Sleep Medicine

Restless legs syndrome occurred in approximately 40% of adults with X-linked adrenoleukodystrophy, based on data from 32 individuals.

Patients with X-linked adrenoleukodystrophy (ALD), a neurodegenerative disease, often experience gait and balance problems, as well as leg discomfort, sleep disturbances, and pain, wrote John W. Winkelman, MD, of Massachusetts General Hospital, Boston, and colleagues. Restless legs syndrome (RLS) has been associated with neurological conditions including Parkinson’s disease, but the prevalence of RLS in ALD patients has not been examined, they said.

Courtesy Brigham and Women's Hospital

In a pilot study published in Sleep Medicine, the researchers identified 21 women and 11 men with ALD who were treated at a single center. The median age of the patients was 45.9 years. Twenty-seven patients had symptoms of myelopathy, with a median age of onset of 34 years.

The researchers assessed RLS severity using questionnaires and the Hopkins Telephone Diagnostic Interview (HTDI), a validated RLS assessment tool. They also reviewed patients’ charts for data on neurological examinations, functional gait measures, and laboratory assessments. Functional gait assessments included the 25-Foot Walk test (25-FW), the Timed Up and Go test (TUG), and Six Minute Walk test (6MW).

Thirteen patients (10 women and 3 men) met criteria for RLS based on the HTDI. The median age of RLS onset was 35 years. Six RLS patients (46.2%) reported using medication to relieve symptoms, and eight RLS patients had a history of antidepressant use.

In addition, six patients with RLS reported a history of anemia or iron deficiency. Ferritin levels were available for 14 patients: 8 women with RLS and 4 women and 2 men without RLS; the mean ferritin levels were 74.0 mcg/L in RLS patients and 99.5 mcg/L in those without RLS.

Of the seven ALD patients with brain lesions, all were men, only two were diagnosed with RLS, and all seven cases were mild, the researchers noted.

Overall, patients with RLS had more neurological signs and symptoms than those without RLS; the most significant were pain and gait difficulty. However, patients with RLS also were more likely than were those without RLS to report spasticity, muscle weakness, impaired coordination, hyperreflexia, impaired sensation, and paraesthesia, as well as bladder, bowel, and erectile dysfunction.

The 40.6% prevalence of RLS in patients with ALD is notably higher than that of the general population, in which the prevalence of RLS is 5%-10%, the researchers wrote in their discussion.

“Consistent with patterns observed in the general population, risk factors for RLS in this cohort of adults with ALD included female gender, increased age, lower iron indices, and use of serotonergic antidepressants,” they said.

The study findings were limited by several factors including the small size and the possible contribution of antidepressant use to the high rate of RLS, the researchers noted.

“Awareness of RLS in patients with ALD would allow for its effective treatment, which may improve the functional impairments as well as quality of life, mood, and anxiety issues in those with ALD,” they concluded.

The study received no outside funding.

Dr. Winkelman disclosed ties with Advance Medical, Avadel, Disc Medicine, Eisai, Emalex, Idorsia, Noctrix, UpToDate, and Merck Pharmaceuticals, as well as research support from the National Institute on Drug Abuse and the Baszucki Brain Research Foundation. The study also was supported by grants from the National Institute of Neurological Disorders and Stroke, the European Leukodystrophy Association, the Arrivederci Foundation, the Leblang Foundation, and the Hammer Family Fund Journal Preproof for ALD Research and Therapies for Women.

Restless legs syndrome occurred in approximately 40% of adults with X-linked adrenoleukodystrophy, based on data from 32 individuals.

Patients with X-linked adrenoleukodystrophy (ALD), a neurodegenerative disease, often experience gait and balance problems, as well as leg discomfort, sleep disturbances, and pain, wrote John W. Winkelman, MD, of Massachusetts General Hospital, Boston, and colleagues. Restless legs syndrome (RLS) has been associated with neurological conditions including Parkinson’s disease, but the prevalence of RLS in ALD patients has not been examined, they said.

Courtesy Brigham and Women's Hospital

In a pilot study published in Sleep Medicine, the researchers identified 21 women and 11 men with ALD who were treated at a single center. The median age of the patients was 45.9 years. Twenty-seven patients had symptoms of myelopathy, with a median age of onset of 34 years.

The researchers assessed RLS severity using questionnaires and the Hopkins Telephone Diagnostic Interview (HTDI), a validated RLS assessment tool. They also reviewed patients’ charts for data on neurological examinations, functional gait measures, and laboratory assessments. Functional gait assessments included the 25-Foot Walk test (25-FW), the Timed Up and Go test (TUG), and Six Minute Walk test (6MW).

Thirteen patients (10 women and 3 men) met criteria for RLS based on the HTDI. The median age of RLS onset was 35 years. Six RLS patients (46.2%) reported using medication to relieve symptoms, and eight RLS patients had a history of antidepressant use.

In addition, six patients with RLS reported a history of anemia or iron deficiency. Ferritin levels were available for 14 patients: 8 women with RLS and 4 women and 2 men without RLS; the mean ferritin levels were 74.0 mcg/L in RLS patients and 99.5 mcg/L in those without RLS.

Of the seven ALD patients with brain lesions, all were men, only two were diagnosed with RLS, and all seven cases were mild, the researchers noted.

Overall, patients with RLS had more neurological signs and symptoms than those without RLS; the most significant were pain and gait difficulty. However, patients with RLS also were more likely than were those without RLS to report spasticity, muscle weakness, impaired coordination, hyperreflexia, impaired sensation, and paraesthesia, as well as bladder, bowel, and erectile dysfunction.

The 40.6% prevalence of RLS in patients with ALD is notably higher than that of the general population, in which the prevalence of RLS is 5%-10%, the researchers wrote in their discussion.

“Consistent with patterns observed in the general population, risk factors for RLS in this cohort of adults with ALD included female gender, increased age, lower iron indices, and use of serotonergic antidepressants,” they said.

The study findings were limited by several factors including the small size and the possible contribution of antidepressant use to the high rate of RLS, the researchers noted.

“Awareness of RLS in patients with ALD would allow for its effective treatment, which may improve the functional impairments as well as quality of life, mood, and anxiety issues in those with ALD,” they concluded.

The study received no outside funding.

Dr. Winkelman disclosed ties with Advance Medical, Avadel, Disc Medicine, Eisai, Emalex, Idorsia, Noctrix, UpToDate, and Merck Pharmaceuticals, as well as research support from the National Institute on Drug Abuse and the Baszucki Brain Research Foundation. The study also was supported by grants from the National Institute of Neurological Disorders and Stroke, the European Leukodystrophy Association, the Arrivederci Foundation, the Leblang Foundation, and the Hammer Family Fund Journal Preproof for ALD Research and Therapies for Women.

Restless legs syndrome occurred in approximately 40% of adults with X-linked adrenoleukodystrophy, based on data from 32 individuals.

Patients with X-linked adrenoleukodystrophy (ALD), a neurodegenerative disease, often experience gait and balance problems, as well as leg discomfort, sleep disturbances, and pain, wrote John W. Winkelman, MD, of Massachusetts General Hospital, Boston, and colleagues. Restless legs syndrome (RLS) has been associated with neurological conditions including Parkinson’s disease, but the prevalence of RLS in ALD patients has not been examined, they said.

Courtesy Brigham and Women's Hospital

In a pilot study published in Sleep Medicine, the researchers identified 21 women and 11 men with ALD who were treated at a single center. The median age of the patients was 45.9 years. Twenty-seven patients had symptoms of myelopathy, with a median age of onset of 34 years.

The researchers assessed RLS severity using questionnaires and the Hopkins Telephone Diagnostic Interview (HTDI), a validated RLS assessment tool. They also reviewed patients’ charts for data on neurological examinations, functional gait measures, and laboratory assessments. Functional gait assessments included the 25-Foot Walk test (25-FW), the Timed Up and Go test (TUG), and Six Minute Walk test (6MW).

Thirteen patients (10 women and 3 men) met criteria for RLS based on the HTDI. The median age of RLS onset was 35 years. Six RLS patients (46.2%) reported using medication to relieve symptoms, and eight RLS patients had a history of antidepressant use.

In addition, six patients with RLS reported a history of anemia or iron deficiency. Ferritin levels were available for 14 patients: 8 women with RLS and 4 women and 2 men without RLS; the mean ferritin levels were 74.0 mcg/L in RLS patients and 99.5 mcg/L in those without RLS.

Of the seven ALD patients with brain lesions, all were men, only two were diagnosed with RLS, and all seven cases were mild, the researchers noted.

Overall, patients with RLS had more neurological signs and symptoms than those without RLS; the most significant were pain and gait difficulty. However, patients with RLS also were more likely than were those without RLS to report spasticity, muscle weakness, impaired coordination, hyperreflexia, impaired sensation, and paraesthesia, as well as bladder, bowel, and erectile dysfunction.

The 40.6% prevalence of RLS in patients with ALD is notably higher than that of the general population, in which the prevalence of RLS is 5%-10%, the researchers wrote in their discussion.

“Consistent with patterns observed in the general population, risk factors for RLS in this cohort of adults with ALD included female gender, increased age, lower iron indices, and use of serotonergic antidepressants,” they said.

The study findings were limited by several factors including the small size and the possible contribution of antidepressant use to the high rate of RLS, the researchers noted.

“Awareness of RLS in patients with ALD would allow for its effective treatment, which may improve the functional impairments as well as quality of life, mood, and anxiety issues in those with ALD,” they concluded.

The study received no outside funding.

Dr. Winkelman disclosed ties with Advance Medical, Avadel, Disc Medicine, Eisai, Emalex, Idorsia, Noctrix, UpToDate, and Merck Pharmaceuticals, as well as research support from the National Institute on Drug Abuse and the Baszucki Brain Research Foundation. The study also was supported by grants from the National Institute of Neurological Disorders and Stroke, the European Leukodystrophy Association, the Arrivederci Foundation, the Leblang Foundation, and the Hammer Family Fund Journal Preproof for ALD Research and Therapies for Women.

Insomnia rates continue to rise in the setting of the pandemic,¹ contributing to increasing rates of depression and anxiety, as well as worsening symptoms of other severe mental illnesses. Data suggests this symptom, defined as chronic sleep onset and/or sleep continuity problems associated with impaired daytime functioning, is common in psychiatric illnesses, and can worsen their course.²

The incidence of psychiatric illness in patients with insomnia is estimated at near 50%, with the highest rates found in mood disorders such as depression and bipolar disorder, as well as anxiety disorders.³ In patients with diagnosed major depressive disorder, insomnia rates can approach 90%.^4-6

Courtesy Dr. Jennifer Reid

Insomnia has been identified as a risk factor for development of mental illness, including doubling the risk of major depressive disorder and tripling the risk of any depressive or anxiety disorder.^7,8 It can also significantly increase the risk of alcohol abuse and psychosis.⁸

Sleep disturbances can worsen symptoms of diagnosed mental illness, including substance abuse, mood and psychotic disorders.^9-10 In one study, nearly 75% of patients with a diagnosis of schizophrenia or bipolar spectrum disorder had at least one type of sleep disturbance (insomnia, hypersomnia, or delayed sleep phase).¹⁰ This was almost twice the rate in healthy controls. Importantly, compared with well-rested subjects with mental illness in this study, sleep-disordered participants had higher rates of negative and depressive symptoms on the Positive and Negative Syndrome Scale, as well as significantly lower function via the global assessment of functioning.^11,12

Additional data suggests simply being awake during the night (00:00-05:59) elevates risk of suicide. The mean incident rate of completed suicide in one study was a striking four times the rate noted during daytime hours (06:00-23:59 ) (P < .001).¹³

Although insomnia symptoms can resolve after relief from a particular life stressor, as many as half of patients with more severe symptoms develop a chronic course.¹⁴ This then leads to an extended use of many types of sedative-hypnotics designed and studied primarily for short-term use.¹⁵ In a survey reviewing national use of prescription drugs for insomnia, as many as 20% of individuals use a medication to target insomnia in a given month.¹⁶

Fortunately, despite the many challenges posed by COVID-19, particularly for those with psychiatric illness and limited access to care, telehealth has become more readily available. Additionally, digital versions of evidence-based treatments specifically for sleep problems, such as cognitive-behavioral therapy for insomnia (CBT-I), are regularly being developed.

The benefits of CBT-I have been demonstrated repeatedly and it is recommended as the first line treatment for insomnia by the Clinical Guidelines of the American Academy of Sleep Medicine, the Centers for Disease Control and Prevention, and the National Institutes of Health.^17-21 Studies suggest benefits persist long-term, even after completing the therapy sessions, which differ in durability from medication choices.¹⁸

One group that may be particularly suited for treatment with CBT-I is women with insomnia during pregnancy or the postpartum period. In these women, options for treatment may be limited by risk of medication during breastfeeding, as well as difficulty traveling to a physician’s or therapist’s office to receive psychotherapy. However, two recent studies evaluated the use of digital CBT-I to treat insomnia during pregnancy and in the postpartum period, respectively.^22-23

In both studies,the same group of women with insomnia diagnosed during pregnancy were given six weekly 20-minute sessions of digital CBT-I or standard treatment for insomnia, including medication and psychotherapy per their usual provider.

By study end, the pregnant women receiving the CBT-I intervention not only had significantly improved severity of insomnia, they also experienced improved depression and anxiety symptoms, and a decrease in the use of prescription or over-the-counter sleep aides, compared with the standard treatment group, lowering the fetal exposure to medication during pregnancy.²²

In the more recent study, the same group was followed for 6 months post partum.²³ Results were again notable, with the women who received CBT-I reporting significantly less insomnia, as well as significantly lower rates of probable major depression at 3 and 6 months (18% vs. 4%, 10% vs. 0%, respectively.) They also exhibited lower rates of moderate to severe anxiety (17% vs. 4%) at 3 months, compared with those receiving standard care. With as many as one in seven women suffering from postpartum depression, these findings represent a substantial public health benefit.

In summary, insomnia is a critical area of focus for any provider diagnosing and treating psychiatric illness. Attempts to optimize sleep, whether through CBT-I or other psychotherapy approaches, or evidence-based medications dosed for appropriate lengths and at safe doses, should be a part of most, if not all, clinical encounters.

Dr. Reid is a board-certified psychiatrist and award-winning medical educator with a private practice in Philadelphia, as well as a clinical faculty role at the University of Pennsylvania, also in Philadelphia. She attended medical school at Columbia University, New York, and completed her psychiatry residency at the University of California, Los Angeles. Dr. Reid is a regular contributor to Psychology Today with her blog, “Think Like a Shrink,” and writes and podcasts as The Reflective Doc.

References

1. Voitsidis P et al. Psychiatry Res. 2020 Jul;289:113076. doi: 10.1016/j.psychres.2020.113076.

2. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, Va.: American Psychiatric Publishing, 2013.

3. Ford DE and Kamerow DB. JAMA. 1989;262(11):1479-84. doi: 10.1001/jama.1989.03430110069030.

4. Ohayon MM and Roth T. J Psychiatr Res. Jan-Feb 2003;37(1):9-15. doi: 10.1016/s0022-3956(02)00052-3.

5. Seow LSE et al. J Ment Health. 2016 Dec;25(6):492-9. doi: 10.3109/09638237.2015.1124390.

6. Thase ME. J Clin Psychiatry. 1999;60 Suppl 17:28-31; discussion 46-8.

7. Baglioni C et al. J Affect Disord. 2011 Dec;135(1-3):10-9. doi: 10.1016/j.jad.2011.01.011.

8. Hertenstein E et al. Sleep Med Rev. 2019 Feb;43:96-105. doi: 10.1016/j.smrv.2018.10.006.

9. Brower KJ et al. Medical Hypotheses. 2010;74(5):928-33. doi: 10.1016/j.mehy.2009.10.020.

10. Laskemoen JF et al. Compr Psychiatry. 2019 May;91:6-12. doi: 10.1016/j.comppsych.2019.02.006.

11. Kay SR et al. Schizophr Bull. 1987;13(2):261-76. doi: 10.1093/schbul/13.2.261.

12. Hall R. Psychosomatics. May-Jun 1995;36(3):267-75. doi: 10.1016/S0033-3182(95)71666-8.

13. Perlis ML et al. J Clin Psychiatry. 2016 Jun;77(6):e726-33. doi: 10.4088/JCP.15m10131.

14. Morin CM et al. Arch Intern Med. 2009 Mar 9. doi: 10.1001/archinternmed.2008.610.

15. Cheung J et al. Sleep Med Clin. 2019 Jun;14(2):253-65. doi: 10.1016/j.jsmc.2019.01.006.

16. Bertisch SM et al. Sleep. 2014 Feb 1. doi: 10.5665/sleep.3410.

17. Okajima I et al. Sleep Biol Rhythms. 2010 Nov 28. doi: 10.1111/j.1479-8425.2010.00481.x.

18. Trauer JM et al. Ann Intern Med. 2015 Aug 4. doi: 10.7326/M14-2841.

19. Edinger J et al. J Clin Sleep Med. 2021 Feb 1. doi: 10.5664/jcsm.8986.

20. U.S. Centers for Disease Control and Prevention. https://www.cdc.gov/sleep/for-clinicians.html.

21. National Institutes of Health. Sleep Health. https://www.nhlbi.nih.gov/health-topics/education-and-awareness/sleep-health.

22. Felder JN et al. JAMA Psychiatry. 2020;77(5):484-92. doi:10.1001/jamapsychiatry.2019.4491.

23. Felder JN et al. Sleep. 2022 Feb 14. doi: 10.1093/sleep/zsab280.

Insomnia rates continue to rise in the setting of the pandemic,¹ contributing to increasing rates of depression and anxiety, as well as worsening symptoms of other severe mental illnesses. Data suggests this symptom, defined as chronic sleep onset and/or sleep continuity problems associated with impaired daytime functioning, is common in psychiatric illnesses, and can worsen their course.²

The incidence of psychiatric illness in patients with insomnia is estimated at near 50%, with the highest rates found in mood disorders such as depression and bipolar disorder, as well as anxiety disorders.³ In patients with diagnosed major depressive disorder, insomnia rates can approach 90%.^4-6

Courtesy Dr. Jennifer Reid

Insomnia has been identified as a risk factor for development of mental illness, including doubling the risk of major depressive disorder and tripling the risk of any depressive or anxiety disorder.^7,8 It can also significantly increase the risk of alcohol abuse and psychosis.⁸

Sleep disturbances can worsen symptoms of diagnosed mental illness, including substance abuse, mood and psychotic disorders.^9-10 In one study, nearly 75% of patients with a diagnosis of schizophrenia or bipolar spectrum disorder had at least one type of sleep disturbance (insomnia, hypersomnia, or delayed sleep phase).¹⁰ This was almost twice the rate in healthy controls. Importantly, compared with well-rested subjects with mental illness in this study, sleep-disordered participants had higher rates of negative and depressive symptoms on the Positive and Negative Syndrome Scale, as well as significantly lower function via the global assessment of functioning.^11,12

Additional data suggests simply being awake during the night (00:00-05:59) elevates risk of suicide. The mean incident rate of completed suicide in one study was a striking four times the rate noted during daytime hours (06:00-23:59 ) (P < .001).¹³

Although insomnia symptoms can resolve after relief from a particular life stressor, as many as half of patients with more severe symptoms develop a chronic course.¹⁴ This then leads to an extended use of many types of sedative-hypnotics designed and studied primarily for short-term use.¹⁵ In a survey reviewing national use of prescription drugs for insomnia, as many as 20% of individuals use a medication to target insomnia in a given month.¹⁶

Fortunately, despite the many challenges posed by COVID-19, particularly for those with psychiatric illness and limited access to care, telehealth has become more readily available. Additionally, digital versions of evidence-based treatments specifically for sleep problems, such as cognitive-behavioral therapy for insomnia (CBT-I), are regularly being developed.

The benefits of CBT-I have been demonstrated repeatedly and it is recommended as the first line treatment for insomnia by the Clinical Guidelines of the American Academy of Sleep Medicine, the Centers for Disease Control and Prevention, and the National Institutes of Health.^17-21 Studies suggest benefits persist long-term, even after completing the therapy sessions, which differ in durability from medication choices.¹⁸

One group that may be particularly suited for treatment with CBT-I is women with insomnia during pregnancy or the postpartum period. In these women, options for treatment may be limited by risk of medication during breastfeeding, as well as difficulty traveling to a physician’s or therapist’s office to receive psychotherapy. However, two recent studies evaluated the use of digital CBT-I to treat insomnia during pregnancy and in the postpartum period, respectively.^22-23

In both studies,the same group of women with insomnia diagnosed during pregnancy were given six weekly 20-minute sessions of digital CBT-I or standard treatment for insomnia, including medication and psychotherapy per their usual provider.

By study end, the pregnant women receiving the CBT-I intervention not only had significantly improved severity of insomnia, they also experienced improved depression and anxiety symptoms, and a decrease in the use of prescription or over-the-counter sleep aides, compared with the standard treatment group, lowering the fetal exposure to medication during pregnancy.²²

In the more recent study, the same group was followed for 6 months post partum.²³ Results were again notable, with the women who received CBT-I reporting significantly less insomnia, as well as significantly lower rates of probable major depression at 3 and 6 months (18% vs. 4%, 10% vs. 0%, respectively.) They also exhibited lower rates of moderate to severe anxiety (17% vs. 4%) at 3 months, compared with those receiving standard care. With as many as one in seven women suffering from postpartum depression, these findings represent a substantial public health benefit.

In summary, insomnia is a critical area of focus for any provider diagnosing and treating psychiatric illness. Attempts to optimize sleep, whether through CBT-I or other psychotherapy approaches, or evidence-based medications dosed for appropriate lengths and at safe doses, should be a part of most, if not all, clinical encounters.

Dr. Reid is a board-certified psychiatrist and award-winning medical educator with a private practice in Philadelphia, as well as a clinical faculty role at the University of Pennsylvania, also in Philadelphia. She attended medical school at Columbia University, New York, and completed her psychiatry residency at the University of California, Los Angeles. Dr. Reid is a regular contributor to Psychology Today with her blog, “Think Like a Shrink,” and writes and podcasts as The Reflective Doc.

References

1. Voitsidis P et al. Psychiatry Res. 2020 Jul;289:113076. doi: 10.1016/j.psychres.2020.113076.

2. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, Va.: American Psychiatric Publishing, 2013.

3. Ford DE and Kamerow DB. JAMA. 1989;262(11):1479-84. doi: 10.1001/jama.1989.03430110069030.

4. Ohayon MM and Roth T. J Psychiatr Res. Jan-Feb 2003;37(1):9-15. doi: 10.1016/s0022-3956(02)00052-3.

5. Seow LSE et al. J Ment Health. 2016 Dec;25(6):492-9. doi: 10.3109/09638237.2015.1124390.

6. Thase ME. J Clin Psychiatry. 1999;60 Suppl 17:28-31; discussion 46-8.

7. Baglioni C et al. J Affect Disord. 2011 Dec;135(1-3):10-9. doi: 10.1016/j.jad.2011.01.011.

8. Hertenstein E et al. Sleep Med Rev. 2019 Feb;43:96-105. doi: 10.1016/j.smrv.2018.10.006.

9. Brower KJ et al. Medical Hypotheses. 2010;74(5):928-33. doi: 10.1016/j.mehy.2009.10.020.

10. Laskemoen JF et al. Compr Psychiatry. 2019 May;91:6-12. doi: 10.1016/j.comppsych.2019.02.006.

11. Kay SR et al. Schizophr Bull. 1987;13(2):261-76. doi: 10.1093/schbul/13.2.261.

12. Hall R. Psychosomatics. May-Jun 1995;36(3):267-75. doi: 10.1016/S0033-3182(95)71666-8.

13. Perlis ML et al. J Clin Psychiatry. 2016 Jun;77(6):e726-33. doi: 10.4088/JCP.15m10131.

14. Morin CM et al. Arch Intern Med. 2009 Mar 9. doi: 10.1001/archinternmed.2008.610.

15. Cheung J et al. Sleep Med Clin. 2019 Jun;14(2):253-65. doi: 10.1016/j.jsmc.2019.01.006.

16. Bertisch SM et al. Sleep. 2014 Feb 1. doi: 10.5665/sleep.3410.

17. Okajima I et al. Sleep Biol Rhythms. 2010 Nov 28. doi: 10.1111/j.1479-8425.2010.00481.x.

18. Trauer JM et al. Ann Intern Med. 2015 Aug 4. doi: 10.7326/M14-2841.

19. Edinger J et al. J Clin Sleep Med. 2021 Feb 1. doi: 10.5664/jcsm.8986.

20. U.S. Centers for Disease Control and Prevention. https://www.cdc.gov/sleep/for-clinicians.html.

21. National Institutes of Health. Sleep Health. https://www.nhlbi.nih.gov/health-topics/education-and-awareness/sleep-health.

22. Felder JN et al. JAMA Psychiatry. 2020;77(5):484-92. doi:10.1001/jamapsychiatry.2019.4491.

23. Felder JN et al. Sleep. 2022 Feb 14. doi: 10.1093/sleep/zsab280.

Insomnia rates continue to rise in the setting of the pandemic,¹ contributing to increasing rates of depression and anxiety, as well as worsening symptoms of other severe mental illnesses. Data suggests this symptom, defined as chronic sleep onset and/or sleep continuity problems associated with impaired daytime functioning, is common in psychiatric illnesses, and can worsen their course.²

The incidence of psychiatric illness in patients with insomnia is estimated at near 50%, with the highest rates found in mood disorders such as depression and bipolar disorder, as well as anxiety disorders.³ In patients with diagnosed major depressive disorder, insomnia rates can approach 90%.^4-6

Courtesy Dr. Jennifer Reid

Insomnia has been identified as a risk factor for development of mental illness, including doubling the risk of major depressive disorder and tripling the risk of any depressive or anxiety disorder.^7,8 It can also significantly increase the risk of alcohol abuse and psychosis.⁸

Sleep disturbances can worsen symptoms of diagnosed mental illness, including substance abuse, mood and psychotic disorders.^9-10 In one study, nearly 75% of patients with a diagnosis of schizophrenia or bipolar spectrum disorder had at least one type of sleep disturbance (insomnia, hypersomnia, or delayed sleep phase).¹⁰ This was almost twice the rate in healthy controls. Importantly, compared with well-rested subjects with mental illness in this study, sleep-disordered participants had higher rates of negative and depressive symptoms on the Positive and Negative Syndrome Scale, as well as significantly lower function via the global assessment of functioning.^11,12

Additional data suggests simply being awake during the night (00:00-05:59) elevates risk of suicide. The mean incident rate of completed suicide in one study was a striking four times the rate noted during daytime hours (06:00-23:59 ) (P < .001).¹³

Although insomnia symptoms can resolve after relief from a particular life stressor, as many as half of patients with more severe symptoms develop a chronic course.¹⁴ This then leads to an extended use of many types of sedative-hypnotics designed and studied primarily for short-term use.¹⁵ In a survey reviewing national use of prescription drugs for insomnia, as many as 20% of individuals use a medication to target insomnia in a given month.¹⁶

Fortunately, despite the many challenges posed by COVID-19, particularly for those with psychiatric illness and limited access to care, telehealth has become more readily available. Additionally, digital versions of evidence-based treatments specifically for sleep problems, such as cognitive-behavioral therapy for insomnia (CBT-I), are regularly being developed.

The benefits of CBT-I have been demonstrated repeatedly and it is recommended as the first line treatment for insomnia by the Clinical Guidelines of the American Academy of Sleep Medicine, the Centers for Disease Control and Prevention, and the National Institutes of Health.^17-21 Studies suggest benefits persist long-term, even after completing the therapy sessions, which differ in durability from medication choices.¹⁸

One group that may be particularly suited for treatment with CBT-I is women with insomnia during pregnancy or the postpartum period. In these women, options for treatment may be limited by risk of medication during breastfeeding, as well as difficulty traveling to a physician’s or therapist’s office to receive psychotherapy. However, two recent studies evaluated the use of digital CBT-I to treat insomnia during pregnancy and in the postpartum period, respectively.^22-23

In both studies,the same group of women with insomnia diagnosed during pregnancy were given six weekly 20-minute sessions of digital CBT-I or standard treatment for insomnia, including medication and psychotherapy per their usual provider.

By study end, the pregnant women receiving the CBT-I intervention not only had significantly improved severity of insomnia, they also experienced improved depression and anxiety symptoms, and a decrease in the use of prescription or over-the-counter sleep aides, compared with the standard treatment group, lowering the fetal exposure to medication during pregnancy.²²

In the more recent study, the same group was followed for 6 months post partum.²³ Results were again notable, with the women who received CBT-I reporting significantly less insomnia, as well as significantly lower rates of probable major depression at 3 and 6 months (18% vs. 4%, 10% vs. 0%, respectively.) They also exhibited lower rates of moderate to severe anxiety (17% vs. 4%) at 3 months, compared with those receiving standard care. With as many as one in seven women suffering from postpartum depression, these findings represent a substantial public health benefit.

In summary, insomnia is a critical area of focus for any provider diagnosing and treating psychiatric illness. Attempts to optimize sleep, whether through CBT-I or other psychotherapy approaches, or evidence-based medications dosed for appropriate lengths and at safe doses, should be a part of most, if not all, clinical encounters.

Dr. Reid is a board-certified psychiatrist and award-winning medical educator with a private practice in Philadelphia, as well as a clinical faculty role at the University of Pennsylvania, also in Philadelphia. She attended medical school at Columbia University, New York, and completed her psychiatry residency at the University of California, Los Angeles. Dr. Reid is a regular contributor to Psychology Today with her blog, “Think Like a Shrink,” and writes and podcasts as The Reflective Doc.

References

1. Voitsidis P et al. Psychiatry Res. 2020 Jul;289:113076. doi: 10.1016/j.psychres.2020.113076.

2. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, Va.: American Psychiatric Publishing, 2013.

3. Ford DE and Kamerow DB. JAMA. 1989;262(11):1479-84. doi: 10.1001/jama.1989.03430110069030.

4. Ohayon MM and Roth T. J Psychiatr Res. Jan-Feb 2003;37(1):9-15. doi: 10.1016/s0022-3956(02)00052-3.

5. Seow LSE et al. J Ment Health. 2016 Dec;25(6):492-9. doi: 10.3109/09638237.2015.1124390.

6. Thase ME. J Clin Psychiatry. 1999;60 Suppl 17:28-31; discussion 46-8.

7. Baglioni C et al. J Affect Disord. 2011 Dec;135(1-3):10-9. doi: 10.1016/j.jad.2011.01.011.

8. Hertenstein E et al. Sleep Med Rev. 2019 Feb;43:96-105. doi: 10.1016/j.smrv.2018.10.006.

9. Brower KJ et al. Medical Hypotheses. 2010;74(5):928-33. doi: 10.1016/j.mehy.2009.10.020.

10. Laskemoen JF et al. Compr Psychiatry. 2019 May;91:6-12. doi: 10.1016/j.comppsych.2019.02.006.

11. Kay SR et al. Schizophr Bull. 1987;13(2):261-76. doi: 10.1093/schbul/13.2.261.

12. Hall R. Psychosomatics. May-Jun 1995;36(3):267-75. doi: 10.1016/S0033-3182(95)71666-8.

13. Perlis ML et al. J Clin Psychiatry. 2016 Jun;77(6):e726-33. doi: 10.4088/JCP.15m10131.

14. Morin CM et al. Arch Intern Med. 2009 Mar 9. doi: 10.1001/archinternmed.2008.610.

15. Cheung J et al. Sleep Med Clin. 2019 Jun;14(2):253-65. doi: 10.1016/j.jsmc.2019.01.006.

16. Bertisch SM et al. Sleep. 2014 Feb 1. doi: 10.5665/sleep.3410.

17. Okajima I et al. Sleep Biol Rhythms. 2010 Nov 28. doi: 10.1111/j.1479-8425.2010.00481.x.

18. Trauer JM et al. Ann Intern Med. 2015 Aug 4. doi: 10.7326/M14-2841.

19. Edinger J et al. J Clin Sleep Med. 2021 Feb 1. doi: 10.5664/jcsm.8986.

20. U.S. Centers for Disease Control and Prevention. https://www.cdc.gov/sleep/for-clinicians.html.

21. National Institutes of Health. Sleep Health. https://www.nhlbi.nih.gov/health-topics/education-and-awareness/sleep-health.

22. Felder JN et al. JAMA Psychiatry. 2020;77(5):484-92. doi:10.1001/jamapsychiatry.2019.4491.

23. Felder JN et al. Sleep. 2022 Feb 14. doi: 10.1093/sleep/zsab280.

There has been a flattening of sleep apnea–related mortality rates in the United States over the past 10 years. The exception is among Black men, for whom mortality from sleep apnea has continuously increased over the past 21 years, new research shows.

“OSA (obstructive sleep apnea) has been recognized as an important cause of medical morbidity and mortality and contributes to the development of systemic hypertension, cardiovascular disease, and abnormalities in glucose metabolism,” noted Yu-Che Lee, MD, University at Buffalo–Catholic Health System, Buffalo, N.Y., and colleagues.

“This study provides the first systematic assessment and demonstrates remarkable demographic disparities of age-adjusted sleep apnea–related mortality in the U.S., with higher rates in males than females and Blacks than Whites,” they concluded.

The study was published online in Sleep Medicine.
 

Twenty-one year interval

Data on sleep apnea–related mortality were obtained from the National Center for Health Statistics and were provided by the Centers for Disease Control and Prevention for the years 1999-2019. Over that 21-year interval, sleep apnea was documented as the underlying cause of death in 17,053 decedents, including 2,593 Black patients and 14,127 White patients.

The age-adjusted mortality rate attributed to sleep apnea was 2.5 per 1,000,000 population. The mortality rate was higher for men, at 3.1 per 1,000,000, than among women, 1.9 per 1,000,000 (P < .001). For both sexes, “unadjusted mortality rates were higher in groups aged ≥ 35 years, and the highest mortality rates were observed in groups aged 75-84,” the authors noted. The rate was 11.3 per 1,000,000 for those aged 75-84 and 13.3 per 1,000,000 for those older than 85.

This was also true among Black and White patients, the authors added, although the age-adjusted mortality rate was higher among Black patients than among other racial groups, at 3.5 per 1,000,000 (P < .001). “Over the 21-year study period, the overall age-adjusted mortality rate rose from 1.2 per 1,000,000 population in 1999 to 2.8 per 1,000,000 in 2019,” Dr. Lee and colleagues noted. While the annual percentage change in sleep apnea–related mortality rose by 10.2% (95% confidence interval [CI], 8.4%-12.0%) between 1999 and 2018, no significant change was observed between 2008 and 2019.

On the other hand, when examined by race and sex, age-adjusted mortality rates increased significantly by an annual percentage change of 7.5% (95% CI, 3.3%-11.9%) among Black women and by 8.2% (95% CI, 6.8%-9.6%) between 1999 and 2009 in White men and by 11.5% (95% CI, 8.9%-14.1%) in White women. “Again, these uptrends were no longer observed after that time interval,” the authors stressed.

Only among Black men was there no turning point in age-adjusted mortality rates; they experienced a steady, significant, 2.7% (95% CI, 1.2%-4.2%) annual percent increase in age-adjusted mortality rate between 1999 and 2019. The highest age-adjusted mortality rate for Black persons was recorded in Indiana, at 6.5 per 1,000,000 population; Utah recorded the highest mortality rate for White persons, at 5.7 per 1,000,000.

For both Black persons and White persons, the lowest mortality rates were in New York, at 1.2 per 1,000,000 and 1.5 per 1,000,000, respectively. Among four geographic regions analyzed, the highest age-adjusted mortality rates were in the Midwest for both sexes; Black men in the West and those in three other regional groups in the Northwest had the lowest mortality rates.
 

Multiple causes of death

Black women were more likely to have multiple causes of death, including cardiac arrest, heart failure, and hypertension. White women were more likely to die of arrhythmia, respiratory failure, pneumonia, and depression. Black men were also more likely to die of cardiac arrest, hypertension, and obesity; arrhythmias, ischemic heart disease, and chronic obstructive pulmonary disease were more common in White men.

The authors pointed out that continuous positive airway pressure (CPAP) is the mainstay of therapy for adults with OSA, but many studies have demonstrated decreased CPAP adherence among Black persons. For example, one report indicated that Black persons use CPAP on average 92 minutes less a day after 1 month of therapy than do White persons, for reasons that are not well understood. Asked by this news organization why Black men are so adversely affected by sleep apnea, Dr. Lee pointed out that studies have shown that sleep apnea is more severe in Black men when first diagnosed.

“We know that the severity of sleep apnea is a risk factor for mortality and cardiovascular outcomes,” he said, “so maybe delayed diagnosis, delayed treatment, and noncompliance with CPAP among Black men may help explain why mortality from sleep apnea among Black men has continued to increase.” Why nonadherence to CPAP is higher among Black men is also not clear. Even when access to CPAP is equal for Black patients and White patients, studies have found that rates of noncompliance to CPAP are higher among Black persons than among White patients.

“This is again a hypothesis,” Dr. Lee emphasized, “but perhaps health literacy among Blacks is lower than it is among White patients, and they may not realize that CPAP can improve health outcomes from sleep apnea,” he suggested. The use of CPAP requires a high level of self-advocacy, which might explain part of their noncompliance.

Other health behaviors and environmental factors may contribute to the tendency among Black patients to be noncompliant with CPAP. “I think this is the first study to show that there is a significant racial disparity in mortality from sleep apnea among Black males, and it should give physicians some insight into the problem; they can develop strategies or interventions to try and reduce racial disparities in outcomes from sleep apnea,” Dr. Lee said.

“So, this study is only the beginning, and we need to have more insight and strategies to improve outcomes among Black males,” he affirmed.

Asked to comment on the findings, Diego Mazzotti, PhD, said the study helps bring attention to existing health disparities related to sleep disorders. “Some of the trends observed by the authors seem to explain the increased recognition that sleep apnea may be a risk factor for cardiovascular morbidity and mortality,” said Dr. Mazzotti, assistant professor in the division of medical informatics at the University of Kansas Medical Center in Kansas City.

“Trends in certain minority groups and certain regions in the U.S. suggest that physicians need to recognize the impact of untreated sleep apnea on the cardiovascular health of these patients,” he said.

Dr. Lee and Dr. Mazzotti have disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

There has been a flattening of sleep apnea–related mortality rates in the United States over the past 10 years. The exception is among Black men, for whom mortality from sleep apnea has continuously increased over the past 21 years, new research shows.

“OSA (obstructive sleep apnea) has been recognized as an important cause of medical morbidity and mortality and contributes to the development of systemic hypertension, cardiovascular disease, and abnormalities in glucose metabolism,” noted Yu-Che Lee, MD, University at Buffalo–Catholic Health System, Buffalo, N.Y., and colleagues.

“This study provides the first systematic assessment and demonstrates remarkable demographic disparities of age-adjusted sleep apnea–related mortality in the U.S., with higher rates in males than females and Blacks than Whites,” they concluded.

The study was published online in Sleep Medicine.
 

Twenty-one year interval

Data on sleep apnea–related mortality were obtained from the National Center for Health Statistics and were provided by the Centers for Disease Control and Prevention for the years 1999-2019. Over that 21-year interval, sleep apnea was documented as the underlying cause of death in 17,053 decedents, including 2,593 Black patients and 14,127 White patients.

The age-adjusted mortality rate attributed to sleep apnea was 2.5 per 1,000,000 population. The mortality rate was higher for men, at 3.1 per 1,000,000, than among women, 1.9 per 1,000,000 (P < .001). For both sexes, “unadjusted mortality rates were higher in groups aged ≥ 35 years, and the highest mortality rates were observed in groups aged 75-84,” the authors noted. The rate was 11.3 per 1,000,000 for those aged 75-84 and 13.3 per 1,000,000 for those older than 85.

This was also true among Black and White patients, the authors added, although the age-adjusted mortality rate was higher among Black patients than among other racial groups, at 3.5 per 1,000,000 (P < .001). “Over the 21-year study period, the overall age-adjusted mortality rate rose from 1.2 per 1,000,000 population in 1999 to 2.8 per 1,000,000 in 2019,” Dr. Lee and colleagues noted. While the annual percentage change in sleep apnea–related mortality rose by 10.2% (95% confidence interval [CI], 8.4%-12.0%) between 1999 and 2018, no significant change was observed between 2008 and 2019.

On the other hand, when examined by race and sex, age-adjusted mortality rates increased significantly by an annual percentage change of 7.5% (95% CI, 3.3%-11.9%) among Black women and by 8.2% (95% CI, 6.8%-9.6%) between 1999 and 2009 in White men and by 11.5% (95% CI, 8.9%-14.1%) in White women. “Again, these uptrends were no longer observed after that time interval,” the authors stressed.

Only among Black men was there no turning point in age-adjusted mortality rates; they experienced a steady, significant, 2.7% (95% CI, 1.2%-4.2%) annual percent increase in age-adjusted mortality rate between 1999 and 2019. The highest age-adjusted mortality rate for Black persons was recorded in Indiana, at 6.5 per 1,000,000 population; Utah recorded the highest mortality rate for White persons, at 5.7 per 1,000,000.

For both Black persons and White persons, the lowest mortality rates were in New York, at 1.2 per 1,000,000 and 1.5 per 1,000,000, respectively. Among four geographic regions analyzed, the highest age-adjusted mortality rates were in the Midwest for both sexes; Black men in the West and those in three other regional groups in the Northwest had the lowest mortality rates.
 

Multiple causes of death

Black women were more likely to have multiple causes of death, including cardiac arrest, heart failure, and hypertension. White women were more likely to die of arrhythmia, respiratory failure, pneumonia, and depression. Black men were also more likely to die of cardiac arrest, hypertension, and obesity; arrhythmias, ischemic heart disease, and chronic obstructive pulmonary disease were more common in White men.

The authors pointed out that continuous positive airway pressure (CPAP) is the mainstay of therapy for adults with OSA, but many studies have demonstrated decreased CPAP adherence among Black persons. For example, one report indicated that Black persons use CPAP on average 92 minutes less a day after 1 month of therapy than do White persons, for reasons that are not well understood. Asked by this news organization why Black men are so adversely affected by sleep apnea, Dr. Lee pointed out that studies have shown that sleep apnea is more severe in Black men when first diagnosed.

“We know that the severity of sleep apnea is a risk factor for mortality and cardiovascular outcomes,” he said, “so maybe delayed diagnosis, delayed treatment, and noncompliance with CPAP among Black men may help explain why mortality from sleep apnea among Black men has continued to increase.” Why nonadherence to CPAP is higher among Black men is also not clear. Even when access to CPAP is equal for Black patients and White patients, studies have found that rates of noncompliance to CPAP are higher among Black persons than among White patients.

“This is again a hypothesis,” Dr. Lee emphasized, “but perhaps health literacy among Blacks is lower than it is among White patients, and they may not realize that CPAP can improve health outcomes from sleep apnea,” he suggested. The use of CPAP requires a high level of self-advocacy, which might explain part of their noncompliance.

Other health behaviors and environmental factors may contribute to the tendency among Black patients to be noncompliant with CPAP. “I think this is the first study to show that there is a significant racial disparity in mortality from sleep apnea among Black males, and it should give physicians some insight into the problem; they can develop strategies or interventions to try and reduce racial disparities in outcomes from sleep apnea,” Dr. Lee said.

“So, this study is only the beginning, and we need to have more insight and strategies to improve outcomes among Black males,” he affirmed.

Asked to comment on the findings, Diego Mazzotti, PhD, said the study helps bring attention to existing health disparities related to sleep disorders. “Some of the trends observed by the authors seem to explain the increased recognition that sleep apnea may be a risk factor for cardiovascular morbidity and mortality,” said Dr. Mazzotti, assistant professor in the division of medical informatics at the University of Kansas Medical Center in Kansas City.

“Trends in certain minority groups and certain regions in the U.S. suggest that physicians need to recognize the impact of untreated sleep apnea on the cardiovascular health of these patients,” he said.

Dr. Lee and Dr. Mazzotti have disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

There has been a flattening of sleep apnea–related mortality rates in the United States over the past 10 years. The exception is among Black men, for whom mortality from sleep apnea has continuously increased over the past 21 years, new research shows.

“OSA (obstructive sleep apnea) has been recognized as an important cause of medical morbidity and mortality and contributes to the development of systemic hypertension, cardiovascular disease, and abnormalities in glucose metabolism,” noted Yu-Che Lee, MD, University at Buffalo–Catholic Health System, Buffalo, N.Y., and colleagues.

“This study provides the first systematic assessment and demonstrates remarkable demographic disparities of age-adjusted sleep apnea–related mortality in the U.S., with higher rates in males than females and Blacks than Whites,” they concluded.

The study was published online in Sleep Medicine.
 

Twenty-one year interval

Data on sleep apnea–related mortality were obtained from the National Center for Health Statistics and were provided by the Centers for Disease Control and Prevention for the years 1999-2019. Over that 21-year interval, sleep apnea was documented as the underlying cause of death in 17,053 decedents, including 2,593 Black patients and 14,127 White patients.

The age-adjusted mortality rate attributed to sleep apnea was 2.5 per 1,000,000 population. The mortality rate was higher for men, at 3.1 per 1,000,000, than among women, 1.9 per 1,000,000 (P < .001). For both sexes, “unadjusted mortality rates were higher in groups aged ≥ 35 years, and the highest mortality rates were observed in groups aged 75-84,” the authors noted. The rate was 11.3 per 1,000,000 for those aged 75-84 and 13.3 per 1,000,000 for those older than 85.

This was also true among Black and White patients, the authors added, although the age-adjusted mortality rate was higher among Black patients than among other racial groups, at 3.5 per 1,000,000 (P < .001). “Over the 21-year study period, the overall age-adjusted mortality rate rose from 1.2 per 1,000,000 population in 1999 to 2.8 per 1,000,000 in 2019,” Dr. Lee and colleagues noted. While the annual percentage change in sleep apnea–related mortality rose by 10.2% (95% confidence interval [CI], 8.4%-12.0%) between 1999 and 2018, no significant change was observed between 2008 and 2019.

On the other hand, when examined by race and sex, age-adjusted mortality rates increased significantly by an annual percentage change of 7.5% (95% CI, 3.3%-11.9%) among Black women and by 8.2% (95% CI, 6.8%-9.6%) between 1999 and 2009 in White men and by 11.5% (95% CI, 8.9%-14.1%) in White women. “Again, these uptrends were no longer observed after that time interval,” the authors stressed.

Only among Black men was there no turning point in age-adjusted mortality rates; they experienced a steady, significant, 2.7% (95% CI, 1.2%-4.2%) annual percent increase in age-adjusted mortality rate between 1999 and 2019. The highest age-adjusted mortality rate for Black persons was recorded in Indiana, at 6.5 per 1,000,000 population; Utah recorded the highest mortality rate for White persons, at 5.7 per 1,000,000.

For both Black persons and White persons, the lowest mortality rates were in New York, at 1.2 per 1,000,000 and 1.5 per 1,000,000, respectively. Among four geographic regions analyzed, the highest age-adjusted mortality rates were in the Midwest for both sexes; Black men in the West and those in three other regional groups in the Northwest had the lowest mortality rates.
 

Multiple causes of death

Black women were more likely to have multiple causes of death, including cardiac arrest, heart failure, and hypertension. White women were more likely to die of arrhythmia, respiratory failure, pneumonia, and depression. Black men were also more likely to die of cardiac arrest, hypertension, and obesity; arrhythmias, ischemic heart disease, and chronic obstructive pulmonary disease were more common in White men.

The authors pointed out that continuous positive airway pressure (CPAP) is the mainstay of therapy for adults with OSA, but many studies have demonstrated decreased CPAP adherence among Black persons. For example, one report indicated that Black persons use CPAP on average 92 minutes less a day after 1 month of therapy than do White persons, for reasons that are not well understood. Asked by this news organization why Black men are so adversely affected by sleep apnea, Dr. Lee pointed out that studies have shown that sleep apnea is more severe in Black men when first diagnosed.

“We know that the severity of sleep apnea is a risk factor for mortality and cardiovascular outcomes,” he said, “so maybe delayed diagnosis, delayed treatment, and noncompliance with CPAP among Black men may help explain why mortality from sleep apnea among Black men has continued to increase.” Why nonadherence to CPAP is higher among Black men is also not clear. Even when access to CPAP is equal for Black patients and White patients, studies have found that rates of noncompliance to CPAP are higher among Black persons than among White patients.

“This is again a hypothesis,” Dr. Lee emphasized, “but perhaps health literacy among Blacks is lower than it is among White patients, and they may not realize that CPAP can improve health outcomes from sleep apnea,” he suggested. The use of CPAP requires a high level of self-advocacy, which might explain part of their noncompliance.

Other health behaviors and environmental factors may contribute to the tendency among Black patients to be noncompliant with CPAP. “I think this is the first study to show that there is a significant racial disparity in mortality from sleep apnea among Black males, and it should give physicians some insight into the problem; they can develop strategies or interventions to try and reduce racial disparities in outcomes from sleep apnea,” Dr. Lee said.

“So, this study is only the beginning, and we need to have more insight and strategies to improve outcomes among Black males,” he affirmed.

Asked to comment on the findings, Diego Mazzotti, PhD, said the study helps bring attention to existing health disparities related to sleep disorders. “Some of the trends observed by the authors seem to explain the increased recognition that sleep apnea may be a risk factor for cardiovascular morbidity and mortality,” said Dr. Mazzotti, assistant professor in the division of medical informatics at the University of Kansas Medical Center in Kansas City.

“Trends in certain minority groups and certain regions in the U.S. suggest that physicians need to recognize the impact of untreated sleep apnea on the cardiovascular health of these patients,” he said.

Dr. Lee and Dr. Mazzotti have disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

The pandemic not only disrupted sleep but may have also triggered an uptick in the use of wearable tech. Sleep tracking was featured at the Cardiovascular Health Tech virtual conference 2022, sponsored by the Institute of Electrical and Electronics Engineers Engineering in Medicine & Biology Society technical committee on Cardiopulmonary Systems and Physiology-Based Engineering.

This news organization interviewed presenter Kelly Glazer Baron, PhD, MPH, DBSM, an associate professor at the University of Utah, Salt Lake City, and a clinical psychologist specializing in behavioral sleep medicine.

The interview has been edited for length and clarity.
 

Question: Are consumer sleep trackers mainly divided into “nearables” – things that you put at the side of the bed or under the pillow – vs. wearables?

Dr. Baron: There are so many different devices these days. There are things that you put under your mattress or pillow; there are bedside recording devices; then there are headbands, rings, wrist-worn, all kinds of things.

Q: At the conference, Philip de Chazal, PhD, (University of Sydney) described the evidence on sleep tracking smartphone apps as woeful. Would you agree with that?

A: Yes. I would agree if you’re looking at how accurate they are at recording sleep, particularly compared with what we would define as the gold standard, which is a sleep study wherein you have electrodes on the scalp and you’re measuring the electrical activity directly.

Devices that go under the pillow are extremely poor at deciphering sleep from wake time, which is really the main goal. They are best at detecting when you get into the bed and when you get out. But even then, there isn’t good evidence that they do that accurately when there are two people in the bed.

Overall, they may give you a general gist of what’s happening in terms of time in and out of bed, but we’re doubtful on their recording ability to tell sleep from wake time.
 

Q: Are the wrist-worn devices better for sleep tracking?

A: They’re getting better. We’ve used wrist activity monitors in research for years. They use an accelerometer to measure movement, and then an algorithm determines whether an interval of time is called sleep or wake.

Recently, they’ve incorporated more sensors, such as heart rate, and they can more accurately decipher rapid eye movement (REM) sleep from non-REM. They’re still not as good as doing a full sleep study. But they’re getting closer.
 

Q: If asked how you slept, most of us think we can answer without needing to look at a smartphone, but maybe not. Can you explain “paradoxical insomnia”?

A: You can’t really know if you’re sleeping because if you know you’re asleep, then you can’t be asleep because it’s a state of unconsciousness. How people decide whether they had a good night’s sleep probably depends on a lot of things about how they feel when they wake up in the morning or if they remember being up in the night.

Quality of sleep is not really something that people can directly ascertain. There is a selection of people who feel awake all night but they actually are sleeping. They feel that their sleep quality is poor: They’re suffering; they have insomnia, but from the objective data, they are sleeping fine.
 

Q: Is this related to non-REM stage 1 sleep, when you may not be aware that you’re asleep?

A: No. I’m talking about people who come into the sleep lab for an overnight study and get hooked up. And in the morning, they’ll tell the tech I was awake all night, but the tech will see that their sleep was just fine.

There is a disconnect between how people perceive their sleep and how they actually sleep. For most people it’s impossible to be completely accurate to know how much you’re sleeping. Then there are some people who perceive it very differently.

Sleep trackers don’t have the level of detail of sleep studies that use scalp electrodes. When we get into the details of sleep measurement, we’re measuring 30-second epochs (sampling periods), where we look at broad measures of electrical activity. There is even more detail there that can be pulled out using other techniques, such as analyzing the spectrum of the EEG. For example, some studies have found a beta frequency in the EEG of people with insomnia, so even though they are sleeping, they often feel awake.

Basically, the subjective experience of sleep somewhat overlaps with the objective recording of what’s happening on a sleep study, but not completely.
 

Q: You said that first thing in the morning might not be the best time to assess your sleep – if you wake up groggy and are already thinking, “The day is shot.”

A: In general, people really feel worst in the morning. Their circadian drive is low, especially if they’re a little sleep deprived. You shouldn’t judge the day on the first hour after waking – most people are pretty cognitively impaired. I tell people they need some boot-up time.

You feel differently as the day goes on and even at different points of the day. There’s a lull in the early afternoon because of your circadian dip and then we get a second wind in the evening. How you feel isn’t one flat line; it’s really a rhythm throughout the day
 

Q: Would you say that consumer sleep trackers are okay for individuals to use to see a pattern but are maybe not accurate enough to use more globally in research?

A: I think there is a huge opportunity to understand sleep at a population level. For example, if there’s been a hurricane or an earthquake or Superbowl Sunday, companies have an opportunity to look at the impact – say, daylight saving time and how it affects sleep across different countries, or men vs. women, or different age groups.

There was a paper about sleep among hospital workers in Wuhan during the outbreak of the pandemic. That was a creative use of wearable devices to look at sleep in a large population.

Now, of course, the devices are not given out randomly; the people who buy them are probably a little bit healthier, maybe a little bit younger – that sort of thing. It is a biased sample.
 

Q: As you note, mobile health trackers tend to be used by the “worried well.” Can you tell us about your paper that introduced the term “orthosomnia,” or “a perfectionistic quest for the ideal sleep in order to optimize daytime function”?

A: As these devices came out, more people were coming into the clinic and shoving their data in front of us saying, “I don’t feel well, and I don’t sleep 7 hours.” They were focused on this specific number. Back when we wrote this paper, the devices were primarily movement based (now the devices are a bit more accurate). Some would say, “My sleep is light, and it’s not deep.” We’d do a sleep study that showed that they have deep sleep, but they would still believe their device even though the device really wasn’t able to classify sleep accurately.

We even found people making their sleep worse because of the device. For example, trying to get the number higher by spending more time lying in bed trying to sleep which is the opposite of what you want someone with insomnia to do. These people held the data so tight and really felt that it characterized their experience, even though we sleep medicine practitioners didn’t find it very accurate and felt that it was somewhat unhelpful to their treatment.
 

Q: What advice would you give the harried primary care physician presented with a patient’s hypnogram or sleep pattern?

A: As someone once pointed out to me, it’s a conversation opener about their sleep. Did they buy the device because they’re worried about their sleep? It’s unlikely that you can glean anything clinically useful from the data.

I briefly look at it to see the duration of their sleep, the regularity in their sleep pattern – the pattern of awakenings during the night might suggest that they have some insomnia. But it doesn’t take the place of clinical assessment for conditions like sleep apnea: Are they snoring? Are they unrefreshed?

I had a patient in the orthosomnia study who was given a sleep tracker by a family member. He brought the data to his doctor who ordered a sleep study that found he had sleep apnea. He would say, “The device diagnosed my sleep apnea.” But that wasn’t actually the case; it just opened the conversation and the clinician said, “Well, let’s order a sleep study.”
 

Q: The device told him he wasn’t getting much sleep and then the sleep study told him it was apnea.

A: Right. It’s impossible to pick up sleep apnea. Some of the latest devices have some oximetry reading but it is not a clinically validated oximetry that could diagnose sleep apnea.

When these first came out I thought I’d get more referrals. So far, I haven’t had a single person come in and ask if they have sleep apnea. If you have a patient saying, “Hey, I’m worried about my oxygen level and here’s my data,” then the clinician should consider whether they need a sleep study for sleep apnea.
 

Q: You did a survey that suggests that clinicians are less keen on these devices than consumers. Conor Heneghan of Fitbit/Google also mentioned a study using the Fitbit Charge and a SleepLife portal. The patients were very engaged but only one physician (out of 49) logged into the portal to look at the data.

A: Our survey of sleep professionals (which we need to publish) showed that they were wary of the data. They found it frustrating in some ways because it took time out of the clinical encounter.

Some of them said that parents are putting trackers on their children and then catastrophizing their children’s sleep.
 

Q: Is there such a thing as an ideal hypnogram or does it vary by individual?

A: I would say that it depends on a lot of things. If you think about a hypnogram from a sleep study, the patient is not sleeping in their home environment, and it’s only one night. There’s a range of what would be considered normal, and it’s related to your sex and your age.

One night is not going to be sufficient to characterize your percentage in this or that sleep stage. Our patients come in saying, “I’m not getting enough REM.” But there isn’t a sleep disorder called lack of REM; there’s no treatment for that. It’s probably pretty normal for them or maybe they’re taking medications that suppress their REM, such as antidepressants.

The tech world is very interested to sense REM properly and to display it. But on the treatment side of things, there’s not much that we do with that data. We’re more interested in the consolidation of their sleep, the duration of their sleep, breathing-related sleep disorders, those sorts of things.
 

Q: Is there any reason to be concerned about the amount of REM sleep in terms of outcomes? We know that poor sleep can lead to bad cardiovascular outcomes, but has any of that correlated to sleep stage?

A: There are studies where they’ve experimentally deprived people of certain stages of sleep, but they’re not very useful in the real world. We’re looking at sleep holistically: Do you have a good sleep pattern? Any breathing-related sleep disorders? Insomnia? We don’t treat sleep by the stage.

Q: Any concern that people who are focused on a device may be ignoring the basic tenets of good sleep hygiene?

A: If people are doing things that are obviously bad for their sleep, like working too late, not exercising enough, sleeping in on weekends to compensate for being up late during the week, or probably the biggest thing contributing to insomnia – stress. A device itself won’t fix those things but it could show you the evidence.

If somebody really has a sleep disorder, then sleep hygiene alone is probably not going to be enough. They’re going to need to engage in a more extensive program to improve their sleep, such as cognitive-behavioral therapy for insomnia.
 

Q: Is there anything else you want to mention?

A: I don’t want to leave with a reputation of being against sleep trackers. I think they are a great opportunity for people to get excited about and learn about their sleep and try to improve it. We have a lot to learn about what people want from their data and how we can use that data to improve people’s sleep.

As providers, we can engage with our patients – sleep is an automatic process, but improving sleep takes some effort. Buying a device is not going to automatically make you sleep better. It takes work to establish a better sleep pattern; it may require some cognitive-behavioral therapy or treating a sleep disorder. That takes some work.

Dr. Baron reported no conflicts of interest.A version of this article first appeared on Medscape.com.

The pandemic not only disrupted sleep but may have also triggered an uptick in the use of wearable tech. Sleep tracking was featured at the Cardiovascular Health Tech virtual conference 2022, sponsored by the Institute of Electrical and Electronics Engineers Engineering in Medicine & Biology Society technical committee on Cardiopulmonary Systems and Physiology-Based Engineering.

This news organization interviewed presenter Kelly Glazer Baron, PhD, MPH, DBSM, an associate professor at the University of Utah, Salt Lake City, and a clinical psychologist specializing in behavioral sleep medicine.

The interview has been edited for length and clarity.
 

Question: Are consumer sleep trackers mainly divided into “nearables” – things that you put at the side of the bed or under the pillow – vs. wearables?

Dr. Baron: There are so many different devices these days. There are things that you put under your mattress or pillow; there are bedside recording devices; then there are headbands, rings, wrist-worn, all kinds of things.

Q: At the conference, Philip de Chazal, PhD, (University of Sydney) described the evidence on sleep tracking smartphone apps as woeful. Would you agree with that?

A: Yes. I would agree if you’re looking at how accurate they are at recording sleep, particularly compared with what we would define as the gold standard, which is a sleep study wherein you have electrodes on the scalp and you’re measuring the electrical activity directly.

Devices that go under the pillow are extremely poor at deciphering sleep from wake time, which is really the main goal. They are best at detecting when you get into the bed and when you get out. But even then, there isn’t good evidence that they do that accurately when there are two people in the bed.

Overall, they may give you a general gist of what’s happening in terms of time in and out of bed, but we’re doubtful on their recording ability to tell sleep from wake time.
 

Q: Are the wrist-worn devices better for sleep tracking?

A: They’re getting better. We’ve used wrist activity monitors in research for years. They use an accelerometer to measure movement, and then an algorithm determines whether an interval of time is called sleep or wake.

Recently, they’ve incorporated more sensors, such as heart rate, and they can more accurately decipher rapid eye movement (REM) sleep from non-REM. They’re still not as good as doing a full sleep study. But they’re getting closer.
 

Q: If asked how you slept, most of us think we can answer without needing to look at a smartphone, but maybe not. Can you explain “paradoxical insomnia”?

A: You can’t really know if you’re sleeping because if you know you’re asleep, then you can’t be asleep because it’s a state of unconsciousness. How people decide whether they had a good night’s sleep probably depends on a lot of things about how they feel when they wake up in the morning or if they remember being up in the night.

Quality of sleep is not really something that people can directly ascertain. There is a selection of people who feel awake all night but they actually are sleeping. They feel that their sleep quality is poor: They’re suffering; they have insomnia, but from the objective data, they are sleeping fine.
 

Q: Is this related to non-REM stage 1 sleep, when you may not be aware that you’re asleep?

A: No. I’m talking about people who come into the sleep lab for an overnight study and get hooked up. And in the morning, they’ll tell the tech I was awake all night, but the tech will see that their sleep was just fine.

There is a disconnect between how people perceive their sleep and how they actually sleep. For most people it’s impossible to be completely accurate to know how much you’re sleeping. Then there are some people who perceive it very differently.

Sleep trackers don’t have the level of detail of sleep studies that use scalp electrodes. When we get into the details of sleep measurement, we’re measuring 30-second epochs (sampling periods), where we look at broad measures of electrical activity. There is even more detail there that can be pulled out using other techniques, such as analyzing the spectrum of the EEG. For example, some studies have found a beta frequency in the EEG of people with insomnia, so even though they are sleeping, they often feel awake.

Basically, the subjective experience of sleep somewhat overlaps with the objective recording of what’s happening on a sleep study, but not completely.
 

Q: You said that first thing in the morning might not be the best time to assess your sleep – if you wake up groggy and are already thinking, “The day is shot.”

A: In general, people really feel worst in the morning. Their circadian drive is low, especially if they’re a little sleep deprived. You shouldn’t judge the day on the first hour after waking – most people are pretty cognitively impaired. I tell people they need some boot-up time.

You feel differently as the day goes on and even at different points of the day. There’s a lull in the early afternoon because of your circadian dip and then we get a second wind in the evening. How you feel isn’t one flat line; it’s really a rhythm throughout the day
 

Q: Would you say that consumer sleep trackers are okay for individuals to use to see a pattern but are maybe not accurate enough to use more globally in research?

A: I think there is a huge opportunity to understand sleep at a population level. For example, if there’s been a hurricane or an earthquake or Superbowl Sunday, companies have an opportunity to look at the impact – say, daylight saving time and how it affects sleep across different countries, or men vs. women, or different age groups.

There was a paper about sleep among hospital workers in Wuhan during the outbreak of the pandemic. That was a creative use of wearable devices to look at sleep in a large population.

Now, of course, the devices are not given out randomly; the people who buy them are probably a little bit healthier, maybe a little bit younger – that sort of thing. It is a biased sample.
 

Q: As you note, mobile health trackers tend to be used by the “worried well.” Can you tell us about your paper that introduced the term “orthosomnia,” or “a perfectionistic quest for the ideal sleep in order to optimize daytime function”?

A: As these devices came out, more people were coming into the clinic and shoving their data in front of us saying, “I don’t feel well, and I don’t sleep 7 hours.” They were focused on this specific number. Back when we wrote this paper, the devices were primarily movement based (now the devices are a bit more accurate). Some would say, “My sleep is light, and it’s not deep.” We’d do a sleep study that showed that they have deep sleep, but they would still believe their device even though the device really wasn’t able to classify sleep accurately.

We even found people making their sleep worse because of the device. For example, trying to get the number higher by spending more time lying in bed trying to sleep which is the opposite of what you want someone with insomnia to do. These people held the data so tight and really felt that it characterized their experience, even though we sleep medicine practitioners didn’t find it very accurate and felt that it was somewhat unhelpful to their treatment.
 

Q: What advice would you give the harried primary care physician presented with a patient’s hypnogram or sleep pattern?

A: As someone once pointed out to me, it’s a conversation opener about their sleep. Did they buy the device because they’re worried about their sleep? It’s unlikely that you can glean anything clinically useful from the data.

I briefly look at it to see the duration of their sleep, the regularity in their sleep pattern – the pattern of awakenings during the night might suggest that they have some insomnia. But it doesn’t take the place of clinical assessment for conditions like sleep apnea: Are they snoring? Are they unrefreshed?

I had a patient in the orthosomnia study who was given a sleep tracker by a family member. He brought the data to his doctor who ordered a sleep study that found he had sleep apnea. He would say, “The device diagnosed my sleep apnea.” But that wasn’t actually the case; it just opened the conversation and the clinician said, “Well, let’s order a sleep study.”
 

Q: The device told him he wasn’t getting much sleep and then the sleep study told him it was apnea.

A: Right. It’s impossible to pick up sleep apnea. Some of the latest devices have some oximetry reading but it is not a clinically validated oximetry that could diagnose sleep apnea.

When these first came out I thought I’d get more referrals. So far, I haven’t had a single person come in and ask if they have sleep apnea. If you have a patient saying, “Hey, I’m worried about my oxygen level and here’s my data,” then the clinician should consider whether they need a sleep study for sleep apnea.
 

Q: You did a survey that suggests that clinicians are less keen on these devices than consumers. Conor Heneghan of Fitbit/Google also mentioned a study using the Fitbit Charge and a SleepLife portal. The patients were very engaged but only one physician (out of 49) logged into the portal to look at the data.

A: Our survey of sleep professionals (which we need to publish) showed that they were wary of the data. They found it frustrating in some ways because it took time out of the clinical encounter.

Some of them said that parents are putting trackers on their children and then catastrophizing their children’s sleep.
 

Q: Is there such a thing as an ideal hypnogram or does it vary by individual?

A: I would say that it depends on a lot of things. If you think about a hypnogram from a sleep study, the patient is not sleeping in their home environment, and it’s only one night. There’s a range of what would be considered normal, and it’s related to your sex and your age.

One night is not going to be sufficient to characterize your percentage in this or that sleep stage. Our patients come in saying, “I’m not getting enough REM.” But there isn’t a sleep disorder called lack of REM; there’s no treatment for that. It’s probably pretty normal for them or maybe they’re taking medications that suppress their REM, such as antidepressants.

The tech world is very interested to sense REM properly and to display it. But on the treatment side of things, there’s not much that we do with that data. We’re more interested in the consolidation of their sleep, the duration of their sleep, breathing-related sleep disorders, those sorts of things.
 

Q: Is there any reason to be concerned about the amount of REM sleep in terms of outcomes? We know that poor sleep can lead to bad cardiovascular outcomes, but has any of that correlated to sleep stage?

A: There are studies where they’ve experimentally deprived people of certain stages of sleep, but they’re not very useful in the real world. We’re looking at sleep holistically: Do you have a good sleep pattern? Any breathing-related sleep disorders? Insomnia? We don’t treat sleep by the stage.

Q: Any concern that people who are focused on a device may be ignoring the basic tenets of good sleep hygiene?

A: If people are doing things that are obviously bad for their sleep, like working too late, not exercising enough, sleeping in on weekends to compensate for being up late during the week, or probably the biggest thing contributing to insomnia – stress. A device itself won’t fix those things but it could show you the evidence.

If somebody really has a sleep disorder, then sleep hygiene alone is probably not going to be enough. They’re going to need to engage in a more extensive program to improve their sleep, such as cognitive-behavioral therapy for insomnia.
 

Q: Is there anything else you want to mention?

A: I don’t want to leave with a reputation of being against sleep trackers. I think they are a great opportunity for people to get excited about and learn about their sleep and try to improve it. We have a lot to learn about what people want from their data and how we can use that data to improve people’s sleep.

As providers, we can engage with our patients – sleep is an automatic process, but improving sleep takes some effort. Buying a device is not going to automatically make you sleep better. It takes work to establish a better sleep pattern; it may require some cognitive-behavioral therapy or treating a sleep disorder. That takes some work.

Dr. Baron reported no conflicts of interest.A version of this article first appeared on Medscape.com.

The pandemic not only disrupted sleep but may have also triggered an uptick in the use of wearable tech. Sleep tracking was featured at the Cardiovascular Health Tech virtual conference 2022, sponsored by the Institute of Electrical and Electronics Engineers Engineering in Medicine & Biology Society technical committee on Cardiopulmonary Systems and Physiology-Based Engineering.

This news organization interviewed presenter Kelly Glazer Baron, PhD, MPH, DBSM, an associate professor at the University of Utah, Salt Lake City, and a clinical psychologist specializing in behavioral sleep medicine.

The interview has been edited for length and clarity.
 

Question: Are consumer sleep trackers mainly divided into “nearables” – things that you put at the side of the bed or under the pillow – vs. wearables?

Dr. Baron: There are so many different devices these days. There are things that you put under your mattress or pillow; there are bedside recording devices; then there are headbands, rings, wrist-worn, all kinds of things.

Q: At the conference, Philip de Chazal, PhD, (University of Sydney) described the evidence on sleep tracking smartphone apps as woeful. Would you agree with that?

A: Yes. I would agree if you’re looking at how accurate they are at recording sleep, particularly compared with what we would define as the gold standard, which is a sleep study wherein you have electrodes on the scalp and you’re measuring the electrical activity directly.

Devices that go under the pillow are extremely poor at deciphering sleep from wake time, which is really the main goal. They are best at detecting when you get into the bed and when you get out. But even then, there isn’t good evidence that they do that accurately when there are two people in the bed.

Overall, they may give you a general gist of what’s happening in terms of time in and out of bed, but we’re doubtful on their recording ability to tell sleep from wake time.
 

Q: Are the wrist-worn devices better for sleep tracking?

A: They’re getting better. We’ve used wrist activity monitors in research for years. They use an accelerometer to measure movement, and then an algorithm determines whether an interval of time is called sleep or wake.

Recently, they’ve incorporated more sensors, such as heart rate, and they can more accurately decipher rapid eye movement (REM) sleep from non-REM. They’re still not as good as doing a full sleep study. But they’re getting closer.
 

Q: If asked how you slept, most of us think we can answer without needing to look at a smartphone, but maybe not. Can you explain “paradoxical insomnia”?

A: You can’t really know if you’re sleeping because if you know you’re asleep, then you can’t be asleep because it’s a state of unconsciousness. How people decide whether they had a good night’s sleep probably depends on a lot of things about how they feel when they wake up in the morning or if they remember being up in the night.

Quality of sleep is not really something that people can directly ascertain. There is a selection of people who feel awake all night but they actually are sleeping. They feel that their sleep quality is poor: They’re suffering; they have insomnia, but from the objective data, they are sleeping fine.
 

Q: Is this related to non-REM stage 1 sleep, when you may not be aware that you’re asleep?

A: No. I’m talking about people who come into the sleep lab for an overnight study and get hooked up. And in the morning, they’ll tell the tech I was awake all night, but the tech will see that their sleep was just fine.

There is a disconnect between how people perceive their sleep and how they actually sleep. For most people it’s impossible to be completely accurate to know how much you’re sleeping. Then there are some people who perceive it very differently.

Sleep trackers don’t have the level of detail of sleep studies that use scalp electrodes. When we get into the details of sleep measurement, we’re measuring 30-second epochs (sampling periods), where we look at broad measures of electrical activity. There is even more detail there that can be pulled out using other techniques, such as analyzing the spectrum of the EEG. For example, some studies have found a beta frequency in the EEG of people with insomnia, so even though they are sleeping, they often feel awake.

Basically, the subjective experience of sleep somewhat overlaps with the objective recording of what’s happening on a sleep study, but not completely.
 

Q: You said that first thing in the morning might not be the best time to assess your sleep – if you wake up groggy and are already thinking, “The day is shot.”

A: In general, people really feel worst in the morning. Their circadian drive is low, especially if they’re a little sleep deprived. You shouldn’t judge the day on the first hour after waking – most people are pretty cognitively impaired. I tell people they need some boot-up time.

You feel differently as the day goes on and even at different points of the day. There’s a lull in the early afternoon because of your circadian dip and then we get a second wind in the evening. How you feel isn’t one flat line; it’s really a rhythm throughout the day
 

Q: Would you say that consumer sleep trackers are okay for individuals to use to see a pattern but are maybe not accurate enough to use more globally in research?

A: I think there is a huge opportunity to understand sleep at a population level. For example, if there’s been a hurricane or an earthquake or Superbowl Sunday, companies have an opportunity to look at the impact – say, daylight saving time and how it affects sleep across different countries, or men vs. women, or different age groups.

There was a paper about sleep among hospital workers in Wuhan during the outbreak of the pandemic. That was a creative use of wearable devices to look at sleep in a large population.

Now, of course, the devices are not given out randomly; the people who buy them are probably a little bit healthier, maybe a little bit younger – that sort of thing. It is a biased sample.
 

Q: As you note, mobile health trackers tend to be used by the “worried well.” Can you tell us about your paper that introduced the term “orthosomnia,” or “a perfectionistic quest for the ideal sleep in order to optimize daytime function”?

A: As these devices came out, more people were coming into the clinic and shoving their data in front of us saying, “I don’t feel well, and I don’t sleep 7 hours.” They were focused on this specific number. Back when we wrote this paper, the devices were primarily movement based (now the devices are a bit more accurate). Some would say, “My sleep is light, and it’s not deep.” We’d do a sleep study that showed that they have deep sleep, but they would still believe their device even though the device really wasn’t able to classify sleep accurately.

We even found people making their sleep worse because of the device. For example, trying to get the number higher by spending more time lying in bed trying to sleep which is the opposite of what you want someone with insomnia to do. These people held the data so tight and really felt that it characterized their experience, even though we sleep medicine practitioners didn’t find it very accurate and felt that it was somewhat unhelpful to their treatment.
 

Q: What advice would you give the harried primary care physician presented with a patient’s hypnogram or sleep pattern?

A: As someone once pointed out to me, it’s a conversation opener about their sleep. Did they buy the device because they’re worried about their sleep? It’s unlikely that you can glean anything clinically useful from the data.

I briefly look at it to see the duration of their sleep, the regularity in their sleep pattern – the pattern of awakenings during the night might suggest that they have some insomnia. But it doesn’t take the place of clinical assessment for conditions like sleep apnea: Are they snoring? Are they unrefreshed?

I had a patient in the orthosomnia study who was given a sleep tracker by a family member. He brought the data to his doctor who ordered a sleep study that found he had sleep apnea. He would say, “The device diagnosed my sleep apnea.” But that wasn’t actually the case; it just opened the conversation and the clinician said, “Well, let’s order a sleep study.”
 

Q: The device told him he wasn’t getting much sleep and then the sleep study told him it was apnea.

A: Right. It’s impossible to pick up sleep apnea. Some of the latest devices have some oximetry reading but it is not a clinically validated oximetry that could diagnose sleep apnea.

When these first came out I thought I’d get more referrals. So far, I haven’t had a single person come in and ask if they have sleep apnea. If you have a patient saying, “Hey, I’m worried about my oxygen level and here’s my data,” then the clinician should consider whether they need a sleep study for sleep apnea.
 

Q: You did a survey that suggests that clinicians are less keen on these devices than consumers. Conor Heneghan of Fitbit/Google also mentioned a study using the Fitbit Charge and a SleepLife portal. The patients were very engaged but only one physician (out of 49) logged into the portal to look at the data.

A: Our survey of sleep professionals (which we need to publish) showed that they were wary of the data. They found it frustrating in some ways because it took time out of the clinical encounter.

Some of them said that parents are putting trackers on their children and then catastrophizing their children’s sleep.
 

Q: Is there such a thing as an ideal hypnogram or does it vary by individual?

A: I would say that it depends on a lot of things. If you think about a hypnogram from a sleep study, the patient is not sleeping in their home environment, and it’s only one night. There’s a range of what would be considered normal, and it’s related to your sex and your age.

One night is not going to be sufficient to characterize your percentage in this or that sleep stage. Our patients come in saying, “I’m not getting enough REM.” But there isn’t a sleep disorder called lack of REM; there’s no treatment for that. It’s probably pretty normal for them or maybe they’re taking medications that suppress their REM, such as antidepressants.

The tech world is very interested to sense REM properly and to display it. But on the treatment side of things, there’s not much that we do with that data. We’re more interested in the consolidation of their sleep, the duration of their sleep, breathing-related sleep disorders, those sorts of things.
 

Q: Is there any reason to be concerned about the amount of REM sleep in terms of outcomes? We know that poor sleep can lead to bad cardiovascular outcomes, but has any of that correlated to sleep stage?

A: There are studies where they’ve experimentally deprived people of certain stages of sleep, but they’re not very useful in the real world. We’re looking at sleep holistically: Do you have a good sleep pattern? Any breathing-related sleep disorders? Insomnia? We don’t treat sleep by the stage.

Q: Any concern that people who are focused on a device may be ignoring the basic tenets of good sleep hygiene?

A: If people are doing things that are obviously bad for their sleep, like working too late, not exercising enough, sleeping in on weekends to compensate for being up late during the week, or probably the biggest thing contributing to insomnia – stress. A device itself won’t fix those things but it could show you the evidence.

If somebody really has a sleep disorder, then sleep hygiene alone is probably not going to be enough. They’re going to need to engage in a more extensive program to improve their sleep, such as cognitive-behavioral therapy for insomnia.
 

Q: Is there anything else you want to mention?

A: I don’t want to leave with a reputation of being against sleep trackers. I think they are a great opportunity for people to get excited about and learn about their sleep and try to improve it. We have a lot to learn about what people want from their data and how we can use that data to improve people’s sleep.

As providers, we can engage with our patients – sleep is an automatic process, but improving sleep takes some effort. Buying a device is not going to automatically make you sleep better. It takes work to establish a better sleep pattern; it may require some cognitive-behavioral therapy or treating a sleep disorder. That takes some work.

Dr. Baron reported no conflicts of interest.A version of this article first appeared on Medscape.com.

A controlled study of sleep-deprived young adults has provided the first causal evidence linking the lack of sleep to abdominal obesity and harmful visceral, or “belly” fat. In what the researchers claim is the first-ever study evaluating the relationship between sleep restriction and body fat distribution, they’ve reported the novel finding that the expansion of abdominal adipose tissue, and especially visceral fat, occurred as a function of shortened sleep.

Naima Covassin, PhD, a researcher in cardiovascular medicine at Mayo Clinic in Rochester, Minn., led the randomized, controlled study of 12 healthy, nonobese people randomized to controlled sleep restriction – 2 weeks of 4 hours of sleep a night – or controlled sleep of 9 hours a night, followed by a 3-day recovery period. The study was conducted in the hospital, monitored participants’ caloric intake, and used accelerometry to monitor energy expense. Participants ranged in age from 19 to 39 years.

“What we found was that at the end of 2 weeks these people put on just about a pound, 0.5 kg, of extra weight, which was significant but still very modest,” senior author Virend K. Somers, MD, PhD, said in an interview. “The average person who sleeps 4 hours a night thinks they’re doing OK if they only put on a pound.” Dr. Somers is the Alice Sheets Marriott Professor in Cardiovascular Medicine at Mayo Clinic.

“The problem is,” he said, “that when you do a more specific analysis you find that actually with the 1 pound the significant increase of the fat is in the belly area, particularly inside the belly.”

The study found that the patients on curtailed sleep ate on average an additional 308 calories a day more than their controlled sleep counterparts (95% confidence interval, 59.2-556.8 kcal/day; P = .015), and while that translated into a 0.5-kg weight gain (95% CI, 0.1-0.8 kg; P = .008), it also led to a 7.8-cm² increase visceral adipose tissue (VAT) (95% CI, 0.3-15.3 cm²; P = .042), representing an increase of around 11%. The study used CT on day 1 and day 18 (1 day after the 3-day recovery period) to evaluate the distribution of abdominal fat.

VAT findings post recovery

After the recovery period, however, the study found that VAT in the sleep-curtailed patients kept rising, yet body weight and subcutaneous fat dropped, and the increase in total abdominal fat flattened. “They slept a lot, they ate fewer calories and their weight came down, but, very importantly, their belly fat went up even further,” Dr. Somers said. On average, it increased another 3.125 cm² by day 21.

The findings raised a number of questions that need further exploration, Dr. Somers said. “There’s some biochemical message in the body that’s continuing to send fat to the visceral compartment,” he said. “What we don’t know is whether repetitive episodes of inadequate sleep actually accumulate over the years to give people a preponderance of belly fat.”

The study also showed that the traditional parameters used for evaluating cardiovascular risk are not enough, Dr. Somers said. “If we just did body weight, body mass index, and overall body fat percentage, we’d completely miss this,” he said.

Future investigations should focus on two points, he said: identifying the mechanisms that cause VAT accumulation with less sleep, and whether extending sleep can reverse the process.

“The big worry is obviously the heart,” Dr. Somers said. “Remember, these are not sick people. These are young healthy people who are doing the wrong thing with their body fat; they’re sending the fat to the completely wrong place.”

In an invited editorial, endocrinologist Harold Bays, MD, wrote that the study confirmed the need for evaluating sleep disorders as a potential cause of accumulated VAT. Dr. Bays of the University of Louisville (Ky.) is medical director and president of the Louisville Metabolic and Atherosclerosis Research Center.

“The biggest misconception of many clinicians, and some cardiologists, is that obesity is not a disease,” Dr. Bays said in an interview. “Even when some clinicians believe obesity is a disease, they believe its pathogenic potential is limited to visceral fat.” He noted that subcutaneous fat can lead to accumulation of VAT and epicardial fat, as well as fatty infiltration of the liver and other vital organs, resulting in increased epicardial adipose tissue and indirect adverse effects on the heart.

“Thus, even if disruption of sleep does not increase body weight, if disruption of sleep results in fat dysfunction – “sick fat” or adiposopathy – then this may result in increased CVD risk factors and unhealthy body composition, including an increase in visceral fat,” Dr. Bays said.

The study received funding from the National Institutes of Health. Dr. Somers disclosed relationships with Baker Tilly, Jazz Pharmaceuticals, Bayer, Sleep Number and Respicardia. Coauthors had no disclosures. Dr. Bays is medical director of Your Body Goal and chief science officer of the Obesity Medical Association.

A controlled study of sleep-deprived young adults has provided the first causal evidence linking the lack of sleep to abdominal obesity and harmful visceral, or “belly” fat. In what the researchers claim is the first-ever study evaluating the relationship between sleep restriction and body fat distribution, they’ve reported the novel finding that the expansion of abdominal adipose tissue, and especially visceral fat, occurred as a function of shortened sleep.

Naima Covassin, PhD, a researcher in cardiovascular medicine at Mayo Clinic in Rochester, Minn., led the randomized, controlled study of 12 healthy, nonobese people randomized to controlled sleep restriction – 2 weeks of 4 hours of sleep a night – or controlled sleep of 9 hours a night, followed by a 3-day recovery period. The study was conducted in the hospital, monitored participants’ caloric intake, and used accelerometry to monitor energy expense. Participants ranged in age from 19 to 39 years.

“What we found was that at the end of 2 weeks these people put on just about a pound, 0.5 kg, of extra weight, which was significant but still very modest,” senior author Virend K. Somers, MD, PhD, said in an interview. “The average person who sleeps 4 hours a night thinks they’re doing OK if they only put on a pound.” Dr. Somers is the Alice Sheets Marriott Professor in Cardiovascular Medicine at Mayo Clinic.

“The problem is,” he said, “that when you do a more specific analysis you find that actually with the 1 pound the significant increase of the fat is in the belly area, particularly inside the belly.”

The study found that the patients on curtailed sleep ate on average an additional 308 calories a day more than their controlled sleep counterparts (95% confidence interval, 59.2-556.8 kcal/day; P = .015), and while that translated into a 0.5-kg weight gain (95% CI, 0.1-0.8 kg; P = .008), it also led to a 7.8-cm² increase visceral adipose tissue (VAT) (95% CI, 0.3-15.3 cm²; P = .042), representing an increase of around 11%. The study used CT on day 1 and day 18 (1 day after the 3-day recovery period) to evaluate the distribution of abdominal fat.

VAT findings post recovery

After the recovery period, however, the study found that VAT in the sleep-curtailed patients kept rising, yet body weight and subcutaneous fat dropped, and the increase in total abdominal fat flattened. “They slept a lot, they ate fewer calories and their weight came down, but, very importantly, their belly fat went up even further,” Dr. Somers said. On average, it increased another 3.125 cm² by day 21.

The findings raised a number of questions that need further exploration, Dr. Somers said. “There’s some biochemical message in the body that’s continuing to send fat to the visceral compartment,” he said. “What we don’t know is whether repetitive episodes of inadequate sleep actually accumulate over the years to give people a preponderance of belly fat.”

The study also showed that the traditional parameters used for evaluating cardiovascular risk are not enough, Dr. Somers said. “If we just did body weight, body mass index, and overall body fat percentage, we’d completely miss this,” he said.

Future investigations should focus on two points, he said: identifying the mechanisms that cause VAT accumulation with less sleep, and whether extending sleep can reverse the process.

“The big worry is obviously the heart,” Dr. Somers said. “Remember, these are not sick people. These are young healthy people who are doing the wrong thing with their body fat; they’re sending the fat to the completely wrong place.”

In an invited editorial, endocrinologist Harold Bays, MD, wrote that the study confirmed the need for evaluating sleep disorders as a potential cause of accumulated VAT. Dr. Bays of the University of Louisville (Ky.) is medical director and president of the Louisville Metabolic and Atherosclerosis Research Center.

“The biggest misconception of many clinicians, and some cardiologists, is that obesity is not a disease,” Dr. Bays said in an interview. “Even when some clinicians believe obesity is a disease, they believe its pathogenic potential is limited to visceral fat.” He noted that subcutaneous fat can lead to accumulation of VAT and epicardial fat, as well as fatty infiltration of the liver and other vital organs, resulting in increased epicardial adipose tissue and indirect adverse effects on the heart.

“Thus, even if disruption of sleep does not increase body weight, if disruption of sleep results in fat dysfunction – “sick fat” or adiposopathy – then this may result in increased CVD risk factors and unhealthy body composition, including an increase in visceral fat,” Dr. Bays said.

The study received funding from the National Institutes of Health. Dr. Somers disclosed relationships with Baker Tilly, Jazz Pharmaceuticals, Bayer, Sleep Number and Respicardia. Coauthors had no disclosures. Dr. Bays is medical director of Your Body Goal and chief science officer of the Obesity Medical Association.

A controlled study of sleep-deprived young adults has provided the first causal evidence linking the lack of sleep to abdominal obesity and harmful visceral, or “belly” fat. In what the researchers claim is the first-ever study evaluating the relationship between sleep restriction and body fat distribution, they’ve reported the novel finding that the expansion of abdominal adipose tissue, and especially visceral fat, occurred as a function of shortened sleep.

Naima Covassin, PhD, a researcher in cardiovascular medicine at Mayo Clinic in Rochester, Minn., led the randomized, controlled study of 12 healthy, nonobese people randomized to controlled sleep restriction – 2 weeks of 4 hours of sleep a night – or controlled sleep of 9 hours a night, followed by a 3-day recovery period. The study was conducted in the hospital, monitored participants’ caloric intake, and used accelerometry to monitor energy expense. Participants ranged in age from 19 to 39 years.

“What we found was that at the end of 2 weeks these people put on just about a pound, 0.5 kg, of extra weight, which was significant but still very modest,” senior author Virend K. Somers, MD, PhD, said in an interview. “The average person who sleeps 4 hours a night thinks they’re doing OK if they only put on a pound.” Dr. Somers is the Alice Sheets Marriott Professor in Cardiovascular Medicine at Mayo Clinic.

“The problem is,” he said, “that when you do a more specific analysis you find that actually with the 1 pound the significant increase of the fat is in the belly area, particularly inside the belly.”

The study found that the patients on curtailed sleep ate on average an additional 308 calories a day more than their controlled sleep counterparts (95% confidence interval, 59.2-556.8 kcal/day; P = .015), and while that translated into a 0.5-kg weight gain (95% CI, 0.1-0.8 kg; P = .008), it also led to a 7.8-cm² increase visceral adipose tissue (VAT) (95% CI, 0.3-15.3 cm²; P = .042), representing an increase of around 11%. The study used CT on day 1 and day 18 (1 day after the 3-day recovery period) to evaluate the distribution of abdominal fat.

VAT findings post recovery

After the recovery period, however, the study found that VAT in the sleep-curtailed patients kept rising, yet body weight and subcutaneous fat dropped, and the increase in total abdominal fat flattened. “They slept a lot, they ate fewer calories and their weight came down, but, very importantly, their belly fat went up even further,” Dr. Somers said. On average, it increased another 3.125 cm² by day 21.

The findings raised a number of questions that need further exploration, Dr. Somers said. “There’s some biochemical message in the body that’s continuing to send fat to the visceral compartment,” he said. “What we don’t know is whether repetitive episodes of inadequate sleep actually accumulate over the years to give people a preponderance of belly fat.”

The study also showed that the traditional parameters used for evaluating cardiovascular risk are not enough, Dr. Somers said. “If we just did body weight, body mass index, and overall body fat percentage, we’d completely miss this,” he said.

Future investigations should focus on two points, he said: identifying the mechanisms that cause VAT accumulation with less sleep, and whether extending sleep can reverse the process.

“The big worry is obviously the heart,” Dr. Somers said. “Remember, these are not sick people. These are young healthy people who are doing the wrong thing with their body fat; they’re sending the fat to the completely wrong place.”

In an invited editorial, endocrinologist Harold Bays, MD, wrote that the study confirmed the need for evaluating sleep disorders as a potential cause of accumulated VAT. Dr. Bays of the University of Louisville (Ky.) is medical director and president of the Louisville Metabolic and Atherosclerosis Research Center.

“The biggest misconception of many clinicians, and some cardiologists, is that obesity is not a disease,” Dr. Bays said in an interview. “Even when some clinicians believe obesity is a disease, they believe its pathogenic potential is limited to visceral fat.” He noted that subcutaneous fat can lead to accumulation of VAT and epicardial fat, as well as fatty infiltration of the liver and other vital organs, resulting in increased epicardial adipose tissue and indirect adverse effects on the heart.

“Thus, even if disruption of sleep does not increase body weight, if disruption of sleep results in fat dysfunction – “sick fat” or adiposopathy – then this may result in increased CVD risk factors and unhealthy body composition, including an increase in visceral fat,” Dr. Bays said.

The study received funding from the National Institutes of Health. Dr. Somers disclosed relationships with Baker Tilly, Jazz Pharmaceuticals, Bayer, Sleep Number and Respicardia. Coauthors had no disclosures. Dr. Bays is medical director of Your Body Goal and chief science officer of the Obesity Medical Association.

Study Overview

Objective: This study evaluated whether a clinical-decision-support (CDS) tool that utilizes a real-time algorithm incorporating patient vital sign data can identify hospitalized patients who can forgo overnight vital sign checks and thus reduce delirium incidence.

Design: This was a parallel randomized clinical trial of adult inpatients admitted to the general medical service of a tertiary care academic medical center in the United States. The trial intervention consisted of a CDS notification in the electronic health record (EHR) that informed the physician if a patient had a high likelihood of nighttime vital signs within the reference ranges based on a logistic regression model of real-time patient data input. This notification provided the physician an opportunity to discontinue nighttime vital sign checks, dismiss the notification for 1 hour, or dismiss the notification until the next day.

Setting and participants: This clinical trial was conducted at the University of California, San Francisco Medical Center from March 11 to November 24, 2019. Participants included physicians who served on the primary team (eg, attending, resident) of 1699 patients on the general medical service who were outside of the intensive care unit (ICU). The hospital encounters were randomized (allocation ratio of 1:1) to sleep promotion vitals CDS (SPV CDS) intervention or usual care.

Main outcome and measures: The primary outcome was delirium as determined by bedside nurse assessment using the Nursing Delirium Screening Scale (Nu-DESC) recorded once per nursing shift. The Nu-DESC is a standardized delirium screening tool that defines delirium with a score ≥2. Secondary outcomes included sleep opportunity (ie, EHR-based sleep metrics that reflected the maximum time between iatrogenic interruptions, such as nighttime vital sign checks) and patient satisfaction (ie, patient satisfaction measured by standardized Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS] survey). Potential balancing outcomes were assessed to ensure that reduced vital sign checks were not causing harms; these included ICU transfers, rapid response calls, and code blue alarms. All analyses were conducted on the basis of intention-to-treat.

Main results: A total of 3025 inpatient encounters were screened and 1930 encounters were randomized (966 SPV CDS intervention; 964 usual care). The randomized encounters consisted of 1699 patients; demographic factors between the 2 trial arms were similar. Specifically, the intervention arm included 566 men (59%) and mean (SD) age was 53 (15) years. The incidence of delirium was similar between the intervention and usual care arms: 108 (11%) vs 123 (13%) (P = .32). Compared to the usual care arm, the intervention arm had a higher mean (SD) number of sleep opportunity hours per night (4.95 [1.45] vs 4.57 [1.30], P < .001) and fewer nighttime vital sign checks (0.97 [0.95] vs 1.41 [0.86], P < .001). The post-discharge HCAHPS survey measuring patient satisfaction was completed by only 5% of patients (53 intervention, 49 usual care), and survey results were similar between the 2 arms (P = .86). In addition, safety outcomes including ICU transfers (49 [5%] vs 47 [5%], P = .92), rapid response calls (68 [7%] vs 55 [6%], P = .27), and code blue alarms (2 [0.2%] vs 9 [0.9%], P = .07) were similar between the study arms.

Conclusion: In this randomized clinical trial, a CDS tool utilizing a real-time prediction algorithm embedded in EHR did not reduce the incidence of delirium in hospitalized patients. However, this SPV CDS intervention helped physicians identify clinically stable patients who can forgo routine nighttime vital sign checks and facilitated greater opportunity for patients to sleep. These findings suggest that augmenting physician judgment using a real-time prediction algorithm can help to improve sleep opportunity without an accompanying increased risk of clinical decompensation during acute care.

Commentary

High-quality sleep is fundamental to health and well-being. Sleep deprivation and disorders are associated with many adverse health outcomes, including increased risks for obesity, diabetes, hypertension, myocardial infarction, and depression.¹ In hospitalized patients who are acutely ill, restorative sleep is critical to facilitating recovery. However, poor sleep is exceedingly common in hospitalized patients and is associated with deleterious outcomes, such as high blood pressure, hyperglycemia, and delirium.^2,3 Moreover, some of these adverse sleep-induced cardiometabolic outcomes, as well as sleep disruption itself, may persist after hospital discharge.⁴ Factors that precipitate interrupted sleep during hospitalization include iatrogenic causes such as frequent vital sign checks, nighttime procedures or early morning blood draws, and environmental factors such as loud ambient noise.³ Thus, a potential intervention to improve sleep quality in the hospital is to reduce nighttime interruptions such as frequent vital sign checks.

In the current study, Najafi and colleagues conducted a randomized trial to evaluate whether a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, can be utilized to identify patients in whom vital sign checks can be safely discontinued at nighttime. The authors found a modest but statistically significant reduction in the number of nighttime vital sign checks in patients who underwent the SPV CDS intervention, and a corresponding higher sleep opportunity per night in those who received the intervention. Importantly, this reduction in nighttime vital sign checks did not cause a higher risk of clinical decompensation as measured by ICU transfers, rapid response calls, or code blue alarms. Thus, the results demonstrated the feasibility of using a real-time, patient data-driven CDS tool to augment physician judgment in managing sleep disruption, an important hospital-associated stressor and a common hazard of hospitalization in older patients.

Delirium is a common clinical problem in hospitalized older patients that is associated with prolonged hospitalization, functional and cognitive decline, institutionalization, death, and increased health care costs.⁵ Despite a potential benefit of SPV CDS intervention in reducing vital sign checks and increasing sleep opportunity, this intervention did not reduce the incidence of delirium in hospitalized patients. This finding is not surprising given that delirium has a multifactorial etiology (eg, metabolic derangements, infections, medication side effects and drug toxicity, hospital environment). A small modification in nighttime vital sign checks and sleep opportunity may have limited impact on optimizing sleep quality and does not address other risk factors for delirium. As such, a multicomponent nonpharmacologic approach that includes sleep enhancement, early mobilization, feeding assistance, fluid repletion, infection prevention, and other interventions should guide delirium prevention in the hospital setting. The SPV CDS intervention may play a role in the delivery of a multifaceted, nonpharmacologic delirium prevention intervention in high-risk individuals.

Sleep disruption is one of the multiple hazards of hospitalization frequently experience by hospitalized older patients. Other hazards, or hospital-associated stressors, include mobility restriction (eg, physical restraints such as urinary catheters and intravenous lines, bed elevation and rails), malnourishment and dehydration (eg, frequent use of no-food-by-mouth order, lack of easy access to hydration), and pain (eg, poor pain control). Extended exposures to these stressors may lead to a maladaptive state called allostatic overload that transiently increases vulnerability to post-hospitalization adverse events, including emergency department use, hospital readmission, or death (ie, post-hospital syndrome).⁶ Thus, the optimization of sleep during hospitalization in vulnerable patients may have benefits that extend beyond delirium prevention. It is perceivable that a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, may be applied to reduce some of these hazards of hospitalization in addition to improving sleep opportunity.

Applications for Clinical Practice

Findings from the current study indicate that a CDS tool embedded in EHR that utilizes a real-time prediction algorithm of patient data may help to safely improve sleep opportunity in hospitalized patients. The participants in the current study were relatively young (53 [15] years). Given that age is a risk factor for delirium, the effects of this intervention on delirium prevention in the most susceptible population (ie, those over the age of 65) remain unknown and further investigation is warranted. Additional studies are needed to determine whether this approach yields similar results in geriatric patients and improves clinical outcomes.

—Fred Ko, MD

Study Overview

Objective: This study evaluated whether a clinical-decision-support (CDS) tool that utilizes a real-time algorithm incorporating patient vital sign data can identify hospitalized patients who can forgo overnight vital sign checks and thus reduce delirium incidence.

Design: This was a parallel randomized clinical trial of adult inpatients admitted to the general medical service of a tertiary care academic medical center in the United States. The trial intervention consisted of a CDS notification in the electronic health record (EHR) that informed the physician if a patient had a high likelihood of nighttime vital signs within the reference ranges based on a logistic regression model of real-time patient data input. This notification provided the physician an opportunity to discontinue nighttime vital sign checks, dismiss the notification for 1 hour, or dismiss the notification until the next day.

Setting and participants: This clinical trial was conducted at the University of California, San Francisco Medical Center from March 11 to November 24, 2019. Participants included physicians who served on the primary team (eg, attending, resident) of 1699 patients on the general medical service who were outside of the intensive care unit (ICU). The hospital encounters were randomized (allocation ratio of 1:1) to sleep promotion vitals CDS (SPV CDS) intervention or usual care.

Main outcome and measures: The primary outcome was delirium as determined by bedside nurse assessment using the Nursing Delirium Screening Scale (Nu-DESC) recorded once per nursing shift. The Nu-DESC is a standardized delirium screening tool that defines delirium with a score ≥2. Secondary outcomes included sleep opportunity (ie, EHR-based sleep metrics that reflected the maximum time between iatrogenic interruptions, such as nighttime vital sign checks) and patient satisfaction (ie, patient satisfaction measured by standardized Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS] survey). Potential balancing outcomes were assessed to ensure that reduced vital sign checks were not causing harms; these included ICU transfers, rapid response calls, and code blue alarms. All analyses were conducted on the basis of intention-to-treat.

Main results: A total of 3025 inpatient encounters were screened and 1930 encounters were randomized (966 SPV CDS intervention; 964 usual care). The randomized encounters consisted of 1699 patients; demographic factors between the 2 trial arms were similar. Specifically, the intervention arm included 566 men (59%) and mean (SD) age was 53 (15) years. The incidence of delirium was similar between the intervention and usual care arms: 108 (11%) vs 123 (13%) (P = .32). Compared to the usual care arm, the intervention arm had a higher mean (SD) number of sleep opportunity hours per night (4.95 [1.45] vs 4.57 [1.30], P < .001) and fewer nighttime vital sign checks (0.97 [0.95] vs 1.41 [0.86], P < .001). The post-discharge HCAHPS survey measuring patient satisfaction was completed by only 5% of patients (53 intervention, 49 usual care), and survey results were similar between the 2 arms (P = .86). In addition, safety outcomes including ICU transfers (49 [5%] vs 47 [5%], P = .92), rapid response calls (68 [7%] vs 55 [6%], P = .27), and code blue alarms (2 [0.2%] vs 9 [0.9%], P = .07) were similar between the study arms.

Conclusion: In this randomized clinical trial, a CDS tool utilizing a real-time prediction algorithm embedded in EHR did not reduce the incidence of delirium in hospitalized patients. However, this SPV CDS intervention helped physicians identify clinically stable patients who can forgo routine nighttime vital sign checks and facilitated greater opportunity for patients to sleep. These findings suggest that augmenting physician judgment using a real-time prediction algorithm can help to improve sleep opportunity without an accompanying increased risk of clinical decompensation during acute care.

Commentary

High-quality sleep is fundamental to health and well-being. Sleep deprivation and disorders are associated with many adverse health outcomes, including increased risks for obesity, diabetes, hypertension, myocardial infarction, and depression.¹ In hospitalized patients who are acutely ill, restorative sleep is critical to facilitating recovery. However, poor sleep is exceedingly common in hospitalized patients and is associated with deleterious outcomes, such as high blood pressure, hyperglycemia, and delirium.^2,3 Moreover, some of these adverse sleep-induced cardiometabolic outcomes, as well as sleep disruption itself, may persist after hospital discharge.⁴ Factors that precipitate interrupted sleep during hospitalization include iatrogenic causes such as frequent vital sign checks, nighttime procedures or early morning blood draws, and environmental factors such as loud ambient noise.³ Thus, a potential intervention to improve sleep quality in the hospital is to reduce nighttime interruptions such as frequent vital sign checks.

In the current study, Najafi and colleagues conducted a randomized trial to evaluate whether a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, can be utilized to identify patients in whom vital sign checks can be safely discontinued at nighttime. The authors found a modest but statistically significant reduction in the number of nighttime vital sign checks in patients who underwent the SPV CDS intervention, and a corresponding higher sleep opportunity per night in those who received the intervention. Importantly, this reduction in nighttime vital sign checks did not cause a higher risk of clinical decompensation as measured by ICU transfers, rapid response calls, or code blue alarms. Thus, the results demonstrated the feasibility of using a real-time, patient data-driven CDS tool to augment physician judgment in managing sleep disruption, an important hospital-associated stressor and a common hazard of hospitalization in older patients.

Delirium is a common clinical problem in hospitalized older patients that is associated with prolonged hospitalization, functional and cognitive decline, institutionalization, death, and increased health care costs.⁵ Despite a potential benefit of SPV CDS intervention in reducing vital sign checks and increasing sleep opportunity, this intervention did not reduce the incidence of delirium in hospitalized patients. This finding is not surprising given that delirium has a multifactorial etiology (eg, metabolic derangements, infections, medication side effects and drug toxicity, hospital environment). A small modification in nighttime vital sign checks and sleep opportunity may have limited impact on optimizing sleep quality and does not address other risk factors for delirium. As such, a multicomponent nonpharmacologic approach that includes sleep enhancement, early mobilization, feeding assistance, fluid repletion, infection prevention, and other interventions should guide delirium prevention in the hospital setting. The SPV CDS intervention may play a role in the delivery of a multifaceted, nonpharmacologic delirium prevention intervention in high-risk individuals.

Sleep disruption is one of the multiple hazards of hospitalization frequently experience by hospitalized older patients. Other hazards, or hospital-associated stressors, include mobility restriction (eg, physical restraints such as urinary catheters and intravenous lines, bed elevation and rails), malnourishment and dehydration (eg, frequent use of no-food-by-mouth order, lack of easy access to hydration), and pain (eg, poor pain control). Extended exposures to these stressors may lead to a maladaptive state called allostatic overload that transiently increases vulnerability to post-hospitalization adverse events, including emergency department use, hospital readmission, or death (ie, post-hospital syndrome).⁶ Thus, the optimization of sleep during hospitalization in vulnerable patients may have benefits that extend beyond delirium prevention. It is perceivable that a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, may be applied to reduce some of these hazards of hospitalization in addition to improving sleep opportunity.

Applications for Clinical Practice

Findings from the current study indicate that a CDS tool embedded in EHR that utilizes a real-time prediction algorithm of patient data may help to safely improve sleep opportunity in hospitalized patients. The participants in the current study were relatively young (53 [15] years). Given that age is a risk factor for delirium, the effects of this intervention on delirium prevention in the most susceptible population (ie, those over the age of 65) remain unknown and further investigation is warranted. Additional studies are needed to determine whether this approach yields similar results in geriatric patients and improves clinical outcomes.

—Fred Ko, MD

Study Overview

Objective: This study evaluated whether a clinical-decision-support (CDS) tool that utilizes a real-time algorithm incorporating patient vital sign data can identify hospitalized patients who can forgo overnight vital sign checks and thus reduce delirium incidence.

Design: This was a parallel randomized clinical trial of adult inpatients admitted to the general medical service of a tertiary care academic medical center in the United States. The trial intervention consisted of a CDS notification in the electronic health record (EHR) that informed the physician if a patient had a high likelihood of nighttime vital signs within the reference ranges based on a logistic regression model of real-time patient data input. This notification provided the physician an opportunity to discontinue nighttime vital sign checks, dismiss the notification for 1 hour, or dismiss the notification until the next day.

Setting and participants: This clinical trial was conducted at the University of California, San Francisco Medical Center from March 11 to November 24, 2019. Participants included physicians who served on the primary team (eg, attending, resident) of 1699 patients on the general medical service who were outside of the intensive care unit (ICU). The hospital encounters were randomized (allocation ratio of 1:1) to sleep promotion vitals CDS (SPV CDS) intervention or usual care.

Main outcome and measures: The primary outcome was delirium as determined by bedside nurse assessment using the Nursing Delirium Screening Scale (Nu-DESC) recorded once per nursing shift. The Nu-DESC is a standardized delirium screening tool that defines delirium with a score ≥2. Secondary outcomes included sleep opportunity (ie, EHR-based sleep metrics that reflected the maximum time between iatrogenic interruptions, such as nighttime vital sign checks) and patient satisfaction (ie, patient satisfaction measured by standardized Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS] survey). Potential balancing outcomes were assessed to ensure that reduced vital sign checks were not causing harms; these included ICU transfers, rapid response calls, and code blue alarms. All analyses were conducted on the basis of intention-to-treat.

Main results: A total of 3025 inpatient encounters were screened and 1930 encounters were randomized (966 SPV CDS intervention; 964 usual care). The randomized encounters consisted of 1699 patients; demographic factors between the 2 trial arms were similar. Specifically, the intervention arm included 566 men (59%) and mean (SD) age was 53 (15) years. The incidence of delirium was similar between the intervention and usual care arms: 108 (11%) vs 123 (13%) (P = .32). Compared to the usual care arm, the intervention arm had a higher mean (SD) number of sleep opportunity hours per night (4.95 [1.45] vs 4.57 [1.30], P < .001) and fewer nighttime vital sign checks (0.97 [0.95] vs 1.41 [0.86], P < .001). The post-discharge HCAHPS survey measuring patient satisfaction was completed by only 5% of patients (53 intervention, 49 usual care), and survey results were similar between the 2 arms (P = .86). In addition, safety outcomes including ICU transfers (49 [5%] vs 47 [5%], P = .92), rapid response calls (68 [7%] vs 55 [6%], P = .27), and code blue alarms (2 [0.2%] vs 9 [0.9%], P = .07) were similar between the study arms.

Conclusion: In this randomized clinical trial, a CDS tool utilizing a real-time prediction algorithm embedded in EHR did not reduce the incidence of delirium in hospitalized patients. However, this SPV CDS intervention helped physicians identify clinically stable patients who can forgo routine nighttime vital sign checks and facilitated greater opportunity for patients to sleep. These findings suggest that augmenting physician judgment using a real-time prediction algorithm can help to improve sleep opportunity without an accompanying increased risk of clinical decompensation during acute care.

Commentary

High-quality sleep is fundamental to health and well-being. Sleep deprivation and disorders are associated with many adverse health outcomes, including increased risks for obesity, diabetes, hypertension, myocardial infarction, and depression.¹ In hospitalized patients who are acutely ill, restorative sleep is critical to facilitating recovery. However, poor sleep is exceedingly common in hospitalized patients and is associated with deleterious outcomes, such as high blood pressure, hyperglycemia, and delirium.^2,3 Moreover, some of these adverse sleep-induced cardiometabolic outcomes, as well as sleep disruption itself, may persist after hospital discharge.⁴ Factors that precipitate interrupted sleep during hospitalization include iatrogenic causes such as frequent vital sign checks, nighttime procedures or early morning blood draws, and environmental factors such as loud ambient noise.³ Thus, a potential intervention to improve sleep quality in the hospital is to reduce nighttime interruptions such as frequent vital sign checks.

In the current study, Najafi and colleagues conducted a randomized trial to evaluate whether a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, can be utilized to identify patients in whom vital sign checks can be safely discontinued at nighttime. The authors found a modest but statistically significant reduction in the number of nighttime vital sign checks in patients who underwent the SPV CDS intervention, and a corresponding higher sleep opportunity per night in those who received the intervention. Importantly, this reduction in nighttime vital sign checks did not cause a higher risk of clinical decompensation as measured by ICU transfers, rapid response calls, or code blue alarms. Thus, the results demonstrated the feasibility of using a real-time, patient data-driven CDS tool to augment physician judgment in managing sleep disruption, an important hospital-associated stressor and a common hazard of hospitalization in older patients.

Delirium is a common clinical problem in hospitalized older patients that is associated with prolonged hospitalization, functional and cognitive decline, institutionalization, death, and increased health care costs.⁵ Despite a potential benefit of SPV CDS intervention in reducing vital sign checks and increasing sleep opportunity, this intervention did not reduce the incidence of delirium in hospitalized patients. This finding is not surprising given that delirium has a multifactorial etiology (eg, metabolic derangements, infections, medication side effects and drug toxicity, hospital environment). A small modification in nighttime vital sign checks and sleep opportunity may have limited impact on optimizing sleep quality and does not address other risk factors for delirium. As such, a multicomponent nonpharmacologic approach that includes sleep enhancement, early mobilization, feeding assistance, fluid repletion, infection prevention, and other interventions should guide delirium prevention in the hospital setting. The SPV CDS intervention may play a role in the delivery of a multifaceted, nonpharmacologic delirium prevention intervention in high-risk individuals.

Sleep disruption is one of the multiple hazards of hospitalization frequently experience by hospitalized older patients. Other hazards, or hospital-associated stressors, include mobility restriction (eg, physical restraints such as urinary catheters and intravenous lines, bed elevation and rails), malnourishment and dehydration (eg, frequent use of no-food-by-mouth order, lack of easy access to hydration), and pain (eg, poor pain control). Extended exposures to these stressors may lead to a maladaptive state called allostatic overload that transiently increases vulnerability to post-hospitalization adverse events, including emergency department use, hospital readmission, or death (ie, post-hospital syndrome).⁶ Thus, the optimization of sleep during hospitalization in vulnerable patients may have benefits that extend beyond delirium prevention. It is perceivable that a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, may be applied to reduce some of these hazards of hospitalization in addition to improving sleep opportunity.

Applications for Clinical Practice

Findings from the current study indicate that a CDS tool embedded in EHR that utilizes a real-time prediction algorithm of patient data may help to safely improve sleep opportunity in hospitalized patients. The participants in the current study were relatively young (53 [15] years). Given that age is a risk factor for delirium, the effects of this intervention on delirium prevention in the most susceptible population (ie, those over the age of 65) remain unknown and further investigation is warranted. Additional studies are needed to determine whether this approach yields similar results in geriatric patients and improves clinical outcomes.

—Fred Ko, MD

Sleep experts tend to agree with U.S. lawmakers about getting rid of the twice-per-year time shift, with one exception: They typically call for standard time rather than daylight saving time.

After the Senate voted unanimously on March 15 to make daylight saving time permanent, the American Academy of Sleep Medicine issued a statement that urged caution about adopting a fixed, year-round time with potential health risks.

“We do applaud stopping the switching during the course of the year and settling on a permanent time,” Jocelyn Cheng, MD, a member of the association’s public safety committee, told The Washington Post.

But “standard time, for so many scientific and circadian rationales and public health safety reasons, should really be what the permanent time is set to,” she said.

Now it’s up to the House of Representatives to decide what to do next. The legislation, which would take effect in 2023, must be passed by the House and signed by President Joe Biden before becoming a law.

Legislators and health experts have debated the shift in recent years. In 2020, the American Academy of Sleep Medicine released a position statement in the Journal of Clinical Sleep Medicine that recommended that the United States move to year-round standard time. Standard time is more aligned with humans’ circadian rhythms and natural light/dark cycles, the group wrote, and disrupting that rhythm has been linked to higher risks of heart disease, obesity, and depression.

At the same time, few studies have focused on the long-term effects of adopting daylight saving time. Most research has focused on the short-term risks of the seasonal shift, such as reduced sleep and increased car crashes, or circadian misalignment caused by other things. Some health experts have called for more research before deciding on a permanent time, the newspaper reported.

Still, the March 15 statement from sleep experts received support from more than 20 groups, including the National Safety Council, National Parent Teacher Association, and the World Sleep Society.

“We have all enjoyed those summer evenings with seemingly endless dusks,” David Neubauer, MD, an associate professor of psychiatry and behavioral sciences at Johns Hopkins University, Baltimore, told the Post.

But daylight saving time “does not ‘save’ evening light at all, it simply steals it from the morning, when it is necessary to maintain our healthy biological rhythms,” he said.

Permanent daylight saving time would lead to more dark mornings, which opponents have said could be dangerous for kids going to school, adults driving to work, and overall sleep cycles.

“With daylight saving time, we are perpetually out of synchronization with our internal clocks, and we often achieve less nighttime sleep, both circumstances having negative health impacts,” Dr. Neubauer said. “Extra evening light suppresses the melatonin that should be preparing us for falling asleep. The later dawn during daylight saving time deprives our biological clocks of the critical light signal.”

The pros and cons of daylight saving time and standard time were debated during a hearing held by a House Energy and Commerce subcommittee recently. Sleep experts argued in favor of standard time, while other industry experts argued for daylight saving time to reduce crime, save energy, and help businesses that benefit from more daylight in the evenings.

“Everybody advocates a permanent time, but this difference between 1 hour back or 1 hour forward is not so clear in everybody’s mind,” Dr. Cheng said. “I would like to see further debate and some due diligence done on these health consequences and public safety measures before anything else goes forward.”

A version of this article first appeared on WebMD.com.

Sleep experts tend to agree with U.S. lawmakers about getting rid of the twice-per-year time shift, with one exception: They typically call for standard time rather than daylight saving time.

After the Senate voted unanimously on March 15 to make daylight saving time permanent, the American Academy of Sleep Medicine issued a statement that urged caution about adopting a fixed, year-round time with potential health risks.

“We do applaud stopping the switching during the course of the year and settling on a permanent time,” Jocelyn Cheng, MD, a member of the association’s public safety committee, told The Washington Post.

But “standard time, for so many scientific and circadian rationales and public health safety reasons, should really be what the permanent time is set to,” she said.

Now it’s up to the House of Representatives to decide what to do next. The legislation, which would take effect in 2023, must be passed by the House and signed by President Joe Biden before becoming a law.

Legislators and health experts have debated the shift in recent years. In 2020, the American Academy of Sleep Medicine released a position statement in the Journal of Clinical Sleep Medicine that recommended that the United States move to year-round standard time. Standard time is more aligned with humans’ circadian rhythms and natural light/dark cycles, the group wrote, and disrupting that rhythm has been linked to higher risks of heart disease, obesity, and depression.

At the same time, few studies have focused on the long-term effects of adopting daylight saving time. Most research has focused on the short-term risks of the seasonal shift, such as reduced sleep and increased car crashes, or circadian misalignment caused by other things. Some health experts have called for more research before deciding on a permanent time, the newspaper reported.

Still, the March 15 statement from sleep experts received support from more than 20 groups, including the National Safety Council, National Parent Teacher Association, and the World Sleep Society.

“We have all enjoyed those summer evenings with seemingly endless dusks,” David Neubauer, MD, an associate professor of psychiatry and behavioral sciences at Johns Hopkins University, Baltimore, told the Post.

But daylight saving time “does not ‘save’ evening light at all, it simply steals it from the morning, when it is necessary to maintain our healthy biological rhythms,” he said.

Permanent daylight saving time would lead to more dark mornings, which opponents have said could be dangerous for kids going to school, adults driving to work, and overall sleep cycles.

“With daylight saving time, we are perpetually out of synchronization with our internal clocks, and we often achieve less nighttime sleep, both circumstances having negative health impacts,” Dr. Neubauer said. “Extra evening light suppresses the melatonin that should be preparing us for falling asleep. The later dawn during daylight saving time deprives our biological clocks of the critical light signal.”

The pros and cons of daylight saving time and standard time were debated during a hearing held by a House Energy and Commerce subcommittee recently. Sleep experts argued in favor of standard time, while other industry experts argued for daylight saving time to reduce crime, save energy, and help businesses that benefit from more daylight in the evenings.

“Everybody advocates a permanent time, but this difference between 1 hour back or 1 hour forward is not so clear in everybody’s mind,” Dr. Cheng said. “I would like to see further debate and some due diligence done on these health consequences and public safety measures before anything else goes forward.”

A version of this article first appeared on WebMD.com.

Sleep experts tend to agree with U.S. lawmakers about getting rid of the twice-per-year time shift, with one exception: They typically call for standard time rather than daylight saving time.

After the Senate voted unanimously on March 15 to make daylight saving time permanent, the American Academy of Sleep Medicine issued a statement that urged caution about adopting a fixed, year-round time with potential health risks.

“We do applaud stopping the switching during the course of the year and settling on a permanent time,” Jocelyn Cheng, MD, a member of the association’s public safety committee, told The Washington Post.

But “standard time, for so many scientific and circadian rationales and public health safety reasons, should really be what the permanent time is set to,” she said.

Now it’s up to the House of Representatives to decide what to do next. The legislation, which would take effect in 2023, must be passed by the House and signed by President Joe Biden before becoming a law.

Legislators and health experts have debated the shift in recent years. In 2020, the American Academy of Sleep Medicine released a position statement in the Journal of Clinical Sleep Medicine that recommended that the United States move to year-round standard time. Standard time is more aligned with humans’ circadian rhythms and natural light/dark cycles, the group wrote, and disrupting that rhythm has been linked to higher risks of heart disease, obesity, and depression.

At the same time, few studies have focused on the long-term effects of adopting daylight saving time. Most research has focused on the short-term risks of the seasonal shift, such as reduced sleep and increased car crashes, or circadian misalignment caused by other things. Some health experts have called for more research before deciding on a permanent time, the newspaper reported.

Still, the March 15 statement from sleep experts received support from more than 20 groups, including the National Safety Council, National Parent Teacher Association, and the World Sleep Society.

“We have all enjoyed those summer evenings with seemingly endless dusks,” David Neubauer, MD, an associate professor of psychiatry and behavioral sciences at Johns Hopkins University, Baltimore, told the Post.

But daylight saving time “does not ‘save’ evening light at all, it simply steals it from the morning, when it is necessary to maintain our healthy biological rhythms,” he said.

Permanent daylight saving time would lead to more dark mornings, which opponents have said could be dangerous for kids going to school, adults driving to work, and overall sleep cycles.

“With daylight saving time, we are perpetually out of synchronization with our internal clocks, and we often achieve less nighttime sleep, both circumstances having negative health impacts,” Dr. Neubauer said. “Extra evening light suppresses the melatonin that should be preparing us for falling asleep. The later dawn during daylight saving time deprives our biological clocks of the critical light signal.”

The pros and cons of daylight saving time and standard time were debated during a hearing held by a House Energy and Commerce subcommittee recently. Sleep experts argued in favor of standard time, while other industry experts argued for daylight saving time to reduce crime, save energy, and help businesses that benefit from more daylight in the evenings.

“Everybody advocates a permanent time, but this difference between 1 hour back or 1 hour forward is not so clear in everybody’s mind,” Dr. Cheng said. “I would like to see further debate and some due diligence done on these health consequences and public safety measures before anything else goes forward.”

A version of this article first appeared on WebMD.com.

Continuous positive airway pressure (CPAP) may be only modestly effective for ameliorating metabolic syndrome in patients with moderate to severe obstructive sleep apnea (OSA).

That conclusion comes from investigators in a randomized controlled, trial, who found that, among 100 patients with OSA and a recent diagnosis of metabolic syndrome (MS), 18% of those assigned to use CPAP at night had a reversal of MS at 6 months of follow-up, compared with 4% of controls who were assigned to use nasal strips at night (P = .04).

The majority of patients assigned to CPAP still retained their MS diagnoses at 6 months, and CPAP did not significantly reduce individual components of the syndrome. Use of CPAP was, however, associated with small reductions in visceral fat and improvement in endothelial function, reported Sara Q.C. Giampa, PhD, from the University of São Paulo, and colleagues.

“Despite a significant rate of MS reversibility after CPAP therapy, most of the patients maintained the MS diagnosis. The modest effects of CPAP on MS reversibility underscore the need for combined therapy with CPAP, aiming to maximize metabolic syndrome recovery in parallel with improvements in OSA severity and related symptoms,” according to their study, reported in the journal CHEST^®.

Asked whether he still recommends CPAP to patients with OSA and the metabolic syndrome, given the findings, corresponding author Luciano F. Drager, MD, PhD, replied “yes, definitely.”

“Despite the modest rate in reversing metabolic syndrome after CPAP, the rate was 5-fold higher than non-effective treatment (18% vs. 4%),” he said in an interview.

Dr. Drager noted that studies of other single interventions such as physical exercise to reverse MS in patients with OSA also had modest results.

A researcher who studies the relationship between sleep, circadian rhythms, and metabolism commented that, although the patients in the CPAP group were compliant with the assigned equipment and had both reductions in apneic events and improvement in oxygen saturation, the effect of CPAP on the metabolic syndrome was rather small.

“The CPAP was doing what we thought it was supposed to do, but it didn’t have the magnitude of effect on the metabolic syndrome as I expected or I think as the authors expected,” said Deanna Arble, PhD, assistant professor of biological science at Marquette University, Milwaukee.

She noted that the study also failed to detect a significant improvement in the blood pressure component of metabolic syndrome.

“In my experience and my review of the literature, blood pressure tends to be the one that’s improved most dramatically with CPAP,” she said.

Dr. Arble was not involved in the study.

Study details

In the trial, titled TREATOSA-MS, the investigators enrolled 100 patients with a recent diagnosis of metabolic syndrome and moderate to severe OSA, defined as 15 or more apnea-hypopnea index events per hour. The patients were stratified by body mass index and then randomized to undergo therapeutic CPAP or to use nasal strips for 6 months.

At baseline and at the end of each intervention investigators measured anthropometric variables, blood pressure, glucose, and lipid profiles. They also leptin and adiponectin, body composition, food intake, physical activity, subcutaneous and abdominal fat (visceral and hepatic), and endothelial function to control for potential confounders.

As noted previously, they found that after 6 months “most patients with OSA randomized to CPAP retained the MS diagnosis, but the rate of MS reversibility was higher than observed in the placebo group.” The difference in metabolic syndrome reversal, 18% with CPAP versus 4% with nasal strips, translated into a hazard ratio favoring CPAP of 5.27 (P = .04).

Also as noted, in analyses adjusted for baseline values, CPAP did not significantly improve either weight, liver fat, lip profiles, or the adiposity biomarkers leptin and adiponectin, but did have “very modest” influence on reducing visceral fat and improving endothelial function.
 

Rigorous study

Dr. Arble said that most studies of the association between OSA and metabolic syndrome have focused on only one or two of the parameters that were included in the TREATOSA-MS study, giving the findings additional weight.

“This could potentially be a very good, carefully controlled first insight into how obstructive sleep apnea is related to the metabolic syndrome,” she said.

The study was funded by grants Fundação de Amparo Q22 à Pesquisa do Estado de São Paulo and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. The authors and Dr. Arble reported having no conflicts of interest to disclose.

Continuous positive airway pressure (CPAP) may be only modestly effective for ameliorating metabolic syndrome in patients with moderate to severe obstructive sleep apnea (OSA).

That conclusion comes from investigators in a randomized controlled, trial, who found that, among 100 patients with OSA and a recent diagnosis of metabolic syndrome (MS), 18% of those assigned to use CPAP at night had a reversal of MS at 6 months of follow-up, compared with 4% of controls who were assigned to use nasal strips at night (P = .04).

The majority of patients assigned to CPAP still retained their MS diagnoses at 6 months, and CPAP did not significantly reduce individual components of the syndrome. Use of CPAP was, however, associated with small reductions in visceral fat and improvement in endothelial function, reported Sara Q.C. Giampa, PhD, from the University of São Paulo, and colleagues.

“Despite a significant rate of MS reversibility after CPAP therapy, most of the patients maintained the MS diagnosis. The modest effects of CPAP on MS reversibility underscore the need for combined therapy with CPAP, aiming to maximize metabolic syndrome recovery in parallel with improvements in OSA severity and related symptoms,” according to their study, reported in the journal CHEST^®.

Asked whether he still recommends CPAP to patients with OSA and the metabolic syndrome, given the findings, corresponding author Luciano F. Drager, MD, PhD, replied “yes, definitely.”

“Despite the modest rate in reversing metabolic syndrome after CPAP, the rate was 5-fold higher than non-effective treatment (18% vs. 4%),” he said in an interview.

Dr. Drager noted that studies of other single interventions such as physical exercise to reverse MS in patients with OSA also had modest results.

A researcher who studies the relationship between sleep, circadian rhythms, and metabolism commented that, although the patients in the CPAP group were compliant with the assigned equipment and had both reductions in apneic events and improvement in oxygen saturation, the effect of CPAP on the metabolic syndrome was rather small.

“The CPAP was doing what we thought it was supposed to do, but it didn’t have the magnitude of effect on the metabolic syndrome as I expected or I think as the authors expected,” said Deanna Arble, PhD, assistant professor of biological science at Marquette University, Milwaukee.

She noted that the study also failed to detect a significant improvement in the blood pressure component of metabolic syndrome.

“In my experience and my review of the literature, blood pressure tends to be the one that’s improved most dramatically with CPAP,” she said.

Dr. Arble was not involved in the study.

Study details

In the trial, titled TREATOSA-MS, the investigators enrolled 100 patients with a recent diagnosis of metabolic syndrome and moderate to severe OSA, defined as 15 or more apnea-hypopnea index events per hour. The patients were stratified by body mass index and then randomized to undergo therapeutic CPAP or to use nasal strips for 6 months.

At baseline and at the end of each intervention investigators measured anthropometric variables, blood pressure, glucose, and lipid profiles. They also leptin and adiponectin, body composition, food intake, physical activity, subcutaneous and abdominal fat (visceral and hepatic), and endothelial function to control for potential confounders.

As noted previously, they found that after 6 months “most patients with OSA randomized to CPAP retained the MS diagnosis, but the rate of MS reversibility was higher than observed in the placebo group.” The difference in metabolic syndrome reversal, 18% with CPAP versus 4% with nasal strips, translated into a hazard ratio favoring CPAP of 5.27 (P = .04).

Also as noted, in analyses adjusted for baseline values, CPAP did not significantly improve either weight, liver fat, lip profiles, or the adiposity biomarkers leptin and adiponectin, but did have “very modest” influence on reducing visceral fat and improving endothelial function.
 

Rigorous study

Dr. Arble said that most studies of the association between OSA and metabolic syndrome have focused on only one or two of the parameters that were included in the TREATOSA-MS study, giving the findings additional weight.

“This could potentially be a very good, carefully controlled first insight into how obstructive sleep apnea is related to the metabolic syndrome,” she said.

The study was funded by grants Fundação de Amparo Q22 à Pesquisa do Estado de São Paulo and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. The authors and Dr. Arble reported having no conflicts of interest to disclose.

Continuous positive airway pressure (CPAP) may be only modestly effective for ameliorating metabolic syndrome in patients with moderate to severe obstructive sleep apnea (OSA).

That conclusion comes from investigators in a randomized controlled, trial, who found that, among 100 patients with OSA and a recent diagnosis of metabolic syndrome (MS), 18% of those assigned to use CPAP at night had a reversal of MS at 6 months of follow-up, compared with 4% of controls who were assigned to use nasal strips at night (P = .04).

The majority of patients assigned to CPAP still retained their MS diagnoses at 6 months, and CPAP did not significantly reduce individual components of the syndrome. Use of CPAP was, however, associated with small reductions in visceral fat and improvement in endothelial function, reported Sara Q.C. Giampa, PhD, from the University of São Paulo, and colleagues.

“Despite a significant rate of MS reversibility after CPAP therapy, most of the patients maintained the MS diagnosis. The modest effects of CPAP on MS reversibility underscore the need for combined therapy with CPAP, aiming to maximize metabolic syndrome recovery in parallel with improvements in OSA severity and related symptoms,” according to their study, reported in the journal CHEST^®.

Asked whether he still recommends CPAP to patients with OSA and the metabolic syndrome, given the findings, corresponding author Luciano F. Drager, MD, PhD, replied “yes, definitely.”

“Despite the modest rate in reversing metabolic syndrome after CPAP, the rate was 5-fold higher than non-effective treatment (18% vs. 4%),” he said in an interview.

Dr. Drager noted that studies of other single interventions such as physical exercise to reverse MS in patients with OSA also had modest results.

A researcher who studies the relationship between sleep, circadian rhythms, and metabolism commented that, although the patients in the CPAP group were compliant with the assigned equipment and had both reductions in apneic events and improvement in oxygen saturation, the effect of CPAP on the metabolic syndrome was rather small.

“The CPAP was doing what we thought it was supposed to do, but it didn’t have the magnitude of effect on the metabolic syndrome as I expected or I think as the authors expected,” said Deanna Arble, PhD, assistant professor of biological science at Marquette University, Milwaukee.

She noted that the study also failed to detect a significant improvement in the blood pressure component of metabolic syndrome.

“In my experience and my review of the literature, blood pressure tends to be the one that’s improved most dramatically with CPAP,” she said.

Dr. Arble was not involved in the study.

Study details

In the trial, titled TREATOSA-MS, the investigators enrolled 100 patients with a recent diagnosis of metabolic syndrome and moderate to severe OSA, defined as 15 or more apnea-hypopnea index events per hour. The patients were stratified by body mass index and then randomized to undergo therapeutic CPAP or to use nasal strips for 6 months.

At baseline and at the end of each intervention investigators measured anthropometric variables, blood pressure, glucose, and lipid profiles. They also leptin and adiponectin, body composition, food intake, physical activity, subcutaneous and abdominal fat (visceral and hepatic), and endothelial function to control for potential confounders.

As noted previously, they found that after 6 months “most patients with OSA randomized to CPAP retained the MS diagnosis, but the rate of MS reversibility was higher than observed in the placebo group.” The difference in metabolic syndrome reversal, 18% with CPAP versus 4% with nasal strips, translated into a hazard ratio favoring CPAP of 5.27 (P = .04).

Also as noted, in analyses adjusted for baseline values, CPAP did not significantly improve either weight, liver fat, lip profiles, or the adiposity biomarkers leptin and adiponectin, but did have “very modest” influence on reducing visceral fat and improving endothelial function.
 

Rigorous study

Dr. Arble said that most studies of the association between OSA and metabolic syndrome have focused on only one or two of the parameters that were included in the TREATOSA-MS study, giving the findings additional weight.

“This could potentially be a very good, carefully controlled first insight into how obstructive sleep apnea is related to the metabolic syndrome,” she said.

The study was funded by grants Fundação de Amparo Q22 à Pesquisa do Estado de São Paulo and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. The authors and Dr. Arble reported having no conflicts of interest to disclose.

Sleeping with a light on can play havoc with insulin levels and consequently impair the response to glucose, a 2-night sleep-lab study of 20 people indicates.

“The most important finding” is that, compared with one night in a dim light environment, “one night of exposure to a moderate level of room light while sleeping with eyes closed increased heart rate and sympathetic [nervous system] activity during the entire sleep period,” said senior author Phyllis C. Zee, MD, PhD.

And on the morning following the moderate room light condition, a higher amount of insulin secretion was required to normalize glucose levels following ingestion of a bolus of glucose in an oral glucose tolerance test, consistent with higher insulin resistance, Dr. Zee, director of the center for circadian and sleep medicine at Northwestern University, Chicago, told this news organization in an email.

The study by Ivy C. Mason, PhD, also of Northwestern University, and colleagues was published March 14 in the Proceedings of the National Academy of Sciences.

Melatonin levels were similar under the two light conditions, Dr. Zee added, which “suggests that the effect of light during sleep on these cardiometabolic measures were more likely due to activation of the sympathetic [nervous] system and less likely due to changes in sleep or suppression of melatonin by light.”

“Attention to avoiding exposure to light at night during sleep may be beneficial for cardiometabolic health,” the researchers conclude.

That means “turn lights off before sleeping,” Dr. Zee elaborated. If a light is needed for safety reasons, keep it as dim as possible, she advises, and avoid exposure to blue or green light, but instead try red-amber colors.
 

How light during sleep may affect insulin, melatonin, heart rate

Several studies have investigated the effect of light on sleep and metabolic outcomes, the researchers explain.

In one study, light in the bedroom was associated with obesity in women, and in another study, it was associated with risk of type 2 diabetes in an elderly population.

Research has suggested that nighttime light exposure may alter glucose metabolism by increasing insulin resistance; lowering melatonin levels, which alters insulin secretion; and having an arousing effect on the sympathetic autonomic nervous system (increasing the stress hormone cortisol or heart rate, and decreasing heart rate variability).

However, the effect of a single night of moderate room light exposure across the entire nighttime sleep period has not been fully investigated.

The researchers enrolled and randomized 20 healthy young adults who were 18-40 years old and regularly went to sleep between 9 p.m. and 1 a.m. and slept 6.5-8.5 hours, to sleep 2 nights in the sleep laboratory under two conditions.

Ten participants (eight women, two men) slept in a dim light condition on night 1 and in a moderate light condition on night 2. The other 10 participants (six women, four men) slept 2 nights in the dim light condition.

The moderate light condition consisted of four 60-watt incandescent overhead ceiling light bulbs (a total of 100 lux), which “is bright enough to see, but not to read comfortably,” Dr. Zee explained. “It’s like hallway light in an apartment. But the people were sleeping, so about 90% of the light would be blocked by the eyelids.”

The dim light condition was less than 3 lux, which is dimmer than a night light.

When participants were awake, the room lighting was 240 lux.

Participants in each group were a mean age of 27 years and had a mean body mass index of 23 and 24 kg/m².

The week before the study, participants went to bed at 11 p.m. and slept for 7 hours (based on actigraphy measures). During the laboratory stay, the participants were allowed to sleep 8 hours, during which polysomnography was performed.  

They received standard meals at 2.5, 5, and 11 hours after waking and had 30 minutes to eat them. Snacking and caffeine were not permitted.

Participants were instructed to remain seated or standing in their room, but not exercise, when they were not sleeping. Blood samples to determine melatonin levels were collected hourly during wake and sleep via an intravenous line.

Participants slept for a similar time, around 7 hours, in both conditions.

Although melatonin levels were similar in both conditions, this was a relatively small sample, the researchers caution.

In the room light condition, participants spent proportionately more time in stage N2 sleep and less in slow-wave and rapid eye movement sleep. There was no increase in sleep fragmentation or arousals.

The research was partly supported by the Center for Circadian and Sleep Medicine at Northwestern University, the National Center for Advancing Translational Sciences, the National Institutes of Health, and the American Heart Association. The researchers have reported no relevant financial relationships.

A version of this article first appeared on Medscape.com.

Sleeping with a light on can play havoc with insulin levels and consequently impair the response to glucose, a 2-night sleep-lab study of 20 people indicates.

“The most important finding” is that, compared with one night in a dim light environment, “one night of exposure to a moderate level of room light while sleeping with eyes closed increased heart rate and sympathetic [nervous system] activity during the entire sleep period,” said senior author Phyllis C. Zee, MD, PhD.

And on the morning following the moderate room light condition, a higher amount of insulin secretion was required to normalize glucose levels following ingestion of a bolus of glucose in an oral glucose tolerance test, consistent with higher insulin resistance, Dr. Zee, director of the center for circadian and sleep medicine at Northwestern University, Chicago, told this news organization in an email.

The study by Ivy C. Mason, PhD, also of Northwestern University, and colleagues was published March 14 in the Proceedings of the National Academy of Sciences.

Melatonin levels were similar under the two light conditions, Dr. Zee added, which “suggests that the effect of light during sleep on these cardiometabolic measures were more likely due to activation of the sympathetic [nervous] system and less likely due to changes in sleep or suppression of melatonin by light.”

“Attention to avoiding exposure to light at night during sleep may be beneficial for cardiometabolic health,” the researchers conclude.

That means “turn lights off before sleeping,” Dr. Zee elaborated. If a light is needed for safety reasons, keep it as dim as possible, she advises, and avoid exposure to blue or green light, but instead try red-amber colors.
 

How light during sleep may affect insulin, melatonin, heart rate

Several studies have investigated the effect of light on sleep and metabolic outcomes, the researchers explain.

In one study, light in the bedroom was associated with obesity in women, and in another study, it was associated with risk of type 2 diabetes in an elderly population.

Research has suggested that nighttime light exposure may alter glucose metabolism by increasing insulin resistance; lowering melatonin levels, which alters insulin secretion; and having an arousing effect on the sympathetic autonomic nervous system (increasing the stress hormone cortisol or heart rate, and decreasing heart rate variability).

However, the effect of a single night of moderate room light exposure across the entire nighttime sleep period has not been fully investigated.

The researchers enrolled and randomized 20 healthy young adults who were 18-40 years old and regularly went to sleep between 9 p.m. and 1 a.m. and slept 6.5-8.5 hours, to sleep 2 nights in the sleep laboratory under two conditions.

Ten participants (eight women, two men) slept in a dim light condition on night 1 and in a moderate light condition on night 2. The other 10 participants (six women, four men) slept 2 nights in the dim light condition.

The moderate light condition consisted of four 60-watt incandescent overhead ceiling light bulbs (a total of 100 lux), which “is bright enough to see, but not to read comfortably,” Dr. Zee explained. “It’s like hallway light in an apartment. But the people were sleeping, so about 90% of the light would be blocked by the eyelids.”

The dim light condition was less than 3 lux, which is dimmer than a night light.

When participants were awake, the room lighting was 240 lux.

Participants in each group were a mean age of 27 years and had a mean body mass index of 23 and 24 kg/m².

The week before the study, participants went to bed at 11 p.m. and slept for 7 hours (based on actigraphy measures). During the laboratory stay, the participants were allowed to sleep 8 hours, during which polysomnography was performed.  

They received standard meals at 2.5, 5, and 11 hours after waking and had 30 minutes to eat them. Snacking and caffeine were not permitted.

Participants were instructed to remain seated or standing in their room, but not exercise, when they were not sleeping. Blood samples to determine melatonin levels were collected hourly during wake and sleep via an intravenous line.

Participants slept for a similar time, around 7 hours, in both conditions.

Although melatonin levels were similar in both conditions, this was a relatively small sample, the researchers caution.

In the room light condition, participants spent proportionately more time in stage N2 sleep and less in slow-wave and rapid eye movement sleep. There was no increase in sleep fragmentation or arousals.

The research was partly supported by the Center for Circadian and Sleep Medicine at Northwestern University, the National Center for Advancing Translational Sciences, the National Institutes of Health, and the American Heart Association. The researchers have reported no relevant financial relationships.

A version of this article first appeared on Medscape.com.

Sleeping with a light on can play havoc with insulin levels and consequently impair the response to glucose, a 2-night sleep-lab study of 20 people indicates.

“The most important finding” is that, compared with one night in a dim light environment, “one night of exposure to a moderate level of room light while sleeping with eyes closed increased heart rate and sympathetic [nervous system] activity during the entire sleep period,” said senior author Phyllis C. Zee, MD, PhD.

And on the morning following the moderate room light condition, a higher amount of insulin secretion was required to normalize glucose levels following ingestion of a bolus of glucose in an oral glucose tolerance test, consistent with higher insulin resistance, Dr. Zee, director of the center for circadian and sleep medicine at Northwestern University, Chicago, told this news organization in an email.

The study by Ivy C. Mason, PhD, also of Northwestern University, and colleagues was published March 14 in the Proceedings of the National Academy of Sciences.

Melatonin levels were similar under the two light conditions, Dr. Zee added, which “suggests that the effect of light during sleep on these cardiometabolic measures were more likely due to activation of the sympathetic [nervous] system and less likely due to changes in sleep or suppression of melatonin by light.”

“Attention to avoiding exposure to light at night during sleep may be beneficial for cardiometabolic health,” the researchers conclude.

That means “turn lights off before sleeping,” Dr. Zee elaborated. If a light is needed for safety reasons, keep it as dim as possible, she advises, and avoid exposure to blue or green light, but instead try red-amber colors.
 

How light during sleep may affect insulin, melatonin, heart rate

Several studies have investigated the effect of light on sleep and metabolic outcomes, the researchers explain.

In one study, light in the bedroom was associated with obesity in women, and in another study, it was associated with risk of type 2 diabetes in an elderly population.

Research has suggested that nighttime light exposure may alter glucose metabolism by increasing insulin resistance; lowering melatonin levels, which alters insulin secretion; and having an arousing effect on the sympathetic autonomic nervous system (increasing the stress hormone cortisol or heart rate, and decreasing heart rate variability).

However, the effect of a single night of moderate room light exposure across the entire nighttime sleep period has not been fully investigated.

The researchers enrolled and randomized 20 healthy young adults who were 18-40 years old and regularly went to sleep between 9 p.m. and 1 a.m. and slept 6.5-8.5 hours, to sleep 2 nights in the sleep laboratory under two conditions.

Ten participants (eight women, two men) slept in a dim light condition on night 1 and in a moderate light condition on night 2. The other 10 participants (six women, four men) slept 2 nights in the dim light condition.

The moderate light condition consisted of four 60-watt incandescent overhead ceiling light bulbs (a total of 100 lux), which “is bright enough to see, but not to read comfortably,” Dr. Zee explained. “It’s like hallway light in an apartment. But the people were sleeping, so about 90% of the light would be blocked by the eyelids.”

The dim light condition was less than 3 lux, which is dimmer than a night light.

When participants were awake, the room lighting was 240 lux.

Participants in each group were a mean age of 27 years and had a mean body mass index of 23 and 24 kg/m².

The week before the study, participants went to bed at 11 p.m. and slept for 7 hours (based on actigraphy measures). During the laboratory stay, the participants were allowed to sleep 8 hours, during which polysomnography was performed.  

They received standard meals at 2.5, 5, and 11 hours after waking and had 30 minutes to eat them. Snacking and caffeine were not permitted.

Participants were instructed to remain seated or standing in their room, but not exercise, when they were not sleeping. Blood samples to determine melatonin levels were collected hourly during wake and sleep via an intravenous line.

Participants slept for a similar time, around 7 hours, in both conditions.

Although melatonin levels were similar in both conditions, this was a relatively small sample, the researchers caution.

In the room light condition, participants spent proportionately more time in stage N2 sleep and less in slow-wave and rapid eye movement sleep. There was no increase in sleep fragmentation or arousals.

The research was partly supported by the Center for Circadian and Sleep Medicine at Northwestern University, the National Center for Advancing Translational Sciences, the National Institutes of Health, and the American Heart Association. The researchers have reported no relevant financial relationships.

A version of this article first appeared on Medscape.com.

CHICAGO – A randomized trial suggests resistance exercise promotes better sleep than other workouts among inactive adults, particularly those who are poor sleepers.

“We thought resistance exercise would be somewhere in the same neighborhood as aerobic exercise or that maybe combined exercise would be a little bit better but, no, it was consistently resistance exercise, on its own, that seemed to show the most benefits across the board,” Angelique Brellenthin, PhD, told this news organization.

The results were presented at the recent Epidemiology, Prevention/Lifestyle & Cardiometabolic Health meeting sponsored by the American Heart Association.

Even before the pandemic and bedtime “doom scrolling” took hold, research showed that a third of Americans regularly get less than 7 hours of sleep. The AHA recommends aerobic exercise to improve sleep and promote cardiovascular health, yet little is known on how it compares with other types of exercise in the general population, she said.

Dr. Brellenthin and coinvestigator Duck-chul Lee, PhD, both of Iowa State University in Ames, recruited 406 inactive adults, aged 35-70 years, who had obesity or overweight (mean body mass index, 31.2 kg/m²) and had elevated or stage 1 hypertension and randomly assigned them to no exercise or 60 minutes of supervised aerobic, resistance, or combination exercise three times per week for 12 months.

The aerobic exercise group could choose among treadmills, upright or recumbent bikes, and ellipticals, and the participants had their heart rate monitored to ensure they were continuously getting moderate- to vigorous-intensity exercise.

The resistance exercise group performed three sets of 8-16 repetitions at 50%-80% of their one-rep maximum on 12 resistance machines: a leg press, chest press, lat pulldown, leg curl, leg extension, biceps curl, triceps pushdown, shoulder press, abdominal crunch, lower back extension, torso rotation, and hip abduction.

The combination group did 30 minutes of aerobic exercise at moderate to vigorous intensity, and then two sets of 8-16 repetitions of resistance exercise on 9 machines instead of 12.

Exercise adherence over the year was 84%, 77%, and 85%, respectively.

Participants also completed the Pittsburgh Sleep Quality Index (PSQI) at baseline and 12 months. Among the 386 participants (53% women) with evaluable data, 35% had poor-quality sleep, as indicated by a global PSQI score of more than 5, and 42% regularly slept less than 7 hours per night.

In adjusted analyses, sleep duration at 12 months, on average, increased by 13 minutes in the resistance-exercise group (P = .009), decreased by 0.6 minute in the aerobic-exercise group, and increased by 2 minutes in the combined-exercise group and by 4 minutes in the control group.

Among participants who got less than 7 hours of sleep at baseline, however, sleep duration increased by 40 minutes (P < .0001), compared with increases of 23 minutes in the aerobic group, 17 minutes in the combined group, and 15 minutes in the control group.

Overall sleep efficiency, or the ratio of total sleep time to time in bed, improved in the resistance (P = .0005) and combined (P = .03) exercise groups, but not in the aerobic or control groups.

Sleep latency, or the time needed to fall asleep, decreased by 3 minutes in the resistance-exercise group, with no notable changes in the other groups.

Sleep quality and the number of sleep disturbances improved in all groups, including the control group. This could be due to simply being part of a health intervention, which included a month of lifestyle education classes, Dr. Brellenthin suggested.

It’s unclear why the aerobic-exercise group didn’t show greater gains, given the wealth of research showing it improves sleep, she said, but it had fewer poor sleepers at baseline than the resistance group (33% vs. 42%). “So it may be that people who were already getting good sleep didn’t have much room to improve.”

Among the poor-quality sleepers at baseline, resistance exercise significantly improved sleep quality (-2.4 vs. -1.0 points; P = .009) and duration (+36 vs. +3 minutes; P = .02), compared with the control group. It also improved sleep efficiency by 9.0%, compared with 0.9% in the control group (P = .002) and 8.0% for the combined-exercise group (P = .01).

“For a lot of people who know their sleep could be a bit better, this could be a place to start without resorting to medications, if they wanted to focus on a lifestyle intervention,” Dr. Brellenthin said.

It’s not fully understood how resistance exercise improves sleep, but it might contribute to better overall mental health and it might enhance the synthesis and release of certain hormones, such as testosterone and human growth hormone, which are associated with better sleep, Dr. Brellenthin said. Another hypothesis is that it causes direct microscopic damage to muscle tissue, forcing that tissue to adapt and grow over time. “So potentially that microscopic damage could provide that extra signal boost to the brain to replenish and repair, and get this person sleep.”

The study was limited by the use of self-reported sleep outcomes and a lack of detailed information on sleep medications, although 81% of participants reported taking no such medications.

The research was supported by a National Institutes of Health/National Heart, Lung, and Blood Institute grant to Dr. Lee. Dr. Brellenthin reports no relevant financial relationships.
 

A version of this article first appeared on Medscape.com.

CHICAGO – A randomized trial suggests resistance exercise promotes better sleep than other workouts among inactive adults, particularly those who are poor sleepers.

“We thought resistance exercise would be somewhere in the same neighborhood as aerobic exercise or that maybe combined exercise would be a little bit better but, no, it was consistently resistance exercise, on its own, that seemed to show the most benefits across the board,” Angelique Brellenthin, PhD, told this news organization.

The results were presented at the recent Epidemiology, Prevention/Lifestyle & Cardiometabolic Health meeting sponsored by the American Heart Association.

Even before the pandemic and bedtime “doom scrolling” took hold, research showed that a third of Americans regularly get less than 7 hours of sleep. The AHA recommends aerobic exercise to improve sleep and promote cardiovascular health, yet little is known on how it compares with other types of exercise in the general population, she said.

Dr. Brellenthin and coinvestigator Duck-chul Lee, PhD, both of Iowa State University in Ames, recruited 406 inactive adults, aged 35-70 years, who had obesity or overweight (mean body mass index, 31.2 kg/m²) and had elevated or stage 1 hypertension and randomly assigned them to no exercise or 60 minutes of supervised aerobic, resistance, or combination exercise three times per week for 12 months.

The aerobic exercise group could choose among treadmills, upright or recumbent bikes, and ellipticals, and the participants had their heart rate monitored to ensure they were continuously getting moderate- to vigorous-intensity exercise.

The resistance exercise group performed three sets of 8-16 repetitions at 50%-80% of their one-rep maximum on 12 resistance machines: a leg press, chest press, lat pulldown, leg curl, leg extension, biceps curl, triceps pushdown, shoulder press, abdominal crunch, lower back extension, torso rotation, and hip abduction.

The combination group did 30 minutes of aerobic exercise at moderate to vigorous intensity, and then two sets of 8-16 repetitions of resistance exercise on 9 machines instead of 12.

Exercise adherence over the year was 84%, 77%, and 85%, respectively.

Participants also completed the Pittsburgh Sleep Quality Index (PSQI) at baseline and 12 months. Among the 386 participants (53% women) with evaluable data, 35% had poor-quality sleep, as indicated by a global PSQI score of more than 5, and 42% regularly slept less than 7 hours per night.

In adjusted analyses, sleep duration at 12 months, on average, increased by 13 minutes in the resistance-exercise group (P = .009), decreased by 0.6 minute in the aerobic-exercise group, and increased by 2 minutes in the combined-exercise group and by 4 minutes in the control group.

Among participants who got less than 7 hours of sleep at baseline, however, sleep duration increased by 40 minutes (P < .0001), compared with increases of 23 minutes in the aerobic group, 17 minutes in the combined group, and 15 minutes in the control group.

Overall sleep efficiency, or the ratio of total sleep time to time in bed, improved in the resistance (P = .0005) and combined (P = .03) exercise groups, but not in the aerobic or control groups.

Sleep latency, or the time needed to fall asleep, decreased by 3 minutes in the resistance-exercise group, with no notable changes in the other groups.

Sleep quality and the number of sleep disturbances improved in all groups, including the control group. This could be due to simply being part of a health intervention, which included a month of lifestyle education classes, Dr. Brellenthin suggested.

It’s unclear why the aerobic-exercise group didn’t show greater gains, given the wealth of research showing it improves sleep, she said, but it had fewer poor sleepers at baseline than the resistance group (33% vs. 42%). “So it may be that people who were already getting good sleep didn’t have much room to improve.”

Among the poor-quality sleepers at baseline, resistance exercise significantly improved sleep quality (-2.4 vs. -1.0 points; P = .009) and duration (+36 vs. +3 minutes; P = .02), compared with the control group. It also improved sleep efficiency by 9.0%, compared with 0.9% in the control group (P = .002) and 8.0% for the combined-exercise group (P = .01).

“For a lot of people who know their sleep could be a bit better, this could be a place to start without resorting to medications, if they wanted to focus on a lifestyle intervention,” Dr. Brellenthin said.

It’s not fully understood how resistance exercise improves sleep, but it might contribute to better overall mental health and it might enhance the synthesis and release of certain hormones, such as testosterone and human growth hormone, which are associated with better sleep, Dr. Brellenthin said. Another hypothesis is that it causes direct microscopic damage to muscle tissue, forcing that tissue to adapt and grow over time. “So potentially that microscopic damage could provide that extra signal boost to the brain to replenish and repair, and get this person sleep.”

The study was limited by the use of self-reported sleep outcomes and a lack of detailed information on sleep medications, although 81% of participants reported taking no such medications.

The research was supported by a National Institutes of Health/National Heart, Lung, and Blood Institute grant to Dr. Lee. Dr. Brellenthin reports no relevant financial relationships.
 

A version of this article first appeared on Medscape.com.

CHICAGO – A randomized trial suggests resistance exercise promotes better sleep than other workouts among inactive adults, particularly those who are poor sleepers.

“We thought resistance exercise would be somewhere in the same neighborhood as aerobic exercise or that maybe combined exercise would be a little bit better but, no, it was consistently resistance exercise, on its own, that seemed to show the most benefits across the board,” Angelique Brellenthin, PhD, told this news organization.

The results were presented at the recent Epidemiology, Prevention/Lifestyle & Cardiometabolic Health meeting sponsored by the American Heart Association.

Even before the pandemic and bedtime “doom scrolling” took hold, research showed that a third of Americans regularly get less than 7 hours of sleep. The AHA recommends aerobic exercise to improve sleep and promote cardiovascular health, yet little is known on how it compares with other types of exercise in the general population, she said.

Dr. Brellenthin and coinvestigator Duck-chul Lee, PhD, both of Iowa State University in Ames, recruited 406 inactive adults, aged 35-70 years, who had obesity or overweight (mean body mass index, 31.2 kg/m²) and had elevated or stage 1 hypertension and randomly assigned them to no exercise or 60 minutes of supervised aerobic, resistance, or combination exercise three times per week for 12 months.

The aerobic exercise group could choose among treadmills, upright or recumbent bikes, and ellipticals, and the participants had their heart rate monitored to ensure they were continuously getting moderate- to vigorous-intensity exercise.

The resistance exercise group performed three sets of 8-16 repetitions at 50%-80% of their one-rep maximum on 12 resistance machines: a leg press, chest press, lat pulldown, leg curl, leg extension, biceps curl, triceps pushdown, shoulder press, abdominal crunch, lower back extension, torso rotation, and hip abduction.

The combination group did 30 minutes of aerobic exercise at moderate to vigorous intensity, and then two sets of 8-16 repetitions of resistance exercise on 9 machines instead of 12.

Exercise adherence over the year was 84%, 77%, and 85%, respectively.

Participants also completed the Pittsburgh Sleep Quality Index (PSQI) at baseline and 12 months. Among the 386 participants (53% women) with evaluable data, 35% had poor-quality sleep, as indicated by a global PSQI score of more than 5, and 42% regularly slept less than 7 hours per night.

In adjusted analyses, sleep duration at 12 months, on average, increased by 13 minutes in the resistance-exercise group (P = .009), decreased by 0.6 minute in the aerobic-exercise group, and increased by 2 minutes in the combined-exercise group and by 4 minutes in the control group.

Among participants who got less than 7 hours of sleep at baseline, however, sleep duration increased by 40 minutes (P < .0001), compared with increases of 23 minutes in the aerobic group, 17 minutes in the combined group, and 15 minutes in the control group.

Overall sleep efficiency, or the ratio of total sleep time to time in bed, improved in the resistance (P = .0005) and combined (P = .03) exercise groups, but not in the aerobic or control groups.

Sleep latency, or the time needed to fall asleep, decreased by 3 minutes in the resistance-exercise group, with no notable changes in the other groups.

Sleep quality and the number of sleep disturbances improved in all groups, including the control group. This could be due to simply being part of a health intervention, which included a month of lifestyle education classes, Dr. Brellenthin suggested.

It’s unclear why the aerobic-exercise group didn’t show greater gains, given the wealth of research showing it improves sleep, she said, but it had fewer poor sleepers at baseline than the resistance group (33% vs. 42%). “So it may be that people who were already getting good sleep didn’t have much room to improve.”

Among the poor-quality sleepers at baseline, resistance exercise significantly improved sleep quality (-2.4 vs. -1.0 points; P = .009) and duration (+36 vs. +3 minutes; P = .02), compared with the control group. It also improved sleep efficiency by 9.0%, compared with 0.9% in the control group (P = .002) and 8.0% for the combined-exercise group (P = .01).

“For a lot of people who know their sleep could be a bit better, this could be a place to start without resorting to medications, if they wanted to focus on a lifestyle intervention,” Dr. Brellenthin said.

It’s not fully understood how resistance exercise improves sleep, but it might contribute to better overall mental health and it might enhance the synthesis and release of certain hormones, such as testosterone and human growth hormone, which are associated with better sleep, Dr. Brellenthin said. Another hypothesis is that it causes direct microscopic damage to muscle tissue, forcing that tissue to adapt and grow over time. “So potentially that microscopic damage could provide that extra signal boost to the brain to replenish and repair, and get this person sleep.”

The study was limited by the use of self-reported sleep outcomes and a lack of detailed information on sleep medications, although 81% of participants reported taking no such medications.

The research was supported by a National Institutes of Health/National Heart, Lung, and Blood Institute grant to Dr. Lee. Dr. Brellenthin reports no relevant financial relationships.
 

A version of this article first appeared on Medscape.com.