How are Consumer Sleep Technology Data Being Used to Deliver Behavioral Sleep Medicine Interventions? A Systematic Review

Baron, Kelly Glazer; Culnan, Elizabeth; Duffecy, Jennifer; Berendson, M.; Mason, Ivy C.; Lattie, Emily G.; Manalo, Natalie

doi:10.1080/15402002.2021.1898397

Cited by 14 publications

(13 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Use of wearable consumer sleep technology such as heart rate monitors with corresponding smartphone applications (eg, Fitbit, Jawbone Up devices, and the Whoop device) are increasing as a means of monitoring sleep as well as delivering insomnia interventions. 36 Actigraphy has been shown to produce significantly distinct results from selfreporting when measuring total sleep time, sleep-onset latency, wake-after-sleep onset, and sleep efficiency in adult and pediatric patients with insomnia. 37 Actigraphy yields distinct estimates of sleep patterns when compared to sleep logs, which suggests that while both measures are often correlated, actigraphy has utility in assessing sleep continuity in conjunction with sleep logs in terms of diagnostic and posttreatment assessment.…”

Section: Measuring Sleep Performancementioning

confidence: 99%

“…[25][26][27][28][29] Prescribe them at the lowest dose and for the shortest amount of time possible. 32,62 Avoid medications listed in TABLE 4 32,36,59,60,[62][63][64][65][66][67][68][69] because data showing clinically significant improvements in insomnia are lacking, and analysis for potential harms is inadequate. 32 ❚ Melatonin is not recommended for treating insomnia in adults, pregnant patients, older adults, or most children because its effects are clinically insignificant, 32 residual sedation has been reported, 60 and no analysis of harms has been undertaken.…”

Section: As a Sole Intervention Exercise For Insomnia Is Readily Avai...mentioning

confidence: 99%

“…Although antihistamines (eg, hydroxyzine, diphenhydramine) and antipsychotics frequently are prescribed off-label for primary insomnia, there is a lack of evidence to support either type of medication for this purpose. 36,62,73 H1-antihistamines such as hydroxyzine increase REM-onset latency and reduce the duration of REM sleep. 73 Depending on the specific medication, secondgeneration antipsychotics such as olanzapine and quetiapine have mixed effects on REM sleep parameters.…”

Section: As a Sole Intervention Exercise For Insomnia Is Readily Avai...mentioning

confidence: 99%

See 2 more Smart Citations

Insomnia diagnosis and treatment across the lifespan

Smith

Colistra

Shawver

et al. 2023

The Journal of Family Practice

View full text Add to dashboard Cite

Section: Measuring Sleep Performancementioning

confidence: 99%

Section: As a Sole Intervention Exercise For Insomnia Is Readily Avai...mentioning

confidence: 99%

Section: As a Sole Intervention Exercise For Insomnia Is Readily Avai...mentioning

confidence: 99%

See 1 more Smart Citation

Insomnia diagnosis and treatment across the lifespan

Smith

Colistra

Shawver

et al. 2023

The Journal of Family Practice

View full text Add to dashboard Cite

“…Not only are sleep disturbances correlated to underlying physical and mental health problems, but poor sleep behavior can actually increase the risk of developing a subsequent health issue [ 16 , 17 , 18 , 19 , 20 ]. Because of its relationship to health outcomes and also because sleep behavior can be easily changed by the individual, sleep is an attractive target for mHealth behavioral interventions [ 21 , 22 , 23 ]. Fourthly, body-worn sensors can more accurately measure a physiological signal if the wearer is in a resting state such as sleep [ 24 , 25 , 26 , 27 ].…”

Section: Sensors Vs Wearablesmentioning

confidence: 99%

Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market

Devine

Schwartz

Hursh

2022

Sensors

View full text Add to dashboard Cite

Sensors that track physiological biomarkers of health must be successfully incorporated into a fieldable, wearable device if they are to revolutionize the management of remote patient care and preventative medicine. This perspective article discusses logistical considerations that may impede the process of adapting a body-worn laboratory sensor into a commercial-integrated health monitoring system with a focus on examples from sleep tracking technology.

show abstract

“…Despite the rapid growth of mHealth apps and mobile sensing technologies, recent reviews have indicated that scientific trials examining the usefulness of mHealth apps are limited [32], and further research is required to test whether objective data enhance sleep outcomes [37]. This research investigating the usefulness of mHealth apps in enhancing sleep has several limitations.…”

Section: Remaining Issuesmentioning

confidence: 99%

The Effects of Objective Push-Type Sleep Feedback on Habitual Sleep Behavior and Momentary Symptoms in Daily Life: mHealth Intervention Trial Using a Health Care Internet of Things System

Takeuchi¹,

Suwa²,

Kishi³

et al. 2022

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background Sleep is beneficial for physical and mental health. Several mobile and wearable sleep-tracking devices have been developed, and personalized sleep feedback is the most common functionality among these devices. To date, no study has implemented an objective push-type feedback message and investigated the characteristics of habitual sleep behavior and diurnal symptoms when receiving sleep feedback. Objective We conducted a mobile health intervention trial to examine whether sending objective push-type sleep feedback changes the self-reported mood, physical symptoms, and sleep behavior of Japanese office workers. Methods In total, 31 office workers (mean age 42.3, SD 7.9 years; male-to-female ratio 21:10) participated in a 2-arm intervention trial from November 30 to December 19, 2020. The participants were instructed to indicate their momentary mood and physical symptoms (depressive mood, anxiety, stress, sleepiness, fatigue, and neck and shoulder stiffness) 5 times a day using a smartphone app. In addition, daily work performance was rated once a day after work. They were randomly assigned to either a feedback or control group, wherein they did or did not receive messages about their sleep status on the app every morning, respectively. All participants wore activity monitors on their nondominant wrists, through which objective sleep data were registered on the web on a server. On the basis of the estimated sleep data on the server, personalized sleep feedback messages were generated and sent to the participants in the feedback group using the app. These processes were fully automated. Results Using hierarchical statistical models, we examined the differences in the statistical properties of sleep variables (sleep duration and midpoint of sleep) and daily work performance over the trial period. Group differences in the diurnal slopes for mood and physical symptoms were examined using a linear mixed effect model. We found a significant group difference among within-individual residuals at the midpoint of sleep (expected a posteriori for the difference: −15, 95% credible interval −26 to −4 min), suggesting more stable sleep timing in the feedback group. However, there were no significant group differences in daily work performance. We also found significant group differences in the diurnal slopes for sleepiness (P<.001), fatigue (P=.002), and neck and shoulder stiffness (P<.001), which was largely due to better scores in the feedback group at wake-up time relative to those in the control group. Conclusions This is the first mobile health study to demonstrate that objective push-type sleep feedback improves sleep timing of and physical symptoms in healthy office workers. Future research should incorporate specific behavioral instructions intended to improve sleep habits and examine the effectiveness of these instructions.

show abstract

How are Consumer Sleep Technology Data Being Used to Deliver Behavioral Sleep Medicine Interventions? A Systematic Review

Cited by 14 publications

References 31 publications

Insomnia diagnosis and treatment across the lifespan

Insomnia diagnosis and treatment across the lifespan

Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market

The Effects of Objective Push-Type Sleep Feedback on Habitual Sleep Behavior and Momentary Symptoms in Daily Life: mHealth Intervention Trial Using a Health Care Internet of Things System

Contact Info

Product

Resources

About