Promoting Sleep Duration in the Pediatric Setting Using a Mobile Health Platform: A Randomized Optimization Trial
Mitchell, J. A.; Morales, K.; Williamson, A.; Jawahar, A.; Juste, L.; Vajravelu, M. E.; Zemel, B.; Dinges, D.; Fiks, A.
Show abstract
ObjectiveDetermine the optimal combination of digital health intervention component settings that increase average sleep duration by [≥]30 minutes per weeknight. MethodsOptimization trial using a 25 factorial design. The trial included 2 week run-in, 7 week intervention, and 2 week follow-up periods. Typically developing children aged 9-12y, with weeknight sleep duration <8.5 hours were enrolled (N=97). All received sleep monitoring and performance feedback. The five candidate intervention components (with their settings to which participants were randomized) were: 1) sleep goal (guideline-based or personalized); 2) screen time reduction messaging (inactive or active); 3) daily routine establishing messaging (inactive or active); 4) child-directed loss-framed financial incentive (inactive or active); and 5) caregiver-directed loss-framed financial incentive (inactive or active). The primary outcome was weeknight sleep duration (hours per night). The optimization criterion was: [≥]30 minutes average increase in sleep duration on weeknights. ResultsAverage baseline sleep duration was 7.7 hours per night. The highest ranked combination included the core intervention plus the following intervention components: sleep goal (either setting was effective), caregiver-directed loss-framed incentive, messaging to reduce screen time, and messaging to establish daily routines. This combination increased weeknight sleep duration by an average of 39.6 (95% CI: 36.0, 43.1) minutes during the intervention period and by 33.2 (95% CI: 28.9, 37.4) minutes during the follow-up period. ConclusionsOptimal combinations of digital health intervention component settings were identified that effectively increased weeknight sleep duration. This could be a valuable remote patient monitoring approach to treat insufficient sleep in the pediatric setting.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Methodological approach to sleep state misperception in insomnia disorder: comparison between multiple nights of actigraphy recordings and a single night of polysomnography recording 94%
- Effects of cognitive behavioral therapy for insomnia on subjective and objective measures of sleep and cognition 94%
- Effects of mindfulness meditation and Acceptance and Commitment Therapy in patients with obstructive sleep apnea with residual excessive sleepiness: A randomized controlled pilot study 91%
Similar papers in this journal
- The effects of daylight saving time clock changes on accelerometer-measured sleep duration in the UK Biobank 94%
- Sleep improvement for metabolic health: A feasibility trial of a digital sleep treatment in people with insomnia and non-diabetic hyperglycaemia. 94%
- Improving sleep after stroke: a randomised controlled trial of digital cognitive behavioural therapy for insomnia 92%
Similar papers in this journal
- Sleep in Frontline Healthcare Workers on Social Media During the COVID-19 Pandemic 94%
- Circadian Rhythm Analysis Using Wearable Device Data: A Novel Penalized Machine Learning Approach 92%
- Factors Associated with Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic 88%
Similar papers in this journal
- Temporal Trends in Racial and Ethnic Disparities in Sleep Duration in the United States, 2004–2018 93%
- Dementia Risk and Machine Learning-Derived Brain Age Index from Sleep Electroencephalography: A Pooled Cohort Analysis of Over 7,000 Individuals Across Five Community Cohorts 88%
- Effectiveness of a Text Message Intervention Promoting Seat Belt Use Among Targeted Young Adults: A Randomized Clinical Trial 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.