Search

The impact of digital device use on health and well-being is a pressing question to which individuals, families, schools, policy makers, legislators, and digital designers are all demanding answers. However, the scientific literature on this topic to date is marred by small and/or unrepresentative samples, poor measurement of core constructs (e.g., device use, smartphone addiction), and a limited ability to address the psychological and behavioral mechanisms that may underlie the relationships between device use and well-being. A number of recent authoritative reviews have made urgent calls for future research projects to address these limitations. The critical role of research is to identify which patterns of use are associated with benefits versus risks, and who is more vulnerable to harmful versus beneficial outcomes, so that we can pursue evidence-based product design, education, and regulation aimed at maximizing benefits and minimizing risks of smartphones and other digital devices. We describe a protocol for a Digital Well-Being (DWB) study to help answer these questions.

ObjectivesAssess a program of sleep schedule recommendations and behavior change “nudges” algorithmically selected using passively collected, longitudinal sleep data. Improvements were primarily measured by sleep schedule adherence and changes in sleep health (quantified by the RU_SATED framework).MethodsThis study used a convenience sample of self-screening volunteers, responding to recruitment emails. Sleep data was gathered with a commercial under-mattress sensor through three phases: baseline passive data collection-2 weeks; intervention-4 weeks; maintenance monitoring passive data collection-8 weeks. The intervention included sleep schedule recommendations and SMS “nudges,” based on rules and recommendations derived from the extant literature. A daily sleep-health score (based on RU_SATED) was derived from passively collected sleep data and daily self-reports of alertness and sleep quality.ResultsTwenty-six participants (34.92 ± 10.08-years-old; 20M:6F) had adequate data for analysis. The main findings were: (1) Adherence—defined as a wake time (WT) within 30 min of the recommendation—rates did not differ significantly between the three study phases. However, there was a general decline in adherence over the course of the study, with adherence rates dropping by about 1.5%/week in a pattern of progressive delay of WTs. (2) Linear mixed models (LMMs) of individual sleep metrics did not demonstrate a significant change over the course of the intervention, possibly due to widely varying, yet relatively healthy, sleep patterns at baseline. (3) Comparatively, the composite, sleep-health (RU_SATED) score demonstrated general improvement over the intervention period, in association with higher rates of WT adherence.ConclusionsWhile, in general, adherence to a sleep schedule and individual dimensions of sleep health did not demonstrate meaningful improvements during the intervention phase, those individuals that were more consistently meeting the wake-time schedule recommendation had associated improvements in their overall sleep-health. As such, this pilot study demonstrates the feasibility and potential efficacy among more adherent individuals of implementing a sleep wellness coaching framework using passively collected sleep data and a rule-based coaching infrastructure.

Figure S1. An example to show that the QC array is highly reliable for ROH detection. Figure S2 Compound heterozygous deletions detected in patient WH12. (PPT 611 kb)

Sensitivity of CNV detection for the QC array (DOCX 40 kb)

Supplementary Table S1. Correlation of PCNV findings from CMA and the QC array in 496 ES cases (DOCX 44 kb)

Sensitivity of CNV detection for the QC array (DOCX 40 kb)

Supplementary Table S1. Correlation of PCNV findings from CMA and the QC array in 496 ES cases (DOCX 44 kb)

Figure S1. An example to show that the QC array is highly reliable for ROH detection. Figure S2 Compound heterozygous deletions detected in patient WH12. (PPT 611 kb)