Operational Trial of Dayzz App

Sleep deficiency is a hidden cost of our 24-7 society, with 70% of Americans admitting that they routinely obtain insufficient sleep, 30% of US workers report sleeping less than 6 hours per night, and it is estimated that 50-70 million individuals have a sleep disorder. Undiagnosed and untreated sleep disorders are associated with poor health outcomes for the individual and generate substantial costs for the employer. Diminished alertness resulting from sleep deficiency or undiagnosed and untreated sleep disorders contributes to absenteeism, presenteeism (diminished work performance at work), health care expenditure, occupational injuries, workplace accidents, and commute-related motor vehicle crashes; all of which result in substantial direct and indirect costs. The investigators have shown that adverse impacts on employees and employers can be mitigated through a sleep health education and sleep disorder screening program which have been implemented in a variety of industries. Further, smartphone apps are becoming popular. Dayzz has created a personalized sleep training app that evaluates sleep issues and offers a holistic sleep improvement solution. It is unique in that the sleep app uses evidence-based, big-data-based algorithms to provide personalized sleep training plans. The sleep solutions are tailored to the individual user and leverages unique motivational strategies and continuous supportive messaging. The investigators hypothesize that the Dayzz app, offering a personalized, scientifically based sleep training plan, might further promote improved health, safety and well-being. This clinical trial will implement the sleep health education and sleep disorders screening program in combination with the Dayzz app.

Specifically, the investigators will test the hypotheses that: Primary Aim 1: Early Dayzz participants who use the app will report positive changes in sleep behavior as compared to the Late Dayzz participants. Primary Aim 2: Early Dayzz participants who use the app will have increased sleep duration and sleep quality as compared to the Late Dayzz participants. Secondary Aim 1: Early Dayzz participants who use the app will have fewer unscheduled absences and higher performance/productivity as compared to the Late Dayzz participants. Secondary Aim 2: Early Dayzz participants who use the app will have improved mood and alertness and increased energy as compared to the Late Dayzz participants. Secondary Aim 3: Early Dayzz participants who use the app will report fewer adverse health and safety outcomes as compared to the Late Dayzz participants. Secondary Aim 4: Early Dayzz participants who use the app will have lower health care costs over a 9-month follow-up period as compared to the Late Dayzz participants.

sleep, sleep deficiency, workplace health, occupational health, workplace health promotion, mobile health, mHealth

Participants receive a Sleep Health and Wellness education session and download the Dayzz app. Complete monthly questionnaires for up to 9 months and 2 weeks of sleep diaries.

Complete monthly questionnaires for up to 9 months and 2 weeks of sleep diaries. At the end of nine months, participants have the opportunity to receive a Sleep Health and Wellness education session.

Personalized sleep training app that evaluates sleep issues and offers a complete, holistic sleep improvement solution

Sleep behaviors (e.g., sleep on a consistent schedule, sleep in later) will be self-reported on the end-of-study survey. 100 mm visual analog scales anchored with "Not at all" at 0 and "All the time" at 100 will be used. Higher scores indicate a better outcome.

Sleep behaviors will also be tracked on the eDiary (e.g., consistent sleep schedule, as calculated with the regularity index). The Sleep Regularity Index calculates the percentage probability of an individual being in the same state (asleep vs. awake) at any two time-points 24 h apart, averaged across the study. The index is scaled so that an individual who sleeps and wakes at exactly the same times each day scores 100 (better outcome), whereas an individual who sleeps and wakes at random scores 0 (worse outcome).

Outcomes in sleep behaviors will be further detailed by self-report by completing a checklist of potential positive changes. Participants will select the healthy sleep changes they have made while completing the protocol, "During this study, have you changed any sleep-related behaviors to improve your sleep since participating in the study (check all that apply)?" Higher numbers of positive changes selected indicate a better outcome.

Sleep quality will be measured with the Pittsburgh Sleep Quality Index. The Pittsburgh Sleep Quality Index (PSQI) is an effective instrument used to measure the quality and patterns of sleep in adults. It differentiates "poor" from "good" sleep by measuring seven domains: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction over the last month. The participant self rates each of these seven areas of sleep. Scoring of the answers is based on a 0 to 3 scale, whereby 3 reflects the negative extreme on the Likert Scale. A global sum of "5"or greater indicates a "poor" sleeper. Minimum Score = 0 (better outcome); Maximum Score = 21 (worse outcome).

Unscheduled absences and disability day usage will be collected by self-report on the monthly questionnaires.

World Health Organization Health Work Performance Questionnaire Short Form (HPQ) will be used to assess performance/productivity via 4-week absolute absenteeism. Absenteeism is scored in terms of hours lost per month, which is to say that a high score indicates a higher amount of absenteeism. The measure of absolute absenteeism is expressed in raw hours, with a negative lower bound (if the person works more than expected) and an upper bound equal to the number of hours the respondent is expected to work.

World Health Organization Health Work Performance Questionnaire Short Form (HPQ) will be used to assess performance/productivity via absolute presenteeism. Presenteeism has a lower bound of 0 (total lack of performance during time on the job) and an upper bound of 100 (no lack of performance during time on the job). A higher score indicates a lower amount of lost performance.

Mood will be self-reported via a 7-point Likert scale anchored at 0 with "Very poor" and 7 with "Very good." Higher numbers indicate better outcome.

Mood will be self-reported via 100 mm visual analog scales anchored with "Sad" at 0 and "Happy" at 100. Higher scores indicate a better outcome.

Alertness will be self-reported via a 7-point Likert scale anchored at 0 with "Very poor" and 7 with "Very good." Higher numbers indicate better outcome.

Alertness will be self-reported via 100 mm visual analog scales anchored with "Sleepy" at 0 and "Alert" at 100. Higher scores indicate a better outcome.

Energy will be self-reported via a 7-point Likert scale anchored at 0 with "Very low" and 7 with "Very high." Higher numbers indicate better outcome.

Energy will be self-reported via 100 mm visual analog scales anchored with "Sluggish" at 0 and "Energetic" at 100. Higher scores indicate a better outcome.

Attentional failures (nodding off) will be self-reported on the monthly questionnaires (count per month).

Healthcare utilization will be reported on monthly surveys and costs estimated from the reported usages.

Inclusion Criteria: Employee of Harvard University or Harvard-affiliated institution or hospital. Nominally work a day schedule. Own and use a smart phone. Regularly use a smart phone app (i.e., once per week). Exclusion Criteria: Regularly work evening, night or rotating shifts. Pregnant or breast-feeding.

Anonymized study data will be made available upon request, consistent with our institution's IRB policies and procedures. Data and supporting documentation will become available two years following the primary publication and remain available for five years. Interested individuals will be asked to provide a research plan. Requests will be reviewed and granted as appropriate.

Data will become available two years following the primary publication. It will be available for 5 years.

Robbins R, Weaver MD, Quan SF, Sullivan JP, Qadri S, Glasner L, Cohen-Zion M, Czeisler CA, Barger LK. Evaluating the impact of a sleep health education and a personalised smartphone application on sleep, productivity and healthcare utilisation among employees: results of a randomised clinical trial. BMJ Open. 2022 Sep 14;12(9):e062121. doi: 10.1136/bmjopen-2022-062121.

Robbins R, Weaver MD, Quan SF, Sullivan JP, Cohen-Zion M, Glasner L, Qadri S, Czeisler CA, Barger LK. A clinical trial to evaluate the dayzz smartphone app on employee sleep, health, and productivity at a large US employer. PLoS One. 2022 Jan 5;17(1):e0260828. doi: 10.1371/journal.pone.0260828. eCollection 2022.