The goal of this project is to establish the evidence base for equitable accessibility and implementation of the precision sleep medicine mobile application, SHIFT.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
DOUBLE
Enrollment
300
SHIFT is a mobile application designed to improve sleep in night shift workers. The SHIFT mobile application is used to collect data from an Apple Watch to assess an individual shift worker's body-clock timing and make personalized recommendations of light exposure schedules that are designed to align the body-clock with the night shift work schedule.
Henry Ford Columbus Medical Center
Novi, Michigan, United States
RECRUITINGEstablish the effect of SHIFT on stakeholder-centered outcomes.
Aim 1a. Measure the effect of SHIFT on work productivity and satisfaction compared to waitlist control using the Job Satisfaction Index. Effect will be tested using a mixed-effects linear regression model with participants as the random effect and Time, Condition, and the Time × Condition interaction term as the fixed effects. Aim 1b: Measure the effect of SHIFT on global health compared to waitlist control using the NIH PROMIS Global Health questionnaire. Effect will be tested using the same method as Aim 1a. Aim 1c. Measure the effect of SHIFT on turnover compared to waitlist control, measured at 8-month follow-up. Turnover will be operationalized as an individual who has either terminated the position they were in at baseline or is no longer engaged in shift work as operationalized in the study. Effect will be determined using a generalized mixed-effects regression with turnover as a dichotomous outcome.
Time frame: From enrollment to the 8 month point.
Compare use experience and accuracy of SLEEP Android to the original iOS version.
Aim 2a. Measure user experience of Android and iOS versions of SHIFT using the User Experience Questionnaire (UEQ). The following ranges of clinical indifference will be used: ± 3 points on the User Experience Questionnaire based on the bin size of 6 for each of the thresholds (bad, neutral, and good user experience). Aim 2b. Measure accuracy of predicted circadian misalignment (CM), sleep, and depression in Android and iOS versions. CM will be indexed with the outputs of the biomathematical model of the circadian system. Sleep will be measured using the Insomnia Severity Index and sleep diaries. Depression will be measured using the Quick Inventory of Depressive Symptomatology. The following ranges of clinical indifference will be used: 1) predicted CM = ± 3 hours based on approximately 2x the absolute mean error of our model predictions, 2) insomnia severity = ± 6 points, and 3) depression = 28.5% of the QIDS-SR16 score.
Time frame: From enrollment to the 8 month point.
Assess facilitators and barriers to engagement and implementation.
A series of semi-structured interviews will be used for thematic analysis, and a comprehensive roadmap for future app updates based on user feedback. The semi-structured interviews will utilize the interview-guide approach following the CFIR framework selected for this study. Six phases will be followed for thematic analysis: (1) data familiarization, (2) generating initial codes, (3) searching for themes, (4) reviewing themes, (5) defining and naming themes, and (6) producing the report. We will combine deductive and inductive techniques to increase the accuracy of thematic analyses.
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Time frame: Immediately following completion of 8-month treatment period.