Exploring Beliefs and Behaviors Regarding Sleep Health Among Users of a Popular Mobile Wearable Sleep Tracker Device

• In Event: ICA Interactive paper/Poster Session I
  In Interactive Paper Session: Health Communication Interactive Poster Session

Abstract

Intro
Poor sleep is a pressing concern, but also a modifiable risk factor for a variety of negative social and health outcomes. Approximately one third of the population is severely sleep deprived (Liu et al., 2016) , and sleep deprivation increases risk for hypertension, stroke, and type II diabetes (Buxton & Marcelli, 2010; Gupta & Knapp, 2014; Jackson, Redline, & Emmons, 2015). Intervention research shows that sleep behavior is largely modifiable (Brown, Berry, & Schmidt, 2013; Carter, Dyer, & Mikan, 2013), and thus a suitable area for research on evidence-based behavior change approaches. Mobile technologies, like wearable activity trackers, have been touted as promising behavioral methods for increasing population health, including sleep. This study describes the formative steps in a unique collaboration between academic and industry partners (makers of a highly popular sleep and activity tracker) to design an evidence-based sleep health education intervention.

Method
We chose a survey-based methodology for the formative stage of this research project to identify belief and behavior targets for the next phase, in which we will test the impact of specific message. We recruited active users of the tracker for the survey (N = 652). The industry partner identified, recruited, and contacted potential participants for the study. Surveys included validated scales to assess Dysfunctional Beliefs About Sleep (DBAS), sleep hygiene index (SHI), the Pittsburgh Sleep Quality Index (PSQI), as well as general health and demographic components. We computed descriptive statistics and assessed bivariate relationships between beliefs, behaviors and the primary outcome, sleep quality, to identify promising message targets for the next phase of this research.

Results
One third of respondents (32.5%) were female. Most participants (67.0%) were between 25 and 54 years of age. The average respondent had a body mass index (BMI) of 27.7 and recorded (based on data from the sleep tracker function of the mobile application) 6.38 hours of sleep per night in the last 14 days. Using a scale from1 (never) to 5 (almost always), respondents most commonly reported sleeping on an uncomfortable bed (m = 4.1, s.d. = 1.1) and sleeping in an uncomfortable bedroom (m = 4.1, s.d. = 1.1, see Table 1) as negative factors affecting sleep hygiene (Table 1). These responses were also significantly, inversely related to sleep quality (having an uncomfortable bed: r = -.09, p < .05; uncomfortable bedroom: r = 0.16, p < .01). Using a scale of agreement from 1 (strongly disagree) to 7 (strongly agree), many participants also reported a belief that that medication was the only solution to sleeplessness (m = 5.7, s.d. = 1.4) and that they cancel obligations after a night of poor sleep (m = 5.4, s.d. = 1.6; see Table 2). Both beliefs were inversely correlated with sleep quality (medication: r = -.25, p < .01; cancel obligations: r = -.19, p < .01). We used these results to identify the five most prevalent behaviors and beliefs that were significantly associated with sleep quality for inclusion as message targets in the next phase of this research – an evidence-based campaign to promote sleep health in users of the sleep and activity tracker.

Conclusions
Mobile wearable trackers are increasing in popularity and hold promise for improving modifiable risk factors like poor sleep. We (academic researchers) are engaged in a unique partnership with the makers of a widely popular, wrist-based sleep tracker to design an evidence-based program tailored to improve sleep health among current users of these sleep and activity trackers. This study describes survey-based methods to identify the knowledge gaps and poor sleep behaviors to identify relevant message targets for a behavior change program that will be designed based on evidence, then delivered to individuals using these sleep trackers to assess behavioral and attitude change toward healthier sleep habits.

Authors

• Rebecca Robbins, Cornell U

• Jeff Niederdeppe, Cornell U

• Melissa Chen, World Journalism Professional School

• John Allegrante, Columbia U

• Girardin Jean-Louis, New York U