A randomized community trial to advance digital epidemiological and mHealth citizen scientist compliance: A smart platform study

Abstract

Background This study aims to understand how participants’ compliance and response rates to both traditional validated surveys and ecological momentary assessments (EMAs) vary across 4 cohorts who participated in the same mHealth study and received the same surveys and EMAs on their smartphones, however with cohort-specific time-triggers that differed across the 4 cohorts. Methods As part of the Smart Platform, adult citizen scientists residing in Regina and Saskatoon, Canada, were randomly assigned to 4 cohorts in 2018. Citizen Scientists provided a complex series of subjective and objective data during 8 consecutive days using a custom-built smartphone application. All citizen scientists responded to both validated surveys and EMAs that captured physical activity. However, using Smart Platform, we varied the burden of responding to validated surveys and EMAs across cohorts by using different time-triggered push notifications. Participants in Cohort 1 (n = 10) received the full baseline 209-item validated survey on day 1 of the study; whereas participants in cohorts 2 (n = 26), 3 (n = 10), and 4 (n = 25) received the same survey in varied multiple sections over a period of 4 days. We used weighted One-way Analysis of Variance (ANOVA) tests and weighted, linear regression models to assess for differences in compliance rate across the cohort groups controlling for age, gender, and household income. Results Compliance to EMAs that captured prospective physical activity varied across cohorts 1 to 4: 50.0% (95% Confidence Interval [C.I.] = 31.4, 68.6), 63.0% (95% C.I. = 50.7, 75.2), 37.5% (95% C.I. = 18.9, 56.1), and 61.2% (95% C.I. = 47.4, 75.0), respectively. The highest completion rate of physical activity validated surveys was observed in Cohort 4 (mean = 97.9%, 95% C.I. = 95.5, 100.0). This was also true after controlling for age, gender, and household income. The regression analyses showed that citizen scientists in Cohorts 2, 3, and 4 had significantly higher compliance with completing the physical activity validated surveys relative to citizen scientists in cohort group 1 who completed the full survey on the first day. Conclusions & significances The findings show that maximizing the compliance rates of research participants for digital epidemiological and mHealth studies requires a balance between rigour of data collection, minimization of survey burden, and adjustment of time- and user-triggered notifications based on citizen or patient input.

Smartphone Use and Mental Health among Youth: It Is Time to Develop Smartphone-Specific Screen Time Guidelines

“…Citizen scientists also had the option in the settings of the app to not only pause data collection, but also to upload data only when they had Wi-Fi access and/or when they were charging their phones. Clear instructions were provided regarding study withdrawal within the app [31].…”

How can digital citizen science approaches improve ethical smartphone use surveillance among youth: traditional surveys versus ecological momentary assessments

“…This gap in guidelines needs to be addressed, particularly because smartphones are the primary devices of day-to-day functioning, yet ironically excessive smartphone use is also associated with poor youth health outcomes [4,14,30] However, to understand associations between smartphone use and youth health outcomes, it is imperative to obtain valid and reliable data, which, again ironically is possible via smartphones themselves due to their near universal usage among youth [4,31]. This study utilized the Smart platform [18], a digital citizen science platform that engaged youth as citizen scientists to obtain both retrospective and prospective smartphone use via their own smartphones [6].…”

“…To capture these effectively and accurately, it is critical to move towards prospective measures such as EMAs that minimize recall bias, particularly when the reporting is complicated by the range of behaviours [44]. Objective measures provide accurate overall smartphone use estimates [6], however, to capture the variation of smartphone use ethically, while preserving privacy can be challenging [45]. Finally, the contextual and sociodemographic associations of smartphone use also have to be captured prospectively to minimize bias, while again, appreciating the complexity of smartphone access itself [11].…”

“…In scientific research settings, power often lies with those who have knowledge such as researchers, which has been an obstacle for meaningful data collection, particularly if the data are sensitive and need to be obtained via citizen devices [48]. Digital citizen science is an approach that can democratize science through engagement with citizens ethically and directly to obtain prospective big data [49,50]– an approach that was utilized in collected data from youth in this study via their own smartphones [6,51].…”

“…Advanced population health measurement techniques such as ecological momentary assessments (EMAs), can potentially address measurement bias and capture the entire range of smartphone activities [6,7]. EMAs “capture brief, repeated assessments in natural environments with a high degree of ecological validity relative to laboratory-based investigations” [8].…”