Background Smartphones are increasingly used in health research. They provide a continuous connection between participants and researchers to monitor long-term health trajectories of large populations at a fraction of the cost of traditional research studies. However, despite the potential of using smartphones in remote research, there is an urgent need to develop effective strategies to reach, recruit, and retain the target populations in a representative and equitable manner. Objective We aimed to investigate the impact of combining different recruitment and incentive distribution approaches used in remote research on cohort characteristics and long-term retention. The real-world factors significantly impacting active and passive data collection were also evaluated. Methods We conducted a secondary data analysis of participant recruitment and retention using data from a large remote observation study aimed at understanding real-world factors linked to cold, influenza, and the impact of traumatic brain injury on daily functioning. We conducted recruitment in 2 phases between March 15, 2020, and January 4, 2022. Over 10,000 smartphone owners in the United States were recruited to provide 12 weeks of daily surveys and smartphone-based passive-sensing data. Using multivariate statistics, we investigated the potential impact of different recruitment and incentive distribution approaches on cohort characteristics. Survival analysis was used to assess the effects of sociodemographic characteristics on participant retention across the 2 recruitment phases. Associations between passive data-sharing patterns and demographic characteristics of the cohort were evaluated using logistic regression. Results We analyzed over 330,000 days of engagement data collected from 10,000 participants. Our key findings are as follows: first, the overall characteristics of participants recruited using digital advertisements on social media and news media differed significantly from those of participants recruited using crowdsourcing platforms (Prolific and Amazon Mechanical Turk; P<.001). Second, participant retention in the study varied significantly across study phases, recruitment sources, and socioeconomic and demographic factors (P<.001). Third, notable differences in passive data collection were associated with device type (Android vs iOS) and participants’ sociodemographic characteristics. Black or African American participants were significantly less likely to share passive sensor data streams than non-Hispanic White participants (odds ratio 0.44-0.49, 95% CI 0.35-0.61; P<.001). Fourth, participants were more likely to adhere to baseline surveys if the surveys were administered immediately after enrollment. Fifth, technical glitches could significantly impact real-world data collection in remote settings, which can severely impact generation of reliable evidence. Conclusions Our findings highlight several factors, such as recruitment platforms, incentive distribution frequency, the timing of baseline surveys, device heterogeneity, and technical glitches in data collection infrastructure, that could impact remote long-term data collection. Combined together, these empirical findings could help inform best practices for monitoring anomalies during real-world data collection and for recruiting and retaining target populations in a representative and equitable manner.

IntroductionPersonal technology (e.g., smartphones, wearable health devices) has been leveraged extensively for mental health purposes, with upwards of 20,000 mobile applications on the market today and has been considered an important implementation strategy to overcome barriers many people face in accessing mental health care. The main question yet to be addressed is the role consumers feel technology should play in their care. One underserved demographic often ignored in this discussion are people over the age of 60. The population of adults 60 and older is predicted to double by 2,050 signaling a need to address how older adults view technology for their mental health care.ObjectiveThe objective of this study is to better understand why digital mental health tools are not as broadly adopted as predicted, what role people with lived mental health experience feel technology should play in their care and how those results compare across age groups.MethodIn a mixed-methods approach, we analyzed results from a one-time cross-sectional survey that included 998 adults aged 18–83 with lived experience of mental health concerns recruited from Prolific, an online research platform. We surveyed participant's use of technology including their perspectives on using technology in conjunction with their mental health care. We asked participants about their previous use of digital mental health tools, their treatment preferences for mental health care, and the role technology should play in their mental health care.ResultsAcross all age groups, respondents had favorable views of using digital mental health for managing mental health care. However, older adults rated their acceptability of digital mental health tools lower than middle-aged and younger adults. When asked what role technology should play in mental health care in an open-ended response, most participants responded that technology should play a complementary role in mental health care (723/954, 75.8%).ConclusionDigital mental health is seen as a valuable care management tool across all age groups, but preferences for its role in care remain largely administrative and supportive. Future development of digital mental health should reflect these preferences.

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BACKGROUND Smartphones are increasingly used in health research to reach, recruit and assess the health of large and diverse populations. These devices provide a continuous connection between participants and researchers to monitor long-term health and behavior trajectories using multimodal data streams, ranging from health surveys to sensor data at a fraction of the cost of traditional research studies. Despite the potential of real-world data for assessing health and behavior, representative and equitable recruitment and retention of the target population remain key challenges. Remote research has unique challenges, particularly issues with data quality from participants, the choice of recruitment channels, and the most effective ways to retain large and representative populations over a long period. OBJECTIVE We explored the impact of different recruitment and incentive distribution approaches on cohort characteristics and long-term retention. Real-world factors that significantly impact active and passive data collection were also evaluated. METHODS This is a secondary data analysis of research engagement from a large-scale, remote clinical study of flu, cold and COVID detection. Recruitment was conducted in two phases, between March 15, 2020, and January 04, 2022. Over 10,000 smartphone owners in the U.S. were recruited to provide 12 weeks of daily surveys and smartphone-based passive sensing data. Using multivariate statistics, we investigated the impact of different recruitment and incentive distribution approaches on the cohort characteristics. Survival analysis was used to assess the effects of socio-demographics on participant retention across two recruitment phases. Logistic regression was used to evaluate associations between passive data sharing from multiple smartphone sensors and cohort demographics. RESULTS We analyzed data from over 10,000 participants with 269,037 days of active data along with 336,292 days of passive sensor data. Our key findings show that i.) Participants recruited from social media/ads (Phase 1) had significantly lower compliance with the baseline survey completion and participated for a shorter period compared to those recruited from crowdsourcing platforms (Prolific, mTurk; Phase 2) (p < .0001). ii) Socioeconomic and demographic factors such as race/ethnicity, age, income, etc. of the cohort impacted participant retention (p < .0001), however, these factors varied remarkably between two phases. iii) Participants are more likely to adhere to baseline surveys if administered immediately after consent/enrollment. iv) Passive data sharing patterns across Android and iOS showed that Black/African Americans were significantly less likely to share passive sensor data than non-Hispanic whites (OR Range = 0.15 - 0.54; p < .001). CONCLUSIONS Our findings provide valuable empirical evidence about potential best practices to recruit and retain target populations in remote clinical research in the conduct of remote clinical research. Future studies should consider and account for possible biases in participant enrollment and retention based on the choice of recruitment platforms and incentive distribution approaches as well as engaging people from underrepresented populations.