Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study

Abstract

Background Personal sensing may improve digital therapeutics for mental health care by facilitating early screening, symptom monitoring, risk prediction, and personalized adaptive interventions. However, further development and the use of personal sensing requires a better understanding of its acceptability to people targeted for these applications. Objective We aimed to assess the acceptability of active and passive personal sensing methods in a sample of people with moderate to severe alcohol use disorder using both behavioral and self-report measures. This sample was recruited as part of a larger grant-funded project to develop a machine learning algorithm to predict lapses. Methods Participants (N=154; n=77, 50% female; mean age 41, SD 11.9 years; n=134, 87% White and n=150, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) were recruited to participate in a 3-month longitudinal study. Participants were modestly compensated for engaging with active (eg, ecological momentary assessment [EMA], audio check-in, and sleep quality) and passive (eg, geolocation, cellular communication logs, and SMS text message content) sensing methods that were selected to tap into constructs from the Relapse Prevention model by Marlatt. We assessed 3 behavioral indicators of acceptability: participants’ choices about their participation in the study at various stages in the procedure, their choice to opt in to provide data for each sensing method, and their adherence to a subset of the active methods (EMA and audio check-in). We also assessed 3 self-report measures of acceptability (interference, dislike, and willingness to use for 1 year) for each method. Results Of the 192 eligible individuals screened, 191 consented to personal sensing. Most of these individuals (169/191, 88.5%) also returned 1 week later to formally enroll, and 154 participated through the first month follow-up visit. All participants in our analysis sample opted in to provide data for EMA, sleep quality, geolocation, and cellular communication logs. Out of 154 participants, 1 (0.6%) did not provide SMS text message content and 3 (1.9%) did not provide any audio check-ins. The average adherence rate for the 4 times daily EMA was .80. The adherence rate for the daily audio check-in was .54. Aggregate participant ratings indicated that all personal sensing methods were significantly more acceptable (all P<.001) compared with neutral across subjective measures of interference, dislike, and willingness to use for 1 year. Participants did not significantly differ in their dislike of active methods compared with passive methods (P=.23). However, participants reported a higher willingness to use passive (vs active) methods for 1 year (P=.04). Conclusions These results suggest that active and passive sensing methods are acceptable for people with alcohol use disorder over a longer period than has previously been assessed. Important individual differences were observed across people and methods, indicating opportunities for future improvement.

Machine learning models for temporally precise lapse prediction in alcohol use disorder

“…Successful monitoring could also direct patients to engage with the most appropriate digital therapeutic modules, addressing the unique risks present at any given moment throughout their recovery. Such continuous monitoring is now feasible via personal sensing (i.e., in-situ data collection via sensors embedded in individuals' daily lives) (Bae et al, 2018;Chih et al, 2014;Epstein et al, 2020;Moshontz et al, 2021;Soyster et al, 2022;Wyant et al, 2023).…”

“…EMA, like other personal sensing methods, can support the frequent, in-situ, longitudinal measurement necessary for monitoring fluctuating relapse risk. Long-term monitoring with EMA has been well-tolerated by individuals with AUD (Wyant et al, 2023). Additionally, previous research has validated the use of EMA to measure known risk and protective factors for relapse, including craving (Dulin & Gonzalez, 2017), mood (Russell et al, 2020), stressors (Wemm et al, 2019), positive life events (Dvorak et al, 2018), and motivation/efficacy (Dvorak et al, 2014).…”

“…Consequently, the burden of using such systems must be considered. Participants with AUD find three months of 4x daily EMA to be generally acceptable and report that they could hypothetically sustain this for at least a year if there were clinical benefits to them (Wyant et al, 2023). They also report that 1x daily EMA may be more feasible still (Wyant et al, 2023).…”

“…Several large, randomized controlled trials have confirmed that digital therapeutics for AUD improve clinical outcomes (Campbell et al, 2014;Gustafson et al, 2014;Jacobson et al, 2022). Additionally, US adults (including patients with AUD (Wyant et al, 2023)) display high rates of smartphone ownership (over 85% in 2021), with minimal variation across race, ethnicity, socioeconomic status, and geographic settings (Center, 2021).…”