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

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 use of personal sensing first requires better understanding of its acceptability to people targeted for these mental health applications. Objective: We assessed the acceptability of both 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. Method: Participants (N = 154; 50% female; mean age = 41; 87% White, 97% Non-Hispanic) in early recovery (1 – 8 weeks of abstinence) from alcohol use disorder were recruited from the Madison, WI area to participate in a 3-month longitudinal study. Participants engaged with active (ecological momentary assessment; EMA, audio check-in, and sleep quality) and passive (geolocation, cellular communication logs, and text message content) personal sensing methods. We assessed 3 behavioral indicators of acceptability: participants’ choices about their participation in the study at various stages in the study procedure, their choice to opt-in to provide data for each personal sensing method, and their compliance for a subset of the active methods (EMA, 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: All but 1 of the individuals who were eligible to participate consented to the personal sensing procedures. Most of these individuals (88%) also returned 1 week later to formally enroll in the study and begin to provide these data. All participants (100%) opted-in to provide data for EMA, sleep quality, and all passive methods (geolocation, cellular communication logs, text message content). Three participants (2%) did not provide any audio check-ins while on study. The average completion rate for all requested EMAs (4X daily) was 81% for 4x daily and 94% for 1x daily. The completion rate for the daily audio check-in was 55%. Aggregate participant ratings indicated all personal sensing methods to be significantly more acceptable (all P’s < .05) compared to neutral across subjective measures of interference, dislike, and willingness to use for 1 year. Participants did not significantly differ in their dislike of active compared to passive methods (P = .23). However, participants reported a higher willingness to use passive methods for 1 year compared to active methods (P = .04). Conclusions: The results of our study suggest that both active and passive personal sensing methods are generally acceptable to people with alcohol use disorder over a longer period than has previously been assessed. This was true even for data streams that contained potentially more sensitive information (e.g., geolocation, cellular communications). Important individual differences were observed both across people and methods which indicate opportunities for future improvements

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

BackgroundPersonal 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. ObjectiveWe 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. MethodsParticipants (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. ResultsOf 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). ConclusionsThese 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

Prospective Prediction of Lapses in Opioid Use Disorder: Protocol for a Personal Sensing Study

Successful long-term recovery from Opioid Use Disorder requires continuous lapse risk monitoring and appropriately using and adapting recovery-supportive behaviors as lapse risk changes. Available treatments often fail to support long-term recovery by failing to account for the dynamic nature of long-term recovery. This protocol paper describes research that aims to develop a highly contextualized lapse risk prediction model that forecasts the ongoing probability of lapse. Participants will be 480 American adults in their first year of recovery from Opioid Use Disorder. Participants will report lapses and provide data relevant to lapse risk for a year with a digital therapeutic smartphone app, through both self-report and passive personal sensing methods (e.g., cellular communications, geolocation). The lapse risk prediction model will be developed using contemporary rigorous machine learning methods that optimize prediction in new data. The model this project will develop could support long-term recovery from Opioid Use Disorder, for example, by enabling just-in-time interventions within digital therapeutics. This project is funded by the National Institute on Drug Abuse with a funding period from August 2019 to June 2024. Full enrollment began in September 2021