The American journal of psychiatry · 2026
Mobile Health for Alcohol Use Assessment: Longitudinal Effects of Breathalyzer Self-Monitoring in Everyday Contexts.
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Lu Yang, Fairbairn Catharine E, Han Jiaxu, Bosch Nigel
Аннотация
OBJECTIVE: Although mobile health-tracking technologies have burgeoned, offering objective health information to consumers on an unprecedented scale, opportunities to directly test effects of such monitoring have been limited. Low-cost mobile breathalyzers are one tool commonly employed for blood alcohol concentration (BAC) assessment. The authors explored outcomes linked with BAC-tracking technologies, examining effects on alcohol use and self-estimation of BAC levels in a large U.S. sample.
METHODS: Participants (N=32,179) were individuals who voluntarily purchased a mobile breathalyzer and provided at least three ad-lib readings between 2016 and 2022. A paired smartphone application prompted users to enter a BAC self-estimate (a guess) before the measured BAC level was displayed. Analyses included observations collected during active consumption (BAC >0.00%) from breathalyzer users who opted to share anonymized data. Breathalyzer users who displayed inattentive patterns of guessing were excluded from self-estimation analyses. The final dataset comprised 787,393 BAC readings and 387,643 self-estimates.
RESULTS: The accuracy of BAC guesses increased by 2.38% over the course of breathalyzer use. Associations between breathalyzer use and BAC levels varied significantly according to participants' initial drinking levels (b=-0.0062, 95% CI=-0.0065, -0.0059). Among heavy-drinking participants, BAC levels decreased on average from 0.106% to 0.096%, whereas the reverse trend was observed for lighter-drinking participants, whose levels increased from 0.058% to 0.067%. A similar interaction emerged for BAC underestimation (b=-0.0058, 95% CI=-0.0066, -0.0049), with odds of underestimation decreasing among heavy-drinking and increasing among light-drinking participants.
CONCLUSIONS: The results indicate promise for mobile BAC-tracking technologies as a low-impact intervention with the potential to decrease drinking among individuals who drink heavily-a population particularly susceptible to alcohol-related problems. In contrast, inverted trends emerged for light-drinking individuals, highlighting the need for empirical research in the fast-moving landscape of digital health.
METHODS: Participants (N=32,179) were individuals who voluntarily purchased a mobile breathalyzer and provided at least three ad-lib readings between 2016 and 2022. A paired smartphone application prompted users to enter a BAC self-estimate (a guess) before the measured BAC level was displayed. Analyses included observations collected during active consumption (BAC >0.00%) from breathalyzer users who opted to share anonymized data. Breathalyzer users who displayed inattentive patterns of guessing were excluded from self-estimation analyses. The final dataset comprised 787,393 BAC readings and 387,643 self-estimates.
RESULTS: The accuracy of BAC guesses increased by 2.38% over the course of breathalyzer use. Associations between breathalyzer use and BAC levels varied significantly according to participants' initial drinking levels (b=-0.0062, 95% CI=-0.0065, -0.0059). Among heavy-drinking participants, BAC levels decreased on average from 0.106% to 0.096%, whereas the reverse trend was observed for lighter-drinking participants, whose levels increased from 0.058% to 0.067%. A similar interaction emerged for BAC underestimation (b=-0.0058, 95% CI=-0.0066, -0.0049), with odds of underestimation decreasing among heavy-drinking and increasing among light-drinking participants.
CONCLUSIONS: The results indicate promise for mobile BAC-tracking technologies as a low-impact intervention with the potential to decrease drinking among individuals who drink heavily-a population particularly susceptible to alcohol-related problems. In contrast, inverted trends emerged for light-drinking individuals, highlighting the need for empirical research in the fast-moving landscape of digital health.