Evidence map›Paper›PMID 38901024›Full record

ArticleJMIR human factors2024

Using a Smartwatch App to Understand Young Adult Substance Use: Mixed Methods Feasibility Study.

Sahiti Kunchay, Ashley N Linden-Carmichael, Saeed Abdullah

Abstract read
In one paragraph

Article in JMIR human factors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Interpol review of forensic drug chemistry, 2022-2025.Forensic science international. Synergy · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sahiti KunchayCollege of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, United States.ORCID 0000-0001-8468-8794
Ashley N Linden-CarmichaelEdna Bennett Pierce Prevention Research Center, The Pennsylvania State University, University Park, PA, United States.ORCID 0000-0001-8187-6538
Saeed AbdullahCollege of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, United States.ORCID 0000-0002-4371-8173

Funding

Simultaneous Alcohol and Marijuana Use: Momentary Influences and ConsequencesK01AA026854 · NIAAA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LINDEN-CARMICHAEL, ASHLEY NICOLE · 2019 to 2023
$826k
NIAAA NIH HHS K01 AA026854
6 · The paper itself

Abstract

backgroundYoung adults in the United States exhibit some of the highest rates of substance use compared to other age groups. Heavy and frequent substance use can be associated with a host of acute and chronic health and mental health concerns. Recent advances in ubiquitous technologies have prompted interest and innovation in using technology-based data collection instruments to understand substance use and associated harms. Existing methods for collecting granular, real-world data primarily rely on the use of smartphones to study and understand substance use in young adults. Wearable devices, such as smartwatches, show significant potential as platforms for data collection in this domain but remain underused.

objectiveThis study aims to describe the design and user evaluation of a smartwatch-based data collection app, which uses ecological momentary assessments to examine young adult substance use in daily life.

methodsThis study used a 2-phase iterative design and acceptability evaluation process with young adults (aged 18-25 y) reporting recent alcohol or cannabis use. In phase 1, participants (8/15, 53%) used the data collection app for 14 days on their Apple Watches to report their substance use patterns, social contexts of substance use, and psychosocial risk factors (eg, affect). After this 14-day deployment, the participants completed a user experience survey and a semistructured interview to record their perspectives and experiences of using the app. Formative feedback from this phase informed feature modification and refinement of the app. In phase 2, an additional cohort (7/15, 47%) used the modified app for 14 days and provided feedback through surveys and interviews conducted after the app use period.

resultsAnalyses of overall app use patterns indicated high, consistent use of the app, with participants using the app for an average of 11.73 (SD 2.60) days out of 14 days of data collection. Participants reported 67 instances of substance use throughout the study, and our analysis indicates that participants were able to respond to ecological momentary assessment prompts in diverse temporal and situational contexts. Our findings from the user experience survey indicate that participants found the app usable and functional. Comparisons of app use metrics and user evaluation scores indicate that the iterative app design had a measurable and positive impact on users' experience. Qualitative data from the participant interviews highlighted the value of recording substance use patterns, low disruption to daily life, minimal overall burden, preference of platforms (smartphones vs smartwatches), and perspectives relating to privacy and app use in social contexts.

conclusionsThis study demonstrated the acceptability of using a smartwatch-based app to collect intensive, longitudinal substance use data among young adults. The findings document the utility of smartwatches as a novel platform to understand sensitive and often-stigmatized behaviors such as substance use with minimal burden.

Indexed as

Feasibility StudiesMobile ApplicationsSubstance-Related DisordersAdolescentAdultEcological Momentary AssessmentFemaleHumansMaleSmartphoneUnited StatesYoung Adultecological momentary assessmentfeasibility studieshuman-centered designmHealthmobile healthmobile phonesmartwatchessubstance use

Identifiers

PMID38901024
PMCPMC11224702

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.