Feasibility of a Mobile Phone App to Support Recovery From Addiction in China: Secondary Analysis of a Pilot Study
Bibliographic record
Abstract
BACKGROUND: Mobile health technologies have been found to improve the self-management of chronic diseases. However, there is limited research regarding their feasibility in supporting recovery from substance use disorders (SUDs) in China. OBJECTIVE: The objective of this study was to examine the feasibility of a mobile phone-based ecological momentary assessment (EMA) app by testing the concordance of drug use assessed by the EMA, urine testing, and a life experience timeline (LET) assessment. METHODS: A total of 75 participants dependent on heroin or amphetamine-type stimulant (ATS) in Shanghai were recruited to participate in a 4-week pilot study. Of the participants, 50 (67% [50/75]) were randomly assigned to the experimental group and 25 (33% [25/75]) were assigned to the control group. The experimental group used mobile health (mHealth) based EMA technology to assess their daily drug use in natural environments and received 2 short health messages each day, whereas the control group only received 2 short health messages each day from the app. Urine tests and LET assessments were conducted each week and a post-intervention survey was administered to both groups. The correlations among the EMA, the LET assessment, and the urine test were investigated. RESULTS: The mean age of the participants was 41.6 (SD 8.0) years, and 71% (53/75) were male. During the 4 weeks of observation, 690 daily EMA survey data were recorded, with a response rate of 49.29% (690/1400). With respect to drug use, the percent of agreement between the EMA and the LET was 66.7%, 79.2%, 72.4%, and 85.8%, respectively, for each of the 4 weeks, whereas the percent of agreement between the EMA and the urine test was 51.2%, 65.1%, 61.9%, and 71.5%, respectively. The post-intervention survey indicated that 46% (32/70) of the participants preferred face-to-face interviews rather than the mHealth app. CONCLUSIONS: This study demonstrated poor agreement between the EMA data and the LET and found that the acceptance of mHealth among individuals with SUDs in China was not positive. Hence, greater efforts are needed to improve the feasibility of mHealth in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".