Trial reportJMIR mHealth and uHealth2021
Feasibility and Preliminary Efficacy of a Community-Based Addiction Rehabilitation Electronic System in Substance Use Disorder: Pilot Randomized Controlled Trial.
Trial report in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03451344 (A Community-based Addiction Rehabilitation Electronic System to Improve Recovery Outcomes in Patients With Drug Use Disorder), which is not on this map. Cited by 8 papers, 4 of them syntheses that pooled it.
What it found
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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.
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.
A Community-based Addiction Rehabilitation Electronic System to Improve Recovery Outcomes in Patients With Drug Use Disorder: Protocol for a Randomized Controlled Trial
Who cites it
8 citing papers in PubMed, 4 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- Prevalence of Dropout and Influencing Factors in Digital Psychosocial Intervention Trials for Adult Illicit Substance Users: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- What are the functionalities and features of mobile health record apps supporting persons experiencing social exclusion? A systematic literature review.Frontiers in digital health · 2025Pooled it
- Health-promotion interventions targeting multiple behaviors: A meta-analytic review of general and behavior-specific processes of change.Psychological bulletin · 2024Pooled it
- The effectiveness of mental health interventions involving non-specialists and digital technology in low-and middle-income countries - a systematic review.BMC public health · 2024Pooled it
- Evolving role of the internet in China as a medium for promoting psychological well-being and mental health: a scoping review.The Lancet regional health. Western Pacific · 2025Review
- Virtual Digital Psychotherapist App-Based Treatment in Patients With Methamphetamine Use Disorder (Echo-APP): Single-Arm Pilot Feasibility and Efficacy Study.JMIR mHealth and uHealth · 2023Article
- Preliminary efficacy of a digital therapeutics smartphone application for methamphetamine use disorder: An experimental study.Frontiers in psychiatry · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug use disorder has high potential for relapse and imposes an enormous burden on public health in China. Since the promulgation of the Anti-drug law in 2008, community-based rehabilitation has become the primary approach to treat drug addiction. However, multiple problems occurred in the implementation process, leading to a low detoxification rate in the community. Mobile health (mHealth) serves as a promising tool to improve the effectiveness and efficiency of community-based rehabilitation. Community-based addiction rehabilitation electronic system (CAREs) is an interactive system for drug users and their assigned social workers.
objectiveThe study aimed to examine the feasibility and preliminary efficacy of CAREs in community-based rehabilitation from the perspective of drug users and social workers in Shanghai, China.
methodsIn this pilot randomized controlled trial, 40 participants were recruited from the community in Shanghai from January to May 2019. Participants randomized to the intervention group (n=20) received CAREs + community-based rehabilitation, while participants in the control group (n=20) received community-based rehabilitation only for 6 months. CAREs provided education, assessment, and SOS (support) functions for drug users. The assigned social workers provided service and monitored drug use behavior as usual except that the social workers in the intervention group could access the webpage end to obtain drug users' information and fit their routine workflow into CAREs. The primary outcome was the feasibility of CAREs, reflected in the overall proportion and frequency of CAREs features used in both app and webpage end. The secondary outcomes were the effectiveness of CAREs, including the percentage of drug-positive samples, longest period of abstinence, contact times with social workers, and the change of Addiction Severity Index (ASI) from baseline to the 6-month follow-up.
resultsThe number of participants logged in to the app ranged from 7 to 20 per week, and CAREs had relatively high levels of continued patient use. Drug users preferred assessment and education features in the app end while their social workers showed high levels of use in urine results record and viewing assessment results on the webpage end. After the 6-month intervention, 3.3% (17/520) of samples in the intervention group and 7.5% (39/520) in the control group were drug-positive (F=4.358, P=.04). No significant differences were noted between the control and intervention groups in terms of longest duration of abstinence, number of contact times and ASI composite scores.
conclusionsThe study preliminarily demonstrated that with relatively good feasibility and acceptability, CAREs may improve the effectiveness and efficiency of the community-based rehabilitation, which provided instruction for further improvement of the system.
trial registrationClinicalTrials.gov NCT03451344; https://clinicaltrials.gov/ct2/show/NCT03451344. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.3389/fpsyt.2018.00556.
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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.