ArticleJournal of medical Internet research2018
Implementing a Mobile Health System to Integrate the Treatment of Addiction Into Primary Care: A Hybrid Implementation-Effectiveness Study.
Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 9 of them syntheses that pooled 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.
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.
Who cites it
58 citing papers in PubMed, 9 syntheses or guidelines pooled 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
- Evaluation of mobile health applications using the RE-AIM model: systematic review and meta-analysis.Frontiers in public health · 2025Pooled it
- The Impact of Digital Patient Portals on Health Outcomes, System Efficiency, and Patient Attitudes: Updated Systematic Literature Review.Journal of medical Internet research · 2021Pooled it
- Technology-based mental healthcare models: A systematic review of the literature.Revista Colombiana de psiquiatria · 2021Pooled it
- Understanding Clinicians' Adoption of Mobile Health Tools: A Qualitative Review of the Most Used Frameworks.JMIR mHealth and uHealth · 2020Pooled it
- Smartphone Apps for the Treatment of Mental Disorders: Systematic Review.JMIR mHealth and uHealth · 2020Pooled it
- Social, Organizational, and Technological Factors Impacting Clinicians' Adoption of Mobile Health Tools: Systematic Literature Review.JMIR mHealth and uHealth · 2020Pooled it
- Technology-Based Alcohol Interventions in Primary Care: Systematic Review.Journal of medical Internet research · 2019Pooled it
- Research-Tested Mobile Apps for Breast Cancer Care: Systematic Review.JMIR mHealth and uHealth · 2019Pooled it
- Couple-Focused Smartphone Intervention to Reduce Problem Drinking: Pilot Randomized Control Trial.JMIR formative research · 2024Trial
- Effects of Bundling Medication for Opioid Use Disorder With an mHealth Intervention Targeting Addiction: A Randomized Clinical Trial.The American journal of psychiatry · 2024Trial
- Trial
- Lessons learned from service design of a trial of a digital mental health service: Informing implementation in primary care clinics.Translational behavioral medicine · 2020Trial
- Real-Time Expert Support for Addiction Care Across General Medical Settings: Evaluation of a Chat-Based Intervention.Substance use & addiction journal · 2026Article
- Implementation of Mobile-Based Programs for Alcohol Cessation in the Treatment of Alcohol-associated Liver Disease: Protocol for a Type 1 Hybrid Implementation-Effectiveness Trial.JMIR research protocols · 2026Article
- Health System-Level Implementation of Digital Health Support for People Living With HIV and Substance Use Disorders: Protocol for a Cluster-Randomized, Stepped-Wedge Clinical Trial.JMIR research protocols · 2025Article
- Application of a Sociotechnical Framework to Uncover Factors That Influence Effective User Engagement With Digital Mental Health Tools in Clinical Care Contexts: Scoping Review.Journal of medical Internet research · 2025Article
- A framework for designing hybrid effectiveness-implementation trials for digital health interventions.Annals of epidemiology · 2025Article
- Mobile Health Tool to Capture Social Determinants of Health and Their Impact on HIV Treatment Outcomes Among People Who Use Drugs: Pilot Feasibility Study.JMIR formative research · 2025Article
- A randomized trial testing digital medicine support models for mild-to-moderate alcohol use disorder.NPJ digital medicine · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundDespite the near ubiquity of mobile phones, little research has been conducted on the implementation of mobile health (mHealth) apps to treat patients in primary care. Although primary care clinicians routinely treat chronic conditions such as asthma and diabetes, they rarely treat addiction, a common chronic condition. Instead, addiction is most often treated in the US health care system, if it is treated at all, in a separate behavioral health system. mHealth could help integrate addiction treatment in primary care.
objectiveThe objective of this paper was to report the effects of implementing an mHealth system for addiction in primary care on both patients and clinicians.
methodsIn this implementation research trial, an evidence-based mHealth system named Seva was introduced sequentially over 36 months to a maximum of 100 patients with substance use disorders (SUDs) in each of three federally qualified health centers (FQHCs; primary care clinics that serve patients regardless of their ability to pay). This paper reports on patient and clinician outcomes organized according to the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework.
resultsThe outcomes according to the RE-AIM framework are as follows: Reach-Seva reached 8.31% (268/3226) of appropriate patients. Reach was limited by our ability to pay for phones and data plans for a maximum of 100 patients per clinic. Effectiveness-Patients who were given Seva had significant improvements in their risky drinking days (44% reduction, (0.7-1.25)/1.25, P=.04), illicit drug-use days (34% reduction, (2.14-3.22)/3.22, P=.01), quality of life, human immunodeficiency virus screening rates, and number of hospitalizations. Through Seva, patients also provided peer support to one another in ways that are novel in primary care settings. Adoption-Patients sustained high levels of Seva use-between 53% and 60% of the patients at the 3 sites accessed Seva during the last week of the 12-month implementation period. Among clinicians, use of the technology was less robust than use by patients, with only a handful of clinicians using Seva in each clinic and behavioral health providers making most referrals to Seva in 2 of the 3 clinics. Implementation-At 2 sites, implementation plans were realized successfully; they were delayed in the third. Maintenance-Use of Seva dropped when grant funding stopped paying for the mobile phones and data plans. Two of the 3 clinics wanted to maintain the use of Seva, but they struggled to find funding to support this.
conclusionsImplementing an mHealth system can improve care among primary care patients with SUDs, and patients using the system can support one another in their recovery. Among clinicians, however, implementation requires figuring out how information from the mHealth system will be used and making mHealth data available in the electronic health (eHealth) record. In addition, paying for an mHealth system remains a challenge.
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Registered trials
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