Evidence map›Paper›PMID 29382624›Full record

ArticleJournal of medical Internet research2018

Implementing a Mobile Health System to Integrate the Treatment of Addiction Into Primary Care: A Hybrid Implementation-Effectiveness Study.

Andrew Quanbeck, David H Gustafson, Lisa A Marsch, Ming-Yuan Chih, Rachel Kornfield, Fiona McTavish, Roberta Johnson, Randall T Brown, Marie-Louise Mares, Dhavan V Shah

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 9 pooled it
–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

58 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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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

10 authors.

Andrew QuanbeckDepartment of Family Medicine and Community Health, University of Wisconsin - Madison, Madison, WI, United States.ORCID 0000-0003-0762-4804
David H GustafsonCenter for Health Enhancement Systems Studies, University of Wisconsin - Madison, Madison, WI, United States.ORCID 0000-0001-9787-6428
Lisa A MarschCenter for Technology and Behavioral Health, Dartmouth College, Lebanon, NH, United States.ORCID 0000-0001-6429-0965
Ming-Yuan ChihCollege of Health Sciences, University of Kentucky, Lexington, KY, United States.ORCID 0000-0001-7130-1415
Rachel KornfieldSchool of Journalism and Mass Communications, University of Wisconsin - Madison, Madison, WI, United States.ORCID 0000-0001-8542-6913
Fiona McTavishCenter for Health Enhancement Systems Studies, University of Wisconsin - Madison, Madison, WI, United States.ORCID 0000-0002-2434-3820
Roberta JohnsonDepartment of Family Medicine and Community Health, University of Wisconsin - Madison, Madison, WI, United States.ORCID 0000-0002-2582-4378
Randall T BrownDepartment of Family Medicine and Community Health, University of Wisconsin - Madison, Madison, WI, United States.ORCID 0000-0002-5445-8119
Marie-Louise MaresCollege of Letters and Science, University of Wisconsin - Madison, Madison, WI, United States.ORCID 0000-0002-0811-3056
Dhavan V ShahSchool of Journalism and Mass Communications, University of Wisconsin - Madison, Madison, WI, United States.ORCID 0000-0001-5034-2816

Funding

Treatment Development & Evaluation CoreP30DA029926 · NIDA · DARTMOUTH COLLEGE · PI Lisa A. Marsch · 2011 to 2026
$21.5M
Impact on opioid use of bundling medication-assisted treatment with mHealthR01DA040449 · NIDA · UNIVERSITY OF WISCONSIN-MADISON · PI GUSTAFSON, DAVID H · 2015 to 2019
$3.4M
Implementing Technology-Assisted Drug Treatment and relapse prevention in FQHCsR01DA034279 · NIDA · UNIVERSITY OF WISCONSIN-MADISON · PI GUSTAFSON, DAVID H, MARSCH, LISA A. · 2012 to 2016
$3.3M
Conceptualizing the problem of implementation: a systems engineering perspective applied in substance abuse treatmentK01DA039336 · NIDA · UNIVERSITY OF WISCONSIN-MADISON · PI QUANBECK, ANDREW · 2015 to 2019
$741k
NIDA NIH HHS K01 DA039336NIDA NIH HHS P30 DA029926NIDA NIH HHS R01 DA034279NIDA NIH HHS R01 DA040449
6 · The paper itself

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.

Indexed as

AdultBehavior, AddictiveHumansMiddle AgedPrimary Health CareTelemedicineYoung Adultbehavioral medicineevidence-based practicemHealthmobile health

Identifiers

PMID29382624
PMCPMC5811649

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.