Evidence map›Paper›PMID 34982724›Full record

Trial reportJMIR mHealth and uHealth2022

Dose-Response Effects of Patient Engagement on Health Outcomes in an mHealth Intervention: Secondary Analysis of a Randomized Controlled Trial.

Yiran Li, Yan Guo, Y Alicia Hong, Yu Zeng, Aliza Monroe-Wise, Chengbo Zeng, Mengting Zhu, Hanxi Zhang, Jiaying Qiao, Zhimeng Xu and 3 more

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 3 pooled it
5.4field-weighted citation impact, top 4% of its field
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

30 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 citations in OpenAlex.

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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

13 authors at 7 institutions in 3 countries.

Yiran Li *Department of Medical Statistics, School of Public Health, Guangzhou, China.ORCID 0000-0002-4704-8728
Yan Guo *Department of Medical Statistics, School of Public Health, Guangzhou, China.ORCID 0000-0002-8754-1090
Y Alicia HongDepartment of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, VA, United States.ORCID 0000-0002-1481-6495
Yu ZengDepartment of Medical Statistics, School of Public Health, Guangzhou, China.ORCID 0000-0002-2604-9502
Aliza Monroe-WiseDepartment of Global Health, University of Washington, Seattle, WA, United States.ORCID 0000-0002-8843-3462
Chengbo ZengSouth Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID 0000-0002-3512-1115
Mengting ZhuThe Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong.ORCID 0000-0002-7247-7620
Hanxi ZhangNational Center of AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0002-2193-9581
Jiaying QiaoDepartment of Medical Statistics, School of Public Health, Guangzhou, China.ORCID 0000-0003-1171-5098
Zhimeng XuDepartment of Medical Statistics, School of Public Health, Guangzhou, China.ORCID 0000-0003-1042-9786
Weiping CaiDepartment of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.ORCID 0000-0002-1287-0623
Linghua LiDepartment of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.ORCID 0000-0001-5711-8681
Cong LiuDepartment of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.ORCID 0000-0001-7147-8923
Guangzhou Medical University · CNGuangzhou Eighth People's Hospital · CNChinese University of Hong Kong · HKGeorge Mason University · USUniversity of Massachusetts Chan Medical School · USUniversity of South Carolina · USUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe dose-response relationship between patient engagement and long-term intervention effects in mobile health (mHealth) interventions are understudied. Studies exploring long-term and potentially changing relationships between patient engagement and health outcomes in mHealth interventions are needed.

objectiveThis study aims to examine dose-response relationships between patient engagement and 3 psychosocial outcomes in an mHealth intervention, Run4Love, using repeated measurements of outcomes at baseline and 3, 6, and 9 months.

methodsThis study is a secondary analysis using longitudinal data from the Run4Love trial, a randomized controlled trial with 300 people living with HIV and elevated depressive symptoms to examine the effects of a 3-month mHealth intervention on reducing depressive symptoms and improving quality of life (QOL). We examined the relationships between patient engagement and depressive symptoms, QOL, and perceived stress in the intervention group (N=150) using 4-time-point outcome measurements. Patient engagement was assessed using the completion rate of course assignments and frequency of items completed. Cluster analysis was used to categorize patients into high- and low-engagement groups. Generalized linear mixed effects models were conducted to investigate the dose-response relationships between patient engagement and outcomes.

resultsThe cluster analysis identified 2 clusters that were distinctively different from each other. The first cluster comprised 72 participants with good compliance to the intervention, completing an average of 74% (53/72) of intervention items (IQR 0.22). The second cluster comprised 78 participants with low compliance to the intervention, completing an average of 15% (11/72) of intervention items (IQR 0.23). Results of the generalized linear mixed effects models showed that, compared with the low-engagement group, the high-engagement group had a significant reduction in more depressive symptoms (β=-1.93; P=.008) and perceived stress (β=-1.72; P<.001) and an improved QOL (β=2.41; P=.01) over 9 months. From baseline to 3, 6, and 9 months, the differences in depressive symptoms between the 2 engagement groups were 0.8, 1.6, 2.3, and 3.7 points, respectively, indicating widening between-group differences over time. Similarly, between-group differences in QOL and perceived stress increased over time (group differences in QOL: 0.9, 1.9, 4.7, and 5.1 points, respectively; group differences in the Perceived Stress Scale: 0.9, 1.4, 2.3, and 3.0 points, respectively).

conclusionsThis study revealed a positive long-term dose-response relationship between patient engagement and 3 psychosocial outcomes among people living with HIV and elevated depressive symptoms in an mHealth intervention over 9 months using 4 time-point repeat measurement data. The high- and low-engagement groups showed significant and widening differences in depressive symptoms, QOL, and perceived stress at the 3-, 6-, and 9-month follow-ups. Future mHealth interventions should improve patient engagement to achieve long-term and sustained intervention effects.

trial registrationChinese Clinical Trial Registry ChiCTR-IPR-17012606; https://www.chictr.org.cn/showproj.aspx?proj=21019.

Indexed as

HIV InfectionsTelemedicineHumansOutcome Assessment, Health CarePatient ParticipationQuality of Lifedose–response relationshipgeneralized linear mixed effects modellong-term effectmHealthpatient engagement

Identifiers

PMID34982724
PMCPMC8767469
OpenAlexW4206198061

What OpenQuestion holds

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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.