ReviewJMIR mHealth and uHealth2020
Improving Engagement Among Patients With Chronic Cardiometabolic Conditions Using mHealth: Critical Review of Reviews.
Review in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrated disease management interventions for patients with chronic obstructive pulmonary disease.The Cochrane database of systematic reviews · 2021Pooled it
- Trial
- Using Large Language Models for Chronic Disease Management Tasks: Scoping Review.JMIR medical informatics · 2025Article
- Mobile Applications for Longitudinal Data Collection: Web-based Survey Study of Former Intensive Care Patients.Journal of medical systems · 2025Article
- IoT-based remote monitoring system: A new era for patient engagement.Healthcare technology letters · 2024Review
- Improving the management of type 2 diabetes in China using a multifaceted digital health intervention in primary health care: the SMARTDiabetes cluster randomised controlled trial.The Lancet regional health. Western Pacific · 2024Article
- Comprehensive Cardiovascular Risk Factor Control With a Mobile Health Cardiovascular Risk Self-Management Program.Journal of the American Heart Association · 2024Article
- Patterns of Engagement With an Application-Based Dietary Self-Monitoring Tool Within a Randomized Controlled Feasibility Trial.AJPM focus · 2022Article
- Measures of Engagement With mHealth Interventions in Patients With Heart Failure: Scoping Review.JMIR mHealth and uHealth · 2022Article
- Exploring and Characterizing Patient Multibehavior Engagement Trails and Patient Behavior Preference Patterns in Pathway-Based mHealth Hypertension Self-Management: Analysis of Use Data.JMIR mHealth and uHealth · 2022Article
- Text Message Analysis Using Machine Learning to Assess Predictors of Engagement With Mobile Health Chronic Disease Prevention Programs: Content Analysis.JMIR mHealth and uHealth · 2021Article
- Patterns for Patient Engagement with the Hypertension Management and Effects of Electronic Health Care Provider Follow-up on These Patterns: Cluster Analysis.Journal of medical Internet research · 2021Article
- Mobile Health Applications, Cancer Survivors, and Lifestyle Modification: An Integrative Review.Computers, informatics, nursing : CIN · 2021Review
- Monitoring of COVID-19 patients by telemedicine with telemonitoring.Revista clinica espanola · 2020Article
- An AI-Enabled, Patient-Centred Digital Platform for Integrated Chronic Heart Failure Management: Architecture, Validation and Clinical Insights.Healthcare technology lettersArticle
- Developing a mobile health app for chronic illness management: Insights from focus groups.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe burden imposed by cardiometabolic diseases remains a principal health care system concern. Integration of mobile health (mHealth) interventions is helpful for telemonitoring of these patients, which enables patients to be more active and take part in their treatment, while being more conscious and gaining more control over the outcomes. However, little is known about the degree to which users engage, and the extent to which this interaction matches the usage pattern for which mHealth interventions were designed.
objectiveThe aim of this study was to describe the characteristics and results of studies on mHealth solutions that measured the effects of interventions with patient engagement in the context of chronic cardiometabolic diseases.
methodsA critical review of systematic reviews was conducted to recover data on interventions focused on the engagement of patients with chronic cardiometabolic diseases using mHealth technologies. Articles (from January 1, 2010) were searched in the Medlars Online International Literature Medline (Medline/Pubmed), Embase, Cochrane Library, PsycINFO, and Scielo databases. Only studies that quantified a measure of engagement by patients with cardiometabolic disease were included for analysis. The Critical Appraisal Skills Programme (CASP) was used to determine included studies considering the quality of the data provided. The Scottish Intercollegiate Guidelines Network (SIGN) checklist was used to assess the quality of the evidence according to the methodology used in the studies reviewed. Engagement was defined as the level of patient implication or participation in self-care interventions. Engagement measures included number of logs to the website or platform, frequency of usage, number of messages exchanged, and number of tasks completed.
resultsInitially, 638 papers were retrieved after applying the inclusion and exclusion criteria. Finally, only three systematic reviews measuring engagement were included in the analysis. No reviews applying a meta-analysis approach were found. The three review articles described the results of 10 clinical trials and feasibility studies that quantified engagement and met the inclusion criteria assessed through CASP. The sample size varied between 6 and 270 individuals, who were predominantly men. Cardiac disease was the principal target in the comparison of traditional and mHealth interventions for engagement improvement. The level of patient engagement with mHealth technologies varied between 50% and 97%, and technologies incorporating smartphones with a reminder function resulted in the highest level of engagement.
conclusionsmHealth interventions are an effective solution for improving engagement of patients with chronic cardiometabolic diseases. However, there is a need for advanced analysis and higher-quality studies focused on long-term engagement with specific interventions. The use of smartphones with a single app that includes a reminder function appears to result in better improvement in active participation, leading to higher engagement among patients with cardiometabolic diseases.
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What OpenQuestion holds
Registered trials
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.