ArticleJMIR mHealth and uHealth2017
Assessing User Engagement of an mHealth Intervention: Development and Implementation of the Growing Healthy App Engagement Index.
Article in JMIR mHealth and uHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 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
73 citing papers in PubMed, 1 synthesis or guideline pooled it, 127 citations in OpenAlex.
- Exclusively Digital Health Interventions Targeting Diet, Physical Activity, and Weight Gain in Pregnant Women: Systematic Review and Meta-Analysis.JMIR mHealth and uHealth · 2020Pooled it
- The Impact of Dose in an mHealth Intervention to Support Parents and Carers Via Healthy Beginnings for Hunter New England Kids Program: Pragmatic Randomized Controlled Trial.JMIR formative research · 2025Trial
- Six-month intervention effect of a digital movement behavior intervention on parent- and child intermediary outcomes-results from the Let's Grow randomized controlled trial.The international journal of behavioral nutrition and physical activity · 2025Trial
- Predicting Long-Term Engagement in mHealth Apps: Comparative Study of Engagement Indices.Journal of medical Internet research · 2024Trial
- A pilot randomized controlled trial of a cognitive-behavioral therapy guided self-help mobile app for the post-acute treatment of anorexia nervosa: A registered report.The International journal of eating disorders · 2023Trial
- Responsiveness to mHealth Intervention for Cannabis Use in Young Adults Predicts Improved Outcomes.Prevention science : the official journal of the Society for Prevention Research · 2022Trial
- An mHealth Intervention to Reduce the Packing of Discretionary Foods in Children's Lunch Boxes in Early Childhood Education and Care Services: Cluster Randomized Controlled Trial.Journal of medical Internet research · 2022Trial
- Parent Website Engagement and Health Equity Implications in a Child Care-Based Wellness Intervention.Journal of nutrition education and behavior · 2021Trial
- Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.Journal of medical Internet research · 2021Trial
- A Mobile Health Solution Complementing Psychopharmacology-Supported Smoking Cessation: Randomized Controlled Trial.JMIR mHealth and uHealth · 2020Trial
- CHAMP: A cluster randomized-control trial to prevent obesity in child care centers.Contemporary clinical trials · 2019Trial
- Association Between User Engagement of a Mobile Health App for Gout and Improvements in Self-Care Behaviors: Randomized Controlled Trial.JMIR mHealth and uHealth · 2019Trial
- Predicting Outcomes from Engagement With Specific Components of an Internet-Based Physical Activity Intervention With Financial Incentives: Process Analysis of a Cluster Randomized Controlled Trial.Journal of medical Internet research · 2019Trial
- Engagement With a Relaxation-Based Mobile Health Intervention for Perioperative Anxiety: Prospective Longitudinal Cohort Mixed Methods Study.JMIR mHealth and uHealth · 2026Article
- Engagement, Acceptability, and Impact of a Parenting App to Support Infant Sleep and Parent Well-Being: Quasi-Experimental Study.JMIR pediatrics and parenting · 2026Article
- Engagement of Users in Digital Health Applications: Scoping Review.JMIR mHealth and uHealth · 2026Article
- User Engagement With a Mobile Health App for People Living With HIV: Observational Study Based on an Engagement Evaluation Framework.Journal of medical Internet research · 2025Observational
- Parental and Demographic Predictors of Engagement in an mHealth Intervention: Observational Study From the Let's Grow Trial.JMIR mHealth and uHealth · 2025Observational
- Designing a Visual Analytics Tool to Support Data Analysis Tasks of Digital Mental Health Interventions: Case Study.JMIR human factors · 2025Article
- Experiences of social support and the role of engagement in a digital educational support group for adolescent mothers' health in the Dominican Republic.PLOS digital health · 2025Article
13 more citing papers are in PubMed but not listed here.
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 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChildhood obesity is an ongoing problem in developed countries that needs targeted prevention in the youngest age groups. Children in socioeconomically disadvantaged families are most at risk. Mobile health (mHealth) interventions offer a potential route to target these families because of its relatively low cost and high reach. The Growing healthy program was developed to provide evidence-based information on infant feeding from birth to 9 months via app or website. Understanding user engagement with these media is vital to developing successful interventions. Engagement is a complex, multifactorial concept that needs to move beyond simple metrics.
objectiveThe aim of our study was to describe the development of an engagement index (EI) to monitor participant interaction with the Growing healthy app. The index included a number of subindices and cut-points to categorize engagement.
methodsThe Growing program was a feasibility study in which 300 mother-infant dyads were provided with an app which included 3 push notifications that was sent each week. Growing healthy participants completed surveys at 3 time points: baseline (T1) (infant age ≤3 months), infant aged 6 months (T2), and infant aged 9 months (T3). In addition, app usage data were captured from the app. The EI was adapted from the Web Analytics Demystified visitor EI. Our EI included 5 subindices: (1) click depth, (2) loyalty, (3) interaction, (4) recency, and (5) feedback. The overall EI summarized the subindices from date of registration through to 39 weeks (9 months) from the infant's date of birth. Basic descriptive data analysis was performed on the metrics and components of the EI as well as the final EI score. Group comparisons used t tests, analysis of variance (ANOVA), Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests as appropriate. Consideration of independent variables associated with the EI score were modeled using linear regression models.
resultsThe overall EI mean score was 30.0% (SD 11.5%) with a range of 1.8% - 57.6%. The cut-points used for high engagement were scores greater than 37.1% and for poor engagement were scores less than 21.1%. Significant explanatory variables of the EI score included: parity (P=.005), system type including "app only" users or "both" app and email users (P<.001), recruitment method (P=.02), and baby age at recruitment (P=.005).
conclusionsThe EI provided a comprehensive understanding of participant behavior with the app over the 9-month period of the Growing healthy program. The use of the EI in this study demonstrates that rich and useful data can be collected and used to inform assessments of the strengths and weaknesses of the app and in turn inform future interventions.
Indexed as
Identifiers
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.