Evidence map›Paper›PMID 28663164›Full record

ArticleJMIR mHealth and uHealth2017

Assessing User Engagement of an mHealth Intervention: Development and Implementation of the Growing Healthy App Engagement Index.

Sarah Taki, Sharyn Lymer, Catherine Georgina Russell, Karen Campbell, Rachel Laws, Kok-Leong Ong, Rosalind Elliott, Elizabeth Denney-Wilson

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 1 pooled it
28.6field-weighted citation impact, top 1% 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

73 citing papers in PubMed, 1 synthesis or guideline pooled it, 127 citations in OpenAlex.

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  6. Responsiveness to mHealth Intervention for Cannabis Use in Young Adults Predicts Improved Outcomes.Prevention science : the official journal of the Society for Prevention Research · 2022
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13 more citing papers are in PubMed but not listed here.

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

8 authors at 4 institutions in 1 country.

Sarah TakiUniversity of Technology Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0002-7228-8993
Sharyn LymerCentre for Obesity Management and Prevention Research Excellence in Primary Health Care, Sydney, Australia.ORCID http://orcid.org/0000-0002-7876-6722
Catherine Georgina RussellCentre for Obesity Management and Prevention Research Excellence in Primary Health Care, Sydney, Australia.ORCID http://orcid.org/0000-0002-0848-2724
Karen CampbellCentre for Obesity Management and Prevention Research Excellence in Primary Health Care, Sydney, Australia.ORCID http://orcid.org/0000-0002-4499-3396
Rachel LawsCentre for Obesity Management and Prevention Research Excellence in Primary Health Care, Sydney, Australia.ORCID http://orcid.org/0000-0003-4328-1116
Kok-Leong OngLatrobe University, Melbourne, Australia.ORCID http://orcid.org/0000-0003-4688-7674
Rosalind ElliottUniversity of Technology Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0002-9239-7126
Elizabeth Denney-WilsonUniversity of Technology Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0001-9879-4969
Deakin University · AUUniversity of Technology Sydney · AULa Trobe University · AUThe University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

childreninfant developmentinfant obesityinfantsmHealthpractitionersprimary healthcaresocial medium

Identifiers

PMID28663164
PMCPMC5509951
OpenAlexW2730024606

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.