Evidence map›Paper›PMID 35731547›Full record

Trial reportJournal of medical Internet research2022

The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital Phenotypes.

Olivia De-Jongh González, Claire N Tugault-Lafleur, E Jean Buckler, Jill Hamilton, Josephine Ho, Annick Buchholz, Katherine M Morrison, Geoff Dc Ball, Louise C Mâsse

Registry-linked trialOpen access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03651284 (Project LiGHT), which is not on this map. Cited by 12 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 4 pooled it
5.6field-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.

NCT03651284 nacompletednot on this map

Project LiGHT (Living Green and Healthy for Teens): A Novel Weight Management Program That Emphasizes the Benefits of a Healthy Lifestyle

TypeinterventionalSponsorUniversity of British ColumbiaRan2018 to 2022Enrolled200ConditionsObesity, PediatricArmsAim2Be app with Live Coach + Fitbit + BMI tracking tools, Aim2Be app waitlist + Canadian Health Recommendations + Fitbit + BMI tracking tools, then flip to Aim2Be app with Virtual Coach + Fitbit + BMI tracking tools
3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 4 syntheses or guidelines pooled it, 20 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

9 authors at 8 institutions in 1 country.

Olivia De-Jongh GonzálezSchool of Population and Public Health, University of British Columbia, BC Children's Hospital Research Institute, Vancouver, BC, Canada.ORCID 0000-0003-4782-9513
Claire N Tugault-LafleurSchool of Nutrition Sciences, Faculty of Health Sciences, The University of Ottawa., Ottawa, ON, Canada.ORCID 0000-0002-4598-9372
E Jean BucklerSchool of Exercise Science, Physical and Health Education, University of Victoria., Victoria, BC, Canada.ORCID 0000-0002-4561-9419
Jill HamiltonDepartment of Paediatrics, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-1958-2800
Josephine HoCumming School of Medicine, Department of Pediatrics, University of Calgary, Calgary, AB, Canada.ORCID 0000-0003-0379-4427
Annick BuchholzChildren's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada.ORCID 0000-0003-2598-3368
Katherine M MorrisonDepartment of Pediatrics, Center for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON, Canada.ORCID 0000-0002-1737-256X
Geoff Dc BallDepartment of Pediatrics, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0002-9432-3120
Louise C MâsseSchool of Population and Public Health, University of British Columbia, BC Children's Hospital Research Institute, Vancouver, BC, Canada.ORCID 0000-0003-2483-8791
University of British Columbia · CAChildren's Hospital of Eastern Ontario · CAMcMaster University · CAUniversity of Alberta · CAUniversity of Calgary · CAUniversity of Ottawa · CAUniversity of Toronto · CAUniversity of Victoria · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite the growing number of mobile health (mHealth) interventions targeting childhood obesity, few studies have characterized user typologies derived from individuals' patterns of interactions with specific app features (digital phenotypes).

objectiveThis study aims to identify digital phenotypes among 214 parent-child dyads who used the Aim2Be mHealth app as part of a randomized controlled trial conducted between 2019 and 2020, and explores whether participants' characteristics and health outcomes differed across phenotypes.

methodsLatent class analysis was used to identify distinct parent and child phenotypes based on their use of the app's behavioral, gamified, and social features over 3 months. Multinomial logistic regression models were used to assess whether the phenotypes differed by demographic characteristics. Covariate-adjusted mixed-effect models evaluated changes in BMI z scores (zBMI), diet, physical activity, and screen time across phenotypes.

resultsAmong parents, 5 digital phenotypes were identified: socially engaged (35/214, 16.3%), independently engaged (18/214, 8.4%) (socially and independently engaged parents are those who used mainly the social or the behavioral features of the app, respectively), fully engaged (26/214, 12.1%), partially engaged (32/214, 15%), and unengaged (103/214, 48.1%) users. Married parents were more likely to be fullyengaged than independently engaged (P=.02) or unengaged (P=.01) users. Socially engaged parents were older than fullyengaged (P=.02) and unengaged (P=.01) parents. The latent class analysis revealed 4 phenotypes among children: fully engaged (32/214, 15%), partially engaged (61/214, 28.5%), dabblers (42/214, 19.6%), and unengaged (79/214, 36.9%) users. Fully engaged children were younger than dabblers (P=.04) and unengaged (P=.003) children. Dabblers lived in higher-income households than fully and partiallyengaged children (P=.03 and P=.047, respectively). Fully engaged children were more likely to have fully engaged (P<.001) and partiallyengaged (P<.001) parents than unengaged children. Compared with unengaged children, fully and partiallyengaged children had decreased total sugar (P=.006 and P=.004, respectively) and energy intake (P=.03 and P=.04, respectively) after 3 months of app use. Partially engaged children also had decreased sugary beverage intake compared with unengaged children (P=.03). Similarly, children with fully engaged parents had decreased zBMI, whereas children with unengaged parents had increased zBMI over time (P=.005). Finally, children with independently engaged parents had decreased caloric intake, whereas children with unengaged parents had increased caloric intake over time (P=.02).

conclusionsFull parent-child engagement is critical for the success of mHealth interventions. Further research is needed to understand program design elements that can affect participants' engagement in supporting behavior change.

trial registrationClinicalTrials.gov NCT03651284; https://clinicaltrials.gov/ct2/show/NCT03651284. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-020-4080-2.

Indexed as

Pediatric ObesityTelemedicineChildHumansOverweightParentsPhenotypechildhood obesitydigital phenotypeslatent class analysismHealthmobile health

Identifiers

PMID35731547
PMCPMC9221987
OpenAlexW4283316447

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.