Evidence map›Paper›PMID 35171100›Full record

ReviewJMIR mHealth and uHealth2022

The Content, Quality, and Behavior Change Techniques in Nutrition-Themed Mobile Apps for Children in Canada: App Review and Evaluation Study.

Jacqueline Marie Brown, Beatriz Franco-Arellano, Hannah Froome, Amina Siddiqi, Amina Mahmood, JoAnne Arcand

Open access · goldAbstract readReview
In one paragraph

Review 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 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
11.3field-weighted citation impact, top 2% 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

9 citing papers in PubMed, 19 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

6 authors at 1 institution in 1 country.

Jacqueline Marie Brown *Faculty of Health Sciences, Ontario Tech University, Oshawa, ON, Canada.ORCID 0000-0003-3806-0802
Beatriz Franco-Arellano *Faculty of Health Sciences, Ontario Tech University, Oshawa, ON, Canada.ORCID 0000-0003-4506-9792
Hannah FroomeFaculty of Health Sciences, Ontario Tech University, Oshawa, ON, Canada.ORCID 0000-0001-5348-2208
Amina SiddiqiFaculty of Health Sciences, Ontario Tech University, Oshawa, ON, Canada.ORCID 0000-0002-5328-8330
Amina MahmoodFaculty of Health Sciences, Ontario Tech University, Oshawa, ON, Canada.ORCID 0000-0002-3998-8816
JoAnne ArcandFaculty of Health Sciences, Ontario Tech University, Oshawa, ON, Canada.ORCID 0000-0002-1995-6905
Ontario Tech University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChildren increasingly use mobile apps. Strategies to increase child engagement with apps include the use of gamification and images that incite fun and interaction, such as food. However, the foods and beverages that children are exposed to while using apps are unknown and may vary by app type.

objectiveThe aim of this study is to identify the app content (ie, types of foods and beverages) included in nutrition-themed apps intended for children, to assess the use of game-like features, and to examine app characteristics such as overall quality and behavior change techniques (BCTs).

methodsThis analysis used a cross-sectional database of nutrition-themed apps intended for children (≤12 years), collected between May 2018 and June 2019 from the Apple App Store and Google Play Store (n=259). Apps were classified into four types: food games or nongames that included didactic nutrition guides, habit trackers, and other. Food and beverages were identified in apps and classified into 16 food categories, as recommended (8/16, 50%) and as not recommended (8/16, 50%) by dietary guidelines, and quantified by app type. Binomial logistic regression assessed whether game apps were associated with foods and beverages not recommended by guidelines. App quality, overall and by subscales, was determined using the Mobile App Rating Scale. The BCT Taxonomy was used to classify the different behavioral techniques that were identified in a subsample of apps (124/259, 47.9%).

resultsA total of 259 apps displayed a median of 6 (IQR 3) foods and beverages. Moreover, 62.5% (162/259) of apps were classified as food games, 27.4% (71/259) as didactic nutrition guides, 6.6% (17/259) as habit trackers, and 3.5% (9/259) as other. Most apps (198/259, 76.4%) displayed at least one food or beverage that was not recommended by the dietary guidelines. Food game apps were almost 3 times more likely to display food and beverages not recommended by the guidelines compared with nongame apps (β=2.8; P<.001). The overall app quality was moderate, with a median Mobile App Rating Scale score of 3.6 (IQR 0.7). Functionality was the subscale with the highest score (median 4, IQR 0.3). Nutrition guides were more likely to be educational and contain informative content on healthy eating (score 3.7), compared with the other app types, although they also scored significantly lower in engagement (score 2.3). Most apps (105/124, 84.7%) displayed at least one BCT, with the most common BCT being information about health consequences.

conclusionsFindings suggest nutrition-themed apps intended for children displayed food and beverage content not recommended by dietary guidelines, with gaming apps more likely to display not recommended foods than their nongame counterparts. Many apps have a moderate app quality, and the use of consequences (instead of rewards) was the most common BCT.

Indexed as

Mobile ApplicationsBehavior TherapyChildDelivery of Health CareHumansNutritional StatusNutrition Policyapp qualitybehavior change techniquesCanadachild nutritionchildrenmHealthmobile appsmobile phone

Identifiers

PMID35171100
PMCPMC8892278
OpenAlexW4213196230

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.