Evidence map›Paper›PMID 31364606›Full record

ArticleJMIR mHealth and uHealth2019

Nutrition-Related Mobile Apps in the China App Store: Assessment of Functionality and Quality.

Yuan Li, Jingmin Ding, Yishan Wang, Chengyao Tang, Puhong Zhang

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 3 pooled it
–field-weighted citation impact
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

27 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Article
  6. Article
  7. Article
  8. Article
  9. Observational
  10. Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Article
  16. Article
  17. Article
  18. Review
  19. Review
  20. Article
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

5 authors.

Yuan Li *The George Institute for Global Health at Peking University Health Science Center, Beijing, China.ORCID 0000-0003-1887-7065
Jingmin Ding *The George Institute for Global Health at Peking University Health Science Center, Beijing, China.ORCID 0000-0002-7088-1662
Yishan WangThe George Institute for Global Health at Peking University Health Science Center, Beijing, China.ORCID 0000-0002-6917-2420
Chengyao TangPublic Health Graduate School of Medicine, Osaka University, Osaka, Japan.ORCID 0000-0001-6508-3693
Puhong ZhangThe George Institute for Global Health at Peking University Health Science Center, Beijing, China.ORCID 0000-0003-4610-9848

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere are an increasing number of mobile apps that provide dietary guidance to support a healthy lifestyle and disease management. However, the characteristics of these nutrition-related apps are not well analyzed.

objectiveThis study aimed to evaluate the functionality and quality of nutrition-related apps in China.

methodsMobile apps providing dietary guidance were screened in the Chinese iOS and Android app stores in November 2017, using stepwise searching criteria. The first screening consisted of extracting information from the app descriptions. Apps that (1) were free, (2) contain information on diet and nutrition, and (3) were last updated after January 1, 2016, were downloaded for further analysis. Nutritional functionalities were determined according to the Chinese Dietary Guidelines framework. Market-related functionalities were developed from previous studies and tailored to downloaded apps. The quality of apps was assessed with the user version of the Mobile App Rating Scale (uMARS).

resultsOut of 628 dietary guidance apps screened, 44 were nutrition-related. Of these, guidance was provided on diet exclusively (11/44, 25%), fitness (17/44, 39%), disease management (11/44, 25%), or maternal health (5/44, 11%). Nutritional functionalities included nutritional information inquiry (40/44, 91%), nutrition education (35/44, 80%), food record (34/44, 77%), diet analysis (34/44, 77%), and personalized recipes (21/44, 48%). Dietary analysis and suggestions mainly focused on energy intake (33/44, 75%) and less on other factors such as dietary structure (10/44, 23%). Social communication functionalities were available in 42 apps (96%), user incentives were supported in 26 apps (59%), and intelligent recognition technology was available in 8 apps (18%). The median score for the quality of the 44 apps, as determined on a 5-point uMARS scale, was 3.6 (interquartile range 0.7).

conclusionsMost nutrition-related apps are developed for health management rather than for dietary guidance exclusively. Although basic principles of energy balance are used, their nutritional functionality was relatively limited and not individualized. More efforts should be made to develop nutrition-related apps with evidence-based nutritional knowledge, comprehensive and personalized dietary guidance, and innovative technology.

Indexed as

AdultAgedCell PhoneChinaDietDisease ManagementEnergy IntakeExerciseHealth BehaviorHealth EducationHealthy LifestyleHumansMaternal HealthMiddle AgedMobile ApplicationsNutritional Statusappsbehavior changedietfoodmobile appsmobile phonenutrition

Identifiers

PMID31364606
PMCPMC6691672

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.