Evidence map›Paper›PMID 35898287›Full record

ArticleDigital health

A personalized mobile app for physical activity: An experimental mixed-methods study.

Huong Ly Tong, Juan C Quiroz, Ahmet Baki Kocaballi, Kiran Ijaz, Enrico Coiera, Clara K Chow, Liliana Laranjo

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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

10 citing papers in PubMed.

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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

7 authors.

Huong Ly TongWestmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0002-8462-0105
Juan C QuirozCentre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
Ahmet Baki KocaballiSchool of Computer Science, University of Technology Sydney, Sydney, Australia.
Kiran IjazCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Enrico CoieraCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Clara K ChowWestmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
Liliana LaranjoWestmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To investigate the feasibility of the be.well app and its personalization approach which regularly considers users' preferences, amongst university students. Methods: We conducted a mixed-methods, pre-post experiment, where participants used the app for 2 months. Eligibility criteria included: age 18-34 years; owning an iPhone with Internet access; and fluency in English. Usability was assessed by a validated questionnaire; engagement metrics were reported. Changes in physical activity were assessed by comparing the difference in daily step count between baseline and 2 months. Interviews were conducted to assess acceptability; thematic analysis was conducted. Results: Twenty-three participants were enrolled in the study (mean age = 21.9 years, 71.4% women). The mean usability score was 5.6 ± 0.8 out of 7. The median daily engagement time was 2 minutes. Eighteen out of 23 participants used the app in the last month of the study. Qualitative data revealed that people liked the personalized activity suggestion feature as it was actionable and promoted user autonomy. Some users also expressed privacy concerns if they had to provide a lot of personal data to receive highly personalized features. Daily step count increased after 2 months of the intervention (median difference = 1953 steps/day, Conclusions: Incorporating users' preferences in personalized advice provided by a physical activity app was considered feasible and acceptable, with preliminary support for its positive effects on daily step count. Future randomized studies with longer follow up are warranted to determine the effectiveness of personalized mobile apps in promoting physical activity.

Indexed as

digital technology [MeSH]exercise [MeSH]health behavior [MeSH]Mobile applications [MeSH]personalizationphysical activitytailoring

Identifiers

PMID35898287
PMCPMC9309778

What OpenQuestion holds

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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.