Evidence map›Paper›PMID 37997790›Full record

ArticleJMIR mHealth and uHealth2023

Use Patterns of Smartphone Apps and Wearable Devices Supporting Physical Activity and Exercise: Large-Scale Cross-Sectional Survey.

Takeyuki Oba, Keisuke Takano, Kentaro Katahira, Kenta Kimura

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
12.7field-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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Inequalities in Exclusively Mobile Interventions Targeting Weight-Related Behaviors: Systematic Review of Observational Studies.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
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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

4 authors at 1 institution in 1 country.

Takeyuki Oba *Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan.ORCID 0000-0002-9075-2622
Keisuke Takano *Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan.ORCID 0000-0003-0406-8654
Kentaro KatahiraHuman Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan.ORCID 0000-0002-2018-3938
Kenta KimuraHuman Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan.ORCID 0000-0003-2385-2081
National Institute of Advanced Industrial Science and Technology · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Physical inactivity is a global health issue, and mobile health (mHealth) apps are expected to play an important role in promoting physical activity. Empirical studies have demonstrated the efficacy and efficiency of app-based interventions, and an increasing number of apps with more functions and richer content have been released. Regardless of the success of mHealth apps, there are important evidence gaps in the literature; that is, it is largely unknown who uses what app functions and which functions are associated with physical activity. Objective: This study aims to investigate the use patterns of apps and wearables supporting physical activity and exercise in a Japanese-speaking community sample. Methods: We recruited 20,573 web-based panelists who completed questionnaires concerning demographics, regular physical activity levels, and use of apps and wearables supporting physical activity. Participants who indicated that they were using a physical activity app or wearable were presented with a list of app functions (eg, sensor information, goal setting, journaling, and reward), among which they selected any functions they used. Results: Approximately one-quarter (n=4465) of the sample was identified as app users and showed similar demographic characteristics to samples documented in the literature; that is, compared with app nonusers, app users were younger (odds ratio [OR] 0.57, 95% CI 0.50-0.65), were more likely to be men (OR 0.83, 95% CI 0.77-0.90), had higher BMI scores (OR 1.02, 95% CI 1.01-1.03), had higher levels of education (university or above; OR 1.528, 95% CI 1.19-1.99), were more likely to have a child (OR 1.16, 95% CI 1.05-1.28) and job (OR 1.28, 95% CI 1.17-1.40), and had a higher household income (OR 1.40, 95% CI 1.21-1.62). Our results revealed unique associations between demographic variables and specific app functions. For example, sensor information, journaling, and GPS were more frequently used by men than women (ORs <0.84). Another important finding is that people used a median of 2 (IQR 1-4) different functions within an app, and the most common pattern was to use sensor information (ie, self-monitoring) and one other function such as goal setting or reminders. Conclusions: Regardless of the current trend in app development toward multifunctionality, our findings highlight the importance of app simplicity. A set of two functions (more precisely, self-monitoring and one other function) might be the minimum that can be accepted by most users. In addition, the identified individual differences will help developers and stakeholders pave the way for the personalization of app functions.

Indexed as

Mobile ApplicationsWearable Electronic DevicesCross-Sectional StudiesExerciseFemaleHumansMaleSurveys and Questionnairesappappscross sectionalcross-sectionalexercisemHealthmobile healthphysical activitysmartphone appsurveytechnology usetelemedicineusageusewearablewearable deviceswearables

Identifiers

PMID37997790
PMCPMC10690103
OpenAlexW4385973771

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.