Evidence map›Paper›PMID 40695991›Full record

ArticleNPJ digital medicine2025

A randomized controlled trial of mobile intervention using health support bubbles to prevent social frailty.

Chisato Hayashi, Nanae Tanemura, Maki Taniguchi, Tadashi Okano, Hiromitsu Toyoda, Sonoe Mashino, Soshiro Ogata

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

7 authors.

Chisato HayashiResearch Institute of Nursing Care for People and Community, University of Hyogo, Akashi, Hyogo, Japan. chisato_hayashi@cnas.u-hyogo.ac.jp.
Nanae TanemuraFukushima University, Faculty of Food and Agricultural Sciences, Fukushima City, Japan.
Maki TaniguchiInstitute of Science Tokyo, Graduate school of Health Care Sciences, Bunkyo-ku, Tokyo, Japan.
Tadashi OkanoCenter for Senile Degenerative Disorders (CSDD), Graduate School of Medicine, Osaka Metropolitan University, Osaka City, Osaka, Japan.
Hiromitsu ToyodaDepartment of Orthopaedic Surgery, Graduate School of Medicine, Osaka Metropolitan University, Osaka City, Osaka, Japan.
Sonoe MashinoResearch Institute of Nursing Care for People and Community, University of Hyogo, Akashi, Hyogo, Japan.
Soshiro OgataDepartment of Nephrology, School of Medicine, Fujita Health University, Toyoake, Aichi, Japan.

Funding

Japan Society for the Promotion of Science 23K21572
6 · The paper itself

Abstract

Mobile health (mHealth) is gaining interest worldwide. This randomized trial aimed to test the effects of family companionship on the prevention of social frailty among Japanese individuals aged ≥40 years using a mHealth application. We used a participant-coaching system, which included a mobile nutrition management software application and a web portal. We assessed social frailty using Bunt's framework. We enrolled 101 participants who used the application either alone (control group) or with family members (intervention group). The intervention arm showed greater improvement in social behavior and leisure activities (p = 0.004) and the total frailty score (p = 0.037). However, the social isolation did not improve with intervention. Our results suggest that mHealth can influence behavior change, but it does not resolve social isolation. While it provides convenience and enhanced access to healthcare, there is a need to balance digital efficiency with human interaction, ensuring that mHealth solutions complement, rather than replace, personal connections.

Identifiers

PMID40695991
PMCPMC12284115

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Registered trials

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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.