Evidence map›Paper›PMID 40821239›Full record

ArticleCureus2025

Effects of Personalized Messaging From a Social Media Influencer on Followers' Step Counts: A Parallel-Group Randomized Controlled Trial.

Ryosuke Shigematsu, Takumu Ichikawa, Yoshitake Oshima, Hiroyuki Sasai

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ryosuke ShigematsuSchool of Health and Sport Sciences, Chukyo University, Toyota, JPN.
Takumu IchikawaCourse for Health and Physical Education, Faculty of Education, Mie University, Tsu, JPN.
Yoshitake OshimaDepartment of Physical Education, Kyoto University of Education, Kyoto, JPN.
Hiroyuki SasaiResearch Team for Promoting Independence and Mental Health, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPhysical activity during adolescence is crucial for long-term health; however, many young individuals lack adequate activity levels. Social networking services (SNS) and influencers have shown promise in shaping behaviors; however, their potential to promote physical activity remains underexplored, especially when influencers are not fitness-related. This study investigated whether personalized messages from a non-fitness influencer could increase followers' physical activity.

methodsA parallel-group randomized controlled trial was conducted for four weeks in December 2019 among followers of a non-fitness influencer of a Japanese idol group. Participants were assigned to either an individual message group (personalized messages) or an automated message group (standardized messages). Step counts were tracked via a smartphone application. Statistical analyses assessed the differences in daily steps and goal achievement (baseline average plus 1,000 steps) between the groups.

resultsWith a total of 120 participants, the mean difference in step count during Weeks 3 and 4 was significantly greater in the individual message group than in the automated message group. Goal attainment was also greater in the individual message group than in the automated message group at Weeks 2-4.

conclusionPersonalized messages from non-fitness influencers effectively increased physical activity, suggesting a scalable strategy for promoting health in young populations through SNS platforms.

Indexed as

exerciseonline social networkingsocial mediatext messagingyoung adult

Identifiers

PMID40821239
PMCPMC12357767

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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.