ArticleDigital health
Assessing the impact of message relevance and frequency on physical activity change: A secondary data analysis from the random AIM trial.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- StandUPTV: a full-factorial optimization trial to reduce sedentary screen time among adults.The international journal of behavioral nutrition and physical activity · 2025Trial
- Content Validation and Perceived Value of Text Messages to Promote Physical Activity Among U.S. Older Adults and Care Partners.International journal of environmental research and public health · 2026Observational
- Optimizing generative artificial intelligence prompts to engage urges for physical activity in middle-aged and older adults.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2026Article
- Towards the Developing and Designing of an Intervention to Promote Prenatal Physical Activity Using MomConnect (mHealth): A Formative Protocol.Methods and protocols · 2025Article
- Effectiveness and Components of Health Behavior Interventions on Increasing Physical Activity Among Healthy Young and Middle-Aged Adults: A Systematic Review with Meta-Analyses.Behavioral sciences (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Text messages are widely used to deliver intervention content; however, sending more intensive messages may not always improve behavioral outcomes. This study investigated whether message frequency was associated with daily physical activity, either by itself or in interaction with message content relevance. Healthy but insufficiently active young adults (aged 18-29 years) wore Fitbit activity trackers and received text messages for 180 days. Message frequencies varied daily at random, and messages were sent from three content libraries (40% Move More, 40% Sit Less, 20% Inspirational Quotes). Contrary to expectations, the results revealed a null association between total daily text message frequency and physical activity, both for daily step counts and moderate-to-vigorous physical activity (MVPA) duration. Additional analyses revealed that the daily frequency of messages with relevant content (i.e. Move More, Sit Less) was not associated with physical activity, but the daily frequency of messages with irrelevant content (i.e. Inspirational Quotes) was negatively associated with physical activity. We concluded that the effectiveness of text messages in promoting physical activity is impacted by the combination of content relevance and frequency, with frequent irrelevant messages potentially decreasing activity levels. This study suggests that irrelevant message frequency can negatively impact physical activity, highlighting the risks of delivering irrelevant content in digital health interventions.
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Registered trials
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.