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Record W4417076172 · doi:10.2196/79360

Mobile-Based Ecological Momentary Intervention for Improving Physical Activity in Adults Without Regular Physical Activity: Pilot Randomized Controlled Trial

2025· article· en· W4417076172 on OpenAlexvenueno aff
Takeyuki Oba, Chihiro Moriishi, Keisuke Takano, Kentaro Katahira, Kenta Kimura

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialIntervention (counseling)Physical activityPilot trialScope (computer science)Behaviour change

Abstract

fetched live from OpenAlex

Background: The ecological momentary intervention (EMI) is one of the most promising digital-primarily mobile-interventions to enhance physical activity (PA) and other health behaviors. It is a combination of ecological momentary assessment (EMA), in which participants are prompted to indicate their momentary states and ongoing behaviors in daily life, and the just-in-time delivery of interventions tailored to the EMA responses. The EMI has typically been implemented in message-based interventions (eg, activity recommendations tailored to users' physical locations), but its efficacy and feasibility have not been sufficiently established because of the variability in design and implementation. Objective: This pilot, two-arm, parallel-group randomized controlled trial aimed to be an exemplar of EMI for improving PA and establishing efficacy and feasibility among adults without a habit of PA. Methods: A total of 40 participants (23 women; mean age 45.40, SD 10.50 years) were recruited from among community dwellers in northeast Japan and randomly allocated to the EMI or control group. Each participant wore an activity tracker to monitor their daily step count and heart rate (HR) for 4 weeks (fully automated). Simultaneously, they responded to EMA questions about the current weather, location, and social context 3 times during the daytime and an additional evening question about motivations for PA each day. Only the EMI group received messages tailored to their responses to EMA, recommending more active alternative behaviors suited to EMA-reported contexts. Results: Participants wore a Fitbit device for 90.3% (21.66/24 hours per day) of the study period (mean 90.3, SD 10.0), and no dropouts were observed. The EMI group showed no significant improvement in the self-reported amount of PA (P=.44), step count (P=.24), or motivation for PA (from P=.11 to P=.91) compared with the control group. However, the EMI group showed a significantly larger increase in the minutes of 40% HR reserve, a measure of moderate or high intensity of PA (mean 16.03, 95% CI 3.76-28.29; Cohen d=0.20-0.41; P=.02 for the follow-up weeks). The intervention was rated as marginally useful and satisfactory, and approximately half of the participants expressed a willingness to continue the intervention. The timing of the EMA prompting was considered inappropriate. Conclusions: These findings suggest that the EMI with messages tailored to EMA-reported contexts was not effective in increasing the amount or motivation for PA but may increase the intensity as assessed by the HR. The intervention aimed to help individuals implement small but slightly more active behaviors in their daily routine, which may not accompany prominent body movements but may be reflected in the increased HR. Marginal feasibility indicates that the intervention has sufficient scope for improvement, particularly in terms of prompt timing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.058
GPT teacher head0.450
Teacher spread0.392 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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