Health behaviour
Does a step-log app keep people tracking activity?
Open access · cc by · source: Europe PMC
In 10,000 Steps members, a smartphone iStepLog app preserved daily logging and high step counts versus matched controls whose logging fell.
Study at a glance
- Design
- Case-control — Matched case–control of iStepLog users vs similar 10,000 Steps members (not randomised)
- N
- N=50 · 50 app users matched to 150 controls (1:3); 48% women in each group
- Population
- Engaged 10,000 Steps members with iPhone/iPod access
- Outcome
- Daily step-logging frequency and logging >10,000 steps
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
App users maintained logging (~61–62 days) while matched controls declined (61 to 41). App use linked to higher odds of daily logging (OR 3.56) and of logging >10,000 steps (OR 20.64).
Methodology
Intervention participants using iStepLog were matched to similar 10,000 Steps members; researchers compared logging frequency and steps before and during the intervention period.
Limitations
Matched design is not a randomized test of clinical outcomes like weight or CVD events.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
Team walking can add minutes during the programme — advantages often fade months later.
A Facebook team-walking programme increased walking by about 155 min/week during the intervention, but between-group advantages were not maintained 3 months after the stimulus ended.
Scope note — different question — logging adherence, not walking-minute efficacy
Limits the claim's scope: a different population, assay, or outcome.
Digital tools show mixed proximal effects — logging more is not the same as preventing disease.
Digital tools show mixed proximal endpoints: a step-log app was associated with higher odds of continued daily logging versus matched controls, while a small uncontrolled smoking EMI reported 20% biochemically confirmed abstinence at 12 weeks.
Evidence for the claim as stated.
Randomised activity/diet programmes, observational lifestyle indices, and digital pilots measure different endpoints (minutes walked, zBMI, incident disease, logging, abstinence). Treating them as one efficacy number manufactures agreement that the designs do not support.
Evidence for the claim as stated.
Open questions
Tensions this paper is part of
From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.
Randomised activity/diet programmes, observational lifestyle indices, and digital pilots measure different endpoints (minutes walked, zBMI, incident disease, logging, abstinence). Treating them as one efficacy number manufactures agreement that the designs do not support.
- Supports · Web program increased older adults’ measured activity in a trial
- Supports · Healthy lifestyle and multimorbidity risk
- Supports · Child-care diet/activity RCT and zBMI
- Supports · Smart-T smoking EMI app
- Supports · Facebook team walking RCT
History
When this study was placed
Dated entries from the concept change log — when this paper was added or removed as support, challenge, or qualifier on a claim.
Placed as a scope qualifier on Health Behaviour
A Facebook team-walking programme increased walking by about 155 min/week during the intervention, but between-group advantages were not maintained 3 months after the stimulus ended.
Placed as supporting evidence on Health Behaviour
Digital tools show mixed proximal endpoints: a step-log app was associated with higher odds of continued daily logging versus matched controls, while a small uncontrolled smoking EMI reported 20% biochemically confirmed abstinence at 12 weeks.
Placed as supporting evidence on Health Behaviour
Randomised activity/diet programmes, observational lifestyle indices, and digital pilots measure different endpoints (minutes walked, zBMI, incident disease, logging, abstinence). Treating them as one efficacy number manufactures agreement that the designs do not support.
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Same topic cluster — not a recommendation engine.