The effects of step-count monitoring interventions on physical activity: systematic review and meta-analysis of community-based randomised controlled trials in adults
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BACKGROUND: Step-count monitors (pedometers, body-worn trackers and smartphone applications) can increase walking, helping to tackle physical inactivity. We aimed to assess the effect of step-count monitors on physical activity (PA) in randomised controlled trials (RCTs) amongst community-dwelling adults; including longer-term effects, differences between step-count monitors, and between intervention components. METHODS: Systematic literature searches in seven databases identified RCTs in healthy adults, or those at risk of disease, published between January 2000-April 2020. Two reviewers independently selected studies, extracted data and assessed risk of bias. Outcome was mean differences (MD) with 95% confidence intervals (CI) in steps at follow-up between treatment and control groups. Our preferred outcome measure was from studies with follow-up steps adjusted for baseline steps (change studies); but we also included studies reporting follow-up differences only (end-point studies). Multivariate-meta-analysis used random-effect estimates at different time-points for change studies only. Meta-regression compared effects of different step-count monitors and intervention components amongst all studies at ≤4 months. RESULTS: Of 12,491 records identified, 70 RCTs (at generally low risk of bias) were included, with 57 trials (16,355 participants) included in meta-analyses: 32 provided change from baseline data; 25 provided end-point only. Multivariate meta-analysis of the 32 change studies demonstrated step-counts favoured intervention groups: MD of 1126 steps/day 95%CI [787, 1466] at ≤4 months, 1050 steps/day [602, 1498] at 6 months, 464 steps/day [301, 626] at 1 year, 121 steps/day [- 64, 306] at 2 years and 434 steps/day [191, 676] at 3-4 years. Meta-regression of the 57 trials at ≤4 months demonstrated in mutually-adjusted analyses that: end-point were similar to change studies (+ 257 steps/day [- 417, 931]); body-worn trackers/smartphone applications were less effective than pedometers (- 834 steps/day [- 1542, - 126]); and interventions providing additional counselling/incentives were not better than those without (- 812 steps/day [- 1503, - 122]). CONCLUSIONS: Step-count monitoring leads to short and long-term step-count increases, with no evidence that either body-worn trackers/smartphone applications, or additional counselling/incentives offer further benefit over simpler pedometer-based interventions. Simple step-count monitoring interventions should be prioritised to address the public health physical inactivity challenge. SYSTEMATIC REVIEW REGISTRATION: PROSPERO number CRD42017075810 .
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Results suggested strong effects on step counts at 4-6 months (an increase of 1127 [95% CI 710-1543] steps per day), and smaller but statistically significant effects on step counts up to 4 years (an increase of 494 [251-738] steps per day).
“Results suggested strong effects on step counts at 4-6 months (an increase of 1127 [95% CI 710-1543] steps per day), and smaller but statistically significant effects on step counts up to 4 years (an increase of 494 [251-738] steps per day).37”
From Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. (2022) · cites this paper as [37]
It is likely that the increase in steps occurred just by using the Fitbit.
“It is also likely that the increase in steps occurred just by using the Fitbit [43].”
From Feasibility, Usability, and Effectiveness of a Machine Learning–Based Physical Activity Chatbot: Quasi-Experimental Study (2021) · cites this paper as [43]
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- Authors
- Chaudhry, U. A. R., Wahlich, C., Fortescue, R., Cook, D. G., Knightly, R., & Harris, T.
- Affiliations
- St George's, University of London (GB)
- Corresponding author
- Chaudhry, Umar A. R.
- Venue
- International Journal of Behavioral Nutrition and Physical Activity
- Published
- 2020-10-09
- Volume
- 17
- Issue
- 1
- Pages
- 129-129
- ISSN
- 1479-5868 (ISSN-L)
- PMC
- PMC7545847
- OpenAlex
- W3092000252
- Cited by
- 193
- References
- 104
- Citations by year
- 2026: 20 · 2025: 24 · 2024: 57 · 2023: 24 · 2022: 30 · 2021: 37 · 2020: 1
- FWCI
- 2.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal of Behavioral Nutrition and Physical Activity (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- St George's Online Research Archive (St George's University of London) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- step count monitoring, pedometers, mobile application, physical activity, walking, physical inactivity, community-dwelling adults, step count, counseling, incentives, intervention components, long-term effects, physical activity interventions, physical activity counseling
- MeSH terms
- Fitness Trackers, Wearable Electronic Devices, Humans, Exercise, Randomized Controlled Trials as Topic, Walking, Independent Living
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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