Cited by 291Physical Activity and HealthMobile Health and mHealth ApplicationsContext-Aware Activity Recognition Systems

Using computer, mobile and wearable technology enhanced interventions to reduce sedentary behaviour: a systematic review and meta-analysis

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Stephenson, Aoife, McDonough, Suzanne M., Murphy, Marie H., Nugent, Chris D., Mair, Jacqueline L.·2017·International Journal of Behavioral Nutrition and Physical Activity

Abstract

BACKGROUND: High levels of sedentary behaviour (SB) are associated with negative health consequences. Technology enhanced solutions such as mobile applications, activity monitors, prompting software, texts, emails and websites are being harnessed to reduce SB. The aim of this paper is to evaluate the effectiveness of such technology enhanced interventions aimed at reducing SB in healthy adults and to examine the behaviour change techniques (BCTs) used. METHODS: Five electronic databases were searched to identify randomised-controlled trials (RCTs), published up to June 2016. Interventions using computer, mobile or wearable technologies to facilitate a reduction in SB, using a measure of sedentary time as an outcome, were eligible for inclusion. Risk of bias was assessed using the Cochrane Collaboration's tool and interventions were coded using the BCT Taxonomy (v1). RESULTS: Meta-analysis of 15/17 RCTs suggested that computer, mobile and wearable technology tools resulted in a mean reduction of -41.28 min per day (min/day) of sitting time (95% CI -60.99, -21.58, I2 = 77%, n = 1402), in favour of the intervention group at end point follow-up. The pooled effects showed mean reductions at short (≤ 3 months), medium (>3 to 6 months), and long-term follow-up (>6 months) of -42.42 min/day, -37.23 min/day and -1.65 min/day, respectively. Overall, 16/17 studies were deemed as having a high or unclear risk of bias, and 1/17 was judged to be at a low risk of bias. A total of 46 BCTs (14 unique) were coded for the computer, mobile and wearable components of the interventions. The most frequently coded were "prompts and cues", "self-monitoring of behaviour", "social support (unspecified)" and "goal setting (behaviour)". CONCLUSION: Interventions using computer, mobile and wearable technologies can be effective in reducing SB. Effectiveness appeared most prominent in the short-term and lessened over time. A range of BCTs have been implemented in these interventions. Future studies need to improve reporting of BCTs within interventions and address the methodological flaws identified within the review through the use of more rigorously controlled study designs with longer-term follow-ups, objective measures of SB and the incorporation of strategies to reduce attrition. TRIAL REGISTRATION: The review protocol was registered with PROSPERO: CRD42016038187.

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Authors
Stephenson, A., McDonough, S. M., Murphy, M. H., Nugent, C. D., & Mair, J. L.
Affiliations
University of Ulster (GB)
Corresponding author
Stephenson, Aoife
Venue
International Journal of Behavioral Nutrition and Physical Activity
Published
2017-08-11
Volume
14
Issue
1
Pages
105-105
ISSN
1479-5868 (ISSN-L)
OpenAlex
W2744204866
Cited by
290
References
59
Citations by year
2026: 16 · 2025: 28 · 2024: 29 · 2023: 25 · 2022: 44 · 2021: 49 · 2020: 52 · 2019: 22 · 2018: 20 · 2017: 4
FWCI
3.29
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • International Journal of Behavioral Nutrition and Physical Activity (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Ulster University Research Portal (Ulster University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion, cc-by)
Funders
Ulster University (PhD Scholarship)
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
sedentary behavior, technology-based interventions, mobile application, wearable activity trackers, computer-based intervention, behaviour change techniques, sitting time reduction, self-monitoring, prompts and cues, social support, goal setting, healthy adults, intervention effectiveness, attrition, long-term follow-up, risk of bias
MeSH terms
Wearable Electronic Devices, Health Promotion, Humans, Health Behavior, Randomized Controlled Trials as Topic, Databases, Factual, Sedentary Behavior, Mobile Applications

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