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Cited by 43Physical Activity and HealthMobile Health and mHealth ApplicationsContext-Aware Activity Recognition Systems

Balanced: a randomised trial examining the efficacy of two self-monitoring methods for an app-based multi-behaviour intervention to improve physical activity, sitting and sleep in adults

Open access
Duncan, Mitch J., Vandelanotte, Corneel, Trost, Stewart G., Rebar, Amanda L., Rogers, Naomi, Burton, Nicola W., Murawski, Beatrice, Rayward, Anna +2 more·2016·BMC Public Health
DOI 10.1186/s12889-016-3256-xPMC PMC4967346
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Abstract

BACKGROUND: Many adults are insufficiently physically active, have prolonged sedentary behaviour and report poor sleep. These behaviours can be improved by interventions that include education, goal setting, self-monitoring, and feedback strategies. Few interventions have explicitly targeted these behaviours simultaneously or examined the relative efficacy of different self-monitoring methods. METHODS/DESIGN: This study aims to compare the efficacy of two self-monitoring methods in an app-based multi-behaviour intervention to improve objectively measured physical activity, sedentary, and sleep behaviours, in a 9 week 2-arm randomised trial. Participants will be adults (n = 64) who report being physically inactive, sitting >8 h/day and frequent insufficient sleep (≥14 days out of last 30). The "Balanced" intervention is delivered via a smartphone 'app', and includes education materials (guidelines, strategies to promote change in behaviour), goal setting, self-monitoring and feedback support. Participants will be randomly allocated to either a device-entered or user-entered self-monitoring method. The device-entered group will be provided with a activity tracker to self-monitor behaviours. The user-entered group will recall and manually record behaviours. Assessments will be conducted at 0, 3, 6, and 9 weeks. Physical activity, sedentary behaviour and sleep-wake behaviours will be measured using the wrist worn Geneactiv accelerometer. Linear mixed models will be used to examine differences between groups and over time using an alpha of 0.01. DISCUSSION: This study will evaluate an app-based multi-behavioural intervention to improve physical activity, sedentary behaviour and sleep; and the relative efficacy of two different approaches to self-monitoring these behaviours. Outcomes will provide information to inform future interventions and self-monitoring targeting these behaviours. TRIAL REGISTRATION: ACTRN12615000182594 (Australian New Zealand Clinical Trials Registry. Registry URL: www.anzctr.org.au ; registered prospectively on 25 February 2015).

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89 of 89 references verified0 citation errors · 0 not checkable

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  • A total of 26 studies were included across all meta-analyses.

    “A total of 26 studies were included across all meta-analyses [21,22,29-48,50-53].”

    From Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis (2019) · cites this paper as [33]

    The Methods (Participant recruitment and Analysis; page not provided) describe the Balanced trial, so the citation identifies a study. The paper does not report that 26 studies were included across meta-analyses, or establish that this trial was included in them. (LLM, conf=0.78)

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  • Eight studies that reported physical activity outcome measures had greater than 20% loss to follow-up, with three of these reporting a reduction in physical activity participation.

    “In addition, 8 studies [29,32,33,37,42,43,46,48] that reported physical activity outcome measures had greater than 20% loss to follow-up, with 3 of these studies reporting a reduction in physical activity participation.”

    From Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis (2019) · cites this paper as [33]

    The Methods (Secondary outcomes and Analysis; pages unavailable) specify physical activity outcome measures, so this paper supports its identification as a study assessing physical activity. It is a trial protocol describing planned assessments, not results: our copy does not report greater than 20% loss to follow-up or a reduction in physical activity participation. (LLM, conf=0.82)

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  • 17 studies specified that participants must have regular access to the internet, a computer, tablet, and/or smartphone.

    “Furthermore, 17 studies specified that the participant must have regular access to the internet, a computer, tablet, and/or smartphone [21,30,33,35-37,39,41-47,51-53]”

    From Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis (2019) · cites this paper as [33]

  • 13 studies included young adults (≤39.9 years), 15 included middle-aged adults (40-64.9 years), and 1 included adults over 65 years.

    “Thirteen studies included young adults (≤39.9 years) [30-34,37,38,42,44,46-48,50], 15 included middle-aged adults (40-64.9 years) [21,22,29,35,36,39-41, 43,45,51-54], and 1 study included adults aged over 65 years [49].”

    From Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis (2019) · cites this paper as [33]

  • Less time was spent in sedentary behavior for those receiving a consumer-based wearable activity tracker intervention compared with control groups; however, the finding was not significant, and the quality of the evidence was graded as very low.

    “Less time was spent in sedentary behavior for those receiving a consumer-based wearable activity tracker intervention compared with control groups; however, the finding was not significant, and the quality of the evidence was graded as very low [29,31-33,37-40].”

    From Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis (2019) · cites this paper as [33]

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Research works printed but not linked: 1

  1. [44]Davies CA Spence JC Vandelanotte C Caperchione CM Mummery WK Meta-analysis of internet-delivered interventions to increase physical activity levels IJBNPA 2012 9 52 22546283 PMC3464872

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Authors
Duncan, M. J., Vandelanotte, C., Trost, S. G., Rebar, A. L., Rogers, N., Burton, N. W., Murawski, B., Rayward, A., Fenton, S., & Brown, W. J.
Affiliations
  • University of Newcastle Australia (AU)
  • Central Queensland University (AU)
  • Queensland University of Technology (AU)
  • The University of Sydney (AU)
  • The University of Queensland (AU)
Corresponding author
Duncan, Mitch J.
ORCID
Venue
BMC Public Health
Publisher
Springer Science and Business Media LLC
Published
2016-07-30
Volume
16
Issue
1
Pages
670-670
ISSN
1471-2458 (ISSN-L)
DOI
10.1186/s12889-016-3256-x
PMC
PMC4967346
OpenAlex
W2483619682
Cited by
43
References
89
Citations by year
2026: 1 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 5 · 2020: 7 · 2019: 9 · 2018: 9 · 2017: 2
FWCI
5.37
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Public Health (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • QUT ePrints (Queensland University of Technology) (submittedVersion, other-oa)
  • QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by)
  • Acquire (CQUniversity) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eSpace (Curtin University) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Griffith Research Online (submittedVersion, cc-by)
Funders
  • National Heart Foundation of Australia (100029, ID 100427)
  • National Health and Medical Research Council (GNT1105926)
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
self-monitoring, physical activity, sedentary behavior, sleep, mobile application, wearable activity trackers, behavior change, goal setting, feedback, adults, insufficient sleep
MeSH terms
Smartphone, Adolescent, Adult, Behavior Therapy, Female, Humans, Male, Middle Aged, New South Wales, Sleep, Exercise, Treatment Outcome, Young Adult, Sedentary Behavior, Mobile Applications

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