Associations between app usage and behaviour change in a m-health intervention to improve physical activity and sleep health in adults: secondary analyses from two randomised controlled trials

Leah L Murphy¹, Ben J Dascombe², Beatrice Murawski^{1

3}, Anna T Rayward^{1

4

5}, Wendy J Brown^{3

6}, Ronald C Plotnikoff^{1

4

5}, Corneel Vandelanotte⁷, Elizabeth G Holliday¹, Mitch J Duncan^{8

9}

Affiliations

¹ School of Medicine and Public Health, Faculty of Health and Medicine, College of Health, Medicine, and Wellbeing, University of Newcastle, University Drive, Callaghan, 2308, NSW, Australia.
² Applied Sport Science and Exercise Testing Laboratory, School of Environmental and Life Sciences, University of Newcastle, Ourimbah, NSW, 2258, Australia.
³ School of Human Movement and Nutrition Studies, The University of Queensland, Brisbane, QLD, 4072, Australia.
⁴ Active Living Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, 2305, Australia.
⁵ School of Education, University of Newcastle, Callaghan, NSW, 2308, Australia.
⁶ Faculty of Health Sciences and Medicine, Bond University, Gold Coast, QLD, 4226, Australia.
⁷ School of Health, Medical and Applied Sciences, Appleton Institute, Physical Activity Research Group, Central Queensland University, Rockhampton, QLD, 4701, Australia.
⁸ School of Medicine and Public Health, Faculty of Health and Medicine, College of Health, Medicine, and Wellbeing, University of Newcastle, University Drive, Callaghan, 2308, NSW, Australia. Mitch.Duncan@newcastle.edu.au.
⁹ Active Living Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, 2305, Australia. Mitch.Duncan@newcastle.edu.au.

PMID: 40217393
PMCID: PMC11960321
DOI: 10.1186/s44167-022-00013-1

Associations between app usage and behaviour change in a m-health intervention to improve physical activity and sleep health in adults: secondary analyses from two randomised controlled trials

Leah L Murphy et al. J Act Sedentary Sleep Behav. 2023.

. 2023 Feb 2;2(1):4.

doi: 10.1186/s44167-022-00013-1.

Authors

Affiliations

¹ School of Medicine and Public Health, Faculty of Health and Medicine, College of Health, Medicine, and Wellbeing, University of Newcastle, University Drive, Callaghan, 2308, NSW, Australia.
² Applied Sport Science and Exercise Testing Laboratory, School of Environmental and Life Sciences, University of Newcastle, Ourimbah, NSW, 2258, Australia.
³ School of Human Movement and Nutrition Studies, The University of Queensland, Brisbane, QLD, 4072, Australia.
⁴ Active Living Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, 2305, Australia.
⁵ School of Education, University of Newcastle, Callaghan, NSW, 2308, Australia.
⁶ Faculty of Health Sciences and Medicine, Bond University, Gold Coast, QLD, 4226, Australia.
⁷ School of Health, Medical and Applied Sciences, Appleton Institute, Physical Activity Research Group, Central Queensland University, Rockhampton, QLD, 4701, Australia.
⁸ School of Medicine and Public Health, Faculty of Health and Medicine, College of Health, Medicine, and Wellbeing, University of Newcastle, University Drive, Callaghan, 2308, NSW, Australia. Mitch.Duncan@newcastle.edu.au.
⁹ Active Living Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, 2305, Australia. Mitch.Duncan@newcastle.edu.au.

PMID: 40217393
PMCID: PMC11960321
DOI: 10.1186/s44167-022-00013-1

Abstract

Background: To examine associations between user engagement and activity-sleep patterns in a 12-week m-health behavioural intervention targeting physical activity and sleep.

Methods: This secondary analysis used data pooled from two Randomised Control Trials (RCT, [Synergy and Refresh]) that aimed to improve physical activity and sleep (PAS) among physically inactive adults with poor sleep. Both RCTs include a PAS intervention group (n = 190 [Synergy n = 80; Refresh n = 110]) and a wait list Control (CON n = 135 [Synergy n = 80; Refresh n = 55]). The PAS groups received a pedometer and accessed a smartphone/tablet "app" with behaviour change strategies, and email/SMS support. Activity-sleep patterns were quantified using the activity-sleep behaviour index (ASI) based on self-report measures. Intervention usage was quantified as a composite score of the frequency, intensity and duration of app usage during intervention (range: 0-30). Assessments were conducted at baseline, 3 and 6 months. Relationships between usage and ASI were examined using generalised linear models. Differences in ASI between the control group and intervention usage groups (Low [0-10.0], Mid [10.1-20.0], High [20.1-30.0]) were examined using generalised linear mixed models adjusted for baseline values of the outcome.

Trial registration: ACTRN12617000376347; ACTRN12617000680369.

Results: During the 3-month intervention, the mean (± sd) usage score was 18.9 ± 9.5. At 3 months (regression coefficient [95%CI]: 0.45 [0.22, 0.68]) and 6 months (0.48 [0.22, 0.74]) there was a weak association between usage score and ASI in the intervention group. At 3 months, ASI scores in the Mid (Mean [95%CI] = 57.51 [53.99, 61.04]) and High (60.09 [57.52, 62.67]) usage groups were significantly higher (better) than the control group (51.91 [49.58, 54.24]), but not the Low usage group (47.49 [41.87, 53.12]). Only differences between the high usage and control group remained at 6 months.

Conclusion: These findings suggests that while higher intervention usage is associated with improvements in behaviour, the weak magnitude of this association suggests that other factors are also likely to influence behaviour change in m-health interventions.

Trial registration number: ACTRN12617000376347; ACTRN12617000680369.

Keywords: Attrition; Dose-response; Engagement; Usage; eHealth.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards Informed consent was obtained from all individual participants included in the study. Approval was provided by the University of Newcastle Human Research Ethics Committee: H-2016-0267; H-2016-0181. Competing interests: None of the authors have any competing interests.

Figures

**Fig. 1**
Baseline adjusted ASI-12 at 3 and 6 months by usage score in intervention group. Model only includes the pooled intervention group. Model adjusted for study (i.e., Synergy, Refresh), baseline ASI-12 score, and includes the mean centered usage score and its interaction with assessment. p-value for interaction between usage score and assessment is = 0.857 The association between usage score and ASI-12 at 3 months is = Β=, 95%CI: b = 0.45,95%CI = 0.22, 0.68 and at 6 months is Β = 0.48, 95%CI = 0.22, 0.74

**Fig. 2**
Baseline adjusted ASI-12 at 3 and 6 months by Intervention and Usage Group. Model adjusted for study (i.e., Synergy, Refresh), baseline ASI-12 score, fixed effects for group (Control, Low Usage, Mid Usage, High Usage), assessment (3 months, 6 months) and the group by assessment interaction

See this image and copyright information in PMC

References

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Associations between app usage and behaviour change in a m-health intervention to improve physical activity and sleep health in adults: secondary analyses from two randomised controlled trials

Affiliations

Associations between app usage and behaviour change in a m-health intervention to improve physical activity and sleep health in adults: secondary analyses from two randomised controlled trials

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Grants and funding

LinkOut - more resources

Full Text Sources