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. 2023 Jun 28:9:20552076231183552.
doi: 10.1177/20552076231183552. eCollection 2023 Jan-Dec.

Adherence to unsupervised exercise in sedentary individuals: A randomised feasibility trial of two mobile health interventions

Daniel J Bannell  1 , Madeleine France-Ratcliffe  1 , Benjamin James Roy Buckley  1   2   3 , Anthony Crozier  1 , Andrew P Davies  1 , Katie L Hesketh  1   4 , Helen Jones  1   2 , Matthew Cocks  1   2 , Victoria S Sprung  1   2   3   4 ; MOTIVATE Team
Affiliations

Adherence to unsupervised exercise in sedentary individuals: A randomised feasibility trial of two mobile health interventions

Daniel J Bannell et al. Digit Health. .

Abstract

Introduction: Adherence to unsupervised exercise is poor, yet unsupervised exercise interventions are utilised in most healthcare settings. Thus, investigating novel ways to enhance adherence to unsupervised exercise is essential. This study aimed to examine the feasibility of two mobile health (mHealth) technology-supported exercise and physical activity (PA) interventions to increase adherence to unsupervised exercise.

Methods: Eighty-six participants were randomised to online resources (n = 44, females n = 29) or MOTIVATE (n = 42, females n = 28). The online resources group had access to booklets and videos to assist in performing a progressive exercise programme. MOTIVATE participants received exercise counselling sessions supported via mHealth biometrics which allowed instant participant feedback on exercise intensity, and communication with an exercise specialist. Heart rate (HR) monitoring, survey-reported exercise behaviour and accelerometer-derived PA were used to quantify adherence. Remote measurement techniques were used to assess anthropometrics, blood pressure, HbA1c and lipid profiles.

Results: HR-derived adherence rates were 22 ± 34% and 113 ± 68% in the online resources and MOTIVATE groups, respectively. Self-reported exercise behaviour demonstrated moderate (Cohen's d = 0.63, CI = 0.27 to 0.99) and large effects (Cohen's d = 0.88, CI = 0.49 to 1.26) in favour of online resources and MOTIVATE groups, respectively. When dropouts were included, 84% of remotely gathered data were available, with dropouts removed data availability was 94%.

Conclusion: Data suggest both interventions have a positive impact on adherence to unsupervised exercise but MOTIVATE enables participants to meet recommended exercise guidelines. Nevertheless, to maximise adherence to unsupervised exercise, future appropriately powered trials should explore the effectiveness of the MOTIVATE intervention.

Keywords: Exercise; behaviour; feasibility study; mHealth; physical activity; technology.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, funding and/or publication of this article.

Figures

Figure 1.
Figure 1.
Participant consort diagram.
Figure 2.
Figure 2.
Bland–Altman plots assessing agreement for the number of moderate-to-strenuous exercise sessions recorded by the GLTEQ survey and HR monitor. (a) All participants, (b) MOTIVATE only, (c) online resources only and (d) online resources participants reporting a wear proportion of 66–100%. Solid line shows the mean difference in the number of sessions, and the dashed lines show 95% CI of agreement.
Figure 3.
Figure 3.
Representative accelerometer (mg) and HR data (%HRmax) traces recorded throughout bouts of RT (a), HIIT (b), MICT (c) and VIT (d). The dotted line is indicative of the vigorous intensity threshold (>429 mg/70%HRmax), the dashed line is indicative of the moderate intensity threshold (>101 mg/60%HRmax).
Figure 4.
Figure 4.
Bland–Altman plots assessing agreement for proportion of exercise session in moderate-to-vigorous–intensity activity recorded by the accelerometer and HR monitor. (a) MICT, (b) VIT, (c) HIIT and (d) RT. Solid line shows the mean difference in % time spent in moderate-to-vigorous–intensity activity, and the dashed lines show 95% CI of agreement.
See this image and copyright information in PMC

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