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.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-20 |
| Number of pages | 20 |
| Journal | Digital Health |
| Volume | 9 |
| DOIs | |
| Publication status | Published - 28 Jun 2023 |
Keywords
- feasibility study
- physical activity
- mHealth
- technology
- behaviour
- Exercise