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Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence

Author

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  • Andrea Albergoni

    (Department of Biomedical Sciences for Health, University of Milan, 20133 Milan, Italy
    Centro Polifunzionale di Scienze Motorie, University of Genoa, 16132 Genoa, Italy
    Department of Neuroscience (DINOGMI), University of Genoa, 16148 Genoa, Italy
    Department of Patient Care and Measurements, Philips Research, 5656AE Eindhoven, The Netherlands)

  • Florentina J. Hettinga

    (School of Sport Rehabilitation and Exercise Sciences, University of Essex, Colchester CO4 3WA, UK
    Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle NE1 8ST, UK)

  • Wim Stut

    (Department of Chronic Disease Management, Philips Research, 5656AE Eindhoven, The Netherlands)

  • Francesco Sartor

    (Department of Patient Care and Measurements, Philips Research, 5656AE Eindhoven, The Netherlands
    School of Sport Rehabilitation and Exercise Sciences, University of Essex, Colchester CO4 3WA, UK
    College of Health and Behavioural Science, Bangor University, Bangor LL57 2EF, UK)

Abstract

Background: Monitoring and interfacing technologies may increase physical activity (PA) program adherence in older adults, but they should account for aspects influencing older adults’ PA behavior. This study aimed at gathering preliminary wrist-based PA adherence data in free-living and relate these to the influencing factors. Methods: Ten healthy older adults (4 females, aged 70–78 years) provided health, fatigue, activity levels, attitude towards pacing, and self-efficacy information and performed a 6 min-walk test to assess their fitness. After a baseline week they followed a two-week walking and exercise intervention. Participants saw their progress via a purposely designed mobile application. Results: Walking and exercise adherence did not increase during the intervention ( p = 0.38, p = 0.65). Self-efficacy decreased ( p = 0.024). The baseline physical component of the Short Form Health Survey was the most predictive variable of walking adherence. Baseline perceived risk of over-activity and resting heart rate (HR rest ) were the most predictive variables of exercise adherence. When the latter two were used to cluster participants according to their exercise adherence, the fitness gap between exercise-adherent and non-adherent increased after the intervention ( p = 0.004). Conclusions: Risk of over-activity and HR rest profiled short-term exercise adherence in older adults. If confirmed in a larger and longer study, these could personalize interventions aimed at increasing adherence.

Suggested Citation

  • Andrea Albergoni & Florentina J. Hettinga & Wim Stut & Francesco Sartor, 2020. "Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence," IJERPH, MDPI, vol. 17(17), pages 1-18, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:17:p:6142-:d:403193
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    References listed on IDEAS

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    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
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    1. Andrea Fuente-Vidal & Myriam Guerra-Balic & Oriol Roda-Noguera & Javier Jerez-Roig & Joel Montane, 2022. "Adherence to eHealth-Delivered Exercise in Adults with no Specific Health Conditions: A Scoping Review on a Conceptual Challenge," IJERPH, MDPI, vol. 19(16), pages 1-19, August.

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