Personalized Exercise Programs Based upon Remote Assessment of Motor Fitness: A Pilot Study among Healthy People Aged 65 Years and Older

Abstract

<b><i>Background:</i></b> The World Health Organization has recently updated exercise guidelines for people aged >65 years, emphasizing the inclusion of multiple fitness components. However, without adequate recognition of individual differences, these guidelines may be applied using an approach that “one-size-fits-all.” Within the shifting paradigm toward an increasingly personalized approach to medicine and health, it is apparent that fitness components display a significant age-related increase in variability. Therefore, it is both logical and necessary to perform an accurate individualized assessment of multiple fitness components prior to optimal prescription for a personalized exercise program. <b><i>Objective:</i></b> The aim of the study was to test the feasibility and effectiveness of a novel tool able to remotely assess balance, flexibility, and strength using smartphone sensors (accelerometer/gyroscope), and subsequently deliver personalized exercise programs via the smartphone. <b><i>Methods:</i></b> We enrolled 52 healthy volunteers (34 females) aged 65+ years, with normal cognition and low fall risk. Baseline data from remote smartphone fitness assessment were analyzed to generate 42 fitness digital markers (DMs), used to guide personalized exercise programs (×5/week for 6 weeks) delivered via smartphone. Programs included graded exercises for upper/lower body, flexibility, strength, and balance (dynamic, static, and vestibular). Participants were retested after 6 weeks. <b><i>Results:</i></b> Average age was 74.7 ± 6.4 years; adherence was 3.6 ± 1.7 exercise sessions/week. Significant improvement for pre-/posttesting was observed for 10/12 DMs of strength/flexibility for upper/lower body (sit-to-stand repetitions/duration; arm-lift duration; torso rotation; and arm extension/flexion). Balance improved significantly for 6/10 measures of tandem stance, with consistent (nonsignificant) trends observed across 20 balance DMs of tandem walk and 1 leg stance. Balance tended to improve among the 37 participants exercising ≥3/week. <b><i>Discussion:</i></b> These preliminary results provide a proof of concept, with high adherence and improved fitness confirming the benefits of remote fitness assessment for guiding home personalized exercise programs among healthy adults aged >65 years. Further examination of the application within a randomized control study is necessary, comparing the personalized exercise program to general guidelines among healthy older adults, as well as specific populations, such as those with frailty, deconditioning, cognitive, or functional impairment. The study tool offers the opportunity to collect big data, including additional variables, with subsequent utilization of artificial intelligence to optimize the personalized exercise program.

Going digital – a commentary on the terminology used at the intersection of physical activity and digital health

“…There is an increasing number of mobile apps that are used to provide physical interventions in a non-clinical context to counteract the age-related decline of physical and cognitive capabilities [ 92 – 95 ]).…”

“…and based on the fact that the physical interventions in the first example rely on mobile apps (i.e., delivered/received via smartphone or tablet), these interventions fulfill the criteria of Digital Health, Electronic Health, Mobile Health, and Telehealth. However, as the apps are used in a non-clinical context (e.g., physical interventions to prevent the age-related decline of physical and cognitive capabilities [ 92 – 95 ]), they do not fall within the definitions of Telemedicine and Telerehabilitation. Thus, these interventions should be referred to as Telehealth and/or Mobile Health applications.…”

Effects of an App-Based Physical Exercise Program on Selected Parameters of Physical Fitness of Females in Retirement: A Randomized Controlled Trial

“…Additionally, there is preliminary evidence suggesting that unsupervised programs delivered using digital technologies (e.g., virtual reality, smartphone, or tablet apps) may have an impact on physical fitness in people of older age. ( Van Het Reve et al, 2014 ; Park and Yim, 2016 ; Neumann et al, 2018 ; Yerrakalva et al, 2019 ; Gao et al, 2020 ; Van den Helder et al, 2020 ; Geraedts et al, 2021 ; Jungreitmayr et al, 2021 ; Netz et al, 2021 ). Findings point toward an promising but often not significant ( Yerrakalva et al, 2019 ; Van den Helder et al, 2020 ; Geraedts et al, 2021 ), increase in lower body strength, balance, and flexibility, whereas others show significant effects on these markers ( Van Het Reve et al, 2014 ; Park and Yim, 2016 ; Neumann et al, 2018 ; Jungreitmayr et al, 2021 ; Netz et al, 2021 ).…”

“…( Van Het Reve et al, 2014 ; Park and Yim, 2016 ; Neumann et al, 2018 ; Yerrakalva et al, 2019 ; Gao et al, 2020 ; Van den Helder et al, 2020 ; Geraedts et al, 2021 ; Jungreitmayr et al, 2021 ; Netz et al, 2021 ). Findings point toward an promising but often not significant ( Yerrakalva et al, 2019 ; Van den Helder et al, 2020 ; Geraedts et al, 2021 ), increase in lower body strength, balance, and flexibility, whereas others show significant effects on these markers ( Van Het Reve et al, 2014 ; Park and Yim, 2016 ; Neumann et al, 2018 ; Jungreitmayr et al, 2021 ; Netz et al, 2021 ). In these studies, measurement of muscle strength, balance, and flexibility was mostly performed via stand-alone tests, such as chair rise ( Geraedts et al, 2021 ; Jungreitmayr et al, 2021 ; Netz et al, 2021 ) or uni-pedal stance ( Jungreitmayr et al, 2021 ; Netz et al, 2021 ), via short physical performance battery ( Van Het Reve et al, 2014 ; Treacy and Hassett, 2018 ; Van den Helder et al, 2020 ) or Senior Fitness Test, respectively, ( Rikli and Jones, 2013 ; Neumann et al, 2018 ).…”

“…Remarkably, positive effects on outcomes of these tests have also been demonstrated in studies that used fixed durations of exercise sessions but had the option to self-select training frequency. ( Yerrakalva et al, 2019 ; Geraedts et al, 2021 ; Jungreitmayr et al, 2021 ; Mehra et al, 2021 ; Netz et al, 2021 ). The above-mentioned evidence suggests that unsupervised training with self-selected training frequency can improve measures of muscular strength, and balance ( Geraedts et al, 2021 ; Jungreitmayr et al, 2021 ; Netz et al, 2021 ) and can lead to high adherence rates when it is delivered by modern technologies (i.e., tablets; Van Het Reve et al, 2014 ; Mehra et al, 2021 ).…”