Fitness Applications for Home-Based Training

Khaghani-Far, Iman; Nikitina, Svetlana; Báez, Marcos; Taran, Ekaterina A.; Casati, Fabio

doi:10.1109/mprv.2016.76

Cited by 24 publications

(7 citation statements)

References 25 publications

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“…These fitness and health apps primarily focus on three aspects (i.e. training apps, tracking apps and fitness games) to improve individuals' activity levels (Khaghani-Far et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

Consumers' continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model

Chiu

Cho

Christina

2020

ITP

129

130

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PurposeThis study aimed to explore consumers' continuance intention to use a fitness and health app by applying two theoretical models: the expectation–confirmation model (ECM) and the investment model (IM).Design/methodology/approachAn online survey was administered to consumers who are currently using fitness and health apps (N = 342). Structural equation modeling (SEM) was conducted using Amos 22.0 software to examine the hypothesized relationships in the research model.FindingsResults revealed that users' continuance intention was significantly predicted by the two models. Within the ECM, the positive and significant relationships among variables were found. Moreover, users' satisfaction and investment size had positive impacts on their commitment, which, in turn, positively affected the intention. Also, confirmation of expectations had a positive impact on investment size.Originality/valueThe integrated model helps better understand fitness and health app users' decision-making process from the perspective of relationship commitment and suggests practical implications for health and fitness app providers.

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“…These fitness and health apps primarily focus on three aspects (i.e. training apps, tracking apps and fitness games) to improve individuals' activity levels (Khaghani-Far et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

Consumers' continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model

Chiu

Cho

Christina

2020

ITP

129

130

View full text Add to dashboard Cite

show abstract

“…Fitness apps for home-based training have been widely explored in technology-supported interventions (see [22] for a review); however, we are not aware of interventions supporting online group exercising for individuals of different levels of fitness. Consequently, there is very limited research on the effects of level of fitness, social support, and subjective well-being in online group settings.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Virtual Tablet-Based Group Exercise Among Older Adults in Siberia: Findings From Two Pilot Trials

Nikitina

Didino

Báez

et al. 2018

JMIR Mhealth Uhealth

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BackgroundRegular physical activity has a positive effect on physical health, well-being, and life satisfaction of older adults. However, engaging in regular physical activity can be challenging for the elderly population because of reduced mobility, low motivation, or lack of the proper infrastructures in their communities.ObjectiveThe objective of this paper was to study the feasibility of home-based online group training—under different group cohesion settings—and its effects on adherence and well-being among Russian older adults. We focused particularly on the technology usability and usage and on the adherence to the training (in light of premeasures of social support, enjoyment of physical activity, and leg muscle strength). As a secondary objective, we also explored the effects of the technology-supported intervention on subjective well-being and loneliness.MethodsTwo pilot trials were carried out exploring two different group cohesion settings (weak cohesion and strong cohesion) in the period from 2015 to 2016 in Tomsk, Russian Federation. A total of 44 older adults (59-83 years) participated in the two pilots and followed a strength and balance training program (Otago) for 8 weeks with the help of a tablet-based virtual gym app. Participants in each pilot were assigned to an interaction condition, representing the online group exercising, and an individual condition, representing a home-based individual training. Both conditions featured persuasion strategies but differed in the ability to socialize and train together.ResultsBoth interaction and individual groups reported a high usability of the technology. Trainees showed a high level of technology acceptance and, particularly, a high score in intention to future use (4.2-5.0 on a 5-point Likert scale). Private texting (short service message [SMS]) was used more than public texting, and the strong cohesion condition resulted in more messages per user. Joint participations to training sessions (copresence) were higher for the social group with higher cohesion. The overall adherence to the training was 74% (SD 27%). Higher levels of social support at baseline were associated with higher adherence in the low cohesion condition (F1,18=5.23, P=.03), whereas in the high cohesion, such association was not found. Overall improvement in the satisfaction with life score was observed between pre and post measures (F1,31=5.85, P=.02), but no decrease in loneliness.ConclusionsOnline group exercising was proven feasible among healthy independently living older adults in Russia. The pilots suggest that a physical training performed in a virtual environment positively affect the life satisfaction of the trainees, but it does not provide support for a decrease in loneliness. High cohesion groups are preferable for group exercising, especially to mitigate effects of low social support on adherence. Further research in motivating group interactions in training settings is needed.

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“…As noted by Khaghani-Far et al [ 26 ], the present computer-generated support, mostly in the form of a virtual coach, is not capable of replacing the emotional support provided by a human coach. The contribution of our work is the demonstration of how PA in older adults can be stimulated by a blended approach.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, there are also limitations in the scope of mHealth. Automated feedback and guidance (ie, avatar coach) do not correspond well with the subtlety and social support that a person can provide [ 26 ]. A blended intervention, where personal guidance by a coach is matched with the possibilities technology can deliver, can be an effective approach.…”

Section: Introductionmentioning

confidence: 99%

Translating Behavior Change Principles Into a Blended Exercise Intervention for Older Adults: Design Study

Mehra¹,

Visser²,

Dadema³

et al. 2018

JMIR Res Protoc

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BackgroundPhysical activity can prevent or delay age-related impairments and prolong the ability of older adults to live independently. Community-based programs typically offer classes where older adults can exercise only once a week under the guidance of an instructor. The health benefits of such programs vary. Exercise frequency and the duration of the program play a key role in realizing effectiveness. An auxiliary home-based exercise program can provide older adults the opportunity to exercise more regularly over a prolonged period of time in the convenience of their own homes. Furthermore, mobile electronic devices can be used to motivate and remotely guide older adults to exercise in a safe manner. Such a blended intervention, where technology is combined with personal guidance, needs to incorporate behavior change principles to ensure effectiveness.ObjectiveThe aim of this study was to identify theory-based components of a blended intervention that supports older adults to exercise at home.MethodsThe Medical Research Council framework was used to develop the blended intervention. Insights from focus group, expert panels, and literature were combined into leading design considerations.ResultsA client-server system had been developed that combined a tablet app with a database in the cloud and a Web-based dashboard that can be used by a personal coach to remotely monitor and guide older adults. The app contains several components that facilitate behavior change—an interactive module for goal setting, the ability to draw up a personal training schedule from a library containing over 50 exercise videos, progress monitoring, and possibilities to receive remote feedback and guidance of a personal coach.ConclusionsAn evidence-based blended intervention was designed to promote physical activity among older adults. The underlying design choices were underpinned by behavior change techniques that are rooted in self-regulation. Key components of the tablet-supported intervention were a tailored program that accommodates individual needs, demonstrations of functional exercises, monitoring, and remote feedback. The blended approach combines the convenience of a home-based exercise program for older adults with the strengths of mobile health and personal guidance.

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Fitness Applications for Home-Based Training

Cited by 24 publications

References 25 publications

Consumers' continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model

Consumers' continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model

Feasibility of Virtual Tablet-Based Group Exercise Among Older Adults in Siberia: Findings From Two Pilot Trials

Translating Behavior Change Principles Into a Blended Exercise Intervention for Older Adults: Design Study

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