Smartphone-Based Gait Measurement Application for Exercise and Its Effects on the Lifestyle of Senior Citizens

Miura, Takahiro; Yabu, Ken-ichiro; Hiyama, Atsushi; Inamura, Noriko; Hirose, Michitaka; Ifukube, Tohru

doi:10.1007/978-3-319-22698-9_7

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…Providing health-related apps for gait assessment would help older adults improve their health outcomes and reduce the burden of care. Several studies have tested validity and reliability of gait analysis using a smartphone [14][15][16][17]. Nishiguchi et al [18] found that the reliability and validity of a smartphone in measuring step variability, autocorrelation, and acceleration RMS was comparable with an external accelerometer.…”

Section: Introductionmentioning

confidence: 99%

A Mobile Phone–Based Gait Assessment App for the Elderly: Development and Evaluation

Zhong¹,

Rau²

2020

JMIR Mhealth Uhealth

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Background Gait disorders are common among older adults. With an increase in the use of technology among older adults, a mobile phone app provides a solution for older adults to self-monitor their gait quality in daily life. Objective This study aimed to develop a gait-monitoring mobile phone app (Pocket Gait) and evaluate its acceptability and usability among potential older users. Methods The app was developed to allow older adults to track their gait quality, including step frequency, acceleration root mean square (RMS), step regularity, step symmetry, and step variability. We recruited a total of 148 community-dwelling older adults aged 60 years and older from two cities in China: Beijing and Chongqing. They walked in three ways (single task, dual task, and fast walking) using a smartphone with the gait-monitoring app installed and completed an acceptability and usability survey after the walk test. User acceptability was measured by a questionnaire including four quantitative measures: perceived ease of use, perceived usefulness, ease of learning, and intention to use. Usability was measured using the System Usability Scale (SUS). Interviews were conducted with participants to collect open-ended feedback questions. Results Task type had a significant effect on all gait parameters, namely, step frequency, RMS, step variability, step regularity, and step symmetry (all P values <.001). Age had a significant effect on step frequency (P=.01), and region had a significant effect on step regularity (P=.04). The acceptability of the gait-monitoring app was positive among older adults. Participants identified the usability of the system with an overall score of 59.7 (SD 10.7) out of 100. Older adults from Beijing scored significantly higher SUS compared with older adults from Chongqing (P<.001). The age of older adults was significantly associated with their SUS score (P=.048). Older adults identified improvements such as a larger font size, inclusion of reference values for gait parameters, and inclusion of heart rate and blood pressure monitoring. Conclusions This mobile phone app is a health management tool for older adults to self-manage their gait quality and prevent adverse outcomes. In the future, it will be important to take factors such as age and region into consideration while designing a mobile phone–based gait assessment app. The feedback of the participants would help to design more elderly-friendly products.

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Section: Introductionmentioning

confidence: 99%

A Mobile Phone–Based Gait Assessment App for the Elderly: Development and Evaluation

Zhong¹,

Rau²

2020

JMIR Mhealth Uhealth

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“…However, no increase was found in water intake, no PA, no weight loss, and no increase in social contacts. Active lifestyle & healthy lifestyle Germany 60< Mixed 30 4 weeks Miura et al, 2015 [49] Activity tracking…”

Section: Smartphonementioning

confidence: 99%

The use of mHealth solutions in active and healthy ageing promotion: an explorative scoping review

Marcussen¹,

Marinus²

2021

jidhealth

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The global population aged 60 years and over is expected to almost double between 2015 and 2050 from 12.0% to 22.0%, which will directly impact countries' labor market composition and increase the economic pressure on their healthcare systems. One way to address these challenges is to promote Active and Healthy Ageing (AHA) using mobile Health (mHealth). This research aims to provide an initial overview of the width and the depth of contemporary preventive mHealth solutions that promote AHA among healthy, independent older adults (individuals aged 60 years and over). To do so, an explorative scoping review was applied to search online databases for recent studies (March 2015 - March 2020) addressing the promotion of mHealth solutions targeting healthy and independent older adults. We identified 31 publications that met the inclusion criteria. Most of them utilized either mobile (n=25) and/or wearable (n=11) devices. mHealth solutions mostly promoted AHA by targeting older adults’ active lifestyles or independence. Most of the studies (n=27) did not apply a theoretical framework on which the mHealth promotion was based. User-experience was positive (n=12) when the solution was easy to use but negative (n=11) when the participants were resistant or faced challenges using the device and/or technology. The review concludes that mHealth offers the opportunity to combat the issues faced by an unhealthy and dependent aging population by promoting AHA through focusing on older adults’ Lifestyle, Daily functioning, and Participation. Future research should use multidisciplinary integrated approaches and strong theoretical and methodological foundations to investigate mHealth solutions' impact on AHA behavioral change.

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“…The question is thus: how to help people aging with good health? Several studies suggest that monitoring people's level of activity has the potential to foster health and well-being by transforming it from an individual goal towards a collective and social goal [2,3]. Some experiments have concluded that the use of information and communication technology (ICT) enabled devices to collect individual performance data, a communication network for sharing those data, can contribute to increase the activity levels and to promote community building.…”

Section: Introductionmentioning

confidence: 99%

Supporting Better Physical Activity in a Smart City: a Framework for Suggesting and Supervising Walking Paths

Rodrigues¹,

Santos²,

Queirós³

et al. 2019

Adv. sci. technol. eng. syst. j.

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The increase of elderly population creates the need to promote healthy aging, with autonomy and independence, for preserving the functional capacity and quality of life as much as possible. To achieve this goal the recommendations include walking a certain amount of steps daily, given that the exact amount of steps changes with age, lesions, and chronic health conditions that can affect health and well-being. Understanding how much exercise is adequate for an individual requires specialized knowledge and training. It is important to avoid risks that include: (1) be too aggressive in exercising and increase injuries, create new ones, and later refrain in exercising more; or (2) be too conservative and in the long run have an activity level under each one's potential and below what is recommended. In this article is proposed and discussed a framework that aims to support people having an adequate level of activity for its particular condition and to do it without disrupting daily routines. The framework is supported by a software system built for monitoring well-being and physical activity in the context of Smart Cities. The system is composed by: (1) smartphone applications that interact with the end-user for showing possible exercises and walk routes, and that collects some relevant data; (2) a back-office application that collects and presents data obtained from the smartphone applications. The back-office application is designed for health professionals to follow users' progression over time, to recommend new exercises or to correct less optimal situations. The first tests with a system implementing this framework show that the solution is robust and able to be used in a large scale.

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Smartphone-Based Gait Measurement Application for Exercise and Its Effects on the Lifestyle of Senior Citizens

Cited by 7 publications

References 41 publications

A Mobile Phone–Based Gait Assessment App for the Elderly: Development and Evaluation

A Mobile Phone–Based Gait Assessment App for the Elderly: Development and Evaluation

The use of mHealth solutions in active and healthy ageing promotion: an explorative scoping review

Supporting Better Physical Activity in a Smart City: a Framework for Suggesting and Supervising Walking Paths

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