Validity and reliability of the Apple Health app on iPhone for measuring gait parameters in children, adults, and seniors

Werner, Christian; Hezel, Natalie; Dongus, Fabienne; Spielmann, Jan; Mayer, Jan; Becker, Clemens; Bauer, Jürgen

doi:10.1038/s41598-023-32550-3

Cited by 15 publications

(9 citation statements)

References 70 publications

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“…The literature reveals wearable body sensors, including accelerometers and gyroscopes, as commonly used tools for this purpose [18][19][20][21]. Figure 1 illustrates some commercially available systems [22][23][24].…”

Section: Background and Related Work 21 Methods And Technologies For ...mentioning

confidence: 99%

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Community-Based and Everyday Life Gait Analysis: Approach to an Automatic Balance Assessment and Fall Risk Prediction in the Elderly

Arom Gómez Arias,

Gonzalo Chávez Orellana,

Cecilia Ortega-Bastidas

et al. 2024

Biomedical Engineering

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This chapter discusses the potential of wearable technologies in predicting fall risks among older adults, a demographic susceptible to falls due to age-related walking ability decline. We aimed to explore the feasibility of portable body sensors, mobile apps, and smartwatches for real-time gait analysis in non-clinical, everyday settings. We used classification models like Random Forest, Support Vector Machine with a radial basis function kernel, and Logistic Regression to predict fall risks based on gait parameters. Notably, both Random Forest and Support Vector Machine models demonstrated over 72% accuracy, underscoring the critical role of feature selection and model choice in fall risk prediction. These technologies can enhance older adults’ quality of life by predicting fall risks. However, future developments should focus on technologies adapted to non-clinical environments, predictivity, and high-risk group usability. The integration of these features may enable more efficient fall risk assessment systems.

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Section: Background and Related Work 21 Methods And Technologies For ...mentioning

confidence: 99%

“…Smartwatches and other commercially available wearables, as mentioned in Refs. [18,21,34,35], offer an attractive, non-intrusive solution. These devices provide continuous monitoring and recording of motion data, allowing for real-time and longitudinal gait analysis.…”

Section: Background and Related Work 21 Methods And Technologies For ...mentioning

confidence: 99%

Community-Based and Everyday Life Gait Analysis: Approach to an Automatic Balance Assessment and Fall Risk Prediction in the Elderly

Arom Gómez Arias,

Gonzalo Chávez Orellana,

Cecilia Ortega-Bastidas

et al. 2024

Biomedical Engineering

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“…Double support percentage was reported to a have higher, but still clinically acceptable error percentage (18.4%) and a moderate ICC (0.58). Test–retest was conducted one week apart and good to excellent ICCs (0.75 to 0.93) were reported for walking speed, step length, and double support percentage [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

Stepping beyond Counts in Recovery of Total Knee Arthroplasty: A Prospective Study on Passively Collected Gait Metrics

Fary

Cholewa

Abshagen

et al. 2023

Sensors

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Advances in algorithms developed from sensor-based technology data allow for the passive collection of qualitative gait metrics beyond step counts. The purpose of this study was to evaluate pre- and post-operative gait quality data to assess recovery following primary total knee arthroplasty. This was a multicenter, prospective cohort study. From 6 weeks pre-operative through to 24 weeks post-operative, 686 patients used a digital care management application to collect gait metrics. Average weekly walking speed, step length, timing asymmetry, and double limb support percentage pre- and post-operative values were compared with a paired-samples t-test. Recovery was operationally defined as when the respective weekly average gait metric was no longer statistically different than pre-operative. Walking speed and step length were lowest, and timing asymmetry and double support percentage were greatest at week two post-operative (p < 0.0001). Walking speed recovered at 21 weeks (1.00 m/s, p = 0.063) and double support percentage recovered at week 24 (32%, p = 0.089). Asymmetry percentage was recovered at 13 weeks (14.0%, p = 0.23) and was consistently superior to pre-operative values at week 19 (11.1% vs. 12.5%, p < 0.001). Step length did not recover during the 24-week period (0.60 m vs. 0.59 m, p = 0.004); however, this difference is not likely clinically relevant. The data suggests that gait quality metrics are most negatively affected two weeks post-operatively, recover within the first 24-weeks following TKA, and follow a slower trajectory compared to previously reported step count recoveries. The ability to capture new objective measures of recovery is evident. As more gait quality data is accrued, physicians may be able to use passively collected gait quality data to help direct post-operative recovery using sensor-based care pathways.

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“…Users can exploit one of the many available step counting apps, or apps embedded in their systems and start tracking their steps daily . The Apple Health app available on iPhone, for instance, not only allows to count steps but also estimates other gait parameters ( 20 ). Some apps can be paired with other wearables, while other apps can function independently, generally by retrieving data from apps like Google Fit or Apple Health, and provide step counting-related features.…”

Section: Technologies For Step Countingmentioning

confidence: 99%

Digital technologies for step counting: between promises of reliability and risks of reductionism

Angelucci,

Canali,

Aliverti

2023

Front. Digit. Health

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Step counting is among the fundamental features of wearable technology, as it grounds several uses of wearables in biomedical research and clinical care, is at the center of emerging public health interventions and recommendations, and is gaining increasing scientific and political importance. This paper provides a perspective of step counting in wearable technology, identifying some limitations to the ways in which wearable technology measures steps and indicating caution in current uses of step counting as a proxy for physical activity. Based on an overview of the current state of the art of technologies and approaches to step counting in digital wearable technologies, we discuss limitations that are methodological as well as epistemic and ethical—limitations to the use of step counting as a basis to build scientific knowledge on physical activity (epistemic limitations) as well as limitations to the accessibility and representativity of these tools (ethical limitations). As such, using step counting as a proxy for physical activity should be considered a form of reductionism. This is not per se problematic, but there is a need for critical appreciation and awareness of the limitations of reductionistic approaches. Perspective research should focus on holistic approaches for better representation of physical activity levels and inclusivity of different user populations.

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Validity and reliability of the Apple Health app on iPhone for measuring gait parameters in children, adults, and seniors

Cited by 15 publications

References 70 publications

Community-Based and Everyday Life Gait Analysis: Approach to an Automatic Balance Assessment and Fall Risk Prediction in the Elderly

Community-Based and Everyday Life Gait Analysis: Approach to an Automatic Balance Assessment and Fall Risk Prediction in the Elderly

Stepping beyond Counts in Recovery of Total Knee Arthroplasty: A Prospective Study on Passively Collected Gait Metrics

Digital technologies for step counting: between promises of reliability and risks of reductionism

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