Sensor-Based Frailty Assessment Using Fitbit

This study evaluated the reliability of Fitbit in assessing frailty based on motor and heart rate (HR) parameters through a validated upper extremity function (UEF) test, which involves 20 s of rapid elbow flexion. For motor performance, participants completed six trials of full elbow flexion using their right arm, with and without weight. Fitbit and a commercial motion sensor were worn on the right arm. For HR measurements, an ECG system was placed on the left chest alongside the Fitbit on the left wrist. Motor parameters assessing speed, flexibility, weakness, exhaustion, and HR before, during, and after UEF were measured. A total of 42 participants (age = 22 ± 3) were recruited. For motor parameters, excellent agreement was observed between the wearable sensor and Fitbit, except for flexibility (ICC = 0.87 ± 0.09). For HR parameters, ICC values showed weak agreement between ECG and Fitbit for HR increase and recovery (ICC = 0.24 ± 0.11), while moderate to stronger agreement was seen for mean HR during baseline, task, and post-task (ICC = 0.81 ± 0.13). Fitbit is a reliable tool for assessing frailty through motor parameters and provides reasonably accurate HR estimates during baseline, task, and recovery periods. However, Fitbit's ability to track rapid HR changes during activity is limited.

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Publication Details

Journal
Sensors
Published
2024-12-07
DOI
https://doi.org/10.3390/s24237827
Citations
3
Primary Topic
Frailty in Older Adults
Type
article
Field-Weighted Citation Impact
1.03
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article

Sensor-Based Frailty Assessment Using Fitbit

Mehran Asghari, Nima Toosizadeh, Mohammad Hosseinalizadeh
3 citations
Sensors
Frailty in Older Adults
1.03
article

Sensor-Based Frailty Assessment Using Fitbit

Mehran Asghari, Nima Toosizadeh, Mohammad Hosseinalizadeh
article en
3 citations

Abstract

This study evaluated the reliability of Fitbit in assessing frailty based on motor and heart rate (HR) parameters through a validated upper extremity function (UEF) test, which involves 20 s of rapid elbow flexion. For motor performance, participants completed six trials of full elbow flexion using their right arm, with and without weight. Fitbit and a commercial motion sensor were worn on the right arm. For HR measurements, an ECG system was placed on the left chest alongside the Fitbit on the left wrist. Motor parameters assessing speed, flexibility, weakness, exhaustion, and HR before, during, and after UEF were measured. A total of 42 participants (age = 22 ± 3) were recruited. For motor parameters, excellent agreement was observed between the wearable sensor and Fitbit, except for flexibility (ICC = 0.87 ± 0.09). For HR parameters, ICC values showed weak agreement between ECG and Fitbit for HR increase and recovery (ICC = 0.24 ± 0.11), while moderate to stronger agreement was seen for mean HR during baseline, task, and post-task (ICC = 0.81 ± 0.13). Fitbit is a reliable tool for assessing frailty through motor parameters and provides reasonably accurate HR estimates during baseline, task, and recovery periods. However, Fitbit's ability to track rapid HR changes during activity is limited.

SensorsVol. 24(23)
Rutgers, The State University of New Jersey (US)
Openalex Percentile: Top 23%
Frailty in Older Adults
1.03
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Sensor-Based Frailty Assessment Using Fitbit — Mehran Asghari, Nima Toosizadeh, et al. · Sensors (2024) | TGRS Research Map | TGRS