Accuracy of wearable physical activity trackers in people with Parkinson’s disease

Lamont, Robyn; Daniel, Hannah L.; Payne, Caitlyn L.; Brauer, Sandra G.

doi:10.1016/j.gaitpost.2018.04.034

Cited by 65 publications

(64 citation statements)

References 26 publications

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“…Both the Garmin Vivosmart HR+ and the Leaf Health Tracker had no significant mean differences between the measured values and the manual count for walking or jogging. For the Garmin Vivosmart HR+, two studies had similar results (Lamont et al, 2018;Wahl, Duking, Droszez, Wahl, & Mester, 2017). The only known study on the Leaf Health Tracker also concurred (Navalta et al, 2018).…”

Section: Heart Ratementioning

confidence: 76%

“…Consequentially, researchers have used a variety of methodologies to establish device validity. For heart rate, protocols involving resistance training and cycling (Boudreaux et al, 2018), treadmill walking (Montes, Young, Tandy, & Navalta, 2018), separately evaluated indoor and outdoor free motion walking (Lamont, Daniel, Payne, & Brauer, 2018), and measurements taken while seated, supine, during treadmill walking and running, and when cycling (Wallen, Gomersall, Keating, Wisloff, & Coombes, 2016) have been utilized. For step count, protocols have looked at values compared to a predetermined number of steps (El-Amrawy & Nounou, 2015), steps taken in a predetermined distance (Floegel, Florez-Pregonero, Hekler, & Buman, 2017), values from walking up and down stairs (Huang, Xu, Yu, & Shull, 2016), and treadmill walking (Montes, Young, Tandy, & Navalta, 2017).…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Multiple Wearable Technology Devices Heart Rate and Step Count Measurements During Free Motion and Treadmill Based Measurements

Montes

Tandy

Young

et al. 2019

IJKSS

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Introduction: Wearable Technology Devices are used to promote physical activity. It is unknown whether different devices measure heart rate and step count consistently during walking or jogging in a free motion setting and on a treadmill. Purpose: To compare heart rate and step count values for the Samsung Gear 2, FitBit Surge, Polar A360, Garmin Vivosmart HR+, Scosche Rhythm+ and the Leaf Health Tracker in walking and jogging activities. Methods: Forty volunteers participated. Devices were worn simultaneously in randomized configurations. 5-minute intervals of walking and jogging were completed in free motion and treadmill settings with matching paces. Heart rates at minutes 3, 4, and 5 were averaged for the devices along with the criterion measure, the Polar T31 monitor. Step count criterion measure was the mean of two manual counters. A 2x6 (environment vs device) repeated measures ANOVA with Bonferroni post-hoc was performed with significance set at p<0.05. Results: There was no significant interaction or main effects for walking heart rate. Jogging heart rate saw significant environment and device main effects. Walking step count had a significant interaction between the devices and the environment. Jogging step count had a significant device main effect. Conclusions: There may be some conditions such as heart rate measurements taken while walking or step count measurements taken while jogging/running that may only require treadmill-based validity testing.

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Section: Heart Ratementioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

A Comparison of Multiple Wearable Technology Devices Heart Rate and Step Count Measurements During Free Motion and Treadmill Based Measurements

Montes

Tandy

Young

et al. 2019

IJKSS

View full text Add to dashboard Cite

show abstract

“…10 Another study examined the accuracy of two wrist-worn motion sensors during 2-minute bouts of over-ground walking in measuring steps and physical activity intensity in persons with PD and reported that a wrist-worn motion sensor was accurate in measuring steps but not physical activity intensity. 12 Collectively, commercially available motion sensors may be accurate and precise in measuring steps in PD, and this supports the interest in developing step-rate thresholds for quantifying MVPA that can be applied for interpreting the output from such devices for prescribing, monitoring, and tracking physical activity in persons with PD.…”

mentioning

confidence: 80%

Step‐rate threshold for physical activity intensity in Parkinson’s disease

et al. 2020

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Objectives: To examine the relationship between step-rate and energy expenditure during treadmill walking in persons with PD and then further develop a step-rate cutpoint for moderate-to-vigorous physical activity (MVPA) for persons with PD. Materials and Methods: The sample consisted of 30 persons with mild-to-moderate PD and 30 controls matched by age and sex. Participants performed a 6-minute bout of over-ground walking at comfortable speed, and then completed three, 6-minute bouts of treadmill walking at 13.4 m/min slower, comfortable, and 13.4 m/min faster than comfortable speeds. The three treadmill speeds were based on the initial overground walking speed. The total number of steps per treadmill walking bout was recorded using a hand-tally counter, and energy expenditure was measured using a portable, indirect spirometry system. Results: The results indicated a strong association between step-rate and energy expenditure for persons with PD (R 2 = .92) and controls (R 2 = .92). The analyses further indicated a steeper slope of the association for persons with PD compared with controls (t(58) = −1.87, P < .05), resulting in a lower step-rate threshold (t(58) = 2.19, P < .05) for persons with PD (~80 steps•per minutes) than controls (~93 steps•per minutes). Conclusion: Collectively, these results support the application of this disease-specific step-rate threshold for MVPA among persons with PD. This has important implications for physical activity promotion, prescription, and monitoring using accelerometers and pedometers for persons with PD to manage health and symptoms of PD.

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“…This has prompted healthcare providers to develop integrated care models [2] and explore technological solutions such as Mobile Health (mHealth) to enhance care, help People with Parkinson's (PwP) manage their symptoms, and improve quality of life [3][4][5][6][7][8]. mHealth technologies are increasingly being explored by the healthcare industry to help PwP monitor and track their symptoms, improve medication adherence, provide automated assessments and transmit data to healthcare providers and/or personal devices, in realtime [9][10][11][12][13]. Self-monitoring using mHealth technologies can be a powerful way to yield insights into unique disease patterns and help clinicians tailor individualized treatments [7,[14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Designing socially acceptable mHealth technologies for Parkinson's disease self-management

2020

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Mobile health (mHealth) technologies for Parkinson’s disease management have developed quickly in recent years. Research in this area typically focuses on evaluation of the accuracy and reliability of the technology, often to the exclusion of social factors and patient perspectives. This qualitative systematic review aimed to investigate the barriers to and facilitators of use mHealth technologies for disease self-management from the perspective of People with Parkinson's (PwP). Findings revealed that technological, as well as social, and financial factors are key considerations for mHealth design, to ensure its acceptability, and long-term use by PwP. This study proposes that a co-design approach could contribute to the design and development of mHealth that are socially acceptable to PwP, and enable their successful long-term use in the context of daily life.

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Accuracy of wearable physical activity trackers in people with Parkinson’s disease

Cited by 65 publications

References 26 publications

A Comparison of Multiple Wearable Technology Devices Heart Rate and Step Count Measurements During Free Motion and Treadmill Based Measurements

A Comparison of Multiple Wearable Technology Devices Heart Rate and Step Count Measurements During Free Motion and Treadmill Based Measurements

Step‐rate threshold for physical activity intensity in Parkinson’s disease

Designing socially acceptable mHealth technologies for Parkinson's disease self-management

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