Comparison of Wrist- and Hip-Worn Activity Monitors When Meeting Step Guidelines

Nelson, Rachael K.; Hasanaj, Kristina; Millen, Laramy; Muench, Joshua P.; Bidolli, Nicole S.C.; Preston, Michael; Montoye, Alexander H.K.

doi:10.5888/pcd19.210343

Cited by 13 publications

(10 citation statements)

References 29 publications

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“…Many previous studies have demonstrated that the waist-worn pedometers have higher accuracy and underestimated the number of steps compared to the wrist-worn pedometer during walking 15,19,20 , which aligns with our observations. For instance, one study reported that the wrist-worn pedometers underestimated the number of steps by 2.7 to 10.2%, whereas the waist-worn pedometers presented only a -0.4% underestimation during 5 minutes of walking 15 .…”

Section: Discussionsupporting

confidence: 92%

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Accuracy and reliability of accelerometer-based pedometers in step counts during walking, running, and climbing stairs in different wearing positions

Pan,

Wei

2024

Preprint

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This study aimed to investigate the accuracy and reliability of accelerometer-based pedometers placed on the wrist, waist, and shoe's midsole during walking, running, and up & down stairs. Twenty healthy adults were recruited. Steps were recorded by the pedometers and visually assessed from simultaneously recorded video to evaluate the accuracy of each pedometer in different wearing positions. One week later, steps were recorded again by the pedometers only to evaluate the reliability of each pedometer in different wearing positions. The wrist-worn pedometer presented significantly greater error scores compared to the midsole-worn pedometer during walking (p < .001), running (p = .006), and up & down stairs (p = .003). Additionally, Bland-Altman plots indicated that the accuracy is the midsole-worn > waist-worn > wrist-worn pedometers during running and up & down stairs. Furthermore, the midsole-worn and waist-worn pedometers showed strong reliability during walking and running, but only the midsole-worn pedometer presented acceptable reliability during ascending and descending stairs. The pedometer’s position impacts the accuracy and reliability of step counts, especially for walking and up & down stairs. Embedding the pedometer into the midsole seems an effective approach to improve the accuracy and reliability of step counts.

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Section: Discussionsupporting

confidence: 92%

“…Compared to the wrist-worn pedometers, the waist-worn pedometers have higher accuracy in estimating steps during activities 15,[17][18][19][20] . For example, a study observed that the waist-worn pedometers underestimated steps by only 0.4% for a ve-minute walk 15 .…”

Section: Introductionmentioning

confidence: 99%

Accuracy and reliability of accelerometer-based pedometers in step counts during walking, running, and climbing stairs in different wearing positions

Pan,

Wei

2024

Preprint

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“…While commercial wearable activity trackers are limited by proprietary summaries of the data, often provided at a daily or hourly level, smartphones offer an opportunity to access raw sub-second level data which can be used to generate reproducible and interpretable digital biomarkers, such as step counts, with research software. Moreover, smartphones are typically carried close to the center of the body, which produces less noisy inertial sensor data compared with the locations that many available wearables are worn (i.e., on the wrist), which provides more accurate estimations of whole-body motion (18,19,(41)(42)(43)(44).…”

Section: Discussionmentioning

confidence: 99%

Validation of an open-source smartphone step counting algorithm in clinical and non-clinical settings

Strączkiewicz

Keating

Thompson

et al. 2023

Preprint

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Background: Step counts are increasingly used in public health and clinical research to assess wellbeing, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states. Objective: Our goal was to evaluate an open-source step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations (internal validation), manually ascertained ground truth (manual validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer (wearable validation). Methods: We used eight independent datasets collected in controlled, semi-controlled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. Five datasets (N=103) were used for internal validation, two datasets (N=107) for manual validation, and one dataset (N=45) used for wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw sub-second level accelerometer data. We calculated mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis. Results: In the internal validation datasets, participants performed 751.7+/-581.2 (mean+/-SD) steps, and the mean bias was -7.2 steps (LoA -47.6, 33.3) or -0.5%. In the manual validation datasets, the ground truth step count was 367.4+/-359.4 steps while the mean bias was -0.4 steps (LoA -75.2, 74.3) or 0.1 %. In the wearable validation dataset, Fitbit devices indicated mean step counts of 1931.2+/-2338.4, while the calculated bias was equal to -67.1 steps (LoA -603.8, 469.7) or a difference of 0.3 %. Conclusions: This study demonstrates that our open-source step counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.

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“…Using activities of daily living, previous analyses have shown that estimates of step count were ~1200 steps/day higher when derived from consumer-grade wrist-worn devices. compared with a pedometer worn at the waist [34,35] and direct observation [36]. When comparing ActiGraph accelerometers worn synonymously on the waist and wrist during 7 days of free-living, Tudor-Locke et al [37] reported the device worn on the wrist estimated 2500 more steps/day compared with that on the waist.…”

Section: Discussionmentioning

confidence: 99%

Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm

Maylor

Edwardson

Dempsey

et al. 2022

Sensors

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Stepping-based targets such as the number of steps per day provide an intuitive and commonly used method of prescribing and self-monitoring physical activity goals. Physical activity surveillance is increasingly being obtained from wrist-worn accelerometers. However, the ability to derive stepping-based metrics from this wear location still lacks validation and open-source methods. This study aimed to assess the concurrent validity of two versions (1. original and 2. optimized) of the Verisense step-count algorithm at estimating step-counts from wrist-worn accelerometry, compared with steps from the thigh-worn activPAL as the comparator. Participants (n = 713), across three datasets, had >24 h continuous concurrent accelerometry wear on the non-dominant wrist and thigh. Compared with activPAL, total daily steps were overestimated by 913 ± 141 (mean bias ± 95% limits of agreement) and 742 ± 150 steps/day with Verisense algorithms 1 and 2, respectively, but moderate-to-vigorous physical activity (MVPA) steps were underestimated by 2207 ± 145 and 1204 ± 103 steps/day in Verisense algorithms 1 and 2, respectively. In summary, the optimized Verisense algorithm was more accurate in detecting total and MVPA steps. Findings highlight the importance of assessing algorithm performance beyond total step count, as not all steps are equal. The optimized Verisense open-source algorithm presents acceptable accuracy for derivation of stepping-based metrics from wrist-worn accelerometry.

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Comparison of Wrist- and Hip-Worn Activity Monitors When Meeting Step Guidelines

Cited by 13 publications

References 29 publications

Accuracy and reliability of accelerometer-based pedometers in step counts during walking, running, and climbing stairs in different wearing positions

Accuracy and reliability of accelerometer-based pedometers in step counts during walking, running, and climbing stairs in different wearing positions

Validation of an open-source smartphone step counting algorithm in clinical and non-clinical settings

Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm

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