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

Accuracy of consumer-based activity trackers to measure and coach patients with lower limb lymphoedema

PURPOSE

This study investigated the accuracy of activity trackers in chronic lower limb lymphoedema (LLL) patients and in comparison to matched controls.

MATERIALS AND METHODS

Seventeen LLL patients and 35 healthy subjects wore an activity tracker at the hip (Fitbit Zip/Inspire; hip-AT) and one at the wrist (Fitbit Alta/Inspire; wrist-AT) combined with a reference activity monitor (Dynaport Movemonitor; DAM), for 14 consecutive days. To analyze accuracy and agreement, mean daily step count from both AT's were compared to DAM. To evaluate the accuracy as coaching tool, day-by-day differences were calculated. The Kendall correlation coefficient was used to test consistency of ranking daily steps between the AT's and the DAM.

RESULTS

The wrist-AT significantly overestimated daily step count compared to DAM in the LLL group (+1221 ± 1754 steps per day, p = 0.011) while the hip-AT underestimated the step count, although not significantly. Similar results were found in the healthy control group. As a coaching tool, both wrist-AT and hip-AT showed a moderate correlation with the DAM (r = 0.507 and 0.622, respectively) in the LLL group regarding consistency of ranking from most to least active days.

CONCLUSION

Wrist-AT's significantly overestimate daily step count in a LLL population. As a coaching tool, both trackers show moderate validity, indicating applicability to improve physical activity.

Comparison of step-count outcomes across seven different activity trackers: a free-living experiment with young and older adults

BACKGROUND

There are now many different types of activity trackers, including pedometers and accelerometers, to estimate step counts per day. Previous research has extensively examined step-count measurements using activity trackers across various settings while simultaneously wearing different devices.; however, older adults frequently display distinct walking patterns and gait speeds compared to younger adults. This study aimed to compare the step-count between older and younger adults by having them simultaneously wear seven different activity trackers in free-living experiments.

METHODS

This study included 35 younger adults (21-43 yrs) and 57 physically independent older adults (65-91 yrs). All participants simultaneously wore one pedometer and six activity trackers: ActiGraph GT3X + Wrist and Hip, Omron Active Style Pro HJA-350IT, Panasonic Actimarker, TANITA EZ-064, Yamasa TH-300, and Yamasa AS-200 for seven days. A regression equation was also used to assess inter-device compatibility.

RESULTS

When comparing wrist-worn ActiGraph to the six hip-worn activity trackers, the wrist-worn ActiGraph consistently recorded step counts over 4,000 steps higher than hip-worn activity trackers in both groups (range, 3000-5000 steps). Moreover, when comparing the ActiGraph worn on the wrist to that worn on the hip, the proportion was higher among older adults compared to younger ones (younger: 131%, older: 180%). The Actimarker recorded the highest average step counts among six hip-worn devices, with 8,569 ± 4,881 overall, 9,624 ± 5,177 for younger adults, and 7,890 ± 4,562 for older adults. The difference between the hip-worn ActiGraph and Active Style Pro was just about 70 steps/day overall. The correlation among all devices demonstrated a very high consistency, except for the wrist-worn ActiGraph (r = 0.874-0.978).

CONCLUSIONS

Step counts recorded from seven selected consumer-based and research-grade activity trackers and one pedometer, except for the wrist-worn ActiGraph. showed a variation of approximately 1700 steps (range, 1265-2275 steps) steps for both groups, yet maintained a high correlation with each other. These findings will be valuable for researchers and clinicians as they compare step counts across different studies or representative surveys conducted globally.

Comparison of daily step count between the Fitbit Inspire 3 and the activPAL 3 in adults with transtibial amputation

Introduction

Physical activity has significant positive effects on health. Accelerometers can be used to track daily physical activity. The Fitbit Inspire 3 is a commercially available health and fitness tracker, but its validity for tracking steps among individuals with transtibial amputation has not been examined. Therefore, the purpose of this study was to evaluate the concurrent validity of the Fitbit Inspire 3 for assessing free-living daily steps in adults with transtibial amputation.

Methods

Participants (n = 79) completed a general health survey and were provided with a Fitbit Inspire 3 and activPAL 3 accelerometer to wear concurrently for seven days in their home environment. Relationships between the activPAL and Fitbit Inspire 3 were examined using Pearson's Correlation. Paired samples t-tests, mean difference, mean absolute difference, and equivalence testing were used to compared daily step counts between Fitbit Inspire 3 and activPAL 3.

Results

Average step counts were 5,768 ± 3,750 (mean ± SD) and 4,674 ± 3,081 by the Fitbit Inspire 3 and activPAL, respectively. A high correlation (r = 0.93) but significant mean difference was found between the activPAL 3 and Fitbit Inspire 3 (p < 0.001). The mean absolute difference between the devices was 1,347 ± 1,184 steps. On average, the Fitbit Inspire 3 counted 1,094 ± 1,423 more daily steps than the activPAL 3. Equivalency could not be claimed between the devices.

Discussion

The Fitbit Inspire 3 counted more steps compared to the activPAL. Because of the significant mean differences and the large mean absolute difference between the devices, the activPAL 3 and Fitbit Inspire 3 are not interchangeable for estimating physical activity in individuals with transtibial amputation. However, due to the high correlation, the devices will produce similar classification rankings based on step counts.

Combining Wearable Technology and Telehealth Counseling for Rehabilitation After Lumbar Spine Surgery: Feasibility and Acceptability of a Physical Activity Intervention

OBJECTIVE

The purpose of this study was to examine the feasibility and acceptability of a wearable device and telehealth counseling physical activity intervention early after lumbar spine surgery.

METHODS

Sixteen patients were randomized to an 8-session physical activity intervention or to usual postoperative care after surgery. The intervention included a wearable device (ie, Fitbit) and telehealth counseling by a licensed physical therapist. The feasibility of study procedures was assessed through recruitment, randomization, retention, and participation rates. Acceptability was assessed through a satisfaction survey and median within-participant change in objective physical activity (steps per day and time spent in moderate-to-vigorous physical activity [MVPA]) and patient-reported outcomes.

RESULTS

Of 64 participants who were eligible, recruitment and randomization rates were 41 and 62%, respectively. Retention for objective physical activity and patient-reported outcomes was 94 and 100%, respectively, at 6-month follow-up. Seven (88%) participants in the intervention group completed all telehealth sessions, and 6 (75%) met step goals over the 8 sessions. All participants in the intervention group found the wearable device and telehealth counseling to be helpful and reported it much or somewhat more important than other postoperative services. Median within-participant change for steps per day improved from baseline (preoperative) to 6 months after surgery for both the intervention (1070) and usual care (679) groups, while MVPA only improved for the intervention group (2.2. minutes per day). Improvements in back and leg pain and disability were noted for both groups. No adverse events were reported in the study.

CONCLUSION

Combining wearable technology and telehealth counseling is a feasible approach to promote the physical activity during the early postoperative period after spine surgery. Future randomized controlled trials are needed to investigate the efficacy of leveraging wearables and telehealth during postoperative rehabilitation.

IMPACT

This study has implications for the clinical dissemination of physical activity strategies in the rehabilitation setting.

Fitbit's accuracy to measure short bouts of stepping and sedentary behaviour: validation, sensitivity and specificity study

Objective

This study aims to assess the suitability of Fitbit devices for real-time physical activity (PA) and sedentary behaviour (SB) monitoring in the context of just-in-time adaptive interventions (JITAIs) and event-based ecological momentary assessment (EMA) studies.

Methods

Thirty-seven adults (18-65 years) and 32 older adults (65+) from Belgium and the Czech Republic wore four devices simultaneously for 3 days: two Fitbit models on the wrist, an ActiGraph GT3X+ at the hip and an ActivPAL at the thigh. Accuracy measures included mean (absolute) error and mean (absolute) percentage error. Concurrent validity was assessed using Lin's concordance correlation coefficient and Bland-Altman analyses. Fitbit's sensitivity and specificity for detecting stepping events across different thresholds and durations were calculated compared to ActiGraph, while ROC curve analyses identified optimal Fitbit thresholds for detecting sedentary events according to ActivPAL.

Results

Fitbits demonstrated validity in measuring steps on a short time scale compared to ActiGraph. Except for stepping above 120 steps/min in older adults, both Fitbit models detected stepping bouts in adults and older adults with sensitivities and specificities exceeding 87% and 97%, respectively. Optimal cut-off values for identifying prolonged sitting bouts achieved sensitivities and specificities greater than 93% and 89%, respectively.

Conclusions

This study provides practical insights into using Fitbit devices in JITAIs and event-based EMA studies among adults and older adults. Fitbits' reasonable accuracy in detecting short bouts of stepping and SB makes them suitable for triggering JITAI prompts or EMA questionnaires following a PA or SB event of interest.

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

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.

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

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.