Validity of Fitbit Inspire 2 in step count and distance measurement during treadmill walking

Wearable activity trackers provide a simple and objective measurement of postoperative mobilization. However, few have validated the accuracy of trackers in patients after major abdominal surgery. To examine the accuracy of wrist-worn activity trackers to measure steps of patients in early mobilization after major abdominal surgery, and to explore the influence of clinical variables and gait parameters on the accuracy of trackers. Forty-five patients after major abdominal surgery were recruited to participate in modified six-minute walk tests wearing three trackers simultaneously, the Fitbit Inspire HR, Xiaomi MI 4, and HONOR 5. The differences in displayed steps before and after the walking test were considered as the step counts measured by the trackers; the actual steps taken were determined as the average of the values manually counted by two researchers. The intraclass correlation coefficient, Bland-Altman method, mean percentage error, and mean absolute percentage error were used to assess the accuracy of trackers with reference to manual step counts. The three trackers undercounted postoperative steps by -65.5% to -23.5%. Analysis showed low-to-good agreement between step counts recorded by trackers and actual steps (ICC = 0.35-0.75); the mean absolute percentage errors ranged from 24.5% to 65.7%. For all trackers, mean absolute percentage errors correlated negatively with postoperative days (r = -0.626 to -0.744), walking speed (r = -0.714 to -0.854), step length (r = -0.466 to -0.615), and cadence (r = -0.681 to -0.790), while there were positive correlations between mean absolute percentage errors and the number of abdominal drains (r = 0.450-0.514). The specific activity trackers used in this study might not be reliable tools for measuring steps counts during the walking test in the early postoperative period for patients undergoing major abdominal surgery.

The growing popularity of wearable physical activity (PA) monitors and fitness applications (apps) in recent years and the vast amounts of data that they generate present attractive possibilities for surveillance. However, measurement accuracy is indispensable when tracking PA variables to provide meaningful measures of PA. The purpose of this study was to examine the criterion validity of wearable PA monitors and a combination of GPS and accelerometer free of charge smartphone apps, during self-paced outdoor walking and running. Thirty-eight healthy adolescents (15.3±2.0 years) participated in this cross-sectional study. They were fitted with Garmin Forerunner 310XT, Garmin Vivofit, Medisana Vifit, and smartphones running the Runkeeper, Runtastic, Sports Tracker (GPS), Pedometer, Accupedo, Pedometer and Pedometer 2.0 (accelerometer) apps. They were asked to walk and run 1.22 km for each trial and two researchers counted every step taken during trials with a digital tally counter. Validity was evaluated by comparing each device with the criterion measure using Repeated measures analysis of variance (ANOVA), Mean Absolute Percentage Errors (MAPE) and Bland-Altman plots. MAPE were low for Forerunner and GPS apps for distance in both conditions (2.27%- 9.73%), and significantly higher for the accelerometer monitors and apps (6.92%-39.02%). Vivofit (MAPE=6.51%) and Vifit (MAPE=6.66%) accurately estimated the number of steps during walking, however only Vivofit (MAPE=3.95%) was accurate during running. All accelerometer-based apps had high MAPE for step counting (9.87%-40.26%). The findings suggested that GPS monitors and apps were accurate tools for counting distance during walking and running, while accelerometer- based monitors and apps had higher errors. Vivofit provided accurate estimates of step count in both conditions, and Medisana Vifit was valid during walking. Accupedo was the only app with an acceptable step count error.

Background: Smartphones and wrist-worn activity trackers are increasingly popular for step counting purposes and physical activity promotion. Although trackers from popular brands have frequently been validated, the accuracy of low-cost devices under free-living conditions has not been adequately determined. Objective: To investigate the criterion validity of smartphones and low-cost wrist-worn activity trackers under free-living conditions. Methods: Participants wore a waist-worn Yamax pedometer and seven different low-cost wrist-worn activity trackers continuously over 3 days, and an activity log was completed at the end of each day. At the end of the study, the number of step counts reflected on the participants’ smartphone for each of the 3 days was also recorded. To establish criterion validity, step counts from smartphones and activity trackers were compared with the pedometers using Pearson’s correlation coefficient, mean absolute percentage error, and intraclass correlation coefficient. Results: Five of the seven activity trackers underestimated step counts and the remaining two and the smartphones overestimated step counts. Criterion validity was consistently higher for the activity trackers (r = .78–.92; mean absolute percentage error 14.5%–36.1%; intraclass correlation coefficient: .51–.91) than the smartphone (r = .37; mean absolute percentage error 55.7%; intraclass correlation coefficient: .36). Stratified analysis showed better validity of smartphones among female than for male participants. Phone wearing location also affected accuracy. Conclusions: Low-cost trackers demonstrated high accuracy in recording step counts and can be considered with confidence for research purposes or large-scale health promotion programs. The accuracy of using a smartphone for measuring step counts was substantially lower. Factors such as phone wear location and gender should also be considered when using smartphones to track step counts.

Commercial smartwatches could be useful for step counting and monitoring ambulatory activity. However, in Parkinson's disease (PD) patients, an altered gait, pharmacological condition, and symptoms lateralization may affect their accuracy and potential usefulness in research and clinical routine. Steps were counted during a 6 min walk in 47 patients with PD and 47 healthy subjects (HS) wearing a Garmin Vivosmart 4 (GV4) on each wrist. Manual step counting was used as a reference. An inertial sensor (BTS G-Walk), placed on the lower back, was used to compute spatial-temporal gait parameters. Intraclass correlation coefficient (ICC) and mean absolute percentage error (MAPE) were used for accuracy evaluation and the Spearman test was used to assess the correlations between variables. The GV4 overestimated steps in PD patients with only a poor-to-moderate agreement. The OFF pharmacological state and wearing the device on the most-affected body side led to an unacceptable accuracy. The GV4 showed an excellent agreement and MAPE in HS at a self-selected speed, but an unacceptable performance at a slow speed. In PD patients, MAPE was not associated with gait parameters and clinical variables. The accuracy of commercial smartwatches for monitoring step counting might be reduced in PD patients and further influenced by the pharmacological condition and placement of the device.

BackgroundSmartwatches are increasingly popular for physical activity and health promotion. However, ongoing validation studies on commercial smartwatches are still needed to ensure their accuracy in assessing daily activity levels, which is important for both promoting activity-related health behaviors and serving research purposes.ObjectiveThis study aimed to evaluate the accuracy of a popular smartwatch, the Huawei Watch GT2, in measuring step count (SC), total daily activity energy expenditure (TDAEE), and total sleep time (TST) during daily activities among Chinese adults, and test whether there are population differences.MethodsA total of 102 individuals were recruited and divided into 2 age groups: young adults (YAs) and middle-aged and older (MAAO) adults. Participants’ daily activity data were collected for 1 week by wearing the Huawei Watch GT2 on their nondominant wrist and the Actigraph GT3X+ (ActiGraph) on their right hip as the reference measure. The accuracy of the GT2 was examined using the intraclass correlation coefficient (ICC), Pearson product-moment correlation coefficient (PPMCC), Bland-Altman analysis, mean percentage error, and mean absolute percentage error (MAPE).ResultsThe GT2 demonstrated reasonable agreement with the Actigraph, as evidenced by a consistency test ICC of 0.88 (P<.001) and an MAPE of 25.77% for step measurement, an ICC of 0.75 (P<.001) and an MAPE of 33.79% for activity energy expenditure estimation, and an ICC of 0.25 (P<.001) and an MAPE of 23.29% for sleep time assessment. Bland-Altman analysis revealed that the GT2 overestimated SC and underestimated TDAEE and TST. The GT2 was better at measuring SC and TDAEE among YAs than among MAAO adults, and there was no significant difference between these 2 groups in measuring TST (P=.12).ConclusionsThe Huawei Watch GT2 demonstrates good accuracy in step counting. However, its accuracy in assessing activity energy expenditure and sleep time measurement needs further examination. The GT2 demonstrated higher accuracy in measuring SC and TDAEE in the YA group than in the MAAO group. However, the measurement errors for TST did not differ significantly between the 2 age groups. Therefore, the watch may be suitable for monitoring several key parameters (eg, SC) of daily activity, yet caution is advised for its use in research studies that require high accuracy.

When tested in a controlled clinic environment, individuals with neuromuscular-related symptoms may complete motor tasks within normal predicted ranges. However, measuring activity at home may better reflect typical motor performance. The accuracy of accelerometry measurements in individuals with congenital muscular dystrophy (CMD) is unknown. We aimed to compare accelerometry and manual step counts and assess free-living physical activity intensity in individuals with CMD using accelerometry. Ambulatory pediatric CMD participants (n = 9) performed the 6-minute walk test in clinic while wearing ActiGraph GT3X accelerometer devices. During the test, manual step counting was conducted to assess concurrent validity of the ActiGraph step count in this population using Bland-Altman analysis. In addition, activity intensity of 6 pediatric CMD participants was monitored at home with accelerometer devices for an average of 7 days. Cut-point values previously validated for neuromuscular disorders were used for data analysis. Bland-Altman and intraclass correlation analyses showed no concurrent validity between manual and ActiGraph-recorded step counts. Fewer steps were recorded by ActiGraph step counts compared with manual step counts (411 ± 74 vs 699 ± 43, respectively; P = .004). Although improved, results were in the same direction with the application of low-frequency extension filters (587 ± 40 vs 699 ± 43, P = .03). ActiGraph step-count data did not correlate with manual step count (Spearman ρ = 0.32, P = .41; with low-frequency extension: Spearman ρ = 0.45, P = .22). Seven-day physical activity monitoring showed that participants spent more than 80% of their time in the sedentary activity level. In a controlled clinic setting, step count was significantly lower by ActiGraph GT3X than by manual step counting, possibly because of the abnormal gait in this population. Additional studies using triaxial assessment are needed to validate accelerometry measurement of activity intensity in individuals with CMD. Accelerometry outcomes may provide valuable measures and complement the 6-minute walk test in the assessment of treatment efficacy in CMD.

BackgroundFew studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real life, as opposed to laboratory settings.ObjectiveThis study explored the performance of two wrist-worn trackers (Fitbit Charge 2 and Garmin vivosmart HR+) in estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (total sleep time [TST] and wake after sleep onset [WASO]) against gold-standard technologies in a cohort of healthy older adults in a free-living environment.MethodsOverall, 20 participants (>65 years) took part in the study. The devices were worn by the participants for 24 hours, and the results were compared against validated technology (ActiGraph and New-Lifestyles NL-2000i). Mean error, mean percentage error (MPE), mean absolute percentage error (MAPE), intraclass correlation (ICC), and Bland-Altman plots were computed for all the parameters considered.ResultsFor step counting, all trackers were highly correlated with one another (ICCs>0.89). Although the Fitbit tended to overcount steps (MPE=12.36%), the Garmin and ActiGraph undercounted (MPE 9.36% and 11.53%, respectively). The Garmin had poor ICC values when energy expenditure was compared against the criterion. The Fitbit had moderate-to-good ICCs in comparison to the other activity trackers, and showed the best results (MAPE=12.25%), although it underestimated calories burned. For MVPA levels estimation, the wristband trackers were highly correlated (ICC=0.96); however, they were moderately correlated against the criterion and they overestimated MVPA activity minutes. For the sleep parameters, the ICCs were poor for all cases, except when comparing the Fitbit with the criterion, which showed moderate agreement. The TST was slightly overestimated with the Fitbit, although it provided good results with an average MAPE equal to 10.13%. Conversely, WASO estimation was poorer and was overestimated by the Fitbit but underestimated by the Garmin. Again, the Fitbit was the most accurate, with an average MAPE of 49.7%.ConclusionsThe tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, although clinicians should be cautious in considering other parameters for clinical and research purposes.

BackgroundUsing infrared counters is a promising unobtrusive method of assessing footfall in urban parks. However, infrared counters are susceptible to reliability and validity issues, and there is limited guidance for their use. The aims of this study were to (1) determine how many weeks of automated active infrared count data would provide behaviourally stable estimates of urban park footfall for each meteorological season, and (2) determine the validity of automated active infrared count estimates of footfall in comparison to direct manual observation counts.MethodsThree automated active infrared counters collected daily footfall counts for 365 days on three footpaths in an urban park within Northampton, England, between May 2021 – May 2022. Intraclass correlation coefficients were used to compare the behavioural stability of abbreviated data collection schedules with total median footfall within each meteorological season (Spring, Summer, Autumn, Winter). Public holidays, events, and extreme outliers were removed. Ten one-hour manual observations were conducted at the site of an infrared counter to determine the validity of the infrared counter.ResultsAt least four-weeks (28 days) of infrared counts are required to provide ‘good’ to ‘excellent’ (Intraclass correlation > 0.75, > 0.9, respectively) estimates of median daily footfall per meteorological season in an urban park. Infrared counters had, on average, -4.65 counts per hour (95% LoA -12.4, 3.14; Mean absolute percentage error 13.7%) lower counts compared to manual observation counts during one-hour observation periods (23.2 ± 15.6, 27.9 ± 18.9 counts per hour, respectively). Infrared counts explained 98% of the variance in manual observation counts. The number of groups during an observation period explained 78% of the variance in the difference between infrared and manual counts.ConclusionsAbbreviated data collection schedules can still obtain estimates of urban park footfall. Automated active infrared counts are strongly associated with manual counts; however, they tend to underestimate footfall, often due to people in groups. Methodological and practical recommendations are provided.

The purpose was to examine the validity of three wrist-worn commercial activity trackers (Samsung Galaxy Watch Active 2, Apple Watch Series 5, and Xiaomi Mi Band 5) and six mobile apps (Pedometer and Pacer for android and iPhone mobiles, Google Fit for android, and Apple Health for iPhone mobiles) for estimating high school students' steps and physical activity (PA) under free-living conditions. A sample of 56 (27 females; mean age = 14.7 years) and 51 (25 females; mean age = 14.0 years) high school students participated in Study 1 and 2, respectively. Study 1: Students performed a 200-meter course in four different conditions while wearing the wearables. Step counting through a video record was used as the golden standard. Study 2: Students wore the three wrist-worn commercial activity trackers during the waking time of one day, considering ActiGraph model wGT3X-BT accelerometers as a standard of reference. Afterward, the agreement between the PA scores measured by the commercial activity trackers and the video (study 1) or accelerometers (study 2) were calculated as follows: Equivalence test, Limits of Agreement (LOA); Mean Absolute Error (MAE); Mean Absolute Percentage Error (MAPE); and Intraclass Correlation Coefficient (ICC). Results showed that all the wearables presented excellent validity for assessing steps in structured free-living conditions (study 1; MAPE &lt; 5%), although their validity was between poor-excellent based on ICC (95% confidence interval) values (ICC = 0.56-1.00). Regarding Study 2, the Xiaomi wristband and the Samsung Watch presented acceptable-excellent (MAPE = 9.4-11.4%; ICC = 0.91-0.97) validity for assessing steps under unstructured free-living conditions (study 2). However, the Apple Watch presented questionable-excellent validity (MAPE = 18.0%; ICC = 0.69-0.95). Regarding moderate-to-vigorous PA (MVPA) and total PA, only the Apple Watch showed low-acceptable validity for MAPE value and questionable-excellent validity for the ICC values for MVPA assessment (MAPE = 22.6; ICC = 0.67-0.93). All wearables checked in this study have shown adequate validity results in order to assess steps in both structured and unstructured free-living conditions for both continuous and dichotomous variables. Moreover, for assessing MVPA, only the Apple Watch reported valid results for compliance or non-compliance with the daily PA recommendations. However, the results showed low validity for total PA and MVPA as continuous variables. In conclusion, depending on the user's/researcher's aim and context, one or another wearable activity tracker could be more adequate, mainly because of its valid measurements and its costs.

Monitoring the real-life mobility of stroke patients could be extremely useful for clinicians. Step counters are a widely accessible, portable, and cheap technology that can be used to monitor patients in different environments. The aim of this study was to validate a low-cost commercial tri-axial accelerometer-based step counter for stroke patients and to determine the best positioning of the step counter (wrists, ankles, and waist). Ten healthy subjects and 43 post-stroke patients were enrolled and performed four validated clinical tests (10 m, 50 m, and 6 min walking tests and timed up and go tests) while wearing five step counters in different positions while a trained operator counted the number of steps executed in each test manually. Data from step counters and those collected manually were compared using the intraclass coefficient correlation and mean average percentage error. The Bland–Altman plot was also used to describe agreement between the two quantitative measurements (step counter vs. manual counting). During walking tests in healthy subjects, the best reliability was found for lower limbs and waist placement (intraclass coefficient correlations (ICCs) from 0.46 to 0.99), and weak reliability was observed for upper limb placement in every test (ICCs from 0.06 to 0.38). On the contrary, in post-stroke patients, moderate reliability was found only for the lower limbs in the 6 min walking test (healthy ankle ICC: 0.69; pathological ankle ICC: 0.70). Furthermore, the Bland–Altman plot highlighted large average discrepancies between methods for the pathological group. However, while the step counter was not able to reliably determine steps for slow patients, when applied to the healthy ankle of patients who walked faster than 0.8 m/s, it counted steps with excellent precision, similar to that seen in the healthy subjects (ICCs from 0.36 to 0.99). These findings show that a low-cost accelerometer-based step counter could be useful for measuring mobility in select high-performance patients and could be used in clinical and real-world settings.

Evaluation of the SenseWear activity monitor during exercise in cystic fibrosis and in health

The purpose of this study was to assess the validity of the step count measurement of commercial electronic activity monitor devices. Two popular models, Fitbit Charge HR and Mi Band 2, were selected for treadmill walking in a single session. Thirty healthy volunteers walked at five predetermined speeds (0.90, 1.12, 1.33, 1.54, and 1.78 m/s) on a treadmill with both Fitbit Charge HR and Mi Band 2 worn on their dominant hand's wrist. Observers counted the steps, with the aid of taped video, which was taken as the criterion measure for steps. The validity of the electronic activity devices was assessed by (1) Paired sample t test with the criterion measures and (2) Pearson's correlation coefficients and the corresponding p-values were calculated to compare the output of devices with manual step count. In addition, Bland-Altman plots were constructed to visually inspect the data and to assess agreement with the criterion measures. There were no significant differences in step measurement between Fitbit Charge HR and Mi Band 2 with the criterion measures. Besides, there was a very strong agreement between step count measurements obtained using the Fitbit Charge HR (r = 0.99) and the Mi Band 2 (r = 0.99), at five predetermined speeds while comparing with the observed step counts. Both Fitbit Charge HR and Mi Band 2 provided accurate step count measurement in the treadmill walking test.

Introduction: This research aimed to assess the accuracy of a portable multi-sensor device (SenseWear armband) measuring energy expenditure in people with stroke compared with indirect calorimetry at rest and during treadmill walking. Secondary aims were to determine if there was a difference depending on which arm the device was placed and to determine the accuracy of the armband step count. Materials and Methods: Ten stroke survivors (mean age 64.3 SD 7.7 years; 70% male) wore an armband on each arm and metabolic facemask. Energy expenditure was measured at rest and during two 10-minute bouts of treadmill walking at different speeds separated with a seated rest. Results: The armband was accurate for measuring energy expenditure at rest (Intraclass correlations (ICC) > 0.869), with poor to fair accuracy during treadmill walking (ICC>0.306). The non-hemiplegic arm provided more accurate energy expenditure measurement (ICC>0.409), underestimating with 10%-15% absolute percentage error. SenseWear armband was inaccurate for measuring step count (absolute percentage error approximately 30%). Conclusion: These results suggest that SenseWear armband lacks accuracy for measuring energy expenditure during treadmill walking in people with stroke; with the non-hemiplegic arm the most accurate. The armband is inaccurate measuring step count. Revision of algorithms specific for people following stroke may improve accuracy.

Accuracy of Apple Watch fitness tracker for wheelchair use varies according to movement frequency and task.

Wearable technology can track unusual exercise, providing data for improving fitness. The aim of the study was to determine validity and reliability during walking, jogging, and skipping. Eighteen volunteers completed 5 min self-paced activities interspersed with 5 min rest. Variables and devices were step count (Garmin Instinct), estimated energy expenditure (Garmin Instinct, Polar Vantage M2), and heart rate (Garmin Instinct, Polar Vantage M2, Polar OH1, Polar Verity Sense). Validity measures were mean absolute percent error (MAPE) and Lin’s Concordance (CCC), and reliability were coefficient of variation (CV), and intraclass correlation (ICC). Thresholds were MAPE ≤5%, CCC≥0.90, CV≤10%, ICC≥0.70. Garmin Instinct step count during skipping was not considered valid (MAPE=90.2%, CCC=0.008) or reliable (CV=6%, ICC CI=0.4). Energy expenditure during skipping was not valid or reliable in the Garmin Instinct (MAPE=28%, CCC=0.27; CV=19%, ICC=0.61) or the Polar Vantage M2 (MAPE=19%, CCC=0.57; CV=13%). While the Polar Vantage M2 was reliable for estimated energy expenditure during walking and jogging activities, wrist-worn devices (Garmin Instinct, Polar Vantage M2) were neither valid nor reliable in returning estimated energy expenditure during overground skipping. From a wider perspective, wearable device algorithms for estimating energy expenditure should continue to be refined until they return the same level of accuracy as what is currently observed for heart rate, and to a lesser extent step count. Skipping may be an excellent unusual activity for testing wearable devices.