Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures.
The purpose of this systematic review was to examine the validity and reliability of ...commercial wearables in measuring step count, heart rate, and energy expenditure.
We identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only articles published in the English language up to May 2019 were considered. Studies were excluded if they did not identify the device used and if they did not examine the validity or reliability of the device. Studies involving the general population and all special populations were included. We operationalized validity as criterion validity (as compared with other measures) and construct validity (degree to which the device is measuring what it claims). Reliability measures focused on intradevice and interdevice reliability.
We included 158 publications examining nine different commercial wearable device brands. Fitbit was by far the most studied brand. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate and Fitbit tending toward underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand.
Commercial wearable devices are accurate for measuring steps and heart rate in laboratory-based settings, but this varies by the manufacturer and device type. Devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.
Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real life, as opposed to laboratory settings.
This 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.
Overall, 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.
For 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%.
The 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.
Objective
:
To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in ...children and adults.
Methods
:
Participants (
N
= 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit’s SmartTrack, and Garmin’s Move IQ. Trip- and day-level agreement were tested.
Results
:
The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults.
Conclusions
:
The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS’ pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research.
Purpose
Establishing accurate estimates of physical activity at baseline is essential for interventions assessing the potential benefits of exercise in adults with cancer. This study compares ...self-reported physical activity with independent data from activity trackers in women with early breast cancer (BC) recruited into a “walking” intervention during chemotherapy.
Methods
Baseline (pre-intervention) questions inquired about self-reported physical activity—number of walking days/week and minutes/day—in women who were initiating chemotherapy for Stage I–III BC. Activity trackers measured steps per day during the first full week of chemotherapy. Weighted Kappa statistic and Pearson correlation coefficients were used to evaluate agreement and association between self-reported and objectively tracked physical activity levels, respectively. Univariate analyses were conducted to identify variables that may influence congruence between the two measures.
Results
In a sample of 161 women, 77% were white, with mean age 56 years. Agreement between self-reported and objectively tracked physical activity was “fair” (kappa coefficient = 0.31), with most patients (59%) over-reporting their physical activity levels. There was weak correlation between the two measures (
r
= 0.24); however, correlation was strong in participants who were not married (
r
= 0.53) and/or living alone (
r
= 0.69).
Conclusions
Objective methods for assessing physical activity (activity trackers, accelerometers) should be used as a complement to self-reported measures to establish credible activity levels for intervention studies seeking to increase physical activity and/or measure the impact of increased physical activity in women with breast cancer.
The development of consumer sleep-tracking technologies has outpaced the scientific evaluation of their accuracy. In this study, five consumer sleep-tracking devices, research-grade actigraphy, and ...polysomnography were used simultaneously to monitor the overnight sleep of fifty-three young adults in the lab for one night. Biases and limits of agreement were assessed to determine how sleep stage estimates for each device and research-grade actigraphy differed from polysomnography-derived measures. Every device, except the Garmin Vivosmart, was able to estimate total sleep time comparably to research-grade actigraphy. All devices overestimated nights with shorter wake times and underestimated nights with longer wake times. For light sleep, absolute bias was low for the Fitbit Inspire and Fitbit Versa. The Withings Mat and Garmin Vivosmart overestimated shorter light sleep and underestimated longer light sleep. The Oura Ring underestimated light sleep of any duration. For deep sleep, bias was low for the Withings Mat and Garmin Vivosmart while other devices overestimated shorter and underestimated longer times. For REM sleep, bias was low for all devices. Taken together, these results suggest that proportional bias patterns in consumer sleep-tracking technologies are prevalent and could have important implications for their overall accuracy.
Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, ...we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations.
As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov-a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords "ActiGraph,""Apple Watch,""Empatica,""Fitbit,""Garmin," and "wearable devices" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables.
Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations.
Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future.
A novel, wearable, stretchable Smart Patch can monitor various aspects of physical activity, including the dynamics of running. However, like any new device developed for such applications, it must ...first be tested for validity and reliability. Here, we compare the step rate while running on a treadmill measured by this smart patch with the corresponding values obtained with the "gold standard" OptoGait, as well as with other devices commonly used to assess running dynamics, that is, the MEMS accelerometer and commercially available and widely used Garmin Running Dynamic Pod. The 14 healthy, physically active volunteers completed two identical sessions with a 5-min rest between. Each session involved two 1-min runs at 11 and 14 km/h separated by a 1-min rest. The major finding was that the Smart Patch demonstrated fair to good test-retest reliability. The best test-retest reliability for the Running Pod was observed in connection with running at 11 km/h and both velocities combined (good and excellent, respectively) and for the OptoGait when running at 14 km/h (good). The best concurrent validity was achieved with the Smart Patch, as reflected in the highest Pearson correlation coefficient for this device when running at 11 or 14 km/h, as well as for both velocities combined. In conclusion, this study demonstrates that the novel wearable Smart Patch shows promising reliability and excellent concurrent validity in measuring step rate during treadmill running, making it a viable tool for both research and practical applications in sports and exercise science.
In order to study the relationship between human physical activity and the design of the built environment, it is important to measure the location of human movement accurately. In this study, we ...compared an inexpensive GPS receiver (Holux RCV-3000) and a frequently used Garmin Forerunner 35 smart watch, with a device that has been validated and recommended for physical activity research (Qstarz BT-Q1000XT). These instruments were placed on six geodetic points, which represented a range of different environments (e.g., residential, open space, park). The coordinates recorded by each device were compared with the known coordinates of the geodetic points. There were no differences in accuracy among the three devices when averaged across the six sites. However, the Garmin was more accurate in the city center and the Holux was more accurate in the park and housing estate areas compared to the other devices. We consider the location accuracy of the Holux and the Garmin to be comparable to that of the Qstarz. Therefore, we consider these devices to be suitable instruments for locating physical activity. Researchers must also consider other differences among these devices (such as battery life) when determining if they are suitable for their research studies.
Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should ...be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory. Therefore, the main objective of this study was to investigate whether algorithms can be developed for predicting patellar and Achilles tendon force and impulse during running using measures that can be easily collected by runners using commercially available devices. A secondary objective was to evaluate the predictive performance of the algorithms against the commonly used running distance. Trials of 24 recreational runners were collected with an Xsens suit and a Garmin Forerunner 735XT at three different intended running speeds. Data were analyzed using a mixed-effects multiple regression model, which was used to model the association between the estimated forces in anatomical structures and the training load variables during the fixed running speeds. This provides twelve algorithms for predicting patellar or Achilles tendon peak force and impulse per stride. The algorithms developed in the current study were always superior to the running distance algorithm.
Energy Consumption in the agricultural sector consists of diesel, gasoline, and kerosene for fuel of agricultural machinery (rice transplanter, tractor, rice milling unit, motor sprayer, and water ...pump) in the sector. The objectives of this study are to determine the total energy consumption of rice planting and to analyse the performance of rice transplanter during rice planting in West Sumatra, Indonesia. This research was conducted on farmer's rice fields in west Sumatera Indonesia. The results obtained from the performance of a rice transplanter machine include working speed of 0.633 m/s, a theoretical work capacity of 0.274 ha/hour, effective work capacity of 0.222 ha/hour and work efficiency of 80.967%. The detail of energy consumption using a rice transplanter are human energy (9.225 MJ/ha), seed energy (255.413 MJ/ha), fuel energy (93.463 MJ/ha) and engine energy (0.821 MJ/ha), so that the total energy consumption is 358.952 MJ/ha.
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