Background: Although designed as a consumer product to help motivate individuals to be physically active, Fitbit activity trackers are becoming increasingly popular as measurement tools in physical activity and health promotion research and are also commonly used to inform health care decisions Objective: The objective of this review was to systematically evaluate and report measurement accuracy for Fitbit activity trackers in controlled and free-living settings Methods: We conducted electronic searches using PubMed, EMBASE, CINAHL, and SPORTDiscus databases with a supplementary Google Scholar search We considered original research published in English comparing Fitbit versus a reference- or research-standard criterion in healthy adults and those living with any health condition or disability We assessed risk of bias using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments We explored measurement accuracy for steps, energy expenditure, sleep, time in activity, and distance using group percentage differences as the common rubric for error comparisons We conducted descriptive analyses for frequency of accuracy comparisons within a ±3% error in controlled and ±10% error in free-living settings and assessed for potential bias of over- or underestimation We secondarily explored how variations in body placement, ambulation speed, or type of activity influenced accuracy Results: We included 67 studies Consistent evidence indicated that Fitbit devices were likely to meet acceptable accuracy for step count approximately half the time, with a tendency to underestimate steps in controlled testing and overestimate steps in free-living settings Findings also suggested a greater tendency to provide accurate measures for steps during normal or self-paced walking with torso placement, during jogging with wrist placement, and during slow or very slow walking with ankle placement in adults with no mobility limitations Consistent evidence indicated that Fitbit devices were unlikely to provide accurate measures for energy expenditure in any testing condition Evidence from a few studies also suggested that, compared with research-grade accelerometers, Fitbit devices may provide similar measures for time in bed and time sleeping, while likely markedly overestimating time spent in higher-intensity activities and underestimating distance during faster-paced ambulation However, further accuracy studies are warranted Our point estimations for mean or median percentage error gave equal weighting to all accuracy comparisons, possibly misrepresenting the true point estimate for measurement bias for some of the testing conditions we examined Conclusions: Other than for measures of steps in adults with no limitations in mobility, discretion should be used when considering the use of Fitbit devices as an outcome measurement tool in research or to inform health care decisions, as there are seemingly a limited number of situations where the device is likely to provide accurate measurement

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Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data

Background: New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. Objective: The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. Methods: We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. Results: We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. Conclusions: The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated. [J Med Internet Res 2018;20(3):e110]

Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables

Background: Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures
Objective: 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
Methods: 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
Results: 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
Conclusions: 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

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Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review.

Background: Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. Objective: The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. Methods: A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). Results: Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). Conclusions: At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states. [JMIR Mhealth Uhealth 2018;6(4):e94]

Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study.

Wearable physical activity trackers are a popular and useful method to collect biometric information at rest and during exercise The purpose of this systematic review was to summarize recent findings of wearable devices for biometric information related to steps, heart rate, and caloric expenditure for several devices that hold a large portion of the market share Searches were conducted in both PubMed and SPORTdiscus Filters included: humans, within the last 5 years, English, full-text, and adult 19+ years Manuscripts were retained if they included an exercise component of 5-min or greater and had 20 or more participants A total of 10 articles were retained for this review Overall, wearable devices tend to underestimate energy expenditure compared to criterion laboratory measures, however at higher intensities of activity energy expenditure is underestimated All wrist and forearm devices had a tendency to underestimate heart rate, and this error was generally greater at higher exercise intensities and those that included greater arm movement Heart rate measurement was also typically better at rest and while exercising on a cycle ergometer compared to exercise on a treadmill or elliptical machine Step count was underestimated at slower walking speeds and in free-living conditions, but improved accuracy at faster speeds The majority of the studies reviewed in the present manuscript employed different methods to assess validity and reliability of wearable technology, making it difficult to compare devices Standardized protocols would provide guidance for researchers to evaluate research-grade devices as well as commercial devices used by the lay public

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Current State of Commercial Wearable Technology in Physical Activity Monitoring 2015-2017.

Wearable activity trackers have become popular for tracking individual’s daily physical activity, but little information is available to substantiate the validity of these devices in step counts. Thirty-five healthy individuals completed three conditions of activity tracker measurement: walking/jogging on a treadmill, walking over-ground on an indoor track, and a 24-hour free-living condition. Participants wore 10 activity trackers at the same time for both treadmill and over-ground protocol. Of these 10 activity trackers three were randomly given for 24-hour free-living condition. Correlations of steps measured to steps observed were r = 0.84 and r = 0.67 on a treadmill and over-ground protocol, respectively. The mean MAPE (mean absolute percentage error) score for all devices and speeds on a treadmill was 8.2% against manually counted steps. The MAPE value was higher for over-ground walking (9.9%) and even higher for the 24-hour free-living period (18.48%) on step counts. Equivalence testing for...

Gregory Jones

Papers

How valid are wearable physical activity trackers for measuring steps