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Activity monitors provide an inexpensive and convenient way to measure sleep, yet relatively few studies have been conducted to validate the use of these devices in examining measures of sleep quality or sleep stages and if other measures, such as thermometry, could inform their accuracy. The purpose of this study was to compare one research-grade and four consumer-grade activity monitors on measures of sleep quality (sleep efficiency, sleep onset latency, and wake after sleep onset) and sleep stages (awake, sleep, light, deep, REM) against an electroencephalography criterion. The use of a skin temperature device was also explored to ascertain whether skin temperature monitoring may provide additional data to increase the accuracy of sleep determination. Twenty adults stayed overnight in a sleep laboratory during which sleep was assessed using electroencephalography and compared to data concurrently collected by five activity monitors (research-grade: ActiGraph GT9X Link; consumer-grade: Fitbit Charge HR, Fitbit Flex, Jawbone UP4, Misfit Flash) and a skin temperature sensor (iButton). The majority of the consumer-grade devices overestimated total sleep time and sleep efficiency while underestimating sleep onset latency, wake after sleep onset, and number of awakenings during the night, with similar results being seen in the research-grade device. The Jawbone UP4 performed better than both the consumer- and research-grade devices, having high levels of agreement overall and in epoch-by-epoch sleep stage data. Changes in temperature were moderately correlated with sleep stages, suggesting that addition of skin temperature could increase the validity of activity monitors in sleep measurement.

Background: Consumer-based activity monitors use accelerometers to estimate Calories (kcals), but it is unknown if monitors measuring heart rate (HR) use HR in kcal prediction. Purpose: Determine if there is a difference in kcal estimations in Fitbits measuring HR compared to those not measuring HR. Methods: Participants (n = 23) wore five Fitbits and performed nine activities for five minutes each, split into four groupings (G1: sitting, standing, cycling 50–150W; G2: level (0%) and inclined (10%) walking at 1.1 m/s; G3: level (0%) and inclined (10%) walking at 1.4 m/s; G4: level (0%) and inclined (3%) jogging at 2.2–4.5 m/s) in the laboratory. Three Fitbits (Blaze, Charge HR, Alta HR) assessed steps, HR, and kcals, and two Fitbits (Alta, Flex2) assessed steps and kcals. Steps, HR, and kcals data from the Fitbits were compared to criterion measures and between Fitbits measuring HR and Fitbits without HR. Results: Fitbits with HR had significantly higher kcal predictions (10.5–23.8% higher, p < .05) during inclined compared to level activities in G2–G4, whereas Fitbits without HR had similar kcal estimates between level and inclined activities. Mean absolute percent errors for kcal predictions were similar for Fitbits measuring HR (33.7–38.3%) and Fitbits without HR (32.4–36.6%). Conclusion: Fitbits measuring HR appear to use HR when predicting kcals. However, kcal prediction accuracies were similarly poor compared to Fitbits without HR compared to criterion measures.

Purpose: We aimed to determine the step-count validity of commonly used physical activity monitors for pregnancy overground walking and during free-living conditions. Methods: Participants (n = 39, 12–38 weeks gestational age) completed six 100-step overground walking trials (three self-selected “normal pace”, three “brisk pace”) while wearing five physical activity monitors: Omron HJ-720 (OM), New Lifestyles 2000 (NL), Fitbit Flex (FF), ActiGraph Link (AG), and Modus StepWatch (SW). For each walking trial, monitor-recorded steps and criterion-measured steps were assessed. Participants also wore all activity monitors for an extended free-living period (72 hours), with the SW used as the criterion device. Mean absolute percent error (MAPE) was calculated for overground walking and free-living protocols and compared across monitors. Results: For overground walking, the OM, NL, and SW performed well (<5% MAPE) for normal and brisk pace walking trials, and also when trials were analyzed by actual speeds. The AG and FF had significantly greater MAPE for overground walking trials (11.9–14.7%). Trimester did affect device accuracy to some degree for the AG, FF, and SW, with error being lower in the third trimester compared to the second. For the free-living period, the OM, NL, AG, and FF significantly underestimated (>32% MAPE) actual steps taken per day as measured by the criterion SW (M [SD] = 9,350 [3,910]). MAPE for the OM was particularly high (45.3%). Conclusion: The OM, NL, and SW monitors are valid measures for overground step-counting during pregnancy walking. However, the OM and NL significantly underestimate steps by second and third trimester pregnant women in free-living conditions.

Speed of movement has been shown to affect the validity of physical activity (PA) monitors during locomotion. Speed of movement may also affect the validity of accelerometer-based PA monitors during other types of exercise. Purpose: To assess the ability of the Atlas Wearables Wristband2 (a PA monitor developed specifically for resistance training [RT] exercise) to identify the individual RT exercise type and count repetitions during RT exercises at various movement speeds. Methods: 50 male and female participants completed seven sets of 10 repetitions for five different upper/lower body RT exercises while wearing a Wristband2 on the left wrist. The speed of each set was completed at different metronome-paced speeds ranging from a slow speed of 4 sec·rep⁻¹ to a fast speed of 1 sec·rep⁻¹. Repeated Measures ANOVAs were used to compare the actual exercise type/number of repetitions among the seven different speeds. Mean absolute percent error (MAPE) and bias were calculated for repetition counting. Results: For each exercise, there tended to be significant differences between the slower speeds and the fastest speed for activity type identification and repetition counting (p < .05). Across all exercises, the highest accuracy for activity type identification (91 ± 1.8% correct overall), repetition counting (8.77 ± 0.17 of 10 reps overall) and the lowest MAPE (14 ± 1.7% overall) and bias (−1.23 ± 0.17 reps overall) occurred during the 1.5 sec·rep⁻¹ speed (the second fastest speed tested). Conclusions: The validity of the Atlas Wearables Wristband2 to identify exercise type and count repetitions varied based on the speed of movement during RT exercises.

Purpose: This purpose of this study was to validate consumer-based and research-grade PA monitors for step counting and Calorie expenditure during treadmill walking. Methods: Participants (n = 40, 24 in second trimester and 16 in third trimester) completed five 2-minute walking activities (1.5–3.5 miles/hour in 0.5 mile/hour increments) while wearing five PA monitors (right hip: ActiGraph Link [AG]; left hip: Omron HJ-720 [OM]; left front pants pocket: New Lifestyles NL 2000 [NL]; non-dominant wrist: Fitbit Flex [FF]; right ankle: StepWatch [SW]). Mean absolute percent error (MAPE) was used to determine device accuracy for step counting (all monitors) and Calorie expenditure (AG with Freedson equations and FF) compared to criterion measures (hand tally for steps, indirect Calorimetry for Calories). Results: For step counting, the SW had MAPE ≤ 10% at all walking speeds, and the OM and NL had MAPE ≤ 10% for all speeds but 1.5 miles/hour. The AG had MAPE ≤ 10% for only 3.0–3.5 miles/hour speeds, and the FF had high MAPE for all speeds. For Calories, the FF and AG had MAPE > 10% for all speeds, with the FF overestimating Calories expended. Trimester did not affect PA monitor accuracy for step counting but did affect accuracy for Calorie expenditure. Conclusion: The ankle-worn SW and hip-worn OM had high accuracy for measuring step counts at all treadmill walking speeds, whereas the NL had high accuracy for speeds ≥2.0 miles/hour. Conversely, the monitors tested for Calorie expenditure have poor accuracy and should be interpreted cautiously for walking behavior.

Background: Resistance training (RT) is an integral component of physical activity guidelines, but methods for the objective assessment of RT have been limited. Recently, the Atlas Wearables Wristband2 has been marketed to measure RT, but its reliability is unknown. Purpose: To determine the reliability of the Wristband2 for measuring RT exercises. Methods: Participants (n = 62) aged 18–52 yrs. wore two Wristband2 monitors on the left wrist and performed 2 sets of 12 repetitions of 14 different resistance training exercises. Test-retest reliability was determined by calculating percent agreement for exercise type and for repetitions recorded by a single Wristband2 between sets 1 and 2 for each exercise, and inter-monitor reliability was determined by calculating percent agreement for exercise type and for repetitions recorded by both Wristband2 monitors in set 1 of each exercise. Results: Test-retest reliability for exercise type was 80.0 ± 1.0% (lowest: 69.4% for bench press; highest: 95.2% for biceps curls) and for repetition count was 47.9 ± 2.2% (lowest: 19.4% for calf raises; highest: 82.3% for lateral raises). Inter-monitor reliability for exercise type was 80.4 ± 1.3% (lowest: 66.1% for bench press; highest: 95.2% for biceps curls) and for repetition count was 59.6 ± 2.2% (lowest: 32.3% for calf raises; highest: 88.7% for lateral raises). Subgroup analyses by gender, RT experience, and participant height revealed minimal differences in reliability. Repetition agreement of ≤1 repetition increased test-retest reliability to 74.7% and inter-monitor reliability to 83.7%. Conclusion: The Wristband2 had acceptable test-retest and inter-monitor reliability for the majority of exercises tested and for counting repetitions to within 1 repetition/set.

Background: Devices for monitoring physical activity have focused mainly on measuring aerobic activity; however, the 2018 Physical Activity Guidelines for Americans also recommend muscle-resistance training two or more days per week. Recently, a wrist-worn activity monitor, the Atlas Wristband2, was developed to recognize resistance training exercises. Purpose: To assess the ability of the Wristband2 to identify the type and number of repetitions of resistance training exercises, when worn on the left wrist as directed by the manufacturer, and when worn on the right wrist. Methods: While wearing monitors on both wrists, 159 participants completed a circuit-style workout consisting of two sets of 12 repetitions of 14 different resistance training exercises. Data from the monitors were used to determine classification accuracies for identifying exercise type verses direct observation. The average repetitions and mean absolute error (MAE) for repetitions were calculated for each exercise. Results: The Wristband2 classification accuracy for exercise type was 78.4 ± 2.5%, ranging from 54.7 ± 3.4% (dumbbell [DB] bench press) to 97.5 ± 1.0% (DB biceps curls), when worn on the left wrist. An average of 11.0 ± 0.2 repetitions, ranging from 9.0 ± 0.3 repetitions (DB lunges) to 11.9 ± 0.1 repetitions (push-ups), were identified. For all exercises, MAE ranged from 0.0–4.6 repetitions. When worn on the right wrist, exercise type classification accuracy dropped to 24.2 ± 5.1%, and repetitions decreased to 8.1 ± 0.8 out of 12. Conclusions: The Wristband2, worn on the left wrist, had acceptable exercise classification and repetition counting capabilities for many of the 14 exercises used in this study, and may be a useful tool to objectively track resistance training.

Background: Machine learning may improve energy expenditure (EE) prediction from body-worn accelerometers. However, machine learning models are rarely cross-validated in an independent sample, and the use of machine learning raises additional questions including the effect of accelerometer placement and data type (count vs. raw) for optimal EE prediction. Purpose: To assess the accuracy of artificial neural network (ANN) models for EE prediction in youth using count-based or raw data from accelerometers worn on the hip, wrist, or in combination, and compare these to count-based, EE regression equations. Methods: Data were collected in two settings; one (n = 27) to calibrate the EE prediction models, and the other (n = 34) for model cross-validation. Participants wore a portable metabolic analyzer (EE criterion) and accelerometers on the left wrist and right hip while completing 30 minutes of exergames (calibration, cross-validation) and a maximal exercise test (calibration only). Six ANNs were created from the calibration data, separately by accelerometer placement (hip, wrist, combination) and data format (count-based, raw) to predict EE (15-second epochs). Three count-based linear regression equations were also developed for comparison to the ANNs. Results: The count-based, hip ANN demonstrated lower error (RMSE: 1.2 METs) than all other ANNs (RMSE: 1.7–3.6 METs) and EE regression equations (RMSE: 1.5–3.2 METs). However, all models showed bias toward the mean. Conclusion: An ANN developed for hip-worn accelerometers had higher accuracy for EE prediction during an exergame session than wrist or combination ANNs, and ANNs developed using count-based data had higher accuracy than ANNs developed using raw data.

Background: ActiGraph is a commonly used, research-grade accelerometer brand, but there is little information regarding intermonitor comparability of newer models. In addition, while sampling rate has been shown to influence accelerometer metrics, its influence on measures of free-living physical activity has not been directly studied. Purpose: To examine differences in physical activity metrics due to intermonitor variability and chosen sampling rate. Methods: Adults (n = 20) wore two hip-worn ActiGraph wGT3X-BT monitors for 1 week, with one accelerometer sampling at 30 Hz and the other at 100 Hz, which was downsampled to 30 Hz. Activity intensity was classified using vector magnitude, Euclidean Norm Minus One (ENMO), and mean amplitude deviation (MAD) cut points. Equivalence testing compared outcomes. Results: There was a lack of intermonitor equivalence for ENMO, time in sedentary/light- or moderate-intensity activity according to ENMO cut points, and time in moderate-intensity activity according to MAD cut points. Between sampling rates, differences existed for time in moderate-intensity activity according to vector magnitude, ENMO, and MAD cut points, and time in sedentary/light-intensity activity according to ENMO cut points. While mean differences were small (0.1–1.7 percentage points), this would equate to differences in moderate-to vigorous-intensity activity over a 10-hr wear day of 3.6 (MAD) to 10.8 (ENMO) min/day for intermonitor comparisons or 3.6 (vector magnitude) to 5.4 (ENMO) min/day for sampling rate. Conclusions: Epoch-level intermonitor differences were larger than differences due to sampling rate, but both may impact outcomes such as time spent in each activity intensity. ENMO was the least comparable metric between monitors or sampling rates.

Background: The activPAL accelerometer is used widely for assessment of free-living activity and postural data. Two algorithms, VANE (traditional) and CREA (new), are available to analyze activPAL data, but the comparability of metrics derived from these algorithms is unknown. Purpose: To determine the comparability of physical activity and sedentary behavior metrics from activPAL’s VANE and CREA algorithms. Methods: Individuals enrolled in the LIFT trial (n = 354) wore an activPAL accelerometer on the right thigh continuously for 7 days on four occasions, resulting in 5,851 valid days of data for analysis. Daily data were downloaded in the PALbatch software using the VANE and CREA algorithms. Correlations, mean absolute percentage error, effect sizes (ES), and equivalence (within 3%) were calculated to evaluate comparability of the algorithms. Results: Steps, activity score, stepping time, bouts of stepping, and upright time metrics were statistically equivalent, highly correlated (r ≥ .98), and had small mean absolute percentage errors (≤2.5%) and trivial ES (ES < 0.07) between algorithms. Stepping bouts also had good comparability. Conversely, sedentary-upright and upright-sedentary transitions and bouts of sitting were not equivalent, with large mean absolute percentage differences (17.4%–141.3%) and small to very large ES (ES = 0.45–3.80) between algorithms. Conclusions: Stepping and upright metrics are highly comparable between activPAL’s VANE and CREA algorithms, but sitting metrics had large differences as the VANE algorithm does not capture nonwear or differentiate between sitting and lying down. Researchers using the activPAL should explicitly describe the analytic algorithms used in their work to facilitate data pooling and comparability across studies.