Alex V. Rowlands

The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study

BackgroundTechnological advances have seen a burgeoning industry for accelerometer-based wearable activity monitors targeted at the consumer market. The purpose of this study was to determine the convergent validity of a selection of consumer-level accelerometer-based activity monitors.Methods21 healthy adults wore seven consumer-level activity monitors (Fitbit One, Fitbit Zip, Jawbone UP, Misfit Shine, Nike Fuelband, Striiv Smart Pedometer and Withings Pulse) and two research-grade accelerometers/multi-sensor devices (BodyMedia SenseWear, and ActiGraph GT3X+) for 48-hours. Participants went about their daily life in free-living conditions during data collection. The validity of the consumer-level activity monitors relative to the research devices for step count, moderate to vigorous physical activity (MVPA), sleep and total daily energy expenditure (TDEE) was quantified using Bland-Altman analysis, median absolute difference and Pearson’s correlation.ResultsAll consumer-level activity monitors correlated strongly (r > 0.8) with research-grade devices for step count and sleep time, but only moderately-to-strongly for TDEE (r = 0.74-0.81) and MVPA (r = 0.52-0.91). Median absolute differences were generally modest for sleep and steps (<10% of research device mean values for the majority of devices) moderate for TDEE (<30% of research device mean values), and large for MVPA (26-298%). Across the constructs examined, the Fitbit One, Fitbit Zip and Withings Pulse performed most strongly.ConclusionsIn free-living conditions, the consumer-level activity monitors showed strong validity for the measurement of steps and sleep duration, and moderate valid for measurement of TDEE and MVPA. Validity for each construct ranged widely between devices, with the Fitbit One, Fitbit Zip and Withings Pulse being the strongest performers.

Validation of the GT3X ActiGraph in children and comparison with the GT1M ActiGraph.

OBJECTIVES The GT1M ActiGraph has been replaced by the triaxial GT3X which includes an inclinometer to detect postures. The purpose of this study was to investigate comparability of the GT3X to the GT1M and to develop activity intensity cut-points for the vector magnitude of the GT3X (VM(GT3X)) in children. Additionally, the study aimed to assess the validity of the GT3X inclinometer for detecting postures. DESIGN Forty-nine children aged 10-15 were tested during semi-structured activities in a laboratory setting (lying, sitting, standing, Nintendo Wii boxing, walking and running). METHODS Expired respiratory gases were measured continuously using the Cosmed K4b(2) portable metabolic system. Simultaneously, ActiGraph counts by a GT3X and a GT1M were recorded. RESULTS Significantly higher counts were found for GT3X vertical mean counts per second (vcps) and antero-posterior counts per second (apcps) during running, relative to the GT1M. Cut-points for the VM(GT3X), developed using Receiver Operator Characteristics (ROC) curves (development group N=32, validation group N=17), were <3 counts s⁻¹ for sedentary (cross-validation: 87% correctly classified), 3-56 counts s⁻¹ for light (cross-validation: 70% correctly classified) and >56 counts s⁻¹ for moderate to vigorous (mvpa) (cross-validation: 86% correctly classified). The inclinometer correctly classified standing 20%, lying 15%, sitting 94% and the off position 45% of the time. CONCLUSIONS The vcps from the two monitors differ for certain activities. Cross-validated cut-points for the classification of VM(GT3X) counts into sedentary, light and mvpa are presented. Posture classification by the GT3X should be interpreted with care, as misclassifications are common.

Physical Activity Classification Using the GENEA Wrist-Worn Accelerometer

INTRODUCTION Most accelerometer-based activity monitors are worn on the waist or lower back for assessment of habitual physical activity. Output is in arbitrary counts that can be classified by activity intensity according to published thresholds. The purpose of this study was to develop methods to classify physical activities into walking, running, household, or sedentary activities based on raw acceleration data from the GENEA (Gravity Estimator of Normal Everyday Activity) and compare classification accuracy from a wrist-worn GENEA with a waist-worn GENEA. METHODS Sixty participants (age = 49.4 ± 6.5 yr, body mass index = 24.6 ± 3.4 kg·m⁻²) completed an ordered series of 10-12 semistructured activities in the laboratory and outdoor environment. Throughout, three GENEA accelerometers were worn: one at the waist, one on the left wrist, and one on the right wrist. Acceleration data were collected at 80 Hz. Features obtained from both fast Fourier transform and wavelet decomposition were extracted, and machine learning algorithms were used to classify four types of daily activities including sedentary, household, walking, and running activities. RESULTS The computational results demonstrated that the algorithm we developed can accurately classify certain types of daily activities, with high overall classification accuracy for both waist-worn GENEA (0.99) and wrist-worn GENEA (right wrist = 0.97, left wrist = 0.96). CONCLUSIONS We have successfully developed algorithms suitable for use with wrist-worn accelerometers for detecting certain types of physical activities; the performance is comparable to waist-worn accelerometers for assessment of physical activity.

Calibration of the GENEA accelerometer for assessment of physical activity intensity in children

OBJECTIVES The purpose of the study was to establish activity intensity cut-points for the GENEA accelerometer via calibration with oxygen consumption (V˙⁡O(2)). DESIGN The study was a lab-based validation and calibration study. METHODS Forty-four children, aged 8-14 years, completed eight activities (ranging from lying supine to a medium paced run) whilst wearing GENEA accelerometers at three locations (each wrist and at the right hip), an ActiGraph GT1M at the hip and a portable gas analyser. ActiGraph output and V˙⁡O(2) were used for assessment of concurrent and criterion validity, respectively. Pearsons r correlations were used to assess validity of the GENEA monitors at each location and location-specific activity intensity cut-points were established via Receiver Operator Characteristic curve analysis. RESULTS The GENEA showed good criterion validity at both wrist locations (right: r=.900; left: r=.910, both p<0.01), although the hip-mounted monitor demonstrated significantly higher criterion validity (r=.965, p<0.05). Similar results were shown for concurrent validity (right: r=.830; left: r=.845; hip: r=.985, all p<0.01). GENEAs, irrespective of wear location, accurately discriminated between all activity intensities (sedentary, light, moderate and vigorous) with the hip mounted monitor recording the largest area under the curve for each intensity (area under the curve=0.94-0.99). CONCLUSIONS The GENEA can be used to accurately assess childrens physical activity intensity when worn at either the wrist or the hip.

Accelerometer counts and raw acceleration output in relation to mechanical loading.

The purpose of this study was to assess the relationship of accelerometer output, in counts (ActiGraph GT1M) and as raw accelerations (ActiGraph GT3X+ and GENEA), with ground reaction force (GRF) in adults. Ten participants (age: 29.4 ± 8.2 yr, mass: 74.3 ± 9.8 kg, height: 1.76 ± 0.09 m) performed eight trials each of: slow walking, brisk walking, slow running, faster running and box drops. GRF data were collected for one step per trial (walking and running) using a force plate. Low jumps and higher jumps (one per second) were performed for 20 s each on the force plate. For box drops, participants dropped from a 35 cm box onto the force plate. Throughout, three accelerometers were worn at the hip: GT1M, GT3X+ and GENEA. A further GT3X+ and GENEA were worn on the left and right wrist, respectively. GT1M counts correlated with peak impact force (r = 0.85, p < 0.05), average resultant force (r = 0.73, p < 0.05) and peak loading rate (r = 0.76, p < 0.05). Accelerations from the GT3X+ and GENEA correlated with average resultant force and peak loading rate irrespective of whether monitors were worn at the hip or wrist (r > 0.82, p < 0.05, r > 0.63 p < 0.05, respectively). In conclusion, accelerometer count and raw acceleration output correlate positively with GRF and thus may be appropriate for the quantification of activity beneficial to bone. Wrist-worn monitors show a similar relationship with GRF as hip-worn monitors, suggesting that wrist-worn monitors may be a viable option for future studies looking at bone health.

Wear compliance and activity in children wearing wrist and hip mounted accelerometers

PURPOSE This study aimed to 1) explore childrens compliance to wearing wrist- and hip-mounted accelerometers, 2) compare childrens physical activity (PA) derived from raw accelerations of wrist and hip, and 3) examine differences in raw and counts PA measured by hip-worn accelerometry. METHODS One hundred and twenty-nine 9- to 10-yr-old children wore a wrist-mounted GENEActiv accelerometer (GAwrist) and a hip-mounted ActiGraph GT3X+ accelerometer (AGhip) for 7 d. Both devices measured raw accelerations, and the AGhip also provided count-based data. RESULTS More children wore the GAwrist than those from the AGhip regardless of wear time criteria applied (P < 0.001-0.035). Raw data signal vector magnitude (r = 0.68), moderate PA (MPA) (r = 0.81), vigorous PA (VPA) (r = 0.85), and moderate-to-vigorous PA (MVPA) (r = 0.83) were strongly associated between devices (P < 0.001). GAwrist signal vector magnitude (P = 0.001), MPA (P = 0.037), VPA (P = 0.002), and MVPA (P = 0.016) were significantly greater than those from the AGhip. According to GAwrist raw data, 86.9% of children engaged in at least 60 min · d(-1) of MVPA, compared with 19% for AGhip. ActiGraph MPA (raw) was 42.00 ± 1.61 min · d(-1) compared with 35.05 ± 0.99 min · d(-1) (counts) (P = 0.02). ActiGraph VPA was 7.59 ± 0.46 min · d(-1) (raw) and 37.06 ± 1.85 min · d(-1) (counts; P = 0.19). CONCLUSIONS In children, accelerometer wrist placement promotes superior compliance than the hip. Raw accelerations were significantly higher for GAwrist compared with those for AGhip possibly because of placement location and technical differences between devices. AGhip PA calculated from raw accelerations and counts differed substantially, demonstrating that PA outcomes derived from cut points for raw output and counts cannot be directly compared.

Utilization and Harmonization of Adult Accelerometry Data: Review and Expert Consensus.

Supplemental digital content is available in the text.

Assessing Sedentary Behavior with the GENEActiv: Introducing the Sedentary Sphere.

BACKGROUND The Sedentary Sphere is a method for the analysis, identification, and visual presentation of sedentary behaviors from a wrist-worn triaxial accelerometer. PURPOSE This study aimed to introduce the concept of the Sedentary Sphere and to determine the accuracy of posture classification from wrist accelerometer data. METHODS Three samples were used: 1) free living (n = 13, ages 20-60 yr); 2) laboratory based (n = 25, ages 30-65 yr); and 3) hospital inpatients (n = 10, ages 60-90 yr). All participants wore a GENEActiv on their wrist and activPAL on their thigh. The free-living sample wore an additional GENEActiv on the thigh and completed the Multimedia Activity Recall for Children and Adults. The laboratory-based sample wore the monitors while seated at a desk for 7 h, punctuated by 2 min of walking every 20 min. The free-living and inpatient samples wore the monitors for 24 h. Posture was classified from wrist-worn accelerometry using the Sedentary Sphere concept. RESULTS Sitting time did not differ between the wrist GENEActiv and the activPAL in the free-living sample and was correlated in the three samples combined (rho = 0.9, P < 0.001), free-living and inpatient samples (r ≃ 0.8, P < 0.01). Mean intraindividual agreement was 85% ± 7%. In the laboratory-based and inpatient samples, sitting time was underestimated by the wrist GENEActiv by 30 min and 2 h relative to the activPAL, respectively (P < 0.05). Posture classification disagreed during reading while standing, cooking while standing, and brief periods during driving. Posture allocation validity was excellent when the GENEActiv was worn on the thigh, evidenced by the near-perfect agreement with the activPAL (96% ± 3%). CONCLUSIONS The Sedentary Sphere enables determination of the most likely posture from the wrist-worn GENEActiv. Visualizing behaviors on the sphere displays the pattern of wrist movement and positions within that behavior.

Age- and Sex-Specific Criterion Validity of the Health Survey for England Physical Activity and Sedentary Behavior Assessment Questionnaire as Compared With Accelerometry

The criterion validity of the 2008 Physical Activity and Sedentary Behavior Assessment Questionnaire (PASBAQ) was examined in a nationally representative sample of 2,175 persons aged ≥16 years in England using accelerometry. Using accelerometer minutes/day greater than or equal to 200 counts as a criterion, Spearmans correlation coefficient (ρ) for PASBAQ-assessed total activity was 0.30 (95% confidence interval (CI): 0.25, 0.35) in women and 0.20 (95% CI: 0.15, 0.26) in men. Correlations between accelerometer counts/minute of wear time and questionnaire-assessed relative energy expenditure (metabolic equivalent-minutes/day) were higher in women (ρ = 0.41, 95% CI: 0.36, 0.46) than in men (ρ = 0.32, 95% CI: 0.26, 0.38). Similar correlations were observed for minutes/day spent in vigorous activity (women: ρ = 0.39, 95% CI: 0.33, 0.46; men: ρ = 0.31, 95% CI: 0.26, 0.36) and moderate-to-vigorous activity (women: ρ = 0.42, 95% CI: 0.36, 0.48; men: ρ = 0.38, 95% CI: 0.32, 0.45). Correlations for time spent being sedentary (<100 counts/minute) were 0.30 (95% CI: 0.24, 0.35) and 0.25 (95% CI: 0.19, 0.30) in women and men, respectively. Sedentary behavior correlations showed no sex difference. The validity of sedentary behavior and total physical activity was higher in older age groups, but validity was higher in younger persons for vigorous-intensity activity. The PASBAQ is a useful and valid instrument for ranking individuals according to levels of physical activity and sedentary behavior.

Children's, physical activity assessed with wrist- and hip-worn accelerometers

BACKGROUND Recently, triaxial raw acceleration accelerometers have become available from GENEActiv and ActiGraph; both are designed for wrist and hip wear. It is important to determine whether the output from these monitors is comparable with the wealth of data already collected from the hip-worn, epoch-based, uniaxial ActiGraph. PURPOSE This study aimed to assess the concurrent validity of measures of total activity and time spent at different activity intensities from the GENEActiv relative to the ActiGraph GT3X+. METHODS Fifty-eight children age 10-12 yr wore two accelerometers at the hip (ActiGraph GT3X+ and GENEActiv) and one at the wrist (GENEActiv) for 7 d. Wear time was matched for all monitors before analysis. RESULTS Mean daily accelerometer output, time spent sedentary, and time in moderate-to-vigorous physical activity (MVPA) from the hip- or wrist-worn GENEActiv were strongly correlated with the corresponding output from the hip-worn ActiGraph (r > 0.83, P < 0.001). However, less time was estimated to be sedentary and more time was estimated to be MVPA using the hip- or wrist-worn GENEActiv (Phillips cut points) than that when using the Evenson vertical axis cut points with the hip-worn ActiGraph. Output from the vertical axis ActiGraph cut points could be predicted with 95% limits of agreement, equating to 23%-28% and 33%-35% of the mean value, by the hip- and wrist-worn GENEActiv, respectively. CONCLUSIONS The assessment of childrens activity level, time spent sedentary, and time in MVPA estimated from the hip- or wrist-worn GENEActiv seems to be comparable with that of the uniaxial ActiGraph. On the basis of the strong linear correlations, ActiGraph output can be predicted from the hip- or wrist-worn GENEActiv for comparative purposes at the group level. However, because of relatively wide limits of agreement, individual-level comparisons are not recommended.