Browse

You are looking at 131 - 140 of 177 items for :

  • Journal for the Measurement of Physical Behaviour x
  • Sport and Exercise Science/Kinesiology x
  • Psychology and Behavior in Sport/Exercise x
  • Refine by Access: All Content x
Clear All
Restricted access

Volume 4 (2021): Issue 1 (Mar 2021)

Restricted access

Accelerometer Calibration: The Importance of Considering Functionality

Scott J. Strath, Taylor W. Rowley, Chi C. Cho, Allison Hyngstrom, Ann M. Swartz, Kevin G. Keenan, Julian Martinez, and John W. Staudenmayer

Purpose: To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions. Methods: 118 adults self-identifying as healthy (n = 44) and those with arthritis (n = 23), multiple sclerosis (n = 18), Parkinson’s disease (n = 17), and stroke (n = 18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit. Results: All activities’ accelerometer counts per minute (CPM) were 29.5–72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, −0.49 (−0.71, −0.27) for arthritis, −0.38 (−0.53, −0.22) for healthy, −0.44 (−0.68, −0.20) for MS, −0.34 (−0.58, −0.09) for Parkinson’s, and −0.30 (−0.54, −0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, −0.37 METs (−0.52, −0.23); Group 2, −0.44 METs (−0.60, −0.28); and Group 3, −0.33 METs (−0.55, −0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition. Conclusion: Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.

Open access

Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification

Supun Nakandala, Marta M. Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan A. Carlson, Andrea Z. LaCroix, Sheri J. Hartman, Dori E. Rosenberg, Jingjing Zou, Arun Kumar, and Loki Natarajan

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

Restricted access

Comparison of Three Algorithms Using Thigh-Worn Accelerometers for Classifying Sitting, Standing, and Stepping in Free-Living Office Workers

Bronwyn Clark, Elisabeth Winker, Matthew Ahmadi, and Stewart Trost

Accurate measurement of time spent sitting, standing, and stepping is important in studies seeking to evaluate interventions to reduce sedentary behavior. In this study, the authors evaluated the agreement in classification of these activities from three algorithms applied to thigh-worn ActiGraph accelerometers using predictions from the widely used activPAL device as a criterion. Participants (n = 29, 72% female, age 23–68 years) wore the activPAL3 micro (processed by PAL software, version 7.2.32) and the ActiGraph GT9X accelerometer on the right front thigh concurrently for working hours on one full workday (7.2 ± 1.2 hr). ActiGraph output was classified via the three test algorithms: ActiGraph’s ActiLife software (inclinometer); an open source method; and, a machine-learning algorithm reported in the literature (Acti4). Performance at an instance level was evaluated by computing classification accuracy (F scores) for 15-s windows. The F scores showed high accuracy relative to the criterion for identifying sitting (96.7–97.1) and were 84.7–85.1 for identifying standing and 78.1–80.6 for identifying stepping. The four methods agreed strongly in total time spent sitting, standing, and stepping, with intraclass correlation coefficients of .96 (95% confidence interval [.92, .96]), .92 (95% confidence interval [.81, .96]), and .87 (95% confidence interval [.53, .95]) but sometimes overestimated sitting time and underestimated standing time relative to activPAL. These algorithms for identifying sitting, standing, and stepping from thigh-worn accelerometers provide estimates that are very similar to those obtained using the activPAL.

Restricted access

Examining the Contribution of Dog Walking to Total Daily Physical Activity Among Dogs and Their Owners

Katie Potter, Robert T. Marcotte, Greg J. Petrucci, Caitlin Rajala, Deborah E. Linder, and Laura B. Balzer

Given high rates of obesity and chronic disease in both people and dogs, it is important to understand how dogs and dog owners influence each other’s health, including physical activity (PA) levels. Research suggests that dog owners who walk their dogs are more likely to meet PA guidelines than those who do not, but few studies have investigated dog walking intensity or its contribution to dog owners’ total moderate-to-vigorous PA using accelerometry. Furthermore, no studies have examined the contribution of dog walking to dogs’ total PA or the relationship between dog and dog owner PA using accelerometers on dogs. The authors used accelerometers on 33 dog owner–dog pairs to investigate (a) the intensity of dog walking behavior, (b) the contribution of dog walking to dog owners’ overall moderate-to-vigorous PA and dogs’ overall PA, and (c) the correlation between dog and dog owner PA. Dog owners wore an ActiGraph accelerometer and logged all dog walking for 7 days; dogs wore a Fitbark activity monitor. On average, 64.1% (95% confidence interval [55.2, 73.1]) of daily dog walking was moderate to vigorous intensity, and dog walking accounted for 51.2% (95% confidence interval [44.1, 58.3]) of dog owners’ daily moderate-to-vigorous PA. Dog walking accounted for 41.2% (95% confidence interval [36.0, 46.4]) of dogs’ daily PA. Dog owners’ daily steps were moderately correlated (r = .54) with dogs’ daily activity points. These findings demonstrate the interdependence of dog and dog owner PA and can inform interventions that leverage the dog–owner bond to promote PA and health in both species.

Restricted access

Validation of Wearable Camera Still Images to Assess Posture in Free-Living Conditions

Julian Martinez, Autumn E. Decker, Chi C. Cho, Aiden Doherty, Ann M. Swartz, John Staudenmayer, and Scott J. Strath

Purpose: To assess the convergent validity of body-worn wearable camera still images (IMGs) for determining posture compared with activPAL (AP) classifications. Methods: The participants (n = 16, mean age 46.7 ± 23.8 years, 9 F) wore an Autographer wearable camera and an AP during three 2-hr free-living visits. IMGs were on average 8.47 s apart and were annotated with output consisting of events, transitory states, unknown, and gaps. The events were annotations that matched AP classifications (sit, stand, and move), consisting of at least three IMGs; the transitory states were posture annotations fewer than three IMGs; the unknowns were IMGs that could not be accurately classified; and the gaps were the time between annotations. For the analyses, the annotation and AP output were converted to 1-s epochs. The total and average length of visits and events were reported in minutes. Bias and 95% confidence intervals for event posture times from IMGs to AP were calculated to determine accuracy and precision. Confusion matrices using total AP posture times were computed to determine misclassification. Results: Forty-three visits were analyzed, with a total visit and event time of 5,027.73 and 4,237.23 min, respectively, and the average visit and event lengths being 116.92 and 98.54 min, respectively. Bias was not statistically significant for sitting, but was significant for standing and movement (0.84, −6.87, and 6.04 min, respectively). From confusion matrices, IMGs correctly classified sitting, standing, and movement (85.69%, 54.87%, and 69.41%, respectively) of total AP time. Conclusion: Wearable camera IMGs provide a good estimation of overall sitting time. Future work is warranted to improve posture classifications and examine the validity of IMGs in assessing activity-type behaviors.

Full access

Comparison of a Thigh-Worn Accelerometer Algorithm With Diary Estimates of Time in Bed and Time Asleep: The 1970 British Cohort Study

Elif Inan-Eroglu, Bo-Huei Huang, Leah Shepherd, Natalie Pearson, Annemarie Koster, Peter Palm, Peter A. Cistulli, Mark Hamer, and Emmanuel Stamatakis

Background: Thigh-worn accelerometers have established reliability and validity for measurement of free-living physical activity-related behaviors. However, comparisons of methods for measuring sleep and time in bed using the thigh-worn accelerometer are rare. The authors compared the thigh-worn accelerometer algorithm that estimates time in bed with the output of a sleep diary (time in bed and time asleep). Methods: Participants (N = 5,498), from the 1970 British Cohort Study, wore an activPAL device on their thigh continuously for 7 days and completed a sleep diary. Bland–Altman plots and Pearson correlation coefficients were used to examine associations between the algorithm derived and diary time in bed and asleep. Results: The algorithm estimated acceptable levels of agreement with time in bed when compared with diary time in bed (mean bias of −11.4 min; limits of agreement −264.6 to 241.8). The algorithm-derived time in bed overestimated diary sleep time (mean bias of 55.2 min; limits of agreement −204.5 to 314.8 min). Algorithm and sleep diary are reasonably correlated (ρ = .48, 95% confidence interval [.45, .52] for women and ρ = .51, 95% confidence interval [.47, .55] for men) and provide broadly comparable estimates of time in bed but not for sleep time. Conclusions: The algorithm showed acceptable estimates of time in bed compared with diary at the group level. However, about half of the participants were outside of the ±30 min difference of a clinically relevant limit at an individual level.

Full access

Agreement of Sedentary Behavior Metrics Derived From Hip- and Thigh-Worn Accelerometers Among Older Adults: With Implications for Studying Physical and Cognitive Health

John Bellettiere, Fatima Tuz-Zahra, Jordan A. Carlson, Nicola D. Ridgers, Sandy Liles, Mikael Anne Greenwood-Hickman, Rod L. Walker, Andrea Z. LaCroix, Marta M. Jankowska, Dori E. Rosenberg, and Loki Natarajan

Little is known about how sedentary behavior (SB) metrics derived from hip- and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL (AP) micro monitors were concurrently worn with hip-worn ActiGraph (AG) GT3X+ accelerometers (with SB measured using the 100 counts per minute [cpm] cut point; AG100cpm) by 953 older adults (age 77 ± 6.6, 54% women) for 4–7 days. Device agreement for sedentary time and five SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with four health outcomes using standardized (i.e., z scores) and unstandardized SB metrics. Mean errors (AP − AG100cpm) and 95% limits of agreement were: sedentary time −54.7 [−223.4, 113.9] min/day; time in 30+ min bouts 77.6 [−74.8, 230.1] min/day; mean bout duration 5.9 [0.5, 11.4] min; usual bout duration 15.2 [0.4, 30] min; breaks in sedentary time −35.4 [−63.1, −7.6] breaks/day; and alpha −.5 [−.6, −.4]. Respective Pearson correlations were: .66, .78, .73, .79, .51, and .40. Concordance correlations were: .57, .67, .40, .50, .14, and .02. The statistical significance and direction of associations were identical for AG100cpm and AP metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 13 of 24 tests for unstandardized and five of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from AG100cpm due to the tendency for it to overestimate breaks in sedentary time relative to AP. However, high correlations between AP and AG100cpm measures and similar standardized associations with health outcomes suggest that studies using AG100cpm are useful, though not ideal, for studying SB in older adults.

Restricted access

Does Preoperative Pain Catastrophizing Influence Objectively Measured Physical Activity Before and After Total Knee Arthroplasty: A Prospective Cohort Study

Sara Birch, Torben Bæk Hansen, Maiken Stilling, and Inger Mechlenburg

Background: Pain catastrophizing is associated with pain both before and after a total knee arthroplasty (TKA). However, it remains uncertain whether pain catastrophizing affects physical activity (PA). The aim was to examine the influence of pain catastrophizing on the PA profile, knee function, and muscle mass before and after a TKA. Methods: The authors included 58 patients with knee osteoarthritis scheduled for TKA. Twenty-nine patients had a score >22 on the Pain Catastrophizing Scale (PCS), and 29 patients had a score <11. PA was measured with a triaxial accelerometer preoperative, 3 months, and 12 months after TKA. Other outcome measures consisted of the Knee Osteoarthritis Outcome Score and dual-energy X-ray absorptiometry scans. Results: The authors found no difference in PA between patients with a better/low or a worse/high score on the PCS, and none of the groups increased their mean number of steps/day from preoperative to 12 months postoperative. Patients with better/low PCS scores had higher/better preoperative scores on the Knee Osteoarthritis Outcome Score subscales (symptoms, pain, and activity of daily living), and they walked longer in the 6-min walk test. Further, they had lower body mass index, lower percent fat mass, and higher percent muscle mass than patients with worse/high PCS scores both before and after a TKA. Conclusion: Preoperative pain catastrophizing did not influence PA before or after a TKA. Although the patients improved substantially in self-reported knee function, their PA did not increase. This may be important to consider when the clinicians are informing the patients about the expected benefits from the operation.

Restricted access

Validity and Interinstrument Reliability of a Medical Grade Physical Activity Monitor in Older Adults

Myles W. O’Brien, William R. Wojcik, and Jonathon R. Fowles

Wearable physical activity monitors are associated with an increase in user’s habitual physical activity levels. Most of the older adult population do not meet the national moderate- to vigorous-intensity physical activity (MVPA) recommendations and may benefit from being prescribed a physical activity monitor. The PiezoRx is a class one medical grade device that uses step rate thresholds to measure MVPA. The validity and reliability of the PiezoRx in measuring MVPA has yet to be determined in older persons. We assessed the validity and interinstrument reliability of the PiezoRx to measure steps and MVPA in older adults. Participants (n = 19; 68.8 ± 2.3 years) wore an Omron HJ-320 pedometer, ActiGraph GT3X accelerometer, and four PiezoRx monitors during a five-stage treadmill walking protocol. The PiezoRx devices were set at moderate physical activity and vigorous physical activity step rate thresholds (steps per minute) of 100/120, 110/130, adjusted for height and adjusted for height + fitness. The PiezoRx exhibited a stronger correlation (intraclass correlation coefficient = .82) with manually counted steps than the ActiGraph (intraclass correlation coefficient = .53) and Omron (intraclass correlation coefficient = .54) and had a low absolute percentage error (3 ± 6%). The PiezoRx with moderate physical activity/vigorous physical activity step thresholds adjusted to 110/130 was strongly correlated to indirect calorimetry (0.84, p < .001) and best distinguished each walking stage as MVPA or not (sensitivity: 88%; specificity: 95%). The PiezoRx monitor is a valid and reliable measure of step count and MVPA among older adults. The device’s ability to measure MVPA in absolute terms was improved when step rate thresholds for moderate physical activity/vigorous physical activity were increased to 110/130 steps per minute in this population.