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An Evaluation of Accelerometer-derived Metrics to Assess Daily Behavioral Patterns.

ABSTRACT: INTRODUCTION:The way physical activity (PA) and sedentary behavior (SB) are accumulated throughout the day (i.e., patterns) may be important for health, but identifying measurable and meaningful metrics of behavioral patterns is challenging. This study evaluated accelerometer-derived metrics to determine whether they predicted PA and SB patterns and were reliably measured. METHODS:We defined and measured 55 metrics that describe daily PA and SB using data collected by using the activPAL monitor in four studies. The first two studies were randomized crossover designs that included recreationally active participants. Study 1 experimentally manipulated time spent in moderate-to-vigorous-intensity PA and sedentary time, and study 2 held time in exercise constant and manipulated SB. Study 3 included inactive participants who increased exercise, decreased sedentary time, or both. The study conditions induced distinct behavioral patterns; thus, we tested whether the new metrics could improve the prediction of an individual's study condition after adjusting for the overall volume of PA or SB using conditional logistic regression. In study 4, we measured the 3-month reliability for the pattern metrics by calculating intraclass correlation coefficients in a community-dwelling sample who wore the activPAL monitor twice for 7 d. RESULTS:In each of the experimental studies, we identified new metrics that could improve the accuracy for predicting condition beyond SB and moderate-to-vigorous-intensity PA volume. In study 1, 23 metrics were predictive of a highly active condition, and in study 2, 24 metrics were predictive of a highly sedentary condition. In study 4, the median intraclass correlation coefficients (25-75th percentiles) of the metrics were 0.59 (0.46-0.65). CONCLUSIONS:Several new metrics were predictive of patterns of SB, exercise, and nonexercise behavior and are moderately reliable for a 3-month period. Applying these metrics to determine whether daily behavioral patterns are associated with health-outcomes is an important area of future research.

SUBMITTER: Keadle SK

PROVIDER: S-EPMC5176102 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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An Evaluation of Accelerometer-derived Metrics to Assess Daily Behavioral Patterns.

Keadle Sarah Kozey SK Sampson Joshua N JN Li Haocheng H Lyden Kate K Matthews Charles E CE Carroll Raymond J RJ

Medicine and science in sports and exercise 20170101 1

<h4>Introduction</h4>The way physical activity (PA) and sedentary behavior (SB) are accumulated throughout the day (i.e., patterns) may be important for health, but identifying measurable and meaningful metrics of behavioral patterns is challenging. This study evaluated accelerometer-derived metrics to determine whether they predicted PA and SB patterns and were reliably measured.<h4>Methods</h4>We defined and measured 55 metrics that describe daily PA and SB using data collected by using the ac ...[more]

PMID: 27992396

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Project description:BackgroundDaily rhythmicity has been observed for a number of hormonal and behavioral variables in mammals. It can be entrained by several external factors, such as light-dark cycle and scheduled feeding. In dromedary camels, daily rhythmicity has been documented only for melatonin secretion and body temperature. In this study, the daily rhythmicity of behavioral repertoire, cortisol and testosterone levels was investigated in captive male camels.MethodsSix clinically healthy male dromedary camels (Camelus dromedarius) were used. The animals were housed in single boxes for 24 h daily and fed twice a day. Over a period of 48 h, behavioral observations were made and blood samples taken every two hours. The data were analyzed using diurnality index, conisor analysis and PROC mixed procedure.ResultsThe diurnality index for rumination and lying down was close to 0 (respectively, 0.09 and 0.19), while the indices for stereotypy, standing, feeding and walking were close to 1 (respectively, 0.74, 0.84, 0.92 and 0.85). Cosinor analysis revealed daily rhythmicity for all behaviors and for cortisol levels (acrophase at 12:57) but not for testosterone. Rumination and lying down (inactive behaviors) reached a peak during the scotophase, whereas feeding, walking and stereotypy (active behaviors) reached a peak during the photophase around midday. Cortisol level and expression of stereotypies peaked before and after food distribution and were negatively correlated (r = - 0.287, P = 0.005). Testosterone levels and expression of sexual behaviors were stimulated by the visual and olfactory contacts with the females and were positively correlated (r = 0.164, P = 0.040). Testosterone was also negatively correlated with cortisol (r = - 0.297; P = 0.003).DiscussionThese preliminary results provided new knowledge about the daily rhythm of behaviors in camels housed in boxes, suggesting that camels exhibit diurnal behavior pattern in the maintenance conditions outlined in the study. Daily rhythmicity seemed to be entrained not only by the light-dark cycle but also by scheduled feeding. The rise in stereotypy after food distribution could be due to the persistence of feeding motivation and frustration after the ingestion of food. Therefore, feeding practices should be improved to satisfy the foraging and feeding motivation of these camels. Behavioral and hormonal daily patterns in camels should be taken in consideration to adapt the management system, giving the animals more freedom during the light period and a diet richer in fiber, so as to improve reproductive performance, health and welfare.

Project description:BACKGROUND:There has been an increasing interest in understanding the usefulness of wrist-based accelerometer data for physical activity (PA) assessment due to the ease of use and higher user compliance than other body placements. PA assessment studies have relied on machine learning methods which take accelerometer data in forms of variables, or feature vectors. METHODS:In this work, we introduce automated shape feature derivation methods to transform epochs of accelerometer data into feature vectors. As the first step, recurring patterns in the collected data are identified and placed in a codebook. Similarities between epochs of accelerometer data and codebook's patterns are the basis of feature calculations. In this paper, we demonstrate supervised and unsupervised approaches to learn codebooks. We evaluated these methods and compared them with the standard statistical measures for PA assessment. The experiments were performed on 146 participants who wore an ActiGraph GT3X+ accelerometer on the right wrist and performed 33 activities of daily living. RESULTS:Our evaluations show that the shape feature derivation methods were able to perform comparably with the standard wrist model (F1-score: 0.89) for identifying sedentary PAs (F1-scores of 0.86 and 0.85 for supervised and unsupervised methods, respectively). This was also observed for identifying locomotion activities (F1-scores: 0.87, 0.83, and 0.81 for the standard wrist, supervised, unsupervised models, respectively). All the wrist models were able to estimate energy expenditure required for PAs with low error (rMSE: 0.90, 0.93, and 0.90 for the standard wrist, supervised, and unsupervised models, respectively). CONCLUSION:The automated shape feature derivation methods offer insights into the performed activities by providing a summary of repeating patterns in the accelerometer data. Furthermore, they could be used as efficient alternatives (or additions) for manually engineered features, especially important for cases where the latter fail to provide sufficient information to machine learning methods for PA assessment.

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An Evaluation of Accelerometer-derived Metrics to Assess Daily Behavioral Patterns.

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An Evaluation of Accelerometer-derived Metrics to Assess Daily Behavioral Patterns.

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