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The International Journal of Behavioral... Oct 2018Physical activity is associated with improved physical and mental health among children. However, physical activity declines and sedentary time increases with age, and...
BACKGROUND
Physical activity is associated with improved physical and mental health among children. However, physical activity declines and sedentary time increases with age, and large proportions of older children do not meet the recommended hour per day of moderate-to-vigorous-intensity physical activity (MVPA). The aim of this paper is to identify profiles of children based on the complex relationship between physical activity and sedentary time at ages 6 and 9 and explore how those profiles are associated with other covariates and how they change over time.
METHODS
Valid accelerometer data were collected for 1132 children aged 6 and 1121 at age 9, with 565 children with data at both ages. We calculated the proportions of total wear time spent in sedentary, light and MVPA activity on both weekdays and weekends. Latent profile (class) analysis was applied separately to the two age groups to identify activity profiles. We then used latent transition analysis to explore transitions between profiles at the two time points.
RESULTS
We identified five profiles of activity at age 6 and six profiles at age 9. Although profiles were not directly equivalent, five classes captured similar patterns at both ages and ranged from very active to inactive. At both ages, active profiles, where the majority achieved the recommended MVPA guidelines, were more likely to be active at weekends than on weekdays. There was substantial movement between classes, with strongest patterns of movement to classes with no change or a decrease in MVPA. Transition between classes was associated with sex, BMI z-score, screen-viewing and participation in out-of-school activities.
CONCLUSIONS
This paper is the first to apply latent profile analysis to the physical activity of UK children as they move through primary school. Profiles were identified at ages 6 and 9, reflecting different weekday and weekend patterns of physical activity and sedentary time. There was substantial movement between profiles between ages 6 and 9, mostly to no change or less active profiles. Weekend differences suggest that greater focus on how weekend activity contributes to an average of 60 min per day of MVPA across the week may be warranted.
Topics: Accelerometry; Age Factors; Child; Child Behavior; Exercise; Female; Humans; Male; Sedentary Behavior
PubMed: 30352597
DOI: 10.1186/s12966-018-0735-8 -
The Journals of Gerontology. Series A,... Dec 2022Hip- and wrist-worn ActiGraph accelerometers are widely used in research on physical activity as they offer an objective assessment of movement intensity across the day....
BACKGROUND
Hip- and wrist-worn ActiGraph accelerometers are widely used in research on physical activity as they offer an objective assessment of movement intensity across the day. Herein we characterize and contrast key structured physical activities and common activities of daily living via accelerometry data collected at the hip and wrist from a sample of community-dwelling older adults.
METHODS
Low-active, older adults with obesity (age 60+ years) were fit with an ActiGraph GT3X+ accelerometer on their nondominant wrist and hip before completing a series of tasks in a randomized order, including sitting/standing, sweeping, folding laundry, stair climbing, ambulation at different intensities, and cycling at different intensities. Participants returned a week later and completed the tasks once again. Vector magnitude counts/second were time-matched during each task and then summarized into counts/minute (CPM).
RESULTS
Monitors at both wear locations similarly characterized standing, sitting, and ambulatory tasks. A key finding was that light home chores (sweeping, folding laundry) produced higher and more variable CPM values than fast walking via wrist ActiGraph. Regression analyses revealed wrist CPM values were poor predictors of hip CPM values, with devices aligning best during fast walking (R2 = 0.25) and stair climbing (R2 = 0.35).
CONCLUSIONS
As older adults spend a considerable portion of their day in nonexercise activities of daily living, researchers should be cautious in the use of simply acceleration thresholds for scoring wrist-worn accelerometer data. Methods for better classifying wrist-worn activity monitor data in older adults are needed.
Topics: Humans; Aged; Wrist; Activities of Daily Living; Hip; Accelerometry; Obesity
PubMed: 34791237
DOI: 10.1093/gerona/glab347 -
Statistics in Medicine Feb 2023Accelerometers are commonly used in human medical and public health research to measure physical movement, which is relevant in a wide range of studies, from physical...
Accelerometers are commonly used in human medical and public health research to measure physical movement, which is relevant in a wide range of studies, from physical activity and sleep behaviours studies, to identification of movement patterns in people affected by diseases of the locomotor system and prediction of risk of injury in high performance sports. The accelerometer output provides the intensity (activity count) and timing (timestamp) of the movement, which can be used to define bouts of activity (periods of sustained movement of a given intensity). In some contexts, it may be important to include both dimensions to obtain a broader and deeper understanding of the phenomenon under study. Such is the case of a large-scale epidemiological investigation on the daily and weekly physical activity behaviours of school-aged children enrolled in the UK Millennium Cohort Study, which has motivated the present article. I present a statistical approach to joint modelling of intensity and timing of activity bouts that takes advantage of the circular nature of the timing. The model, which accounts for the longitudinal structure of the observations, is remarkably simple to implement using standard statistical software.
Topics: Child; Humans; Cohort Studies; Exercise; Public Health; Accelerometry
PubMed: 36562435
DOI: 10.1002/sim.9633 -
Journal of Neuroengineering and... Mar 2014Integrating rehabilitation services through wearable systems has the potential to accurately assess the type, intensity, duration, and quality of movement necessary for... (Review)
Review
BACKGROUND
Integrating rehabilitation services through wearable systems has the potential to accurately assess the type, intensity, duration, and quality of movement necessary for procuring key outcome measures.
OBJECTIVES
This review aims to explore wearable accelerometry-based technology (ABT) capable of assessing mobility-related functional activities intended for rehabilitation purposes in community settings for neurological populations. In this review, we focus on the accuracy of ABT-based methods, types of outcome measures, and the implementation of ABT in non-clinical settings for rehabilitation purposes.
DATA SOURCES
Cochrane, PubMed, Web of Knowledge, EMBASE, and IEEE Xplore. The search strategy covered three main areas, namely wearable technology, rehabilitation, and setting.
STUDY SELECTION
Potentially relevant studies were categorized as systems either evaluating methods or outcome parameters.
METHODS
Methodological qualities of studies were assessed by two customized checklists, depending on their categorization and rated independently by three blinded reviewers.
RESULTS
Twelve studies involving ABT met the eligibility criteria, of which three studies were identified as having implemented ABT for rehabilitation purposes in non-clinical settings. From the twelve studies, seven studies achieved high methodological quality scores. These studies were not only capable of assessing the type, quantity, and quality measures of functional activities, but could also distinguish healthy from non-healthy subjects and/or address disease severity levels.
CONCLUSION
While many studies support ABT's potential for telerehabilitation, few actually utilized it to assess mobility-related functional activities outside laboratory settings. To generate more appropriate outcome measures, there is a clear need to translate research findings and novel methods into practice.
Topics: Accelerometry; Humans; Monitoring, Physiologic; Nervous System Diseases; Telemedicine
PubMed: 24625308
DOI: 10.1186/1743-0003-11-36 -
Health Reports Dec 2018Self-reported and accelerometer-measured physical activity levels generally exhibit low correlation and agreement. The objective of this study is to compare estimates of... (Comparative Study)
Comparative Study
BACKGROUND
Self-reported and accelerometer-measured physical activity levels generally exhibit low correlation and agreement. The objective of this study is to compare estimates of physical activity among adults from a newly developed Canadian questionnaire with those obtained objectively by accelerometry.
DATA AND METHODS
Data for 18- to 79-year-olds (N = 2,372) were collected in 2014 and 2015 as part of the Canadian Health Measures Survey (CHMS). Moderate-to-vigorous physical activity (MVPA) was reported on the household questionnaire by domain (transportation, recreation, and occupational or household) as part of the new Physical Activity Adult Questionnaire (PAAQ) and measured objectively using the Actical accelerometer. Correlation and mean difference analyses were used to assess the relationships between measured and reported physical activity variables. Linear regression was used to test the association between measured and reported physical activity and measures of obesity.
RESULTS
On average, Canadian adults reported more physical activity than they accumulated on an accelerometer (49 minutes versus 23 minutes per day). The highest correlation observed was between accelerometer-measured MVPA and the sum of self-reported recreation and transportation activity (R = 0.36, p ⟨ 0.0001). The sum of activity from all domains (recreation + transportation + occupational or household) exhibited a lower correlation with measured variables because the occupational or household domain was negatively correlated with MVPA (R = -0.04). The occupational or household domain was positively correlated with light-intensity physical activity (R = 0.20, p ⟨ 0.0001). Respondents in the least active quintile were more likely than those in the most active quintile to report more activity than was measured by the accelerometer. On average, the most active quintile reported less activity than was measured by the accelerometer.
DISCUSSION
The newly developed Canadian physical activity questionnaire exhibited modest correlation and agreement with accelerometer-measured physical activity among adults. Accelerometers and questionnaires provide complementary information, about different aspects of physical activity (actual movement versus perceived time). Consequently, one should exercise caution in using estimates derived from these methods interchangeably.
Topics: Accelerometry; Adult; Aged; Canada; Exercise; Female; Health Surveys; Humans; Male; Middle Aged; Sedentary Behavior; Self Report; Surveys and Questionnaires
PubMed: 30566204
DOI: No ID Found -
International Journal of Medical... Apr 2023Although machine learning techniques have been repeatedly used for activity prediction from wearable devices, accurate classification of 24-hour activity behaviour...
OBJECTIVE
Although machine learning techniques have been repeatedly used for activity prediction from wearable devices, accurate classification of 24-hour activity behaviour categories from accelerometry data remains a challenge. We developed and validated a deep learning-based framework for classifying 24-hour activity behaviours from wrist-worn accelerometers.
METHODS
Using an openly available dataset with free-living wrist-based raw accelerometry data from 151 participants (aged 18-91 years), we developed a deep learning framework named AccNet24 to classify 24-hour activity behaviours. First, the acceleration signal (x, y, and z-axes) was segmented into 30-second nonoverlapping windows, and signal-to-image conversion was performed for each segment. Deep features were automatically extracted from the signal images using transfer learning and transformed into a lower-dimensional feature space. These transformed features were then employed to classify the activity behaviours as sleep, sedentary behaviour, and light-intensity (LPA) and moderate-to-vigorous physical activity (MVPA) using a bidirectional long short-term memory (BiLSTM) recurrent neural network. AccNet24 was trained and validated with data from 101 and 25 randomly selected participants and tested with the remaining unseen 25 participants. We also extracted 112 hand-crafted time and frequency domain features from 30-second windows and used them as inputs to five commonly used machine learning classifiers, including random forest, support vector machines, artificial neural networks, decision tree, and naïve Bayes to classify the 24-hour activity behaviour categories.
RESULTS
Using the same training, validation, and test data and window size, the classification accuracy of AccNet24 outperformed the accuracy of the other five machine learning classification algorithms by 16%-30% on unseen data.
CONCLUSION
AccNet24, relying on signal-to-image conversion, deep feature extraction, and BiLSTM achieved consistently high accuracy (>95 %) in classifying the 24-hour activity behaviour categories as sleep, sedentary, LPA, and MVPA. The next generation accelerometry analytics may rely on deep learning techniques for activity prediction.
Topics: Humans; Wrist; Exercise; Bayes Theorem; Deep Learning; Accelerometry
PubMed: 36724729
DOI: 10.1016/j.ijmedinf.2023.105004 -
Translational Psychiatry Jan 2021The integration of technology in clinical care is growing rapidly and has become especially relevant during the global COVID-19 pandemic. Smartphone-based digital...
The integration of technology in clinical care is growing rapidly and has become especially relevant during the global COVID-19 pandemic. Smartphone-based digital phenotyping, or the use of integrated sensors to identify patterns in behavior and symptomatology, has shown potential in detecting subtle moment-to-moment changes. These changes, often referred to as anomalies, represent significant deviations from an individual's baseline, may be useful in informing the risk of relapse in serious mental illness. Our investigation of smartphone-based anomaly detection resulted in 89% sensitivity and 75% specificity for predicting relapse in schizophrenia. These results demonstrate the potential of longitudinal collection of real-time behavior and symptomatology via smartphones and the clinical utility of individualized analysis. Future studies are necessary to explore how specificity can be improved, just-in-time adaptive interventions utilized, and clinical integration achieved.
Topics: Accelerometry; Adult; Boston; Ecological Momentary Assessment; Female; Health Surveys; Humans; Longitudinal Studies; Male; Mobile Applications; Movement; Phenotype; Recurrence; Reproducibility of Results; Risk Assessment; Schizophrenia; Screen Time; Sensitivity and Specificity; Sleep; Smartphone; Social Behavior; Telemedicine
PubMed: 33431818
DOI: 10.1038/s41398-020-01123-7 -
BMC Public Health Jun 2022The day-to-next day predictions between physical activity (PA) and sleep are not well known, although they are crucial for advancing public health by delivering valid...
STUDY OBJECTIVES
The day-to-next day predictions between physical activity (PA) and sleep are not well known, although they are crucial for advancing public health by delivering valid sleep and physical activity recommendations. We used Big Data to examine cross-lagged time-series of sleep and PA over 14 days and nights.
METHODS
Bi-directional cross-lagged autoregressive pathways over 153,154 days and nights from 12,638 Polar watch users aged 18-60 years (M = 40.1 SD = 10.1; 44.5% female) were analyzed with cross-lagged panel data modeling (RI-CPL). We tested the effects of moderate-to-vigorous physical activity (MVPA) vs. high intensity PA (vigorous, VPA) on sleep duration and quality, and vice versa.
RESULTS
Within-subject results showed that more minutes spent in VPA the previous day was associated with shorter sleep duration the next night, whereas no effect was observed for MVPA. Longer sleep duration the previous night was associated with less MVPA but more VPA the next day. Neither MVPA nor VPA were associated with subsequent night's sleep quality, but better quality of sleep predicted more MVPA and VPA the next day.
CONCLUSIONS
Sleep duration and PA are bi-directionally linked, but only for vigorous physical activity. More time spent in VPA shortens sleep the next night, yet longer sleep duration increases VPA the next day. The results imply that a 24-h framing for the interrelations of sleep and physical activity is not sufficient - the dynamics can even extend beyond, and are activated specifically for the links between sleep duration and vigorous activity. The results challenge the view that sleep quality can be improved by increasing the amount of PA. Yet, better sleep quality can result in more PA the next day.
Topics: Accelerometry; Exercise; Female; Humans; Male; Sleep; Time Factors
PubMed: 35681198
DOI: 10.1186/s12889-022-13586-y -
Journal of Biomedical Informatics Jan 2019Smartphone and smartwatch technology is changing the transmission and monitoring landscape for patients and research participants to communicate their healthcare...
Smartphone and smartwatch technology is changing the transmission and monitoring landscape for patients and research participants to communicate their healthcare information in real time. Flexible, bidirectional and real-time control of communication allows development of a rich set of healthcare applications that can provide interactivity with the participant and adapt dynamically to their changing environment. Additionally, smartwatches have a variety of sensors suitable for collecting physical activity and location data. The combination of all these features makes it possible to transmit the collected data to a remote server, and thus, to monitor physical activity and potentially social activity in real time. As smartwatches exhibit high user acceptability and increasing popularity, they are ideal devices for monitoring activities for extended periods of time to investigate the physical activity patterns in free-living condition and their relationship with the seemingly random occurring illnesses, which have remained a challenge in the current literature. Therefore, the purpose of this study was to develop a smartwatch-based framework for real-time and online assessment and mobility monitoring (ROAMM). The proposed ROAMM framework will include a smartwatch application and server. The smartwatch application will be used to collect and preprocess data. The server will be used to store and retrieve data, remote monitor, and for other administrative purposes. With the integration of sensor-based and user-reported data collection, the ROAMM framework allows for data visualization and summary statistics in real-time.
Topics: Accelerometry; Exercise; Humans; Mobile Applications; Monitoring, Physiologic; Smartphone
PubMed: 30414474
DOI: 10.1016/j.jbi.2018.11.003 -
Journal of Sleep Research Jun 2021Actigraphy is a cost-efficient method to estimate sleep-wake patterns over long periods in natural settings. However, the lack of methodological standards in actigraphy... (Review)
Review
Actigraphy is a cost-efficient method to estimate sleep-wake patterns over long periods in natural settings. However, the lack of methodological standards in actigraphy research complicates the generalization of outcomes. A rapidly growing methodological diversity is visible in the field, which increasingly necessitates the detailed reporting of methodology. We address this problem and evaluate the current state of the art and recent methodological developments in actigraphy reporting with a special focus on infants and young children. Through a systematic literature search on PubMed (keywords: sleep, actigraphy, child *, preschool, children, infant), we identified 126 recent articles (published since 2012), which were classified and evaluated for reporting of actigraphy. Results show that all studies report on the number of days/nights the actigraph was worn. Reporting was good with respect to device model, placement and sleep diary, whereas reporting was worse for epoch length, algorithm, artefact identification, data loss and definition of variables. In the studies with infants only (n = 58), the majority of articles (62.1%) reported a recording of actigraphy that was continuous across 24 hr. Of these, 23 articles (63.9%) analysed the continuous 24-hr data and merely a fifth used actigraphy to quantify daytime sleep. In comparison with an evaluation in 2012, we observed small improvements in reporting of actigraphy methodology. We propose stricter adherence to standards in reporting methodology in order to streamline actigraphy research with infants and young children, to improve comparability and to facilitate big data ventures in the sleep community.
Topics: Accelerometry; Actigraphy; Female; Humans; Male; Research Design; Sleep
PubMed: 32638500
DOI: 10.1111/jsr.13134