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The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7–22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab and at-home protocols. We found strong agreement between GT1M and GT9X counts during the combined in-lab activities (mean bias = 2 counts) and between minutes per day with different intensities of activity (e.g., sedentary, light, moderate, and vigorous) classified using cutpoints (mean bias < 5 min/d at all intensities). We generated bin sizes that could be used to generate IGs from the count data (mean bias = −0.15; 95% LOA [−0.65, 0.34]) compared with the original IG. Therefore, the intensity gradient could be used to analyze count data. The count-based intensity gradient metric will be valuable for re-analyzing historical datasets collected using older accelerometer models, such as the GT1M. [ABSTRACT FROM AUTHOR]

Hip-worn accelerometers are commonly used to assess habitual physical activity, but their accuracy in precisely measuring sedentary behavior (SB) is generally considered low. The angle for postural estimation (APE) method has shown promising accuracy in SB measurement. This method relies on the constant nature of Earth's gravity and the assumption that walking posture is typically upright. This study investigated how cardiorespiratory fitness (CRF) and body mass index (BMI) are related to APE output. A total of 3475 participants with adequate accelerometer wear time were categorized into three groups according to CRF or BMI. Participants in low CRF and high BMI groups spent more time in reclining and lying postures (APE ≥ 30°) and less time in sitting and standing postures (APE < 30°) than the other groups. Furthermore, the strongest partial Spearman correlation with CRF (r = 0.284) and BMI (r = −0.320) was observed for APE values typical for standing. The findings underscore the utility of the APE method in studying associations between SB and health outcomes. Importantly, this study emphasizes the necessity of reserving the term "sedentary behavior" for studies wherein the classification of SB is based on both intensity and posture. [ABSTRACT FROM AUTHOR]

The "can do, do do" framework combines measures of poor and normal physical capacity (PC, measured by a 6 min walking test, can do/can't do) and physical activity (PA, measured by accelerometer, do do/don't do) into four domains and is able to categorize patient subgroups with distinct clinical characteristics, including fall and fracture risk factors. This study aims to explore the association between domain categorization and prospective fall, fracture, and mortality outcomes. This 6-year prospective study included patients visiting a Fracture Liaison Service with a recent fracture. Outcomes were first fall (at 3 years of follow-up, measured by fall diaries), first subsequent fracture, and mortality (at 6 years). Cumulative incidences of all three outcomes were calculated. The association between domain categorization and time to the three outcomes was assessed by uni- and multivariate Cox proportional hazard analysis with the "can do, do do" group as reference. The physical performance of 400 patients with a recent fracture was assessed (mean age: 64 years; 70.8% female), of whom 61.5%, 20.3%, and 4.9% sustained a first fall, sustained a subsequent fracture, or had died. Domain categorization using the "can do, do do" framework was not associated with time to first fall, subsequent fracture, or mortality in the multivariate Cox regression analysis for all groups. "Can't do, don't do" group: hazard ratio [HR] for first fall: 0.75 (95% confidence interval [CI]: 0.45–1.23), first fracture HR: 0.58 (95% CI: 0.24–1.41), and mortality HR: 1.19 (95% CI: 0.54–6.95). Categorizing patients into a two-dimensional framework seems inadequate to study complex, multifactorial outcomes. A personalized approach based on known fall and fracture risk factors might be preferable. [ABSTRACT FROM AUTHOR]

Maintenance of brain structure is essential for neurocognitive health. Precision medicine has interests in understanding how maintenance of an individual person's brain, including cerebral cortical structure, interacts with lifestyle factors like physical activity. Cortical structure, including cortical thickness, has recognized relationships with physical activity, but concepts of these relationships come from group, not individual, focused findings. Whether or how group-focused concepts apply to an individual person is fundamental to precision medicine interests but remains unclear. This issue was studied in a healthy man using concurrent micro-longitudinal tracking of magnetic resonance imaging-defined cortical thickness and accelerometer-defined steps/day over six months. These data permitted detailed examination of temporal relationships between thickness maintenance and physical activity at an individual level. Regression analyses revealed graded significant and trend-level temporal interactions between preceding activity vs. subsequent thickness maintenance and between preceding thickness maintenance vs. subsequent activity. Interactions were bidirectional, delayed/prolonged over days/weeks, positive, bilateral, directionally asymmetric, and limited in strength. These novel individual-focused findings in some ways are predicted, but in other ways remain unaddressed or undetected, by group-focused work. We suggest that individual-focused concepts of temporal interactions between maintenance of cortical structure and activity can provide needed new insight for personalized tailoring of physical activity, cortical, and neurocognitive health. [ABSTRACT FROM AUTHOR]

An accurate quantitative assessment of physical activity and sedentary lifestyles enables a better understanding of their relationship with the health records of cancer survivors. The objective of this study was to compare the subjective and objective methods of physical activity measurement in female breast cancer survivors. Materials and methods: In total, 135 female breast cancer survivors at the Holycross Cancer Center, Kielce, Poland, were included in this study. A shortened version of the International Physical Activity Questionnaire (IPAQ) was used to subjectively assess the participants' physical activity (PA), and an ActiGraph GT3X-BT accelerometer was used for an objective assessment. In total, 75% of the studied women did not report any vigorous PA, irrespective of the measurement method. The average values of moderate PA and moderate-to-vigorous PA (MVPA) measured with IPAQ compared with the accelerometer were sevenfold and tenfold higher, respectively. Conversely, the sedentary behavior values measured with the accelerometer were almost three times higher than those measured with IPAQ. The PA and sedentary behavior measurements were significantly different. Irrespective of PA intensity, the accelerometer-based measurements produced significantly lower results than IPAQ, while higher results were observed for sedentary behavior. The measurement differences between these two methods increased as the average differences grew. Regardless of the measurement method, a negative association was observed between moderate PA with general adiposity and adipose tissue distribution, whereas sedentary behavior demonstrated an opposite trend. This indicates the detrimental role of obesity in limiting PA. [ABSTRACT FROM AUTHOR]

ActiGraph® is a valid, frequently used, accelerometer to quantify moderate to vigorous physical activities (MVPA) in people with COPD. The impact of ActiGraph processing techniques on this population is unknown. This study aimed to explore the effect of data reduction techniques on MVPA in people with COPD. MVPA/day, through ActiGraph GT3X+, was estimated using: Troiano, Freedson 98 and FreedsonVM3 cutoffs, 15-s and 60-s epochs, and normal and low-frequency extension (LFE) filters. Cutoff, epoch, and filter effects were explored with Aligned Rank Transform-ANOVA. Lin's concordance correlation coefficients and Bland–Altman plots were used to evaluate agreement and bias between different techniques. The analysis included 136 people with COPD (79% male; 68 ± 8 years; FEV1 51 ± 17% predicted). MVPA/day differed according to cutoff, filter, and epoch selection (p-value < 0.001). FreedsonVM3 cutoff, 15-s epochs, and LFE yielded the highest MVPA (45 min/day, 68% of physically active participants). Troiano cutoff, 60-s epochs, and normal filter yielded the lowest MVPA (8 min/day, 20% of physically active participants). Only comparisons between Troiano and Freedson98 cutoffs presented an almost perfect agreement. ActiGraph data reduction techniques affected MVPA/day estimates and their interpretation at the individual and group level. Studies using different processing criteria should not be compared in people with COPD. Future studies with a gold standard are required to ascertain which processing technique produces the most accurate MVPA estimates in COPD. Meanwhile, future trials employing the ActiGraph GT3X+ may consider estimating MVPA based on Freedson VM3 cutofffs, 60-s epochs, and normal filter. [ABSTRACT FROM AUTHOR]

Measures of physical performance captured within a clinical setting are commonly used as a surrogate for underlying health or disease risk within an individual. By measuring physical behaviour within a free-living setting, we may be able to better quantify physical performance. In our study, we outline an approach to measure maximum free-living step count using a body-worn sensor as an indicator of physical performance. We then use this approach to characterise the maximum step count over a range of window durations within a population of older adults to identify a preferred duration over which to measure the maximum step count. We found that while almost all individuals (97%) undertook at least one instance of continuous stepping longer than two minutes, a sizeable minority of individuals (31%) had no periods of continuous stepping longer than six minutes. We suggest that the maximum step count measured over a six-minute period may be too sensitive to the adults' lack of opportunity to undertake prolonged periods of stepping, and a two-minute window could provide a more representative measure of physical performance. [ABSTRACT FROM AUTHOR]

Background: Health care professionals need a valid tool to assess the physical ability of patients with chronic diseases. We aimed to assess the validity of the results of physical fitness tests estimated by a wrist wearable device in young adults and chronic disease people. Methods: Participants wore a sensor placed on their wrist and performed two physical fitness tests (sit to stand (STS) and time up and go (TUG)). We checked the concordance of sensor-estimated results using Bland–Altman analysis, root-mean-square error, and intraclass coefficient of correlation (ICC). Results: In total, 31 young adults (groups A; median age = 25 ± 5 years) and 14 people with chronic diseases (groups B; median age = 70 ± 15 years) were included. Concordance was high for both STS (ICCA = 0.95, and ICCB = 0.90), and TUG (ICCA = 0.75, ICCB = 0.98). The best estimations were given by the sensor during STS tests in young adults (mean bias = 0.19 ± 2.69; p = 0.12) and chronic disease people (mean bias = −0.14 ± 3.09 s; p = 0.24). The sensor provided the largest estimation errors over 2 s during the TUG test in young adults. Conclusion: This study showed that the results provided by the sensor are consistent with those of the gold standard during STS and TUG in both healthy youth and people with chronic diseases. [ABSTRACT FROM AUTHOR]

Measures of stepping volume and rate are common outputs from wearable devices, such as accelerometers. It has been proposed that biomedical technologies, including accelerometers and their algorithms, should undergo rigorous verification as well as analytical and clinical validation to demonstrate that they are fit for purpose. The aim of this study was to use the V3 framework to assess the analytical and clinical validity of a wrist-worn measurement system of stepping volume and rate, formed by the GENEActiv accelerometer and GENEAcount step counting algorithm. The analytical validity was assessed by measuring the level of agreement between the wrist-worn system and a thigh-worn system (activPAL), the reference measure. The clinical validity was assessed by establishing the prospective association between the changes in stepping volume and rate with changes in physical function (SPPB score). The agreement of the thigh-worn reference system and the wrist-worn system was excellent for total daily steps (CCC = 0.88, 95% CI 0.83–0.91) and moderate for walking steps and faster-paced walking steps (CCC = 0.61, 95% CI 0.53–0.68 and 0.55, 95% CI 0.46–0.64, respectively). A higher number of total steps and faster paced-walking steps was consistently associated with better physical function. After 24 months, an increase of 1000 daily faster-paced walking steps was associated with a clinically meaningful increase in physical function (0.53 SPPB score, 95% CI 0.32–0.74). We have validated a digital susceptibility/risk biomarker—pfSTEP—that identifies an associated risk of low physical function in community-dwelling older adults using a wrist-worn accelerometer and its accompanying open-source step counting algorithm. [ABSTRACT FROM AUTHOR]

Accelerometer data can be used to estimate incident oxygen consumption (VO2) during physical activity. Relationships between the accelerometer metrics and VO2 are typically determined using specific walking or running protocols on a track or treadmill. In this study, we compared the predictive performance of three different metrics based on the mean amplitude deviation (MAD) of the raw three-dimensional acceleration signal during maximal tests performed on a track or treadmill. A total of 53 healthy adult volunteers participated in the study, 29 performed the track test and 24 the treadmill test. During the tests, the data were collected using hip-worn triaxial accelerometers and metabolic gas analyzers. Data from both tests were pooled for primary statistical analysis. For typical walking speeds at VO2 less than 25 mL/kg/min, accelerometer metrics accounted for 71–86% of the variation in VO2. For typical running speeds starting from VO2 of 25 mL/kg/min up to over 60 mL/kg/min, 32–69% of the variation in VO2 could be explained, while the test type had an independent effect on the results, except for the conventional MAD metrics. The MAD metric is the best predictor of VO2 during walking, but the poorest during running. Depending on the intensity of locomotion, the choice of proper accelerometer metrics and test type may affect the validity of the prediction of incident VO2. [ABSTRACT FROM AUTHOR]

Accurate methods for the prediction of the total energy expenditure and physical activity level (PAL) in community-dwelling older adults have not been established. Therefore, we examined the validity of estimating the PAL using an activity monitor (Active style Pro HJA-350IT, [ASP]) and proposed correction formulae for such populations in Japan. Data for 69 Japanese community-dwelling adults aged 65 to 85 years were used. The total energy expenditure in free-living conditions was measured with the doubly labeled water method and the measured basal metabolic rate. The PAL was also estimated from metabolic equivalent (MET) values obtained with the activity monitor. Adjusted MET values were also calculated with the regression equation of Nagayoshi et al. (2019). The observed PAL was underestimated, but significantly correlated, with the PAL from the ASP. When adjusted using the Nagayoshi et al. regression equation, the PAL was overestimated. Therefore, we developed regression equations to estimate the actual PAL (Y) from the PAL obtained with the ASP for young adults (X) as follows: women: Y = 0.949 × X + 0.205, mean ± standard deviation of the prediction error = 0.00 ± 0.20; men: Y = 0.899 × X + 0.371, mean ± standard deviation of the prediction error = 0.00 ± 0.17. [ABSTRACT FROM AUTHOR]

Objective measurement methods using accelerometers have become the mainstream approach for evaluating physical activity (PA) and sedentary behavior (SB). However, several problems face the objective evaluation of PA and SB in patients with chronic obstructive pulmonary disease (COPD). For example, indicators of PA differ depending on whether the accelerometer detects the kind of activity on the one hand, or its intensity on the other. Measured data are also strongly influenced by environmental factors (weather, season, employment status, etc.) and methodological factors (days with uncommon activities, non-wearing time, minimum required wearing time per day, minimum number of valid days required, etc.). Therefore, adjusting for these factors is required when evaluating PA or SB, especially when evaluating the effects of intervention. The exclusion of sleeping time, unification of total measurement time, and minimization of the required wearing time per day might be more important for the evaluation of ST than for evaluating PA. The lying-down-time-to-sitting-time ratio was shown to be larger in COPD patients than in healthy subjects. In this review, we clarified the problems encountered during objective evaluations of PA and SB in patients with COPD and encouraged investigators to recognize the presence of these problems and the importance of adjusting for them. [ABSTRACT FROM AUTHOR]

Activity monitoring combined with machine learning (ML) methods can contribute to detailed knowledge about daily physical behavior in older adults. The current study (1) evaluated the performance of an existing activity type recognition ML model (HARTH), based on data from healthy young adults, for classifying daily physical behavior in fit-to-frail older adults, (2) compared the performance with a ML model (HAR70+) that included training data from older adults, and (3) evaluated the ML models on older adults with and without walking aids. Eighteen older adults aged 70–95 years who ranged widely in physical function, including usage of walking aids, were equipped with a chest-mounted camera and two accelerometers during a semi-structured free-living protocol. Labeled accelerometer data from video analysis was used as ground truth for the classification of walking, standing, sitting, and lying identified by the ML models. Overall accuracy was high for both the HARTH model (91%) and the HAR70+ model (94%). The performance was lower for those using walking aids in both models, however, the overall accuracy improved from 87% to 93% in the HAR70+ model. The validated HAR70+ model contributes to more accurate classification of daily physical behavior in older adults that is essential for future research. [ABSTRACT FROM AUTHOR]

Mobility is the primary indicator of quality of life among older adults. Physical capacity (PC) and physical activity (PA) are two determinants of mobility; however, PC and PA are complex constructs represented by numerous parameters. This research sought to identify the optimal parameters that may be used to represent PC and PA of older adults, while exploring the interrelationship of these two constructs. Participants were 76 community-dwelling older adults (M age = 74.05 ± 5.15 yrs.). The McRoberts MoveTest was used to objectively measure PC in the laboratory with the following tests: the Short Physical Performance Battery, the Sway test, Sit to Stands, and the Timed Up and Go. PA was then measured at home for one week using the McRoberts USB Dynaport. Correlation analyses resulted in 55% and 65% reductions of PC and PA parameters, respectively. Clustering identified five representative PC parameters and five representative PA parameters. Canonical correlation analysis identified a non-significant correlation between the two sets of parameters. A novel approach was used to define PC and PA by systematically reducing these constructs into representative parameters that provide clinically relevant information, suggesting that they are an accurate representation of one's PC and PA. A non-significant correlation between PC and PA suggests that there is no relationship between the two in this sample of community-dwelling older adults. The research provided insight into two important determinants of older adult mobility, and how they influence each other. [ABSTRACT FROM AUTHOR]

Wearable accelerometers record physical activity with high resolution, potentially capturing the rich details of behaviour changes and habits. Detecting these changes as they emerge is valuable information for any strategy that promotes physical activity and teaches healthy behaviours or habits. Indeed, this offers the opportunity to provide timely feedback and to tailor programmes to each participant's needs, thus helping to promote the adherence to and the effectiveness of the intervention. This article presents and illustrates U-BEHAVED, an unsupervised algorithm that periodically scans step data streamed from activity trackers to detect physical activity behaviour changes to assess whether they may become habitual patterns. Using rolling time windows, current behaviours are compared with recent previous ones, identifying any significant change. If sustained over time, these new behaviours are classified as potentially new habits. We validated this detection algorithm using a physical activity tracker step dataset (N = 12,798) from 79 users. The algorithm detected 80% of behaviour changes of at least 400 steps within the same hour in users with low variability in physical activity, and of 1600 steps in those with high variability. Based on a threshold cadence of approximately 100 steps per minute for standard walking pace, this number of steps would suggest approximately 4 and 16 min of physical activity at moderate-to-vigorous intensity, respectively. The detection rate for new habits was 80% with a minimum threshold of 500 or 1600 steps within the same hour in users with low or high variability, respectively. [ABSTRACT FROM AUTHOR]

(1) Background: Actigraphic methods allow prolonged monitoring of human physical activity (PA) by wearable sensors in a real-life unsupervised context. They generally do not characterize the social context, and nearby persons can have a modulating effect on the performed PA. The present study aims to apply an existing method for bimanual actigraphy to both components of a marital dyad to verify the level of association between the two PA profiles. Other dyad comparisons complete the overall figure. (2) Methods: Seven-day actigraphic recordings collected from both components of 20 married couples of retired, cohabiting, healthy subjects (age ranging from 58 to 87 years) were considered. (3) Results: PA profiles of a marital dyad are significantly more correlated (coefficient: 0.444) than unrelated couples (0.278). Interestingly, participants' profiles compared with their own recording shifted by 24 h, evidencing an intermediate level of association (0.335). Data from the literature, the high association (0.875) of individual right and left wrist profiles, enforce the analysis. (4) Conclusions: The proposed method, called "social actigraphy", confirmed that the partner has a relevant effect on one's PA profile, thus suggesting involving the partner in programs concerning lifestyle changes and patient rehabilitation. [ABSTRACT FROM AUTHOR]

Exercise-based cardiac rehabilitation is a highly recommended intervention towards the advancement of the cardiovascular disease (CVD) patients' health profile; though with low participation rates. Although home-based cardiac rehabilitation (HBCR) with the use of wearable sensors is proposed as a feasible alternative rehabilitation model, further investigation is needed. This systematic review and meta-analysis aimed to evaluate the effectiveness of wearable sensors-assisted HBCR in improving the CVD patients' cardiorespiratory fitness (CRF) and health profile. PubMed, Scopus, Cinahl, Cochrane Library, and PsycINFO were searched from 2010 to January 2022, using relevant keywords. A total of 14 randomized controlled trials, written in English, comparing wearable sensors-assisted HBCR to center-based cardiac rehabilitation (CBCR) or usual care (UC), were included. Wearable sensors-assisted HBCR significantly improved CRF when compared to CBCR (Hedges' g = 0.22, 95% CI 0.06, 0.39; I2 = 0%; p = 0.01), whilst comparison of HBCR to UC revealed a nonsignificant effect (Hedges' g = 0.87, 95% CI −0.87, 1.85; I2 = 96.41%; p = 0.08). Effects on physical activity, quality of life, depression levels, modification of cardiovascular risk factors/laboratory parameters, and adherence were synthesized narratively. No significant differences were noted. Technology tools are growing fast in the cardiac rehabilitation era and promote exercise-based interventions into a more home-based setting. Wearable-assisted HBCR presents the potential to act as an adjunct or an alternative to CBCR. [ABSTRACT FROM AUTHOR]

Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodological standards and clinical validation remains a major impediment to step count being accepted as a valid clinical endpoint. Previous works have mainly focused on device-specific step-count algorithms and often employ sensor modalities that may not be widely available. This may limit step-count suitability in clinical scenarios. In this paper, we trained neural network models on publicly available data and tested on an independent cohort using two approaches: generalization and personalization. Specifically, we trained neural networks on accelerometer signals from one device and either directly applied them or adapted them individually to accelerometer data obtained from a separate subject cohort wearing multiple distinct devices. The best models exhibited highly accurate step-count estimates for both the generalization (96–99%) and personalization (98–99%) approaches. The results demonstrate that it is possible to develop device-agnostic, accelerometer-only algorithms that provide highly accurate step counts, positioning step count as a reliable mobility endpoint and a strong candidate for clinical validation. [ABSTRACT FROM AUTHOR]

Simple Summary: This systematic review and meta-analysis aimed to investigate the association between objectively measured physical activity and cardiometabolic health in adult women. After searching four databases (PubMed, Web of Science, Scopus, and the Cochrane library), 23 eligible studies were included (n = 2105). An accelerometer or pedometer determined physical activities (daily steps, total physical activity, minutes engaged in physical activities at different intensities, and the number of physical activity bouts) and cardiometabolic health indicators (blood pressure, lipids, carbohydrate metabolism, insulin, inflammation markers, and metabolic syndrome) were examined in adult women. Overall, it is compelling that being more physically active has favorable effects on the metabolic syndrome. However, the majority of individual cardiometabolic biomarkers hardly improved following increases in physical activity, with the exception that moderate-intensity physical activity appeared to have a more potent effect on high-density lipoprotein. Although higher-intensity physical activity is more effective for women, it is most important to increase the total volume of physical activity. Meanwhile, strategies to improve body composition and cardiorespiratory fitness are required, since these play an important role in mediating the association between physical activity and cardiometabolic health in women. The purpose of this systematic review and meta-analysis was to qualitatively synthesize and quantitatively assess the evidence of the relationship between objectively determined volumes of physical activity (PA) and cardiometabolic health in women. Four databases (PubMed, Web of Science, Scopus, and the Cochrane library) were searched and, finally, 24 eligible studies were included, with a total of 2105 women from eight countries. A correlational meta-analysis shows that moderate-to-vigorous intensity physical activity (MVPA) was favorably associated with high-density lipoprotein (r = 0.16; 95% CI: 0.06, 0.25; p = 0.002); however, there was limited evidence for the effects of most of the other cardiometabolic biomarkers recorded from steps, total physical activity, light- and moderate-intensity physical activity and MVPA. It is most compelling and consistent that being more physically active is beneficial to the metabolic syndrome. Overall, PA levels are low in adult women, suggesting that increasing the total volume of PA is more important than emphasizing the intensity and duration of PA. The findings also indicate that, according to the confounding effects of body composition and cardiorespiratory fitness, meeting the minimal level of 150 min of moderate-intensity physical activity recommended is not enough to obtain a significant improvement in cardiometabolic indicators. Nonetheless, the high heterogeneity between studies inhibits robust conclusions. [ABSTRACT FROM AUTHOR]

Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932   ±   0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models' performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function. [ABSTRACT FROM AUTHOR]

Exercise capacity and physical activity are different concepts: the former refers to what an individual is capable of performing, while the latter refers to what the individual does in daily life. Low levels of physical activity (PA), which are very common in individuals with COPD, are associated with poor health outcomes, including increased symptoms, a more rapid decline in lung function, increased health care utilization and increased mortality risk. Because of these pervasive negative outcomes, attempts have been made to increase physical activity in individuals with COPD, hoping that success in this area will mitigate the negative effects of inactivity. Based on its ability to increase exercise capacity and reduce dyspnea in COPD and other chronic respiratory diseases, pulmonary rehabilitation (PR) would be expected also increase physical activity in these patients. However, accessibility to pulmonary rehabilitation programs is problematic in some areas, and studies testing its effectiveness in this outcome area have had inconsistent results. Using telehealth interventions using technology to provide medical care conveniently over a distance would have the benefit of reaching a larger proportion of individuals with COPD. A systematic review of clinical trials testing telehealth to promote physical activity had mixed results and low-certainty evidence, resulting in the inability to recommend any single type of intervention. Thus, using telehealth interventions to promote physical activity for individuals with chronic respiratory diseases, while promising, remains an area where future investigations are needed to identify its optimal modalities and clarify its benefits. [ABSTRACT FROM AUTHOR]

The present study aimed to clarify the difference in physical activity (PA) due to sarcopenia in community-dwelling older adults with long-term care insurance (LTCI). This was a cross-sectional study that investigated data of 97 consecutive community-dwelling older Japanese adults with LTCI who underwent rehabilitation at one day care center in Japan from November 2018 to May 2019. Sarcopenia was determined according to criteria of the AsianWorking Group for Sarcopenia. Unpaired t-test, Mann-Whitney U test, chi-square test and analysis of covariance were used to compare participant characteristics and clinical parameters between the older adults with and without sarcopenia. A receiver operating characteristic (ROC) curve was constructed to determine the cut-off value of PA for sarcopenia. The sarcopenia group (n = 20) had significantly lower body mass index (BMI), skeletal muscle mass index, gait speed, and PA than those in the no sarcopenia group (n = 28) (p < 0.05). After adjustment for BMI and sex, the sarcopenia group showed significantly lower PA than the no sarcopenia group. Findings showed that the cut-off value of PA indicating sarcopenia by ROC curve analysis was 1494.4 steps/day (p < 0.05); this value may aid in identifying sarcopenia in older adults with LTCI. [ABSTRACT FROM AUTHOR]

MotionSense HRV is a wrist-worn accelerometery-based sensor that is paired with a smartphone and is thus capable of measuring the intensity, duration, and frequency of physical activity (PA). However, little information is available on the validity of the MotionSense HRV. Therefore, the purpose of this study was to assess the concurrent validity of the MotionSense HRV in estimating sedentary behavior (SED) and PA. A total of 20 healthy adults (age: 32.5 ± 15.1 years) wore the MotionSense HRV and ActiGraph GT9X accelerometer (GT9X) on their non-dominant wrist for seven consecutive days during free-living conditions. Raw acceleration data from the devices were summarized into average time (min/day) spent in SED and moderate-to-vigorous PA (MVPA). Additionally, using the Cosemed K5 indirect calorimetry system (K5) as a criterion measure, the validity of the MotionSense HRV was examined in simulated free-living conditions. Pearson correlations, mean absolute percent errors (MAPE), Bland–Altman (BA) plots, and equivalence tests were used to examine the validity of the MotionSense HRV against criterion measures. The correlations between the MotionSense HRV and GT9X were high and the MAPE were low for both the SED (r = 0.99, MAPE = 2.4%) and MVPA (r = 0.97, MAPE = 9.1%) estimates under free-living conditions. BA plots illustrated that there was no systematic bias between the MotionSense HRV and criterion measures. The estimates of SED and MVPA from the MotionSense HRV were significantly equivalent to those from the GT9X; the equivalence zones were set at 16.5% for SED and 29% for MVPA. The estimates of SED and PA from the MotionSense HRV were less comparable when compared with those from the K5. The MotionSense HRV yielded comparable estimates for SED and PA when compared with the GT9X accelerometer under free-living conditions. We confirmed the promising application of the MotionSense HRV for monitoring PA patterns for practical and research purposes. [ABSTRACT FROM AUTHOR]

Long-term assessment of ambulatory behavior and joint motion are valuable tools for the evaluation of therapy effectiveness in patients with neuromuscular disorders and gait abnormalities. Even though there are several tools available to quantify ambulatory behavior in a home environment, reliable measurement of joint motion is still limited to laboratory tests. The aim of this study was to develop and evaluate a novel inertial sensor system for ambulatory behavior and joint motion measurement in the everyday environment. An algorithm for behavior classification, step detection, and knee angle calculation was developed. The validation protocol consisted of simulated daily activities in a laboratory environment. The tests were performed with ten healthy subjects and eleven patients with multiple sclerosis. Activity classification showed comparable performance to commercially available activPAL sensors. Step detection with our sensor system was more accurate. The calculated flexion-extension angle of the knee joint showed a root mean square error of less than 5° compared with results obtained using an electro-mechanical goniometer. This new system combines ambulatory behavior assessment and knee angle measurement for long-term measurement periods in a home environment. The wearable sensor system demonstrated high validity for behavior classification and knee joint angle measurement in a laboratory setting. [ABSTRACT FROM AUTHOR]

Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the development of a clustering utilising the whole activity profile. This was achieved using the robust clustering technique of k-medoids applied to an extensive data set of over 90,000 activity profiles, collected as part of the UK Biobank study. We identified nine distinct activity profiles in these data, which captured both the pattern of activity throughout a week and the intensity of the activity: "Active 9 to 5", "Active", "Morning Movers", "Get up and Active", "Live for the Weekend", "Moderates", "Leisurely 9 to 5", "Sedate" and "Inactive". These patterns are differentiated by sociodemographic, socioeconomic, and health and circadian rhythm data collected by UK Biobank. The utility of these findings are that they sit alongside existing summary measures of physical activity to provide a way to typify distinct activity patterns that may help to explain other health and morbidity outcomes, e.g., BMI or COVID-19. This research will be returned to the UK Biobank for other researchers to use. [ABSTRACT FROM AUTHOR]

Frequent spontaneous facial self-touches, predominantly during outbreaks, have the theoretical potential to be a mechanism of contracting and transmitting diseases. Despite the recent advent of vaccines, behavioral approaches remain an integral part of reducing the spread of COVID-19 and other respiratory illnesses. The aim of this study was to utilize the functionality and the spread of smartwatches to develop a smartwatch application to identify motion signatures that are mapped accurately to face touching. Participants (n = 10, five women, aged 20–83) performed 10 physical activities classified into face touching (FT) and non-face touching (NFT) categories in a standardized laboratory setting. We developed a smartwatch application on Samsung Galaxy Watch to collect raw accelerometer data from participants. Data features were extracted from consecutive non-overlapping windows varying from 2 to 16 s. We examined the performance of state-of-the-art machine learning methods on face-touching movement recognition (FT vs. NFT) and individual activity recognition (IAR): logistic regression, support vector machine, decision trees, and random forest. While all machine learning models were accurate in recognizing FT categories, logistic regression achieved the best performance across all metrics (accuracy: 0.93 ± 0.08, recall: 0.89 ± 0.16, precision: 0.93 ± 0.08, F1-score: 0.90 ± 0.11, AUC: 0.95 ± 0.07) at the window size of 5 s. IAR models resulted in lower performance, where the random forest classifier achieved the best performance across all metrics (accuracy: 0.70 ± 0.14, recall: 0.70 ± 0.14, precision: 0.70 ± 0.16, F1-score: 0.67 ± 0.15) at the window size of 9 s. In conclusion, wearable devices, powered by machine learning, are effective in detecting facial touches. This is highly significant during respiratory infection outbreaks as it has the potential to limit face touching as a transmission vector. [ABSTRACT FROM AUTHOR]

Accurate measurements of physical activity are important for the diagnosis of the exacerbation of chronic diseases. Accelerometers have been widely employed in clinical research for measuring activity intensity and investigating the association between physical activity and adverse health conditions. However, the ability of accelerometers in assessing physical activity intensity such as walking speed has been constrained by the inter-individual variability in sensor output and by the necessity of developing unobtrusive low-power monitoring systems. This paper will present a study aimed at investigating the accuracy of a wearable measuring system of near-body air flow to determine ambulatory speed in the field. [ABSTRACT FROM AUTHOR]

Characteristics of physical activity are indicative of one's mobility level, latent chronic diseases and aging process. Accelerometers have been widely accepted as useful and practical sensors for wearable devices to measure and assess physical activity. This paper reviews the development of wearable accelerometry-based motion detectors. The principle of accelerometry measurement, sensor properties and sensor placements are first introduced. Various research using accelerometry-based wearable motion detectors for physical activity monitoring and assessment, including posture and movement classification, estimation of energy expenditure, fall detection and balance control evaluation, are also reviewed. Finally this paper reviews and compares existing commercial products to provide a comprehensive outlook of current development status and possible emerging technologies. [ABSTRACT FROM AUTHOR]

The health benefits of physical activity have been widely recognized, yet there is limited information on associations between accelerometer-related parameters and established patient-reported health status. This study investigated the association between the waist-worn accelerometer measurements, cardiopulmonary exercise testing (CPX), and results of the Kansas City Cardiomyopathy Questionnaire (KCCQ) in heart failure (HF) patients hospitalized for acute decompensation. A total of 31 patients were enrolled and wore a validated three-axis accelerometer for 2 weeks and completed the short version of the KCCQ after removing the device. Daily step counts, exercise time (metabolic equivalents × hours), and %sedentary time (sedentary time/device-equipped time) were measured. Among the measured parameters, the best correlation was observed between %sedentary time and the KCCQ overall and clinical summary scores (r = −0.65 and −0.65, each p < 0.001). All of the individual domains of the KCCQ (physical limitation, symptom frequency, and quality of life), with the exception of the social limitation domain, showed moderate correlations with %sedentary time. Finally, oxygen consumption assessed by CPX demonstrated only weak associations with the accelerometer-measured parameters. An accelerometer could complement the KCCQ results in accurately assessing the physical activity in HF patients immediately after hospitalization, albeit its correlation with CPX was at most moderate. [ABSTRACT FROM AUTHOR]

The assessment of the functional performance status of persons with multiple sclerosis (PwMS) is a useful tool to optimize healthcare. This concept does not seem to be extensively explored in this population. This study aimed to determine the level of activity of PwMS during weekdays and weekends, and to establish associations between clinical parameters. Forty-one PwMS and 16 healthy persons participated in this study. Their physical activity in real-life conditions was assessed with an accelerometer. For the clinical evaluations, the quality of life, fatigue, gait, and balance were assessed. The level of activity between PwMS for weekdays, weekends, Saturdays, and Sundays was significantly reduced compared with the reference group (p = 0.001–0.00001, d = 0.95–1.76). PwMS had a constant level of activity throughout the week, whereas the reference group increased its level of activity on Saturdays (p = 0.04, d = 0.69). The level of activity was correlated in descending order with multiple sclerosis disability, body mass index, gait velocity, six-minute walk test, and timed up and go test. This study showed that PwMS had a stable level of activity throughout the week, contrary to healthy persons. It could be necessary to develop programs to facilitate physical activity and participation during the weekdays, but especially during weekends. [ABSTRACT FROM AUTHOR]

Understanding periods of the year associated with higher risk for falling and less physical activity may guide fall prevention and activity promotion for older adults. We examined the relationship between weather and seasons on falls and physical activity in a three-year cohort of older adults with glaucoma. Participants recorded falls information via monthly calendars and participated in four one-week accelerometer trials (baseline and per study year). Across 240 participants, there were 406 falls recorded over 7569 person-months, of which 163 were injurious (40%). In separate multivariable regression models incorporating generalized estimating equations, temperature, precipitation, and seasons were not significantly associated with the odds of falling, average daily steps, or average daily active minutes. However, every 10 °C increase in average daily temperature was associated with 24% higher odds of a fall being injurious, as opposed to non-injurious (p = 0.04). The odds of an injurious fall occurring outdoors, as opposed to indoors, were greater with higher average temperatures (OR per 10 °C = 1.46, p = 0.03) and with the summer season (OR = 2.69 vs. winter, p = 0.03). Falls and physical activity should be understood as year-round issues for older adults, although the likelihood of injury and the location of fall-related injuries may change with warmer season and temperatures. [ABSTRACT FROM AUTHOR]

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20–50 years), middle-aged (50–70 years], and older adults (70–89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20–89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost's models were high for sedentary (0.955–0.973), locomotion (0.942–0.964) and lifestyle (0.913–0.949) activity types with no apparent difference across age groups. Low (0.919–0.947), light (0.813–0.828) and moderate (0.846–0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835–1.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults. [ABSTRACT FROM AUTHOR]

In the midst of the COVID-19 pandemic, Remote Patient Monitoring technologies are highly important for clinicians and researchers. These connected-health technologies enable monitoring of patients and facilitate remote clinical trial research while reducing the potential for the spread of the novel coronavirus. There is a growing requirement for monitoring of the full 24 h spectrum of behaviours with a single research-grade sensor. This research describes a free-living and supervised protocol comparison study of the Verisense inertial measurement unit to assess physical activity and sleep parameters and compares it with the Actiwatch 2 actigraph. Fifteen adults (11 males, 23.4 ± 3.4 years and 4 females, 29 ± 12.6 years) wore both monitors for 2 consecutive days and nights in the free-living study while twelve adults (11 males, 23.4 ± 3.4 years and 1 female, 22 ± 0 years) wore both monitors for the duration of a gym-based supervised protocol study. Agreement of physical activity epoch-by-epoch data with activity classification of sedentary, light and moderate-to-vigorous activity and sleep metrics were evaluated using Spearman's rank-order correlation coefficients and Bland–Altman plots. For all activity, Verisense showed high agreement for both free-living and supervised protocol of r = 0.85 and r = 0.78, respectively. For physical activity classification, Verisense showed high agreement of sedentary activity of r = 0.72 for free-living but low agreement of r = 0.36 for supervised protocol; low agreement of light activity of r = 0.42 for free-living and negligible agreement of r = −0.04 for supervised protocol; and moderate agreement of moderate-to-vigorous activity of r = 0.52 for free-living with low agreement of r = 0.49 for supervised protocol. For sleep metrics, Verisense showed moderate agreement for sleep time and total sleep time of r = 0.66 and 0.54, respectively, but demonstrated high agreement for determination of wake time of r = 0.83. Overall, our results showed moderate-high agreement of Verisense with Actiwatch 2 for assessing epoch-by-epoch physical activity and sleep, but a lack of agreement for activity classifications. Future validation work of Verisense for activity cut-point potentially holds promise for 24 h continuous remote patient monitoring. [ABSTRACT FROM AUTHOR]

Understanding the fostering factors of physical activity (PA) and sedentary behavior (SB) in post-stroke chronic survivors is critical to address preventive and health interventions. This cross-sectional study aimed to analyze the association of barriers to PA, fear of falling and severity of fatigue encountered by stroke chronic survivors with device-measured PA and SB. Ambulatory community-dwelling post-stroke subjects (≥six months from stroke onset) were evaluated and answered the Barriers to Physical Activity after Stroke Scale (BAPAS), Short Falls Efficacy Scale-International (Short FES-I) and Fatigue Severity Scale (FSS). SB and PA were measured with an Actigraph GT3X+ accelerometer for ≥seven consecutive days. Stepwise multiple linear regression analysis was employed to identify factors associated with PA and SB. Fifty-seven participants (58.2 ± 11.1 years, 37 men) met the accelerometer wear–time criteria (three days, ≥eight h/day). The physical BAPAS score explained 28.7% of the variance of the prolonged sedentary time (β = 0.547; p < 0.001). Additionally, the walking speed (β = 0.452) together with physical BAPAS (β = −0.319) explained 37.9% of the moderate-to-vigorous PA time (p < 0.001). In chronic post-stroke survivors, not only the walking speed but, also, the perceived physical barriers to PA are accounted for the SB and PA. Interventions to reverse SB and to involve subjects post-stroke in higher levels of PA should consider these factors. [ABSTRACT FROM AUTHOR]

Simple Summary: Altered activity in a dog can be an early indicator of health and welfare concerns. Accelerometers, a type of activity monitor, are being used more frequently in dogs and may be a simple way for owners and veterinarians to monitor a dog's changing health and welfare needs. However, there are few peer-reviewed studies evaluating the accuracy of these devices. Therefore, this study evaluated the accuracy of the FitBark 2 accelerometer (FitBark) by comparing activity data recorded using the FitBark to dog physical activity recorded using video analysis of dog step count. Dog step count and FitBark activity were highly correlated when the dogs were exploring a room off-leash and when they were interacting with their owner. However, when the dogs were being walked on a leash, low correlations between step count and FitBark activity were observed. In conclusion, the FitBark is a valid tool for tracking off-leash activity in dogs; however, more work should be done to identify the best method of tracking activity in on-leash situations. Accelerometers track changes in physical activity which can indicate health and welfare concerns in dogs. The FitBark 2 (FitBark) is an accelerometer for use with dogs; however, no studies have externally validated this tool. The objective of this study was to evaluate FitBark criterion validity by correlating FitBark activity data to dog step count. Dogs (n = 26) were fitted with a collar-mounted FitBark and individually recorded for 30 min using a three-phase approach: (1) off-leash room explore; (2) human–dog interaction; and (3) on-leash walk. Video analysis was used to count the number of times the front right paw touched the ground (step count). Dog step count and FitBark activity were moderately correlated across all phases (r = 0.65, p < 0.001). High correlations between step count and FitBark activity were observed during phases 1 (r = 0.795, p < 0.001) and 2 (r = 0.758, p < 0.001), and a low correlation was observed during phase 3 (r = 0.498, p < 0.001). In conclusion, the FitBark is a valid tool for tracking physical activity in off-leash dogs; however, more work should be done to identify the best method of tracking on-leash activity. [ABSTRACT FROM AUTHOR]

The benefits of daily-living physical activity are clear. Nonetheless, the relationship between physical activity levels and motor subtypes of Parkinson's disease (PD), i.e., tremor dominant (TD) and postural instability gait difficulty (PIGD), have not been well-studied. It is also unclear if patient perspectives and motor symptom severity are related to objective, sensor-based assessment of daily-living activity in those subtypes. To address these questions, total daily-living physical activity was quantified in 73 patients with PD and 29 healthy controls using a 3D-accelerometer worn on the lower back for at least three days. We found that individuals with the PIGD subtype were significantly less active than healthy older adults (p = 0.007), unlike individuals with the TD subtype. Among the PIGD subtype, higher daily physical activity was negatively associated with more severe ON bradykinesia (rS = -0.499, p = 0.002), motor symptoms (higher ON MDS-UPDRS (Unified Parkinson's Disease Rating Scale motor examination)-III scores), gait difficulties (rS = -0.502, p = 0.002), motor complications (rS = 0.466, p = 0.004), and balance (rS = 0.519, p = 0.001). In contrast, among the TD subtype, disease-related characteristics were not related to daily-living physical activity. Intriguingly, physical activity was not related to self-report of ADL difficulties (scores of the MDS-UPDRS Parts I or II) in both motor subtypes. These findings highlight the importance of objective daily-living physical activity monitoring and suggest that self-report does not necessarily reflect objective physical activity levels. Furthermore, the results point to important differences in factors related to physical activity in PD motor subtypes, setting the stage for personalized treatment programs. [ABSTRACT FROM AUTHOR]

Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied. [ABSTRACT FROM AUTHOR]

Strong associations exist between quality of life and physical activity for those living in aged residential care (ARC). Suitable and reliable tools are required to quantify physical activity for descriptive and evaluative purposes. We calculated the number of days required for reliable walking outcomes indicative of physical activity in an ARC population using a trunk-worn device. ARC participants (n = 257) wore the device for up to 7 days. Reasons for data loss were also recorded. The volume, pattern, and variability of walking was calculated. For 197 participants who wore the device for at least 3 days, linear mixed models determined the impact of week structure and number of days required to achieve reliable outcomes, collectively and then stratified by care level. The average days recorded by the wearable device was 5.2 days. Day of the week did not impact walking activity. Depending on the outcome and level of care, 2–5 days was sufficient for reliable estimates. This study provides informative evidence for future studies aiming to use a wearable device located on the trunk to quantify physical activity walking out in the ARC population. [ABSTRACT FROM AUTHOR]

(1) Physical activity is one of the most influencing factors in people' quality of life. Likewise, the costaleros of the Holy Week of Andalusia (Spain) carry out an important effort with high intensity during an extended time without any preparation. This study was the aim of knowing the intensity of the physical activity practiced by the costaleros in relation to their quality of life. (2) A transversal study was carried out with 1057 costaleros in Andalusia (Spain), where 930 were male and 127 female, between the ages of 18–61 years old (31.26 ± 7.60). For this purpose, descriptive, inferential, and correlative analyses were developed. Accelerometers (ActiGraph) were used during the procession to know the intensity of physical activity and the SF-36 test to know the self-perceived state of health and quality of life. (3) The intensity of physical activity practiced by costaleros is moderate, and it is related with their quality of life. In addition, positive associations are found between general health and physical activity. (4) Participants' quality of life is associated with physical activity and freedom from injury. In addition, the measurement by accelerometry provides real data on the intensity of the effort made. [ABSTRACT FROM AUTHOR]

Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hip and non-dominant wrist. Video-based direct observation was used to categorize the children's movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%–86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children's movement behaviors under real-world conditions. [ABSTRACT FROM AUTHOR]

Detailed associations between physical activity (PA) and body composition in preschoolers remain unclear. The aim of this study was to assess body composition among preschool children differentiated according to their levels of PA and to assess whether meeting the current PA recommendations is associated with a lower risk of obesity, determined by body mass index (BMI) and body fat percentage (BFP). Free-living PA was measured using accelerometers for 7 days in children aged 5 to 6 years. Bioelectrical impedance analysis was used to estimate body composition. Significant differences in content of BFP, fat-free mass (FFM), and total body water (TBW) were found between boys meeting and not meeting moderate-to-vigorous PA (MVPA) recommendations. Meeting the MVPA recommendation was associated with a twofold lower risk of obesity determined by BFP in boys but not in girls. In contrast, the total number of recommended steps per day was not related to adiposity in boys or girls. No statistically significant differences were observed in body composition indices and quintiles of MVPA. Boys in the 3rd–5th quintiles of steps per day presented significantly lower BFP and higher muscle mass and TBW than their peers in quintile 1. However, different associations were observed between body composition indices and quintiles of PA. [ABSTRACT FROM AUTHOR]

Asthma is the commonest respiratory disease and one of unceasingly increasing prevalence and burden. As such, asthma has attracted a major share or scientific interest and clinical attention. With the various clinical and pathophysiological aspects of asthma having been extensively investigated, the important association between asthma and physical activity remains underappreciated and insufficiently explored. Asthma impacts adversely on physical activity. Likewise, poor physical activity may lead to worse asthma outcomes. This concise clinical review presents the current recommendations for physical activity, discusses the available evidence on physical activity in asthma, and examines the causes of low physical activity in adult asthmatic patients. It also reviews the effect of daily physical activity and exercise training on the pathology and clinical outcomes of asthma. Finally, it summarizes the evidence on interventions targeting physical activity in asthma. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the relationships between daily step counts and physical fitness in preschool children. Methods: Preschoolers' step counts were assessed by ActiGraph accelerometers consecutively for seven days. Physical fitness was assessed by a 20 m shuttle run test (cardiorespiratory fitness), the handgrip and standing long jump tests (musculoskeletal fitness), and the 2 × 10 m shuttle run test (speed/agility). A composite score was created from the mean of the standardized values of all physical fitness tests. Results: A total of 301 preschoolers (134 girls, mean age 57.40 ± 5.47 months; 167 boys, mean age 58.10 ± 5.34 months) were included in the final analysis. Compared with the lowest tertile, boys and girls in the highest tertile of step counts achieved high physical fitness with odds ratio (OR) being 5.39 (95% CI = 1.65–17.59) and 4.42 (95% CI = 1.30–14.99), respectively, after adjusting for confounders. Meanwhile, a relationship was observed for each 1000 steps/day increment being associated with 43% (OR = 1.43, 95% CI = 1.10–1.85) and 62% (OR = 1.62, 95% CI = 1.20–2.19) increment for high physical fitness in boys and girls, respectively. In addition, significant non-linear relationship was observed between daily steps and physical fitness in boys, which indicated that accumulated 8000 steps/day was associated with the highest ratio to achieve high physical fitness. Conclusions: Positive relationships between step counts and physical fitness were observed in preschool children, and the relationships were strongest for those who accumulated 8000 steps/day in boys. To confirm the findings in this study, well-designed and large-scale longitudinal studies are needed in the future. [ABSTRACT FROM AUTHOR]

This prospective study investigated feasibility and sensitivity of sensor-based physical activity (PA) measures to monitor changes in PA during geriatric rehabilitation and its relation to clinical parameters at admission. PA was routinely measured at day 2 and day 15 after admission in 647 patients (70.2% women, mean age = 82.0 (SD = 7.19) years) of a German geriatric hospital using a thigh-worn accelerometer. Clinical records were used to include age, Barthel Index, diagnosis, mobility, orientation and cognition. Mean values and 95% confidence intervals (95%-CI) of walking duration, walking bout duration and number of sit-to-stand transfers were calculated to quantify different domains of PA. All observed PA parameters improved during rehabilitation, regardless of age, diagnosis or physical and cognitive function at admission. Walking duration increased by 12.1 (95%-CI: 10.3; 13.8) min, walking bout duration by 2.39 (95%-CI: 1.77; 3.00) s, and number of sit-to-stand transfers by 7 (95%-CI: 5; 8). Floor and ceiling effects were not observed. Walking duration at day 2 as well as day 15 was continuously associated with Barthel Index and statistically significant improved for all levels of Barthel Index. In summary, this study showed that sensor-based PA monitoring is feasible to assess the individual progress in geriatric rehabilitation patients. [ABSTRACT FROM AUTHOR]

Few methods for classifying physical activity from accelerometer data have been tested using an independent dataset for cross-validation, and even fewer using multiple independent datasets. The aim of this study was to evaluate whether unsupervised machine learning was a viable approach for the development of a reusable clustering model that was generalisable to independent datasets. We used two labelled adult laboratory datasets to generate a k-means clustering model. To assess its generalised application, we applied the stored clustering model to three independent labelled datasets: two laboratory and one free-living. Based on the development labelled data, the ten clusters were collapsed into four activity categories: sedentary, standing/mixed/slow ambulatory, brisk ambulatory, and running. The percentages of each activity type contained in these categories were 89%, 83%, 78%, and 96%, respectively. In the laboratory independent datasets, the consistency of activity types within the clusters dropped, but remained above 70% for the sedentary clusters, and 85% for the running and ambulatory clusters. Acceleration features were similar within each cluster across samples. The clusters created reflected activity types known to be associated with health and were reasonably robust when applied to diverse independent datasets. This suggests that an unsupervised approach is potentially useful for analysing free-living accelerometer data. [ABSTRACT FROM AUTHOR]

Accelerometer calibration for physical activity (PA) intensity is commonly performed using Metabolic Equivalent of Task (MET) as criterion. However, MET is not an age-equivalent measure of PA intensity, which limits the use of MET-calibrated accelerometers for age-related PA investigations. We investigated calibration using VO2net (VO2gross − VO2stand; mL⋅min−1⋅kg−1) as criterion compared to MET (VO2gross/VO2rest) and the effect on assessment of free-living PA in children, adolescents and adults. Oxygen consumption and hip/thigh accelerometer data were collected during rest, stand and treadmill walk and run. Equivalent speed (Speedeq) was used as indicator of the absolute speed (Speedabs) performed with the same effort in individuals of different body size/age. The results showed that VO2net was higher in younger age-groups for Speedabs, but was similar in the three age-groups for Speedeq. MET was lower in younger age-groups for both Speedabs and Speedeq. The same VO2net-values respective MET-values were applied to all age-groups to develop accelerometer PA intensity cut-points. Free-living moderate-and-vigorous PA was 216, 115, 74 and 71 min/d in children, adolescents, younger and older adults with VO2net-calibration, but 140, 83, 74 and 41 min/d with MET-calibration, respectively. In conclusion, VO2net calibration of accelerometers may provide age-equivalent measures of PA intensity/effort for more accurate age-related investigations of PA in epidemiological research. [ABSTRACT FROM AUTHOR]

Falls are one of the most common problems in the elderly population. Therefore, each year more solutions for automatic fall detection are emerging. This paper proposes a single accelerometer algorithm for wearable devices that works for three different body locations: chest, waist and pocket, without a calibration step being required. This algorithm is able to be fully executed on a wearable device and no external devices are necessary for data processing. Additionally, a study of the accelerometer sampling rate, that allows the algorithm to achieve a better performance, was performed. The algorithm was validated with a continuous dataset with daily living activities and 272 simulated falls. Considering the trade-off between sensitivity and the number of false alarms the most suitable sampling rate found was 50 Hz. The proposed algorithm was able to achieve a trade-off of no false alarms and 89.5% of fall detection rate when wearing the sensor on the user's waist with a medium sensitivity level of the algorithm. In conclusion, this paper presents a reliable solution for automatic fall detection that can be adapted to different usages and conditions, since it can be used in different body locations and its sensitivity can be adapted to different subjects according to their physical activity level. [ABSTRACT FROM AUTHOR]