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Adaptive pacing (AP) is a self-management technique which seeks to balance energy and rest in individuals with chronic health conditions. Adaptive pacing can help people with myalgic encephalomyelitis/chronic fatigue syndrome learn how to manage their energy expenditure thereby reducing their risk of post-exertional malaise (PEM) and other symptoms. Given some symptom similarity, AP also has rehabilitation potential for people experiencing disability from long COVID. The purpose of this study was to explore patient experiences of an AP intervention (the "PaceMe" app) to determine its value for individuals experiencing long COVID. Twenty-five participants each took part in two narrative interviews (at intervention start point and at 3-6 months). Data were analyzed using narrative thematic analysis. Our analysis identified 4 themes relating to key benefits of the PaceMe app: (1) PEM management, (2) Support, (3) Validation, and (4) Control and Agency. By illuminating the critical facts and centring patient voices, these findings contribute a better understanding of the experiences and needs of those with long COVID and highlight the value of a digital health intervention as a vital component of rehabilitation. [ABSTRACT FROM AUTHOR]

Classifying behavior by tracking acceleration has received increased interest lately. Here, we evaluated the performance of three commercial activity trackers in differentiating seven dog behaviors. Adult companion dogs (N = 70) performed still (lying, sitting, standing) and dynamic (walking, sniffing, trotting, playing) tasks, while wearing ActiGraph GT9X Link, Kaunila and FitBark devices placed on the neck collar and ActiGraph GT9X Link placed on the back. Each task was performed for 3 min within a session and repeated in two sessions; the behaviors were confirmed from video recordings. Activity scores of devices were calculated as median values for behavioral differentiation, and as minute-based values for inter-device correlations and cutoff analysis. Measurements of all devices correlated with each other, and median activity scores of all devices − unaffected by dog age, weight or sex − differentiated the still from dynamic behaviors. Dynamic behaviors were also differentiated from each other, with exception of walking vs. sniffing by back-placed ActiGraph GT9X and Kaunila. The definition of cutoffs between behaviors varied from moderate to high accuracy; defined cutoffs for standing and walking were the least accurate. The classification performance of the cutoffs had an accuracy of 80% in all the devices; thus, they performed reasonably well in classifying these behaviors. [ABSTRACT FROM AUTHOR]

Introduction: It is unknown what the benefits of cardio-oncologic rehabilitation programmes on cardiorespiratory fitness and cancer fatigue during anthracycline-based chemotherapies (AC) are with supervised exercise training (ET), compared to PA advice and tracking only. Methods: Patients with breast cancer or lymphoma were recruited from four cancer centres and randomly assigned to three months supervised ET during (EXduringAC) or after (EXpostAC) AC. All patients were counselled on physical activity (PA) and PA was objectively measured with an activity tracker. Primary endpoints were peak VO2, fatigue and quality of life (QoL) after AC (AC-end) and at follow-up (3 months after completion of AC). Secondary endpoints were daily PA and daily steps during AC and follow-up phase, which were compared between days with and without centre-based training sessions. All analyses were performed by linear mixed models. Results: Fifty-seven patients (median [1st and 3rd quartiles] age 47 years [38, 57 years]; 95 ​% women) consented to participate, of whom data from 51 patients were available. Despite the fact that PA on days with centre-based training sessions was 28 (95 ​% confidence interval 24–32) min higher with 4382 (3995–4768) more steps, neither PA nor steps differed between groups in neither AC nor follow-up phase, nor were there between group differences in peak VO2, QoL or fatigue at any time point. Conclusion: In physically active patients with cancer, PA advice and using an activity tracker was equally effective on changes in peak VO2, fatigue, or QoL as enrolling in centre-based ET performed during or after AC. Trial registration: NCT03850171, February 21, 2019.

The continuous growth of the urban electric vehicles market and the rapid progress of the electronics industry create positive prospects towards fostering the development of autonomous robotic solutions for covering critical production sectors. Agriculture can be seen as such, as its digital transformation is a promising necessity for protecting the environment, and for tackling the degradation of natural resources and increasing nutritional needs of the population on Earth. Many studies focus on the potential of agricultural robotic vehicles to perform operations of increased intelligence. In parallel, the study of the activity footprint of these vehicles can be the basis for supervising, detecting the malfunctions, scaling up, modeling, or optimizing the related operations. In this regard, this work, employing a prototype lightweight autonomous electric cargo vehicle, outlines a simple and cost-effective mechanism for a detailed robot's power consumption logging. This process is conducted at a fine time granularity, allowing for detailed tracking. The study also discusses the robot's energy performance across various typical agricultural field operation scenarios. In addition, a comparative analysis has been conducted to evaluate the performance of two different types of batteries for powering the robot for all the operation scenarios. Even non-expert users can conduct the field operation experiments, while directions are provided for the potential use of the data being collected. Given the linear relationship between the size and the consumption of electric robotic vehicles, the energy performance of the prototype agricultural cargo robot can serve as a basis for various studies in the area. [ABSTRACT FROM AUTHOR]

Background: Geriatric depression and anxiety have been identified as mood disorders commonly associated with the onset of dementia. Currently, the diagnosis of geriatric depression and anxiety relies on self‐reported assessments for primary screening purposes, which is uncomfortable for older adults and can be prone to misreporting. When a more precise diagnosis is needed, additional methods such as in‐depth interviews or functional magnetic resonance imaging are used. However, these methods can not only be time‐consuming and costly but also require systematic and cost‐effective approaches. Objective: The main objective of this study was to investigate the feasibility of training an end‐to‐end deep learning (DL) model by directly inputting time‐series activity tracking and sleep data obtained from consumer‐grade wrist‐worn activity trackers to identify comorbid depression and anxiety. Methods: To enhance accuracy, the input of the DL model consisted of step counts and sleep stages as time series data, along with minimal depression and anxiety assessment scores as non‐time‐series data. The basic structure of the DL model was designed to process mixed‐input data and perform multi‐label‐based classification for depression and anxiety. Various DL models, including the convolutional neural network (CNN) and long short‐term memory (LSTM), were applied to process the time‐series data, and model selection was conducted by comparing the performances of the hyperparameters. Results: This study achieved significant results in the multi‐label classification of depression and anxiety, with a Hamming loss score of 0.0946 in the Residual Network (ResNet), by applying a mixed‐input DL model based on activity tracking data. The comparison of hyper‐parameter performance and the development of various DL models, such as CNN, LSTM, and ResNet contributed to the optimization of time series data processing and achievement of meaningful results. Conclusions: This study can be considered as the first to develop a mixed‐input DL model based on activity tracking data for the multi‐label identification of late‐life depression and anxiety. The findings of the study demonstrate the feasibility and potential of using consumer‐grade wrist‐worn activity trackers in conjunction with DL models to improve the identification of comorbid mental health conditions in older adults. The study also established a multi‐label classification framework for identifying the complex symptoms of depression and anxiety. Key points: Deep learning (DL) models, notably ResNet, significantly enhance depression and anxiety classification using activity tracking data, outperforming traditional models in accuracy.Mixed‐input models that combine activity tracking with minimal assessment data demonstrate superior performance, underscoring the advantages of integrating diverse data types for mental health diagnosis.This study confirms the practicality of employing consumer‐grade wearable devices for the multi‐label classification of geriatric mood disorders, presenting an affordable and effective tool for healthcare [ABSTRACT FROM AUTHOR]

Purpose: Accelerometers are used to objectively measure physical behaviors in free-living environments, typically for seven consecutive days or more. We examined whether participants experience "wear fatigue," a decline in wear time day over day, during typical assessment period acquired in a nationally representative sample of 6- to 80-yr-olds in the United States. Methods: Participants were instructed to wear an ActiGraph GT3X+ on their nondominant wrist continuously for seven consecutive days. Participants with seven complete days of recorded data, regardless of wear status, were included in the analyses (N = 13,649). Wear was scored with the sleep, wake, and nonwear algorithm. Results: Participants averaged 1248 ± 3.6 min·d−1 (mean ± SE) of wear over the assessment, but wear time linearly decreased from day 1 (1295 ± 3.2 min) to day 7 (1170 ± 5.3 min), resulting in a wear fatigue of −18.1 ± 0.7 min·d−1 (β ± SE). Wear fatigue did not differ by sex but varied by age-group—highest in adolescents (−26.8 ± 2.4 min·d−1) and lowest in older adults (−9.3 ± 0.9 min·d−1). Wear was lower in evening (1800–2359 h) and early morning (0000–0559 h) compared with the middle of the day and on weekend days compared with weekdays. We verified similar wear fatigue (−23.5 ± 0.7 min·d−1) in a separate sample (N = 14,631) with hip-worn devices and different wear scoring. Applying minimum wear criteria of ≥10 h·d−1 for ≥4 d reduced wear fatigue to −5.3 and −18.7 min·d−1 for the wrist and hip, respectively. Conclusions: Patterns of wear suggest noncompliance may disproportionately affect estimates of sleep and sedentary behavior, particularly for adolescents. Further study is needed to determine the effect of wear fatigue on longer assessments. [ABSTRACT FROM AUTHOR]

Given the importance of young children's postures and movements to health and development, robust objective measures are required to provide high-quality evidence. This study aimed to systematically review the available evidence for objective measurement of young (0–5 years) children's posture and movement using machine learning and other algorithm methods on accelerometer data. From 1663 papers, a total of 20 papers reporting on 18 studies met the inclusion criteria. Papers were quality-assessed and data extracted and synthesised on sample, postures and movements identified, sensors used, model development, and accuracy. A common limitation of studies was a poor description of their sample data, yet over half scored adequate/good on their overall study design quality assessment. There was great diversity in all aspects examined, with evidence of increasing sophistication in approaches used over time. Model accuracy varied greatly, but for a range of postures and movements, models developed on a reasonable-sized (n > 25) sample were able to achieve an accuracy of >80%. Issues related to model development are discussed and implications for future research outlined. The current evidence suggests the rapidly developing field of machine learning has clear potential to enable the collection of high-quality evidence on the postures and movements of young children. [ABSTRACT FROM AUTHOR]

Human activity tracking using wearable technology is an important research field of study and is employed in many applications such as healthcare, aged care, fitness, and tracking activity. Although this field has been well addressed by researchers in the last decade that most of the research is done with tightly fixed sensors (either to the body or to the tight garment), while in contrast, the majority of our daily clothes are loosely fitting so we must be interested in this fields but the main challenge in tracking/recognizing activities in loose clothing is the higher level of noise generated by the movement of clothing compared fixed sensor. This paper reviews recent research on human activity tracking techniques that use sensors that are attached or embedded in loose-fitted clothes. It is predicted from recent studies that many low-cost, reliable, and socially acceptable wearable devices will be available to aid activity tracking. Survey also predicts an increase in the interest for wearables in loose-fitting clothes due to their wide real-time applications. Most of the reviewed work based heavily on off-shelf microcontroller board and sensor, which are characterized by their moderate performance. Hence most of the reviewed work used machine learning to enhance results classification. [ABSTRACT FROM AUTHOR]

All students have different talents and interests; for educators, this is the potential that must be developed to improve the quality of student learning. To achieve this, an instructional model is essential-one that not only stimulates active student engagement but also encourages them to play a active role in the teaching and learning process. Considering the unique characteristics of each child, particularly in physical education where a psychological approach is paramount, there is a need for a tailored approach to developing physical activity. This research aimed to explore how the application of coordination and activity-tracking models can contribute to the improvement of self-efficacy in elementary school children. This study employed an experimental design with a one-group pre-test-post-test structure. The population comprised 125 sixth-grade students, averaging 10 years of age, drawn from 12 schools in the City of Tasikmalaya. The sampling technique used is non-probability sampling with a purposive sampling approach. This technique has specific considerations, including the limitations of researchers in managing research classes. It makes it easier for researchers to determine the sample, namely students who live not far from the research location. So from a population of 125 people, a piece of 87 students was selected consisting of 57 boys and 30 girls. The results of the One way ANOVA test between the coordination model and activity tracking with self-efficacy show the results of the Sig. Obtained a P value (P-value) of 0.002, thus less than 0.05. So this means there is a difference in the average initial test with the final examination on the results of the primary motor movement test and the SES questionnaire. Based on the research findings above, the coordination and activity tracking model significantly affects children's self-efficacy levels. This condition is because MCAT allows children to continue showing their best abilities in carrying out every movement so that the psychological factors become well-honed indirectly. [ABSTRACT FROM AUTHOR]

The Coaching for Healthy Ageing trial evaluated the impact on physical activity (PA) and falls based on a year-long intervention in which participants aged 60+ receive a home visit, regular health coaching by physiotherapists, and a free activity monitor. This interview study describes the participants' experiences of the intervention and ideas for improvement. The authors sampled purposively for maximum variation in experiences. The data were analyzed thematically by two researchers. Most of the 32 participants reported that the intervention increased PA levels, embedded activities, and generated positivity about PA. They were motivated by quantified PA feedback, self-directed goals, and person-centered coaching. Social connectivity motivated some, but the intervention did not support this well. The intervention structure allowed participants to trial and embed activities. Autonomy and relatedness were emphasized and should be included in future program theory. The authors identified synergistic effects, likely "essential ingredients," and potential areas for improving this and similar interventions. [ABSTRACT FROM AUTHOR]

Abstract Background Chronic low back pain can lead to individual suffering, high medical expenditures, and impaired social well-being. Although the role of physical activity in pain management is well established, the underlying mechanisms of biological and clinical outcomes are unknown. This study aimed to assess the feasibility and acceptability of a pain self-management intervention, Problem-Solving Pain to Enhance Living Well, which employs wearable activity tracking technology and nurse consultations for people with chronic low back pain. Methods This one-arm longitudinal study recruited 40 adults aged 18–60 years with chronic low back pain. Over 12 weeks, participants watched 10 short video modules, wore activity trackers, and participated in nurse consultations every 2 weeks. At baseline and the 12-week follow-up, they completed study questionnaires, quantitative sensory testing, and blood sample collection. Results Forty participants were recruited, and their mean age was 29.8. Thirty-two participants completed the survey questionnaire, quantitative sensory testing, Fitbit activity tracker, and bi-weekly nurse consultation, and 25 completed the evaluation of biological markers. The overall satisfaction with the Problem-Solving Pain to Enhance Living Well video modules, nurse consultations, and Fitbit in pain management was rated as excellent. No adverse events were reported. Between the baseline and 12-week follow-up, there was a significant decrease in pain intensity and interference and an increase in the warm detection threshold at the pain site. Conclusions Despite concerns about the participant burden due to multidimensional assessment and intensive education, the feasibility of the Problem-Solving Pain to Enhance Living Well intervention was favorable. Technology-based self-management interventions can offer personalized strategies by integrating pain phenotypes, genetic markers, and physical activity types affecting pain conditions. Trial registration This pilot study was registered with ClinicalTrials.gov [NCT03637998, August 20, 2018]. The first participant was enrolled on September 21, 2018.

Adults with serious mental illness engage in limited physical activity, which contributes to significant health disparities. This study explored the use of both ecological momentary assessments (EMAs) and activity trackers in adults with serious mental illness to examine the bidirectional relationship between activity and affect with multilevel modeling. Affective states were assessed up to seven times per day using EMA across 4 days. The participants (n = 20) were equipped with a waist-worn accelerometer to measure moderate to vigorous physical activity. The participants had a mean EMA compliance rate of 88.3%, and over 90% of completed EMAs were matched with 30-min windows of accelerometer wear. The participants who reported more positive affect than others had a higher probability of engaging in moderate to vigorous physical activity. Engaging in more moderate to vigorous physical activity than one's usual was associated with more negative affect. This study begins to address the effect of momentary mood on physical activity in a population of adults that is typically difficult to reach. [ABSTRACT FROM AUTHOR]

Alerting investigators with practitioners for patient variability in the mobilization of activity undergone in the identification, recognition, and motor impairments subject to patient treatment enhancement. By adapting recognition models to populations, conventional practices associated with machine learning (ML) can enhance patient activity recognition (AR). Here, the supervised ML classifier is stationary, with the observations fed to the hidden Markov model (HMM). Also, empirically test the efficiency of our model with activities generated by the incomplete vision of ambulatory sessions in spinal cord injury (SCI). Walking, wheeling, lying down, sitting up, standing up, and stair climbing were among the exercises. The maximum classification accuracy using static classifiers alone using within-subject cross-validation was 86.3% (85.5–87.0, 95% confidence). We increased the accuracy to 88.9% by adding an HMM to the classification model (88.2–89.6). Compared to other classifiers, the proposed method outcome of an extra 2.8% showed considerable enhancement for precision classification when employing the classifier under hybridization and stationary characteristics. As a result of this improved activity identification, doctors may be able to choose or fine-tune the best physical or pharmacological therapy to increase patient mobility. Classifiers evaluated and trained in the laboratory utilizing within-subject cross-validation had a 91.6% accuracy rate. Conclusions Despite only a slight increase in activity levels, ambulatory people suffering from SCI enable to grow their ranges of Corticotropin-releasing factor (CRF) throughout the initial stage of inpatient recovery. [ABSTRACT FROM AUTHOR]

A prospective observational cohort of 20 primary total hip arthroplasty (n = 12) and total knee arthroplasty (n = 8) patients (mean age: 63 ± 6 years) was passively monitored with a consumer-level wearable activity sensor before and 6 weeks after surgery. Patients were clustered by minimal change or decreased activity using sensor data. Decreased postoperative activity was associated with greater pain reduction (-5.5 vs -2.0, P = .03). All patients surpassed minimal clinical benefit thresholds of total joint arthroplasty (TJA) (Hip Disability and Osteoarthritis Score Junior 30.5 vs 20.8, P = .23; Knee Injury and Osteoarthritis Outcome Score Junior 23.3 vs 18.2, P = .77) within 6 weeks. Patients who objectively "take it easy" after TJA may experience less pain with no difference in early subjective outcome. Remote, passive analysis of outpatient wearable sensor data may permit real-time detection of early problems after TJA.

Background: Chronic low back pain can lead to individual suffering, high medical expenditures, and impaired social well-being. Although the role of physical activity in pain management is well established, the underlying mechanisms of biological and clinical outcomes are unknown. This study aimed to assess the feasibility and acceptability of a pain self-management intervention, Problem-Solving Pain to Enhance Living Well, which employs wearable activity tracking technology and nurse consultations for people with chronic low back pain. Methods: This one-arm longitudinal study recruited 40 adults aged 18–60 years with chronic low back pain. Over 12 weeks, participants watched 10 short video modules, wore activity trackers, and participated in nurse consultations every 2 weeks. At baseline and the 12-week follow-up, they completed study questionnaires, quantitative sensory testing, and blood sample collection. Results: Forty participants were recruited, and their mean age was 29.8. Thirty-two participants completed the survey questionnaire, quantitative sensory testing, Fitbit activity tracker, and bi-weekly nurse consultation, and 25 completed the evaluation of biological markers. The overall satisfaction with the Problem-Solving Pain to Enhance Living Well video modules, nurse consultations, and Fitbit in pain management was rated as excellent. No adverse events were reported. Between the baseline and 12-week follow-up, there was a significant decrease in pain intensity and interference and an increase in the warm detection threshold at the pain site. Conclusions: Despite concerns about the participant burden due to multidimensional assessment and intensive education, the feasibility of the Problem-Solving Pain to Enhance Living Well intervention was favorable. Technology-based self-management interventions can offer personalized strategies by integrating pain phenotypes, genetic markers, and physical activity types affecting pain conditions. Trial registration: This pilot study was registered with ClinicalTrials.gov [NCT03637998, August 20, 2018]. The first participant was enrolled on September 21, 2018. [ABSTRACT FROM AUTHOR]

Hybrid environments and remote work are shifting how employees in more sedentary positions work during the day and how employers can offer opportunities for employees to be physically active. Additionally, organizations with a higher proportion of employees working from home reported their physical work environment to be less likely to support physical activity by providing amenities such as onsite fitness centers, walking trails, and standing desks.[20] A person's employment industry determines how and when they work. Keywords: physical activity; exercise; workplace wellness; workplace physical activity; remote work; technology; activity tracking; exercise is medicine; community partnerships EN physical activity exercise workplace wellness workplace physical activity remote work technology activity tracking exercise is medicine community partnerships 723 730 8 06/06/23 20230601 NES 230601 Introduction How and where we do our work is changing in the United States across industry, government, and non-profit sectors. These employees may get more of their physical activity at work (ie, occupational physical activity) vs in leisure time, so workplace health promotion programs should prioritize the safety of their physical activity and adjust for other health and well-being priorities like work-life balance. [Extracted from the article]

The paper explores activity tracking as a postdigital phenomenon and provides a detailed account of the embodied pedagogy of its practices. Drawing on a case-based, ethnographically inspired, participatory research project with German university students and following a praxeologic approach of transformative Bildung, an empirically grounded heuristic framework is presented that helps to understand how embodied entanglements of person and wearable develop over time and how dynamics of irritation and routinisation that characterise these developments open up processes of learning or Bildung. We discuss how this contributes to researching vital pedagogical questions about self-tracking, and more broadly about yet uncertain futures of postdigital physical culture and society, such as how we constitute ourselves as embodied subjects and continuously develop relations to ourselves and to the world through entangling with digital technology. [ABSTRACT FROM AUTHOR]

Background: Personal Activity Intelligence (PAI) is a novel heart-rate–based metric used to assess cardiorespiratory fitness and quantify physical activity. Objective: The aim of this study was to examine the feasibility, acceptability, and effectiveness of PAI with patients in a clinic setting. Methods: Patients (n = 25) from 2 clinics underwent 12 weeks of heart-rate–monitored physical activity interfaced with aPAI Health phone app. We used a pre-post design with the Physical Activity Vital Sign and the International Physical Activity Questionnaire. Feasibility, acceptability, and PAI measures were used to evaluate the objectives. Results: Twenty-two patients (88%) completed the study. There were significant improvements in International Physical Activity Questionnaire metabolic equivalent task minutes per week (P =.046) and a decrease in sitting hours (P =.0001). The Physical Activity Vital Sign activity increase in minutes per week was not significant (P =.214). Patients achieved a mean PAI score of 116 ± 81.1 and 100 or greater 71% of the days. Most patients (81%) expressed satisfaction with PAI. Conclusions: Personal Activity Intelligence is feasible, acceptable, and effective when used with patients in a clinic setting. [ABSTRACT FROM AUTHOR]

Transcatheter aortic valve replacement (TAVR) is a well-established, safe and effective therapy for severe symptomatic aortic stenosis (AS). The aim of this study was to objectively quantify improvement of physical activity after TAVR, with consideration of different low-gradient AS subtypes. All patients undergoing TAVR for severe AS were screened. Participants received a wearable activity tracker (Fitbit®) at hospital discharge following TAVR and 6 months thereafter. The difference of median daily steps was defined as surrogate outcome for physical activity. For analysis, patients were grouped into high-gradient (HG) AS (dPmean ≥40 mmHg), classical low-flow low-gradient (LFLG) AS (dPmean <40 mmHg, EF <50%), paradoxical LFLG-AS (dPmean <40 mmHg, EF ≥50%, SVi ≤35 ml/m2) and normal-flow low-gradient (NFLG) AS (dPmean <40 mmHg, EF ≥50%, SVi >35 ml/m2) according to mean transvalvular pressure gradient (dPmean), stroke volume index (SVi) and left-ventricular ejection fraction (LVEF). The analysis is based on 230 patients. The median daily step count was 4409 [IQR 2581–7487] after hospital discharge and 5326 [IQR 3045–8668] 6 months thereafter. Median difference of daily steps was ∆529 [IQR -702-2152]). Patients with HG-AS and paradoxical LFLG-AS showed a significant improvement of daily steps (∆951 [IQR -378-2323], p <0.001 and (∆1392 [IQR -609-4444], p = 0.02, respectively). Patients with classical LFLG-AS showed no statistically relevant improvement of daily steps (∆192 [IQR -687-770], p = 0.79). Patients with NFLG-AS showed a numerical decline in daily steps without statistical significance (∆-300 [IQR -1334-1406], p = 0.67). This first prospective study of this sample size shows significant improvement of physical activity after TAVR with an objective and reproducible method. This was mainly driven by an improvement in patients with HG-AS and paradoxical LFLG-AS. • Patients physical activity improves 6-months after TAVR. • There are differences between different types of aortic stenosis. • Patients with high-gradient aortic stenosis improved significantly after TAVR. • Patients with classical low-flow-low-gradient aortic stenosis did not improve. [ABSTRACT FROM AUTHOR]

The continuous growth of the urban electric vehicles market and the rapid progress of the electronics industry create positive prospects towards fostering the development of autonomous robotic solutions for covering critical production sectors. Agriculture can be seen as such, as its digital transformation is a promising necessity for protecting the environment, and for tackling the degradation of natural resources and increasing nutritional needs of the population on Earth. Many studies focus on the potential of agricultural robotic vehicles to perform operations of increased intelligence. In parallel, the study of the activity footprint of these vehicles can be the basis for supervising, detecting the malfunctions, scaling up, modeling, or optimizing the related operations. In this regard, this work, employing a prototype lightweight autonomous electric cargo vehicle, outlines a simple and cost-effective mechanism for a detailed robot’s power consumption logging. This process is conducted at a fine time granularity, allowing for detailed tracking. The study also discusses the robot’s energy performance across various typical agricultural field operation scenarios. In addition, a comparative analysis has been conducted to evaluate the performance of two different types of batteries for powering the robot for all the operation scenarios. Even non-expert users can conduct the field operation experiments, while directions are provided for the potential use of the data being collected. Given the linear relationship between the size and the consumption of electric robotic vehicles, the energy performance of the prototype agricultural cargo robot can serve as a basis for various studies in the area.

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs on Earth and the degradation of natural resources. Toward this direction, the availability of innovative electronic components and of the accompanying software programs can be exploited to detect malfunctions in typical agricultural equipment, such as water pumps, thereby preventing potential failures and water and economic losses. In this context, this article highlights the steps for adding intelligence to sensors installed on pumps in order to intercept and deliver malfunction alerts, based on cheap in situ microcontrollers, sensors, and radios and easy-to-use software tools. This involves efficient data gathering, neural network model training, generation, optimization, and execution procedures, which are further facilitated by the deployment of an experimental platform for generating diverse disturbances of the water pump operation. The best-performing variant of the malfunction detection model can achieve an accuracy rate of about 93% based on the vibration data. The system being implemented follows the on-device intelligence approach that decentralizes processing and networking tasks, thereby aiming to simplify the installation process and reduce the overall costs. In addition to highlighting the necessary implementation variants and details, a characteristic set of evaluation results is also presented, as well as directions for future exploitation. [ABSTRACT FROM AUTHOR]

Given the importance of young children’s postures and movements to health and development, robust objective measures are required to provide high-quality evidence. This study aimed to systematically review the available evidence for objective measurement of young (0–5 years) children’s posture and movement using machine learning and other algorithm methods on accelerometer data. From 1663 papers, a total of 20 papers reporting on 18 studies met the inclusion criteria. Papers were quality-assessed and data extracted and synthesised on sample, postures and movements identified, sensors used, model development, and accuracy. A common limitation of studies was a poor description of their sample data, yet over half scored adequate/good on their overall study design quality assessment. There was great diversity in all aspects examined, with evidence of increasing sophistication in approaches used over time. Model accuracy varied greatly, but for a range of postures and movements, models developed on a reasonable-sized (n > 25) sample were able to achieve an accuracy of >80%. Issues related to model development are discussed and implications for future research outlined. The current evidence suggests the rapidly developing field of machine learning has clear potential to enable the collection of high-quality evidence on the postures and movements of young children.

Background: Commercially available wrist-mounted exercise monitors may offer objective data on disease and recovery. This study is the first to evaluate the potential of such devices in the assessment of frozen shoulder and the effects of treatment. Methods: Twenty-one patients with isolated, unilateral frozen shoulder wore a wrist-mounted accelerometer (Fitbit Fire II, Fitbit Inc. 2007, California, USA) on each wrist for two separate seven-day periods, one week before and six months after treatment. The monitors produced an activity count for each 24-hour period, accounting for all movements of the upper limb. Three values were calculated for each time period: (1) the mean activity count for each limb, (2) the total activity count for both limbs, and (3) an activity count ratio calculated by dividing the activity of the frozen limb by the unaffected limb. Constant score, American Shoulder and Elbow Surgeons, visual analog scale–pain, and range of movement were recorded before and after treatment. Results: Mean activity counts were significantly lower in the frozen shoulder limb than those in the unaffected limb over the initial seven-day period (6066 vs. 7516; P = .04). The activity count ratio significantly improved after treatment (0.83 vs. 096; p 0.01), whereas the mean total activity count remained similar before and after treatment (14915 vs. 12371; P = .18), demonstrating that activity transferred from the unaffected limb back to the previously frozen limb. Range of movement (P

Hibbert, JE, Klawiter, DP, Schubert, MM, Nessler, JA, and Asakawa, DS. Strength, cardiovascular fitness, and blood lipid measures in law enforcement personnel after a 12-week health promotion program. J Strength Cond Res 36(11): 3105–3112, 2022—Law enforcement personnel often have high rates of cardiovascular disease and injury. Health promotion programs have been found to successfully encourage behavior change among law enforcement personnel, but these programs can often be intensive and expensive. Thus, the purpose of this study was to examine the efficacy of a health promotion program on body composition, metabolic health, muscle strength, and cardiovascular endurance in law enforcement personnel. Active duty officers from a local law enforcement agency were invited to participate in a 12-week health promotion program that included activity tracking and exercise and nutrition education. Eighteen subjects underwent measurements of body composition, V̇ o 2max through treadmill test, knee extensor strength, and blood lipids. An a priori alpha level for significance was set at 0.05, and comparisons were assessed using paired t -tests. Overall, subjects improved blood lipid levels evidenced by movement of mean values toward established healthy ranges. Although 8 subjects improved their V̇ o 2max (range: 1.3–30% change), there was large variability and no statistically significant differences in measures of V̇ o 2max (pre: 38.48 ± 5.86 ml·kg·min−1; post: 39.27 ± 5.26 ml·kg·min−1), body composition (pre: 26.52 ± 8.02% body fat; post: 26.44 ± 7.45% body fat), and strength normalized to body mass (isometric pre: 1.45 ± 0.45; post: 1.08 ± 0.36). Although no significant changes were noted, promising trends in these data suggest that health promotion programs with a modified focus may lead to positive changes in overall health. [ABSTRACT FROM AUTHOR]

Cystic fibrosis (CF) is a genetic disorder that primarily affects the respiratory, digestive, and reproductive systems. In the United States, approximately 32,000 individuals, spanning both children and adults, suffer from CF, and roughly 1,000 new cases are diagnosed annually. The current gold standard for CF diagnosis is the sweat test, yet this method is plagued by issues such as being time-consuming, expensive, challenging to replicate, and lacking treatment monitoring capabilities. In contrast, the emerging field of wearable sweat biosensors has gained significant attention due to their potential for noninvasive health monitoring. Despite this, there remains a conspicuous absence of a wearable sweat biosensor tailored specifically for CF diagnosis and monitoring. Here, this study introduces a flexible wearable sweat biosensor, named CFTrack, designed to address the unique challenges associated with CF. This proposed CFTrack biosensor not only facilitates CF diagnosis but also enables the monitoring of medication treatment effectiveness and tracks therapy activities. In addition, it operates in a self-powered and customized manner, ensuring seamless integration into the daily lives of individuals with CF. Given that sweat tests and fitness routines are the predominant methods for diagnosing and treating cystic fibrosis patients, respectively, the proposed CFTrack biosensor leverages ion concentration in sweat for diagnostic purposes. Additionally, it incorporates a motion-tracking function to monitor physical activity, providing a comprehensive approach to CF management. To evaluate the feasibility of the proposed CFTrack biosensor, a comprehensive evaluation has been performed including numerical simulations, theoretical analyses, and experimental tests. The results demonstrate the efficacy of the proposed CFTrack biosensor in diagnosing and monitoring CF conditions while also showcasing its ability to effectively track the progress of patients undergoing physical therapy. The proposed CFTrack biosensor resolves key issues associated with existing sweat sensors including high energy consumption, intricate fabrication procedures, and the absence of continuous monitoring capabilities. By addressing these challenges, the proposed sweat biosensor aims to revolutionize CF diagnosis and monitoring, offering a more efficient and user-friendly alternative to current methods.

Transparency of Caenorhabditis elegans enables microscopic in vivo imaging of cellular processes, but immobilization is required due to high locomotor activity. Here, anesthetic-like effects of dissociate anesthetic ketamine in adult C. elegans are presented using video recordings and infrared-based automated activity tracking. Ketamine caused a reversible blockade of locomotion at a similar concentration (20-50 mM) at which conventionally used immobilizing agent sodium azide (NaN 3 ) produces paralysis. The levels of immobilization at 20 mM ketamine enabled fluorescent and brightfield imaging. The worms' locomotory activity recovered fully after ketamine exposure and no acute toxicity was observed. However, a marked chemosensation deficiency was noted immediately after 20 mM ketamine exposure. Short-term ketamine treatment did not show signs of SKN-1 (skinhead-1) activation, a marker of the stress response associated with NaN 3. In sum, our results show ketamine's potential as a non-toxic nematode immobilizing agent and rationalize C. elegans as a model organism to understand its pharmacology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Study Design: This is a method development and validation study.Objectives: The purpose of this study was to develop and test a method for step detection using accelerometer data in patients with lumbar spinal stenosis (LSS). There are 2 objectives: (1) to describe a method for step detection from accelerations measured at the wrist, hip, lower back, thigh and ankle; (2) to assess the accuracy of the method during walking with and without walking aids and during nonwalking activities.Summary Of Background Data: Loss of walking ability is one of the main symptoms of LSS, and there is no validated measure to assess walking activity in daily living in patients with LSS.Materials and Methods: Thirty patients with LSS performed a standardized movement protocol that included walking with and without walking aids and performing nonwalking activities while wearing accelerometers on five different wear-sites. After the walking tests, a method was designed for optimal step detection and compared with a gold standard of observed step count.Results: The method for step detection applied to accelerations from the lower back, hip, thigh, and ankle provided an accurate step counts during continuous walking without walking aids. Accuracy diminished at all wear-sites when walking with walking aids, except the ankle. The wrist provided the most inaccurate step count, and the accelerometers on the thigh and ankle were prone to falsely detecting steps during bicycling.Conclusion: The ankle-worn accelerometer provided the most accurate step count, but wrongly registered steps during nonwalking activities. The developed step detection method shows potential as a measure of walking activity why further development and testing under free-living conditions should be performed. [ABSTRACT FROM AUTHOR]

The purpose of this scoping review was to examine publications using accelerometers in children aged 6 months to <6 years and report on current methodologies used for data collection and analyses. We examined device make and model, device placement, sampling frequency, data collection protocol, definition of nonwear time, inclusion criteria, epoch duration, and cut points. Five online databases and three gray literature databases were searched. Studies were included if they were published in English between January 2009 and March 2021. A total of 627 articles were included for descriptive analyses. Of the reviewed articles, 75% used ActiGraph devices. The most common device placement was hip or waist. More than 80% of articles did not report a sampling frequency, and 7-day protocols during only waking hours were the most frequently reported. Fifteen-second epoch durations and the cut points developed by Pate et al. in 2006 were the most common. A total of 203 articles did not report which definition of nonwear time was used; when reported, "20 minutes of consecutive zeros" was the most frequently used. Finally, the most common inclusion criteria were "greater or equal to 10 hr/day for at least 3 days" for studies conducted in free-living environments and "greater than 50% of the school day" for studies conducted in preschool or childcare environments. Results demonstrated a major lack of reporting of methods used to analyze accelerometer data from young children. A list of recommended reporting practices was developed to encourage increased reporting of key methodological details for research in this area. [ABSTRACT FROM AUTHOR]

Aims: Tricuspid regurgitation (TR) is associated with high mortality, morbidity and reduced physical capacity. This study was designed to examine the long‐term impact of transcatheter tricuspid valve intervention (TTVI) on physical activity by using the method of actigraphy. Methods and results: Overall, we prospectively included 128 heart failure patients with severe TR (median age 79 years, 48% female) who were scheduled for TTVI. Patients were equipped with activity tracking devices for 1 week before TTVI, and again at 1–6 months and 1 year after TTVI. We compared continuous physical activity (CPA), defined as the mean number of steps/day with New York Heart Association class, quality of life assessments, and 6‐min walk distance (all p <0.01). TTVI reduced TR to grade ≤2+ in 94% of patients. Median (interquartile range [IQR]) CPA at baseline was 3108 (1350–4959) steps/day, which increased by 31.4% to 3958 (1823–5657) steps/day at 1–6 months and 4080 (2293–6514) steps/day at 1 year after TTVI (p <0.001 for both comparisons). The impact of TTVI was significantly higher in advanced heart failure patients with low baseline activity (baseline CPA <1350 steps/day; 1‐year CPA increase: +121.3%; p <0.001), when compared to moderate activity patients (baseline CPA 1350–4959 steps/day; 1‐year CPA increase: +27.5%; p <0.01) or high activity patients (baseline CPA >4959 steps/day; 1‐year CPA change: +2.6%; p = 0.39). Conclusion: One‐week actigraphy demonstrates durable improvement of physical activity after TTVI. Fragile chronic heart failure patients with very low baseline activity, as determined by actigraphy in this study, significantly benefit from transcatheter intervention and should not be excluded from TTVI. [ABSTRACT FROM AUTHOR]

Whether physical activity (PA) tracking devices are associated with PA motivation in young adults is largely unknown. We compared total PA minutes per week, total minutes walking/week, meeting moderate-to vigorous PA guidelines, and past-year activity tracking across motivation cluster profiles among 799 young adults. Participants with "self-determined" profiles reported the highest total PA minutes/week followed by participants with "low intrinsic," "controlled self-determined," and "high external" profiles. A behavior regulation profile X activity tracking frequency interaction was not significant. Behavior regulation profiles may need to be considered in PA interventions using activity trackers. [ABSTRACT FROM AUTHOR]

This paper presents a model for sound classification in construction that leverages a unique combination of Mel spectrograms and Mel-Frequency Cepstral Coefficient (MFCC) values. This model combines deep neural networks like Convolution Neural Networks (CNN) and Long short-term memory (LSTM) to create CNN-LSTM and MFCCs-LSTM architectures, enabling the extraction of spectral and temporal features from audio data. The audio data, generated from construction activities in a real-time closed environment is used to evaluate the proposed model and resulted in an overall Precision, Recall, and F1-score of 91%, 89%, and 91%, respectively. This performance surpasses other established models, including Deep Neural Networks (DNN), CNN, and Recurrent Neural Networks (RNN), as well as a combination of these models as CNN-DNN, CNN-RNN, and CNN-LSTM. These results underscore the potential of combining Mel spectrograms and MFCC values to provide a more informative representation of sound data, thereby enhancing sound classification in noisy environments. • Recognition of various construction activities using audio data inputs. • Combining MFCC-LSTM numerical features with CNN-LSTM spatial maps for enhanced audio representation. • Evaluating model performance on 790 audio samples from four different construction activities. • Comparison of model's performance against five commonly used deep learning and sequential methods. [ABSTRACT FROM AUTHOR]

Abstract Background Numerous studies have examined the association between safety and primary school-aged children’s forms of active mobility. However, variations in studies’ measurement methods and the elements addressed have contributed to inconsistencies in research outcomes, which may be forming a barrier to advancing researchers’ knowledge about this field. To assess where current research stands, we have synthesised the methodological measures in studies that examined the effects of neighbourhood safety exposure (perceived and measured) on children’s outdoor active mobility behaviour and used this analysis to propose future research directions. Method A systematic search of the literature in six electronic databases was conducted using pre-defined eligibility criteria and was concluded in July 2020. Two reviewers screened the literature abstracts to determine the studies’ inclusion, and two reviewers independently conducted a methodological quality assessment to rate the included studies. Results Twenty-five peer-reviewed studies met the inclusion criteria. Active mobility behaviour and health characteristics were measured objectively in 12 out of the 25 studies and were reported in another 13 studies. Twenty-one studies overlooked spatiotemporal dimensions in their analyses and outputs. Delineations of children’s neighbourhoods varied within 10 studies’ objective measures, and the 15 studies that opted for subjective measures. Safety perceptions obtained in 22 studies were mostly static and primarily collected via parents, and dissimilarities in actual safety measurement methods were present in 6 studies. The identified schematic constraints in studies’ measurement methods assisted in outlining a three-dimensional relationship between ‘what’ (determinants), ‘where’ (spatial) and ‘when’ (time) within a methodological conceptual framework. Conclusions The absence of standardised measurement methods among relevant studies may have led to the current diversity in findings regarding active mobility, spatial (locality) and temporal (time) characteristics, the neighbourhood, and the representation of safety. Ignorance of the existing gaps and heterogeneity in measures may impact the reliability of evidence and poses a limitation when synthesising findings, which could result in serious biases for policymakers. Given the increasing interest in children’s health studies, we suggested alternatives in the design and method of measures that may guide future evidence-based research for policymakers who aim to improve children’s active mobility and safety.

Background: The level of physical activity (PA) of people with spinal cord injury (SCI) has an impact on long-term complications. Currently, PA is mostly assessed by interviews. Wearable activity trackers are promising tools to objectively measure PA under everyday conditions. The only off-the-shelf, wearable activity tracker with specific measures for wheelchair users is the Apple Watch.Objectives: This study analyzes the measurement performance of Apple Watch Series 4 for wheelchair users and compares it with an earlier generation of the device.Methods: Fifteen participants with subacute SCI during their first in-patient phase followed a test course using their wheelchair. The number of wheelchair pushes was counted manually by visual inspection and with the Apple Watch. Difference between the Apple Watch and the rater was analyzed with mean absolute percent error (MAPE) and a Bland-Altman plot. To compare the measurement error of Series 4 and an older generation of the device a t-test was calculated using data for Series 1 from a former study.Results: The average of differences was 12.33 pushes (n = 15), whereas participants pushed the wheelchair 138.4 times on average (range 86-271 pushes). The range of difference and the Bland-Altman plot indicate an overestimation by Apple Watch. MAPE is 9.20% and the t-test, testing for an effect of Series 4 on the percentage of error compared with Series 1, was significant with p < 0.05.Conclusion: Series 4 shows a significant improvement in measurement performance compared with Series 1. Series 4 can be considered as a promising data source to capture the number of wheelchair pushes on even grounds. Future research should analyze the long-term measurement performance during everyday conditions of Series 4. [ABSTRACT FROM AUTHOR]

One impact of the Covid-19 lockdowns was a restriction on people's ability to engage in physical activity in previously routine ways. This paper presents a two-stage mixed-method study exploring how people used technology to stay physically active during this period. We found that activity trackers reminded people to be active, while virtual coaching (i.e., video tutorials and online classes) helped them stay connected. The lockdown increased people's awareness of their activity levels and removed barriers to exercise, for example by giving them greater control over their time. However, it also created new challenges, with lack of time and space, injuries due to sudden changes in activity, and anxiety around lockdown, putting limits on physical activity. We highlight future directions that must be addressed to maximise the benefits of physical activity technologies for people trying to stay active during major life disruptions.

Introduction: By means of adding more sensor technology, modern hearing aids (HAs) strive to become better, more personalized, and self-adaptive devices that can handle environmental changes and cope with the day-to-day fitness of the users. The latest HA technology available in the market already combines sound analysis with motion activity classification based on accelerometers to adjust settings. While there is a lot of research in activity tracking using accelerometers in sports applications and consumer electronics, there is not yet much in hearing research.Objective: This study investigates the feasibility of activity tracking with ear-level accelerometers and how it compares to waist-mounted accelerometers, which is a more common measurement location.Method: The activity classification methods in this study are based on supervised learning. The experimental set up consisted of 21 subjects, equipped with two XSens MTw Awinda at ear-level and one at waist-level, performing nine different activities.Results: The highest accuracy on our experimental data as obtained with the combination of Bagging and Classification tree techniques. The total accuracy over all activities and users was 84% (ear-level), 90% (waist-level), and 91% (ear-level + waist-level). Most prominently, the classes, namely, standing, jogging, laying (on one side), laying (face-down), and walking all have an accuracy of above 90%. Furthermore, estimated ear-level step-detection accuracy was 95% in walking and 90% in jogging.Conclusion: It is demonstrated that several activities can be classified, using ear-level accelerometers, with an accuracy that is on par with waist-level. It is indicated that step-detection accuracy is comparable to a high-performance wrist device. These findings are encouraging for the development of activity applications in hearing healthcare.

Background: Numerous studies have examined the association between safety and primary school-aged children's forms of active mobility. However, variations in studies' measurement methods and the elements addressed have contributed to inconsistencies in research outcomes, which may be forming a barrier to advancing researchers' knowledge about this field. To assess where current research stands, we have synthesised the methodological measures in studies that examined the effects of neighbourhood safety exposure (perceived and measured) on children's outdoor active mobility behaviour and used this analysis to propose future research directions. Method: A systematic search of the literature in six electronic databases was conducted using pre-defined eligibility criteria and was concluded in July 2020. Two reviewers screened the literature abstracts to determine the studies' inclusion, and two reviewers independently conducted a methodological quality assessment to rate the included studies. Results: Twenty-five peer-reviewed studies met the inclusion criteria. Active mobility behaviour and health characteristics were measured objectively in 12 out of the 25 studies and were reported in another 13 studies. Twenty-one studies overlooked spatiotemporal dimensions in their analyses and outputs. Delineations of children's neighbourhoods varied within 10 studies' objective measures, and the 15 studies that opted for subjective measures. Safety perceptions obtained in 22 studies were mostly static and primarily collected via parents, and dissimilarities in actual safety measurement methods were present in 6 studies. The identified schematic constraints in studies' measurement methods assisted in outlining a three-dimensional relationship between 'what' (determinants), 'where' (spatial) and 'when' (time) within a methodological conceptual framework. Conclusions: The absence of standardised measurement methods among relevant studies may have led to the current diversity in findings regarding active mobility, spatial (locality) and temporal (time) characteristics, the neighbourhood, and the representation of safety. Ignorance of the existing gaps and heterogeneity in measures may impact the reliability of evidence and poses a limitation when synthesising findings, which could result in serious biases for policymakers. Given the increasing interest in children's health studies, we suggested alternatives in the design and method of measures that may guide future evidence-based research for policymakers who aim to improve children's active mobility and safety. [ABSTRACT FROM AUTHOR]