Longitudinal changes in device-measured physical activity from childhood to young adulthood: the PANCS follow-up study.

BACKGROUND: There is a lack of longitudinal studies examining changes in device-measured physical activity and sedentary time from childhood to young adulthood. We aimed to assess changes in device-measured physical activity and sedentary time from childhood, through adolescence, into young adulthood in a Norwegian sample of ostensibly healthy men and women. METHODS: A longitudinal cohort of 731 Norwegian boys and girls (49% girls) participated at age 9 years (2005-2006) and 15 years (2011-2012), and 258 of these participated again at age 24 years (2019-2021; including the COVID-19 pandemic period). Physical activity and sedentary time were measured using ActiGraph accelerometers. Linear mixed models were used to analyse changes in physical activity and sedentary time and whether low levels of childhood physical activity track, i.e., persist into young adulthood (nchange=721; ntracking=640). RESULTS: The most prominent change occurred between the ages of 9 to 15 years, with an increase in sedentary time (150 min/day) and less time spent in light (125 min/day), moderate (16 min/day), and vigorous physical activity (8 min/day). Only smaller changes were observed between the ages of 15 and 24 years. Changes in moderate-to-vigorous physical activity from childhood to young adulthood differed between subgroups of sex, tertiles of body mass index at baseline and tertiles of peak oxygen uptake at baseline. While the tracking models indicated low absolute stability of physical activity from childhood to young adulthood, children in the lowest quartiles of moderate-to-vigorous (OR:1.88; 95%CI: 1.23, 2.86) and total physical activity (OR: 1.87; 95%CI: 1.21, 2.87) at age 9 years were almost 90% more likely to be in these quartiles at age 24 years compared to those belonging to the upper three quartiles at baseline. CONCLUSIONS: We found a substantial reduction in physical activity and increase in time spent sedentary between age 9 and 15 years. Contrary to previous studies, using mainly self-reported physical activity, little change was observed between adolescence and young adulthood. The least active children were more likely to remain the least active adults and could be targeted for early intervention.

Monitoring Gait and Physical Activity of Elderly Frail Individuals in Free-Living Environment: A Feasibility Study.

INTRODUCTION: Frailty is conventionally diagnosed using clinical tests and self-reported assessments. However, digital health technologies (DHTs), such as wearable accelerometers, can capture physical activity and gait during daily life, enabling more objective assessments. In this study, we assess the feasibility of deploying DHTs in community-dwelling older individuals, and investigate the relationship between digital measurements of physical activity and gait in naturalistic environments and participants' frailty status, as measured by conventional assessments. METHODS: Fried Frailty Score (FFS) was used to classify fifty healthy individuals as non-frail (FFS = 0, n/female = 21/11, mean ± SD age: 71.10 ± 3.59 years), pre-frail (FFS = 1-2, n/female = 23/9, age: 73.74 ± 5.52 years), or frail (FFS = 3+, n/female = 6/6, age: 70.70 ± 6.53 years). Participants wore wrist-worn and lumbar-worn GENEActiv accelerometers (Activinsights Ltd., Kimbolton, UK) during three in-laboratory visits, and at-home for 2 weeks, to measure physical activity and gait. After this period, they completed a comfort and usability questionnaire. Compliant days at-home were defined as follows: those with ≥18 h of wear time, for the wrist-worn accelerometer, and those with ≥1 detected walking bout, for the lumbar-worn accelerometer. For each at-home measurement, a group analysis was performed using a linear regression model followed by ANOVA, to investigate the effect of frailty on physical activity and gait. Correlation between at-home digital measurements and conventional in-laboratory assessments was also investigated. RESULTS: Participants were highly compliant in wearing the accelerometers, as 94% indicated willingness to wear the wrist device, and 66% the lumbar device, for at least 1 week. Time spent in sedentary activity and time spent in moderate activity as measured from the wrist device, as well as average gait speed and its 95th percentile from the lumbar device were significantly different between frailty groups. Moderate correlations between digital measurements and self-reported physical activity were found. CONCLUSIONS: This work highlights the feasibility of deploying DHTs in studies involving older individuals. The potential of digital measurements in distinguishing frailty phenotypes, while unobtrusively collecting unbiased data, thus minimizing participants' travels to sites, will be further assessed in a follow-up study.

Sex differences in physical activity of US children at age 13 months: child and Mother Physical Activity Study (CAMPAS).

BACKGROUND: Lower physical activity (PA) has been observed in females compared to males among preschool-aged and older children. However, the timing of when the sex gap emerges is unclear. The purpose of this study was to investigate whether females have lower PA levels than males in the early toddler age and to explore whether gross motor competency and PA parenting practices might explain a sex difference in PA. METHODS: The study design was cross-sectional. Participants were a community-based sample of 137 children aged 10-16 months residing in US Midwest urban/suburban area. Participants' mothers completed a survey that contained a demographic questionnaire, the Ages and Stages Questionnaire gross motor competency subscale, and a PA parenting practices questionnaire. Participating children wore an ActiGraph accelerometer on their hip for 7 days. Accelerometer-measured time spent in moderate- and vigorous intensity PA (MVPA; minutes/day) and in total PA (minutes/day) were calculated. Multivariable linear regression analysis was conducted to predict MVPA and total PA by sex, gross motor competency, PA parenting practices, and socioeconomic status. RESULTS: Among 137 participants (54.0% female), average age was 13.6 months (SD = 1.7). MVPA was 72 ± 25 and 79 ± 26 min/day for females and males, respectively (p = 0.14). Total PA was 221 ± 48 and 238 ± 47 min/day for females and males, respectively (p = 0.04). Both gross motor competency and PA-encouraging parenting practices were positively associated MVPA (p = 0.01 and p = 0.02, respectively) and total PA (p = 0.02 and p = 0.01, respectively); however, these relationships did not differ by sex (p = 0.11 and p = 0.89, respectively). After accounting for gross motor competency and PA parenting practices, total PA was 15 min/day lower among females than males (p = 0.04). CONCLUSIONS: This cross-sectional study of US children observed a sex gap in total PA at 10-16 months of age. Gross motor development and PA parenting practices did not differ by child sex nor explain the sex difference in PA. A longitudinal investigation should follow to further narrow down when sex differences in PA emerge and to determine the factors that lead to this difference.

Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial.

BACKGROUND: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death. OBJECTIVE: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death. METHODS: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data. RESULTS: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI -277 to -213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change -892), nausea score (-677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. CONCLUSIONS: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-054675.

The use of accelerometer bracelets to evaluate arm motor function over a stroke rehabilitation period - an explorative observational study.

BACKGROUND: Assessments of arm motor function are usually based on clinical examinations or self-reported rating scales. Wrist-worn accelerometers can be a good complement to measure movement patterns after stroke. Currently there is limited knowledge of how accelerometry correlate to clinically used scales. The purpose of this study was therefore to evaluate the relationship between intermittent measurements of wrist-worn accelerometers and the patient's progression of arm motor function assessed by routine clinical outcome measures during a rehabilitation period. METHODS: Patients enrolled in in-hospital rehabilitation following a stroke were invited. Included patients were asked to wear wrist accelerometers for 24 h at the start (T1) and end (T2) of their rehabilitation period. On both occasions arm motor function was assessed by the modified Motor Assessment Scale (M_MAS) and the Motor Activity Log (MAL). The recorded accelerometry was compared to M_MAS and MAL. RESULTS: 20 patients were included, of which 18 completed all measurements and were therefore included in the final analysis. The resulting Spearman's rank correlation coefficient showed a strong positive correlation between measured wrist acceleration in the affected arm and M-MAS and MAL values at T1, 0.94 (p < 0.05) for M_MAS and 0.74 (p < 0.05) for the MAL values, and a slightly weaker positive correlation at T2, 0.57 (p < 0.05) for M_MAS and 0.46 - 0.45 (p = 0.06) for the MAL values. However, no correlation was seen for the difference between the two sessions. CONCLUSIONS: The results confirm that the wrist acceleration can differentiate between the affected and non-affected arm, and that there is a positive correlation between accelerometry and clinical measures. Many of the patients did not change their M-MAS or MAL scores during the rehabilitation period, which may explain why no correlation was seen for the difference between measurements during the rehabilitation period. Further studies should include continuous accelerometry throughout the rehabilitation period to reduce the impact of day-to-day variability.

The Associations Between Parental-Reported and Device-Based Measured Outdoor Play and Health Indicators of Physical, Cognitive, and Social-Emotional Development in Preschool-Aged Children.

PURPOSE: Examine in preschool-aged children: (1) the associations between parental-reported and device-measured outdoor play (OP) and health indicators of physical, cognitive, and social-emotional development and (2) whether associations were independent of outdoor moderate- to vigorous-intensity physical activity (MVPA). METHODS: This cross-sectional study included 107 participants. Children's OP was measured via a parental questionnaire and the lux feature of accelerometers. Children's growth, adiposity, and motor skills were assessed as physical development indicators. Visual-spatial working memory, response inhibition, and expressive language were assessed as cognitive development indicators. Sociability, prosocial behavior, internalizing, externalizing, and self-regulation were assessed as social-emotional development indicators. Regression models were conducted that adjusted for relevant covariates. Additional models further adjusted for outdoor MVPA. RESULTS: Parental-reported total OP, OP in summer/fall months, and OP on weekdays were negatively associated (small effect sizes) with response inhibition and working memory. After adjusting for outdoor MVPA, these associations were no longer statistically significant. OP on weekdays was negatively associated with externalizing (B = -0.04; 95% confidence interval, -0.08 to -0.00; P = .03) after adjusting for outdoor MVPA. A similar pattern was observed for device-based measured total OP (B = -0.49; 95% confidence interval, -1.05 to 0.07; P = .09). CONCLUSIONS: Future research in preschool-aged children should take into account MVPA and contextual factors when examining the association between OP and health-related indicators.

The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements.

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs.

Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals.

Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) identifying the optimal window size for signal pre-processing, and (3) demonstrating the number of observations required to achieve the desired level of model accuracy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS collars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were apparent because of the head-down posture. Increasing the smoothing window size to 10 s improved classification accuracy (p < 0.05), but reducing the number of observations below 50% resulted in a decrease in accuracy for all behaviors (p < 0.05). In-pasture observation increased accuracy and precision (0.05 and 0.08 percent, respectively) compared with animal-borne collar video observations.

A 0.05 m Change in Inertial Measurement Unit Placement Alters Time and Frequency Domain Metrics during Running.

Inertial measurement units (IMUs) provide exciting opportunities to collect large volumes of running biomechanics data in the real world. IMU signals may, however, be affected by variation in the initial IMU placement or movement of the IMU during use. To quantify the effect that changing an IMU's location has on running data, a reference IMU was 'correctly' placed on the shank, pelvis, or sacrum of 74 participants. A second IMU was 'misplaced' 0.05 m away, simulating a 'worst-case' misplacement or movement. Participants ran over-ground while data were simultaneously recorded from the reference and misplaced IMUs. Differences were captured as root mean square errors (RMSEs) and differences in the absolute peak magnitudes and timings. RMSEs were ≤1 g and ~1 rad/s for all axes and misplacement conditions while mean differences in the peak magnitude and timing reached up to 2.45 g, 2.48 rad/s, and 9.68 ms (depending on the axis and direction of misplacement). To quantify the downstream effects of these differences, initial and terminal contact times and vertical ground reaction forces were derived from both the reference and misplaced IMU. Mean differences reached up to -10.08 ms for contact times and 95.06 N for forces. Finally, the behavior in the frequency domain revealed high coherence between the reference and misplaced IMUs (particularly at frequencies ≤~10 Hz). All differences tended to be exaggerated when data were analyzed using a wearable coordinate system instead of a segment coordinate system. Overall, these results highlight the potential errors that IMU placement and movement can introduce to running biomechanics data.

A Wireless Data Acquisition System Based on MEMS Accelerometers for Operational Modal Analysis of Bridges.

This paper illustrates a novel and cost-effective wireless monitoring system specifically developed for operational modal analysis of bridges. The system employs battery-powered wireless sensors based on MEMS accelerometers that dynamically balance power consumption with high processing features and a low-power, low-cost Wi-Fi module that ensures operation for at least five years. The paper focuses on the system's characteristics, stressing the challenges of wireless communication, such as data preprocessing, synchronization, system lifetime, and simple configurability, achieved through the integration of a user-friendly, web-based graphical user interface. The system's performance is validated by a lateral excitation test of a model structure, employing dynamic identification techniques, further verified through FEM modeling. Later, a system composed of 30 sensors was installed on a concrete arch bridge for continuous OMA to assess its behavior. Furthermore, emphasizing its versatility and effectiveness, displacement is estimated by employing conventional and an alternative strategy based on the Kalman filter.

Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study.

Structural health monitoring (SHM) is critical for ensuring the safety of infrastructure such as bridges. This article presents a digital twin solution for the SHM of railway bridges using low-cost wireless accelerometers and machine learning (ML). The system architecture combines on-premises edge computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, the accelerometers stream raw vibration data, which are processed in the frequency domain and analyzed using machine learning to detect anomalies that indicate potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge for which vibration data were collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks that indicate potential changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warnings of bridge damage and also supports the use of in-house-designed sensors with lower cost and edge computing capabilities such as those used in the demonstration. The successful on-premises-cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.

Baseline Measures of Physical Activity and Function Do Not Predict Future Fall Incidence in Sedentary Older Adults: A Prospective Cohort Study.

Physical activity (PA) and physical function (PF) are modifiable risk factors for falls in older adults, but their ability to predict future fall incidence is unclear. The purpose of this study was to determine the predictive ability of baseline measures of PA, PF, and lower limb strength on future falls. A total of 104 participants underwent baseline assessments of PA, PF, and lower limb strength. Falls were monitored prospectively for 12 months. Eighteen participants fell at least once during the 12-month follow-up. Participants recorded almost exclusively sedentary levels of activity. PA, PF, and lower limb strength did not differ between fallers and nonfallers. Twelve participants, who reported a minor musculoskeletal injury in the past 6 months, experienced a fall. The results of this study suggest that in a cohort of highly functioning, sedentary older adults, PA does not distinguish fallers from nonfallers and that the presence of a recent musculoskeletal injury appears to be a possible risk factor for falling.

Associations of device-measured physical activity and sedentary time with quality of life and fatigue in newly diagnosed breast cancer patients: Baseline results from the AMBER cohort study.

BACKGROUND: This study examined associations of device-measured physical activity and sedentary time with quality of life (QOL) and fatigue in newly diagnosed breast cancer patients in the Alberta Moving Beyond Breast Cancer (AMBER) cohort study. METHODS: After diagnosis, 1409 participants completed the SF-36 version 2 and the Fatigue Scale, wore an ActiGraph device on their right hip to measure physical activity, and an activPAL device on their thigh to measure sedentary time (sitting/lying) and steps. ActiGraph data was analyzed using a hybrid machine learning method (R Sojourn package, Soj3x) and activPAL data were analyzed using activPAL algorithms (PAL Software version 8). Quantile regression was used to examine cross-sectional associations of QOL and fatigue with steps, physical activity, and sedentary hours at the 25th, 50th, and 75th percentiles of the QOL and fatigue distributions. RESULTS: Total daily moderate and vigorous physical activity (MVPA) hours was positively associated with better physical QOL at the 25th (ß = 2.14, p = <.001), 50th (ß = 1.98, p = <.001), and 75th percentiles (ß = 1.25, p = .003); better mental QOL at the 25th (ß = 1.73, p = .05) and 50th percentiles (ß = 1.07, p = .03); and less fatigue at the 25th (ß = 4.44, p < .001), 50th (ß = 3.08, p = <.001), and 75th percentiles (ß = 1.51, p = <.001). Similar patterns of associations were observed for daily steps. Total sedentary hours was associated with worse fatigue at the 25th (ß = -0.58, p = .05), 50th (ß = -0.39, p = .06), and 75th percentiles (ß = -0.24, p = .02). Sedentary hours were not associated with physical or mental QOL. CONCLUSIONS: MVPA and steps were associated with better physical and mental QOL and less fatigue in newly diagnosed breast cancer patients. Higher sedentary time was associated with greater fatigue symptoms.

Depression, happiness, and satisfaction with life in women newly diagnosed with breast cancer: Associations with device-measured physical activity and sedentary time.

BACKGROUND: Few studies have examined depression after a cancer diagnosis and before initiating adjuvant or neoadjuvant systemic treatments. In this study, we present baseline data on device-measured physical activity, sedentary behaviour, depression, happiness, and satisfaction with life in newly diagnosed breast cancer survivors. PURPOSE: To examine associations of accelerometer-assessed physical activity and sedentary time with depression symptoms and prevalence, happiness, and satisfaction with life. METHODS: Shortly after diagnosis, 1425 participants completed depression, happiness, and satisfaction with life measures and wore an ActiGraph® device on their hip to measure physical activity and the activPALTM inclinometer on their thigh for 7 days to measure sedentary time (sitting/lying) and steps (1384 completed both device measures). ActiGraph® data were analysed using a hybrid machine learning method (R Sojourn package, Soj3x), and activPALTM data using activPALTM algorithms (PAL Software version 8). We used linear and logistic regression to examine associations of physical activity and sedentary time with depression symptom severity (0-27) and depression prevalence, happiness (0-100), and satisfaction with life (0-35). For the logistic regression analysis, we compared participants with none-minimal depression (n = 895) to participants with some depression (that is, mild, moderate, moderately-severe, or severe depression [n = 530]). RESULTS: Participants reported a mean depression symptom severity score of 4.3 (SD = 4.1), a satisfaction with life score of 25.7 (SD = 7.2), and a happiness score of 70 (SD = 21.8). Higher moderate-to-vigorous physical activity (MVPA) was associated with reduced depression symptom severity scores (ß = -0.51, 95% CI: -0.87 to -0.14, p = 0.007). A 1 hour increase in MVPA was associated with a reduced odds of at least mild or worse depression by 24% (Odds Ratio [OR] = 0.76, 95% CI: 0.62-0.94, p = 0.012). Higher daily step counts were associated with lower depression symptom severity scores (ß = -0.16, 95% CI: -0.24 to -0.10, p < 0.001). Perceptions of happiness was associated with higher MVPA (ß = 2.17, 95% CI: 0.17-4.17, p = 0.033). Sedentary time was not associated with depression severity, but higher sedentary time was associated with lower perceptions of happiness (ß = -0.80, 95% CI: -1.48 to -0.11, p = 0.023). CONCLUSIONS: Higher physical activity was associated with fewer depression symptom severity scores and reduced odds of mild or worse depression in women newly diagnosed with breast cancer. Higher physical activity and daily step counts were also associated with stronger perceptions of happiness and satisfaction with life, respectively. Sedentary time was not associated with depression symptom severity or odds of having depression, but was associated with stronger perceptions of happiness.

The impact of selected methodological factors on data collection outcomes in observational studies of device-measured physical behaviour in adults: A systematic review.

BACKGROUND: Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours. METHODS: The review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss. RESULTS: 123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% [95% CI 18%, 42%] compared to postal distribution), and adhering to minimum wear criteria (+ 15% [4%, 25%]). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% [ 5%, 23%]) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent. CONCLUSION: Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465).

Fall history in older adults impacts acceleration profiles after a near collision with a moving pedestrian hazard.

BACKGROUND: Environmental hazards (e.g., pedestrian traffic) cause falls and testing environment impacts gait in older adults. However, most fall risk evaluations do not assess real-world moving hazard avoidance. AIMS: This study examined the effect of fall history in older adults on acceleration profiles before, during, and after a near collision with a moving hazard, in laboratory and real-world settings. METHODS: Older adults with (n = 14) and without a fall history (n = 15) performed a collision avoidance walking task with a sudden moving hazard in real-world and laboratory settings. Gait acceleration and video data of participants' first-person views were recorded. Four mixed effects multilevel models analyzed the magnitude and variability of mean and peak anteroposterior and mediolateral acceleration while walking before, during, and after the moving hazard in both environments. RESULTS: In the real-world environment, older adults without a fall history increased their mean anteroposterior acceleration after the moving hazard (p = 0.046), but those with a fall history did not (p > 0.05). Older adults without a fall history exhibited more intersubject variability than those with a fall history in mean (p < 0.001) and peak anteroposterior (p = 0.015) acceleration across environments and epochs. Older adults without a fall history exhibited a slower peak mediolateral reaction during the moving hazard (p = 0.014) than those with a fall history. CONCLUSIONS: These results suggest that compared to older adults with a fall history, older adults without a fall history are more adaptable and able to respond last-minute to unexpected hazards. Older adults with a fall history exhibited more homogenous responses.

Wearable accelerometers for measuring and monitoring the motor behaviour of infants with brain damage during CareToy-Revised training.

BACKGROUND: Nowadays, wearable sensors are widely used to quantify physical and motor activity during daily life, and they also represent innovative solutions for healthcare. In the clinical framework, the assessment of motor behaviour is entrusted to clinical scales, but they are dependent on operator experience. Thanks to their intrinsic objectivity, sensor data are extremely useful to provide support to clinicians. Moreover, wearable sensors are user-friendly and compliant to be used in an ecological environment (i.e., at home). This paper aims to propose an innovative approach useful to predict clinical assessment scores of infants' motor activity. MATERIALS AND METHODS: Starting from data acquired by accelerometers placed on infants' wrists and trunk during playtime, we exploit the method of functional data analysis to implement new models combining quantitative data and clinical scales. In particular, acceleration data, transformed into activity indexes and combined with baseline clinical data, represent the input dataset for functional linear models. CONCLUSIONS: Despite the small number of data samples available, results show correlation between clinical outcome and quantitative predictors, indicating that functional linear models could be able to predict the clinical evaluation. Future works will focus on a more refined and robust application of the proposed method, based on the acquisition of more data for validating the presented models. TRIAL REGISTRATION NUMBER: ClincalTrials.gov; NCT03211533. Registered: July, 7th 2017. ClincalTrials.gov; NCT03234959. Registered: August, 1st 2017.

Child temperament and physical activity in young children.

BACKGROUND: Children's temperament has been noted to influence their physical activity (PA) levels. Therefore, understanding the influence of temperament during the early years may be helpful for developing appropriate PA habits and tailoring interventions to align with different personality traits. OBJECTIVE: The purpose of this study was to examine the relationship between child temperament and objectively-measured PA in preschool-aged children. METHODS: Data were collected as part of the Supporting Physical Activity in the Childcare Environment (SPACE) and SPACE extension studies. Temperament data were collected using the validated Very Short Form of the Children's Behavior Questionnaire, which assessed three dimensions of temperament (i.e., surgency, negative affect and effortful control). Physical activity data were measured during childcare hours over the course of 5 days, using Actical® accelerometers. Total PA was summed, along with light and moderate-to-vigorous PA using age-specific cut-points. Three regression analyses were conducted to evaluate the prediction of PA by the dimensions of temperament. RESULTS: A total of 399 participants (Mage = 3.34 years, SD = 0.63) were retained for analyses, wearing an accelerometer an average of 7.21 h/day. Temperament significantly predicted all three PA levels (P < 0.05), with both negative affect and surgency being significantly associated with PA. CONCLUSION: Surgency is typified by a predisposition towards high activity levels; therefore, it is not surprising that it was the primary predictor of young children's PA. Future research may investigate methods of targeting PA interventions towards children with temperaments that may not predispose them to seeking out increased activity levels.

Gyro-Free Inertial Navigation Systems Based on Linear Opto-Mechanical Accelerometers.

High-sensitivity uniaxial opto-mechanical accelerometers provide very accurate linear acceleration measurements. In addition, an array of at least six accelerometers allows the estimation of linear and angular accelerations and becomes a gyro-free inertial navigation system. In this paper, we analyze the performance of such systems considering opto-mechanical accelerometers with different sensitivities and bandwidths. In the six-accelerometer configuration adopted here, the angular acceleration is estimated using a linear combination of accelerometers' read-outs. The linear acceleration is estimated similarly but requires a correcting term that includes angular velocities. Accelerometers' colored noise from experimental data is used to derive, analytically and through simulations, the performance of the inertial sensor. Results for six accelerometers, separated by 0.5 m in a cube configuration show noise levels of 10-7 m s-2 and 10-5 m s-2 (in Allan deviation) for time scales of one second for the low-frequency (Hz) and high-frequency (kHz) opto-mechanical accelerometers, respectively. The Allan deviation for the angular velocity at one second is 10-5 rad s-1 and 5×10-4 rad s-1. Compared to other technologies such as MEMS-based inertial sensors and optical gyroscopes, the high-frequency opto-mechanical accelerometer exhibits better performance than tactical-grade MEMS for time scales shorter than 10 s. For angular velocity, it is only superior for time scales less than a few seconds. The linear acceleration of the low-frequency accelerometer outperforms the MEMS for time scales up to 300 s and for angular velocity only for a few seconds. Fiber optical gyroscopes are orders of magnitude better than the high- and low-frequency accelerometers in gyro-free configurations. However, when considering the theoretical thermal noise limit of the low-frequency opto-mechanical accelerometer, 5×10-11 m s-2, linear acceleration noise is orders of magnitude lower than MEMS navigation systems. Angular velocity precision is around 10-10 rad s-1 at one second and 5×10-7 rad s-1 at one hour, which is comparable to fiber optical gyroscopes. While experimental validation is yet not available, the results shown here indicate the potential of opto-mechanical accelerometers as gyro-free inertial navigation sensors, provided the fundamental noise limit of the accelerometer is reached, and technical limitations such as misalignments and initial conditions errors are well controlled.

Raw Acceleration from Wrist- and Hip-Worn Accelerometers Corresponds with Mechanical Loading in Children and Adolescents.

The purpose of this study was to investigate associations between peak magnitudes of raw acceleration (g) from wrist- and hip-worn accelerometers and ground reaction force (GRF) variables in a large sample of children and adolescents. A total of 269 participants (127 boys, 142 girls; age: 12.3 ± 2.0 yr) performed walking, running, jumping (<5 cm; >5 cm) and single-leg hopping on a force plate. A GENEActiv accelerometer was worn on the left wrist, and an Actigraph GT3X+ was worn on the right wrist and hip throughout. Mixed-effects linear regression was used to assess the relationships between peak magnitudes of raw acceleration and loading. Raw acceleration from both wrist and hip-worn accelerometers was strongly and significantly associated with loading (all p's < 0.05). Body mass and maturity status (pre/post-PHV) were also significantly associated with loading, whereas age, sex and height were not identified as significant predictors. The final models for the GENEActiv wrist, Actigraph wrist and Actigraph hip explained 81.1%, 81.9% and 79.9% of the variation in loading, respectively. This study demonstrates that wrist- and hip-worn accelerometers that output raw acceleration are appropriate for use to monitor the loading exerted on the skeleton and are able to detect short bursts of high-intensity activity that are pertinent to bone health.