Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping.

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.

Validity of the PROMIS® Early Childhood Physical Activity Scale among toddlers.

BACKGROUND: The PROMIS® Early Childhood Physical Activity (PROMIS EC PA) scale is a recently developed PROMIS Early Childhood measure to assess PA among children aged 1-5 years. The purpose of this study was to examine test-retest reliability and convergent validity of the PROMIS EC PA scale among toddlers. METHOD: An ancillary study was conducted in the toddler-mother dyad sample of the Child and Mother Physical Activity Study. Mothers completed the 7-item PROMIS EC PA scale twice: during a study visit (test) and on the last day when their child's wore an ActiGraph accelerometer on the hip for 7 days (retest). The PROMIS EC PA summed score was calculated by totaling scores from items 1-5. Test-retest reliability was assessed using intraclass correlation coefficient (ICC) for test and retest PROMIS EC PA. Convergent validity was assessed using rank correlation coefficients (rho) between PROMIS EC PA scores and accelerometer-measured moderate- and vigorous-intensity PA (MVPA). RESULTS: Among 74 participants (56% female; 19 ± 4 months of mean age with range of 12-30 months), average accelerometer-measured MVPA was 76 ± 24 min/day. The median number of days between PROMIS EC PA test and retest was 8 days (IQR = 6 to 8), with an average PROMIS EC PA summed score of 11.0 ± 3.5 at test and 10.5 ± 3.4 at retest. ICC for the test-retest PROMIS EC PA summed scores was 0.72 (95% CI = 0.59-0.82). The rank correlation between the PROMIS EC PA summed score and accelerometer-measured MVPA was 0.13 (95% CI=-0.10 to 0.35; p = 0.28). CONCLUSION: In a sample of children aged 12-30 months, test-retest reliability for the PROMIS EC PA scale was moderate and its convergent validity against accelerometer-measured MVPA was poor. Prior to a widespread use of the PROMIS EC PA scale in large-scale research and clinical practice, the tool should be further refined and validated to elucidate how young children's lived PA experience as measured in the PROMIS EC PA scale is relevant to their health and wellbeing outcomes.

Assessing the current utilization status of wearable devices in clinical research.

BACKGROUND/AIMS: Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations. METHODS: As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov-a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords "ActiGraph,""Apple Watch,""Empatica,""Fitbit,""Garmin," and "wearable devices" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables. RESULTS: Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations. CONCLUSIONS: Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future.

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.

Current agreement between ActiGraph and CUPAR in measuring moderate to vigorous intensity physical activity for adolescents.

The study aims to develop and validate the Curriculum-related Physical Activity Recall questionnaire (CUPAR) as a measure of physical activity in adolescents. 83 middle-school students (13.23 ± 0.74 yrs) completed the CUPAR and whore ActiGraph accelerometers for seven consecutive days. Correlations and Bland-Altman plots were to examine the agreement between these two measures. Significant correlations were observed between the CUPAR and ActiGraph accelerometer for 5-day MPA (r = 0.29, p < 0.01), and for both 5-day and 7-day VPA (r = 0.47 and 0.79, ps < 0.01), and MVPA (r = 0.79 and 0.42, ps < 0.01). Plots showed reasonable agreement between the CUPAR and ActiGraph estimates of VPA and MVPA. The agreement between CUPAR and ActiGraph was higher for in-school VPA (r = 0.58, p < 0.01) and MVPA (r = 0.44, p < 0.01) as compared to the out-school VPA (r = 0.22, p < 0.05) and MVPA (r = 0.26, p < 0.05). The CUPAR can reduce respondents' burden, representing a reliable and efficient measure of physical activity among adolescents, especially for PA occurred during in-school sessions and at vigorous intensity.

Exploring Factors Associated With Accelerometer Validity Among Ethnically Diverse Toddlers.

PURPOSE: Studying physical activity in toddlers using accelerometers is challenging due to noncompliance with wear time (WT) and activity log (AL) instructions. The aims of this study are to examine relationships between WT and AL completion and (1) demographic and socioeconomic variables, (2) parenting style, and (3) whether sedentary time differs by AL completion. METHODS: Secondary analysis was performed using baseline data from a community wellness program randomized controlled trial for parents with toddlers (12-35 mo). Parents had toddlers wear ActiGraph wGT3x accelerometers and completed ALs. Valid days included ≥600-minute WT. Analysis of variance and chi-square analyses were used. RESULTS: The sample (n = 50) comprised racial and ethnically diverse toddlers (mean age = 27 mo, 58% male) and parents (mean age = 31.7 y, 84% female). Twenty-eight families (56%) returned valid accelerometer data with ALs. Participants in relationships were more likely to complete ALs (P < .05). Toddler sedentary time did not differ between those with ALs and those without. CONCLUSIONS: We found varied compliance with WT instructions and AL completion. Returned AL quality was poor, presenting challenges in correctly characterizing low-activity counts to improve internal validity of WT and physical activity measures. Support from marital partners may be important for adherence to study protocols.

Validation of a Zio XT Patch Accelerometer for the Objective Assessment of Physical Activity in the Atherosclerosis Risk in Communities (ARIC) Study.

BACKGROUND: Combination devices to monitor heart rate/rhythms and physical activity are becoming increasingly popular in research and clinical settings. The Zio XT Patch (iRhythm Technologies, San Francisco, CA, USA) is US Food and Drug Administration (FDA)-approved for monitoring heart rhythms, but the validity of its accelerometer for assessing physical activity is unknown. OBJECTIVE: To validate the accelerometer in the Zio XT Patch for measuring physical activity against the widely-used ActiGraph GT3X. METHODS: The Zio XT and ActiGraph wGT3X-BT (Actigraph, Pensacola, FL, USA) were worn simultaneously in two separately-funded ancillary studies to Visit 6 of the Atherosclerosis Risk in Communities (ARIC) Study (2016-2017). Zio XT was worn on the chest and ActiGraph was worn on the hip. Raw accelerometer data were summarized using mean absolute deviation (MAD) for six different epoch lengths (1-min, 5-min, 10-min, 30-min, 1-h, and 2-h). Participants who had ≥3 days of at least 10 h of valid data between 7 a.m-11 p.m were included. Agreement of epoch-level MAD between the two devices was evaluated using correlation and mean squared error (MSE). RESULTS: Among 257 participants (average age: 78.5 ± 4.7 years; 59.1% female), there were strong correlations between MAD values from Zio XT and ActiGraph (average r: 1-min: 0.66, 5-min: 0.90, 10-min: 0.93, 30-min: 0.93, 1-h: 0.89, 2-h: 0.82), with relatively low error values (Average MSE × 106: 1-min: 349.37 g, 5-min: 86.25 g, 10-min: 56.80 g, 30-min: 45.46 g, 1-h: 52.56 g, 2-h: 54.58 g). CONCLUSIONS: These findings suggest that Zio XT accelerometry is valid for measuring duration, frequency, and intensity of physical activity within time epochs of 5-min to 2-h.

Algorithm Validation for Quantifying ActiGraph™ Physical Activity Metrics in Individuals with Chronic Low Back Pain and Healthy Controls.

Assessing physical activity is important in the treatment of chronic conditions, including chronic low back pain (cLBP). ActiGraph™, a widely used physical activity monitor, collects raw acceleration data, and processes these data through proprietary algorithms to produce physical activity measures. The purpose of this study was to replicate ActiGraph™ algorithms in MATLAB and test the validity of this method with both healthy controls and participants with cLBP. MATLAB code was developed to replicate ActiGraph™'s activity counts and step counts algorithms, to sum the activity counts into counts per minute (CPM), and categorize each minute into activity intensity cut points. A free-living validation was performed where 24 individuals, 12 cLBP and 12 healthy, wore an ActiGraph™ GT9X on their non-dominant hip for up to seven days. The raw acceleration data were processed in both ActiLife™ (v6), ActiGraph™'s data analysis software platform, and through MATLAB (2022a). Percent errors between methods for all 24 participants, as well as separated by cLBP and healthy, were all less than 2%. ActiGraph™ algorithms were replicated and validated for both populations, based on minimal error differences between ActiLife™ and MATLAB, allowing researchers to analyze data from any accelerometer in a manner comparable to ActiLife™.

Adapting the Intensity Gradient for Use with Count-Based Accelerometry Data in Children and Adolescents.

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

Current Knowledge about ActiGraph GT9X Link Activity Monitor Accuracy and Validity in Measuring Steps and Energy Expenditure: A Systematic Review.

Over recent decades, wearable inertial sensors have become popular means to quantify physical activity and mobility. However, research assessing measurement accuracy and precision is required, especially before using device-based measures as outcomes in trials. The GT9X Link is a recent activity monitor available from ActiGraph, recognized as a "gold standard" and previously used as a criterion measure to assess the validity of various consumer-based activity monitors. However, the validity of the ActiGraph GT9X Link is not fully elucidated. A systematic review was undertaken to synthesize the current evidence for the criterion validity of the ActiGraph GT9X Link in measuring steps and energy expenditure. This review followed the PRISMA guidelines and eight studies were included with a combined sample size of 558 participants. We found that (1) the ActiGraph GT9X Link generally underestimates steps; (2) the validity and accuracy of the device in measuring steps seem to be influenced by gait speed, device placement, filtering process, and monitoring conditions; and (3) there is a lack of evidence regarding the accuracy of step counting in free-living conditions and regarding energy expenditure estimation. Given the limited number of included studies and their heterogeneity, the present review emphasizes the need for further validation studies of the ActiGraph GT9X Link in various populations and in both controlled and free-living settings.

Validation of an automated sleep detection algorithm using data from multiple accelerometer brands.

To evaluate the criterion validity of an automated sleep detection algorithm applied to data from three research-grade accelerometers worn on each wrist with concurrent laboratory-based polysomnography (PSG). A total of 30 healthy volunteers (mean [SD] age 31.5 [7.2] years, body mass index 25.5 [3.7] kg/m2 ) wore an Axivity, GENEActiv and ActiGraph accelerometer on each wrist during a 1-night PSG assessment. Sleep estimates (sleep period time window [SPT-window], sleep duration, sleep onset and waking time, sleep efficiency, and wake after sleep onset [WASO]) were generated using the automated sleep detection algorithm within the open-source GGIR package. Agreement of sleep estimates from accelerometer data with PSG was determined using pairwise 95% equivalence tests (±10% equivalence zone), intraclass correlation coefficients (ICCs) with 95% confidence intervals and limits of agreement (LoA). Accelerometer-derived sleep estimates except for WASO were within the 10% equivalence zone of the PSG. Reliability between data from the accelerometers worn on either wrist and PSG was moderate for SPT-window duration (ICCs ≥ 0.65), sleep duration (ICCs ≥ 0.54), and sleep onset (ICCs ≥ 0.61), mostly good for waking time (ICCs ≥ 0.80), but poor for sleep efficiency (ICCs ≥ 0.08) and WASO (ICCs ≥ 0.08). The mean bias between all accelerometer-derived sleep estimates worn on either wrist and PSG were low; however, wide 95% LoA were observed for all sleep estimates, apart from waking time. The automated sleep detection algorithm applied to data from Axivity, GENEActiv and ActiGraph accelerometers, worn on either wrist, provides comparable measures to PSG for SPT-window and sleep duration, sleep onset and waking time, but a poor measure of wake during the sleep period.

Social-cognitive, demographic, clinical, and health-related correlates of physical activity and sedentary behaviour in newly diagnosed women with breast cancer.

PURPOSE: Identifying correlates of physical activity and sedentary behaviour allows for the identification of factors that may be targeted in future behaviour change interventions. This study sought to determine the social-cognitive, demographic, clinical, and health-related correlates of physical activity and sedentary behaviour in individuals recently diagnosed with breast cancer. METHODS: Data were collected from 1381 participants within 90 days of diagnosis in the Alberta Moving Beyond Breast Cancer (AMBER) Cohort Study. Physical activity and sedentary behaviour were measured with ActiGraph GT3X+® and activPALTM devices, respectively, for seven consecutive days. Correlates were collected via a self-reported questionnaire, medical record extraction, or measured by staff. RESULTS: Multivariable models were fitted for sedentary behaviour, light physical activity, and moderate-to-vigorous physical activity. Greater sedentary behaviour was associated with higher body fat percentage (BF%) (ß=0.044; p<0.001) and being single (ß=0.542; p<0.002). Lower light physical activity was associated with higher BF% (ß=-0.044; p<0.001), higher body mass index (ß=-0.039; p<0.001), greater disease barrier influence (ß=-0.006; p<0.001), a HER2-positive diagnosis (ß=-0.278; p=0.001), and being single (ß=-0.385; p= 0.001). Lower moderate-to-vigorous physical activity was associated with higher BF% (ß =-0.011; p=0.001), greater disease barrier influence (ß=-0.002; p<0.001), and being of Asian (ß=-0.189; p=0.002) or Indian/South American (ß=-0.189; p=0.002) descent. Greater moderate-to-vigorous physical activity was associated with having greater intentions (ß=0.049; p=0.033) and planning (ß=0.026; p=0.015) towards physical activity. CONCLUSION: Tailoring interventions to increase physical activity for individuals recently diagnosed with breast cancer may improve long-term outcomes across the breast cancer continuum.

Validity of Actigraph for Measuring Energy Expenditure in Healthy Adults: A Systematic Review and Meta-Analysis.

PURPOSE: The objective of this systematic review and meta-analysis was to assess the validity of the Actigraph triaxial accelerometer device in measuring physical activity energy expenditure (PAEE) in healthy adults, with indirect calorimetry (IC) serving as the validity criterion. METHODS: A comprehensive search was conducted using the PubMed, Web of Science, and sportdiscuss databases, in addition to manual searches for supplementary sources. Search strategies were employed that involved conducting single keyword searches using the terms "gt3x" and "Actigraph gt3x". The literature search encompassed the timeframe spanning from 1 January 2010 to 1 March 2023. The methodological quality of the studies included in the analysis was evaluated using both the Downs and Black checklist and the Consensus-Based Criteria for Selection of Measurement Instruments (COSMIN) checklist. The meta-analysis was conducted using the Review Manager 5.4 software. The standardized mean difference (SMD) was calculated and expressed as a 95% confidence interval (CI). The significance level was set at α = 0.05. A systematic assessment of the Actigraph's performance was conducted through the descriptive analysis of computed effect sizes. RESULTS: A total of 4738 articles were retrieved from the initial search. After eliminating duplicate articles and excluding those deemed irrelevant, a comprehensive analysis was conducted on a total of 20 studies, encompassing a combined sample size of 1247 participants. The scores on the Downs and Black checklist ranged from 10 to 14, with a mean score of 11.35. The scores on the COSMIN checklist varied from 50% to 100%, with an average score of 65.83%. The meta-analysis findings revealed a small effect size (SMD = 0.01, 95% CI = 0.50-0.52, p = 0.97), indicating no statistically significant difference (p > 0.05). CONCLUSIONS: The meta-analysis revealed a small effect size when comparing the Actigraph and IC, suggesting that the Actigraph can be utilized for assessing total PAEE. Descriptive analyses have indicated that the Actigraph device has limited validity in accurately measuring energy expenditure during specific physical activities, such as high-intensity and low-intensity activities. Therefore, caution should be exercised when utilizing this device for such purposes. Furthermore, there was a significant correlation between the activity counts measured by the Actigraph and the PAEE, indicating that activity counts can be utilized as a predictive variable for PAEE.

Opal Actigraphy (Activity and Sleep) Measures Compared to ActiGraph: A Validation Study.

Physical activity and sleep monitoring in daily life provide vital information to track health status and physical fitness. The aim of this study was to establish concurrent validity for the new Opal Actigraphy solution in relation to the widely used ActiGraph GT9X for measuring physical activity from accelerometry epic counts (sedentary to vigorous levels) and sleep periods in daily life. Twenty participants (age 56 + 22 years) wore two wearable devices on each wrist for 7 days and nights, recording 3-D accelerations at 30 Hz. Bland-Altman plots and intraclass correlation coefficients (ICCs) assessed validity (agreement) and test-retest reliability between ActiGraph and Opal Actigraphy sleep durations and activity levels, as well as between the two different versions of the ActiGraph. ICCs showed excellent reliability for physical activity measures and moderate-to-excellent reliability for sleep measures between Opal versus Actigraph GT9X and between GT3X versus GT9X. Bland-Altman plots and mean absolute percentage error (MAPE) also show a comparable performance (within 10%) between Opal and ActiGraph and between the two ActiGraph monitors across activity and sleep measures. In conclusion, physical activity and sleep measures using Opal Actigraphy demonstrate performance comparable to that of ActiGraph, supporting concurrent validation. Opal Actigraphy can be used to quantify activity and monitor sleep patterns in research and clinical studies.

Enabling the ActiGraph GT9X Link's Idle Sleep Mode and Inertial Measurement Unit Settings Directly Impacts Data Acquisition.

The ActiGraph GT9X has been implemented in clinical trials to track physical activity and sleep. Given recent incidental findings from our laboratory, the overall aim of this study was to notify academic and clinical researchers of the idle sleep mode (ISM) and inertial measurement unit (IMU)'s interaction, as well as their subsequent effect on data acquisition. Investigations were undertaken using a hexapod robot to test the X, Y and Z sensing axes of the accelerometers. Seven GT9X were tested at frequencies ranging from 0.5 to 2 Hz. Testing was performed for three sets of setting parameters: Setting Parameter 1 (ISMONIMUON), Setting Parameter 2 (ISMOFFIMUON), Setting Parameter 3 (ISMONIMUOFF). The minimum, maximum and range of outputs were compared between the settings and frequencies. Findings indicated that Setting Parameters 1 and 2 were not significantly different, but both were significantly different from Setting Parameter 3. Upon inspection, it was discovered that the ISM was only active during Setting Parameter 3 testing, despite it being enabled in Setting Parameter 1. Researchers should be aware of this when conducting future research using the GT9X.

Comparison of free-living physical activity measurements between ActiGraph GT3X-BT and Fitbit Charge 3 in young people with haemophilia.

INTRODUCTION: Measurement of physical activity (PA) using commercial activity trackers such as Fitbit devices has become increasingly popular, also for people with haemophilia (PWH). The accuracy of the Fitbit model Charge 3 has not yet been examined. AIMS: To compare the Fitbit Charge 3 against the research-grade accelerometer ActiGraph GT3X-BT in measuring average daily steps and minutes spent in different PA intensities. METHODS: Twenty-four young PWH wore a wrist-worn Fitbit Charge 3 and hip-worn ActiGraph GT3X-BT simultaneously for seven consecutive days in free-living conditions. Correlation of and differences between the devices for daily averages of PA parameters were assessed using Pearson's correlation coefficient and paired t-test, respectively. Agreement between devices was assessed using Bland-Altman plots. RESULTS: Twenty participants (mean age 21.8) were included in the analyses. We found moderate to high correlations between Fitbit and ActiGraph measured daily averages for all PA variables, but statistically significant differences between devices for all variables except daily minutes of moderate PA. Fitbit overestimated average daily steps, minutes of light, vigorous and moderate-to-vigorous PA. Bland-Altman plots showed a measurement bias between devices for all parameters with increasing overestimation by the Fitbit for higher volumes of PA. CONCLUSION: The Fitbit Charge 3 overestimated steps and minutes of light, moderate and moderate-to-vigorous PA as compared to the ActiGraph GT3X-BT, and this bias increased with PA volume. The Fitbit should therefore be used with caution in research, and we advise users of the device to be cognizant of this overestimation.

CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children.

BACKGROUND: Hip-worn accelerometer cut-points have poor validity for assessing children's sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data. METHODS: Participants were 278, 8-11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration). RESULTS: Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL. CONCLUSIONS: There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.

An investigation into the optimal wear time criteria necessary to reliably estimate physical activity and sedentary behaviour from ActiGraph wGT3X+ accelerometer data in older care home residents.

BACKGROUND: Research protocols regarding the use of ActiGraph wGT3X+ accelerometers in care home residents are yet to be established. The purpose of this study was to identify the minimal wear time criteria required to achieve reliable estimates of physical activity (PA) and sedentary behaviour (SB) in older care home residents. METHODS: Ninety-four older adults from 14 care homes wore an ActiGraph wGT3X+ accelerometer on the right hip for 7 consecutive days. A pragmatic, staged approach was adopted in order to explore the effect of: monitoring day; minimum daily wear time and number of wear days on estimates of four outcomes derived from the accelerometer data: counts.day- 1, counts.minute- 1, PA time and SB time. RESULTS: Data from 91 participants (mean age: 84 ± 9 years, 34% male) was included in the analysis. No effect of monitoring day was observed. Lowering the daily wear time to ≥ 8 h (compared to ≥10 h) had no effect on the outcomes of interest. Four days of monitoring was sufficient to provide reliable estimates of all four outcomes. CONCLUSION: In this study, a minimum wear time criterion of ≥ 8 h on any 4 days was required to derive reliable estimates of PA and SB from ActiGraph wGT3X+ accelerometer data in older care home residents.

Cross-sectional association of light sensor-measured time outdoors with physical activity and gross motor competency among U.S. preschool-aged children: the 2012 NHANES National Youth Fitness Survey.

BACKGROUND: Time spent outdoors (outdoor time) has been suggested to be beneficial for physical activity (PA) and healthy development among preschool-aged children. The aim of this study was to quantify PA level and gross motor competency associated with light sensor-measured daily outdoor time in a representative sample of U.S. children aged 3 to 5 years. METHODS: The study sample included 301 participants (149 girls) aged 3 to 5 years from the 2012 U.S. National Health and Examination Survey National Youth Fitness Survey. ActiGraph GT3X+ accelerometers with a built-in ambient light sensor were used to measure PA (expressed in monitor-independent movement summary [MIMS]) and outdoor time. The Test of Gross Motor Development-Second Edition (TGMD-2) was used to assess gross motor skills. Multivariable linear regression models were fit to predict daily and gross motor scores by daily outdoor time. RESULTS: Average daily outdoor time was 95 min (median of 84 min; interquartile range of 52 to 123 min). Means of daily outdoor time and daily MIMS were not significantly different between boys and girls. Among girls, every additional 10 min of daily outdoor time was associated with an additional 540 daily total MIMS (95% CI = 372, 708). Among boys, every additional 10 min of daily outdoor time was associated with an additional 296 daily total MIMS (95% CI = 131, 460). Every additional 10 min of daily outdoor time was associated with a 0.1-point (95% CI = 0.001, 0.130) higher object control standard score. Daily outdoor time was not associated with a locomotor standard score. CONCLUSIONS: In a representative sample of U.S. preschool-aged children, daily outdoor time was positively associated with daily PA. The contribution of outdoor time to PA was greater among girls than boys, suggesting that providing outdoor opportunities is critical for promoting PA, particularly among girls.

Criterion validity of ActiGraph monitoring devices for step counting and distance measurement in adults and older adults: a systematic review.

BACKGROUND: Wearable activity monitors such as ActiGraph monitoring devices are widely used, especially in research settings. Various research studies have assessed the criterion validity of ActiGraph devices for step counting and distance estimation in adults and older adults. Although several studies have used the ActiGraph devices as a reference system for activity monitoring, there is no summarized evidence of the psychometric properties. The main objective of this systematic review was to summarize evidence related to the criterion validity of ActiGraph monitoring devices for step counting and distance estimation in adults and/or older adults. METHODS: Literature searches were conducted in six databases (Medline (OVID), Embase, IEEExplore, CINAHL, Engineering Village and Web of Science). Two reviewers independently conducted selection, a quality analysis of articles (using COSMIN and MacDermid's grids) and data extraction. RESULTS: This review included 21 studies involving 637 participants (age 30.3 ± 7.5 years (for adults) and 82.7 ± 3.3 years (for older adults)). Five ActiGraph devices (7164, GT1M, wGTX +, GT3X +/wGT3X + and wGT3X - BT) were used to collect data at the hip, wrist and ankle to assess various walking and running speeds (ranging from 0.2 m/s to 4.44 m/s) over durations of 2 min to 3 days (13 h 30 mins per day) for step counting and distance estimation. The ActiGraph GT3X +/wGT3X + and wGT3X - BT had better criterion validity than the ActiGraph 7164, wGTX + and GT1M according to walking and running speeds for step counting. Validity of ActiGraph wGT3X + was good for distance estimation. CONCLUSION: The ActiGraph wGT3X - BT and GT3X +/wGT3X + have good criterion validity for step counting, under certain conditions related to walking speeds, positioning and data processing.