Objective Measures Of Physical Activity Research Articles

Physical literacy is associated with children's adherence to physical activity guidelines during COVID-19

ABSTRACT Background: Despite the benefits of physical activity, children’s compliance with physical activity recommendations is very low and this situation worsened during the COVID-19 pandemic. Physical literacy has been theorised as a factor that can enhance and sustain children’s engagement with physical activity. This study aimed to investigate whether physical literacy is associated with children’s adherence to the physical activity guidelines during COVID-19. Methods: Data for this cross-sectional study were collected from 648 Australian children (60% boys) aged 7–12 years (M = 10 years; SD = 1.7) and their parents/carers. Recruitment was conducted through social media and panel sampling. Self-perceived physical literacy was measured using the pictorial Physical Literacy in Children Questionnaire. Children’s adherence to physical activity guidelines was collected using a two-item questionnaire asking parents/guardians about the number of days their children were active for at least 60 min in a typical week before and during COVID-19. The association between physical literacy (predictor) and physical activity (binary outcome: meeting/not meeting guidelines before/during COVID-19) was tested with logistic regressions while adjusting for relevant confounders. Results: Most parents spoke English at home (91%), over half had a university education (62%) and three quarters had paid employment (75%). Children’s physical literacy scores ranged from 32–120 (M = 94; SD = 16). Physical literacy overall score was associated with significantly higher odds of meeting the physical activity recommendations during COVID-19 (OR = 1.03, 95% CI [1.02; 1.05]). Each of the physical literacy subdomains (i.e. physical, psychological, social, cognitive) was also significantly associated with children’s adherence to the physical activity guidelines (ORs = 1.07–1.22). Conclusions: These findings indicate that physical literacy is a predictor of physical activity and may act as a protective factor of such behaviour during limiting events, such as the COVID-19 pandemic. Further research could confirm this relationship using objective measures of physical activity and exploring whether interventions designed to improve physical literacy can benefit physical activity.

117 Standardizing Wearable-based Real-life Physical Activity Monitoring in Patients Undergoing Surgery for Degenerative Lumbar Disorders

INTRODUCTION: Assessment of function in patients with degenerative thoracolumbar disorders (DTD) has traditionally relied solely on the patient’s subjective perception of disability. There is a clear need to better understand an objective measure of physical activity impairment in this patient cohort. METHODS: This analysis is part of the Stanford SpineTrak study, a RCT to assess the feasibility of using a commercially available smartwatch (AppleWatch) to monitor outcome in patients with DTD. Mean preoperative physical activity 2 weeks before surgery was expressed as raw step count, as well as in standardized z-scores that wre referenced to age- and sex-specific values of a control population from a large public database. Step counts were assessed for convergent validity with established patient-reported outcome measures (PROMs) and impairment in activity was stratified into performance groups based on z-score cut-off values. RESULTS: Of 67 patients with DTD, 65 patients (62% female) with a mean age (SD) of 63.8 (12.8) years were available for analysis with a mean (SD) daily step count of 5215 (3868). Physical activity showed significant correlation with the ODI (r = 0.28, 95% CI -0.49-0.03), SF36-PCS (r = -0.34, 95% CI 0.11-0.54) and PROMIS-PF (r = -0.48, 95% CI 0.27-0.65). No, Mild, moderate, and severe impairment in activity performance were defined as corresponding z-scores of >0, 0 to -0.99, -1 to -1.99 and = -2, accounting for 20%, 29%, 43% and 8% of the study population. Each 1-step increase in activity impairment resulted in increased subjective disability as measured by the ODI, SF36-PCS and PROMIS-PF (all < 0.05). CONCLUSIONS: This study establishes a new wearable-based objective measure of real-life physical activity as well as corresponding standard scores that allow for a direct comparison of test results between individual patients and different study populations.

(1338) - Perception vs. Reality: Reported and Objective Measures of Physical Activity in Children with Hypertrophic Cardiomyopathy (HCM)

(1338) - Perception vs. Reality: Reported and Objective Measures of Physical Activity in Children with Hypertrophic Cardiomyopathy (HCM)

The role of physical activity on obstructive sleep apnea severity and hypoxic load, and the mismatch between subjective and objective physical activity assessments.

Obesity is the primary risk factor for the development of obstructive sleep apnea, and physical inactivity plays an important role. However, most studies have either only evaluated physical activity subjectively or objectively in obstructive sleep apnea. The objectives of this study were: (i) to assess the relationship between obstructive sleep apnea severity (both apnea-hypopnea index and desaturation parameters) and both objectively and subjectively measured physical activity after adjustment for anthropometry and body composition parameters; and (ii) to assess the relationship between objective and subjective physical activity parameters and whether obstructive sleep apnea severity has a modulatory effect on this relationship. Fifty-four subjects (age 47.7 ± 15.0 years, 46% males) were categorized into groups according to obstructive sleep apnea severity: no obstructive sleep apnea; mild obstructive sleep apnea; and moderate-to-severe obstructive sleep apnea. All subjects were evaluated with subjective and objective physical activity, anthropometric and body composition measurements, and 3-night self-applied polysomnography. A one-way ANOVA was used to evaluate the differences between the three obstructive sleep apnea severity groups and multiple linear regression to predict obstructive sleep apnea severity. Differences in subjectively reported sitting time (p ≤ 0.004) were found between participants with moderate-to-severe obstructive sleep apnea, and those with either mild or no obstructive sleep apnea (p = 0.004). Age, body mass index and neck circumference explained 63.3% of the variance in the apnea-hypopnea index, and age, body mass index and visceral adiposity explained 67.8% of the variance in desaturation parameters. The results showed that the person's physical activity does not affect obstructive sleep apnea severity. A weak correlation was found between objective and subjective physical activity measures, which could be relevant for healthcare staff encouraging patients with obstructive sleep apnea to increase their physical activity.

Combined effects of sleep and objectively-measured daily physical activity on arterial stiffness in middle-aged and older adults

Although sleep quality and physical activity (PA) may influence on arterial stiffness, the combined effects of these two factors on arterial stiffness remain unknown. A total of 103 healthy middle-aged and older men and women (aged 50–83 years) with no history of cardiovascular disease and depression were included in this study. Arterial stiffness was measured using carotid-femoral pulse wave velocity (cfPWV), brachial-ankle PWV (baPWV), and femoral-ankle PWV (faPWV). Poor sleepers were defined as those with a Pittsburgh Sleep Quality Index score of >5.5. Using an accelerometer for seven consecutive days, low levels of PA were defined as low moderate-to-vigorous-intensity PA (MVPA) <19.0 min/day and low step counts <7100 steps/day, respectively. Poor sleepers with low PA levels, as determined by MVPA and daily steps, showed higher cfPWV, but not faPWV or baPWV, in middle-aged and older adults. Furthermore, in the analysis of covariance (ANCOVA) analyses adjusted for age, obesity, dyslipidemia, and sedentary behavior, the cfPWV result remained significant. Our study revealed that the coexistence of poor sleep quality and decreased PA (low MVPA or daily steps) might increase central arterial stiffness in middle-aged and older adults. Therefore, adequate sleep (good and sufficient sleep quality) and regular PA, especially at appropriate levels of MVPA (i.e., at least of 7100 steps/day), should be encouraged to decrease central arterial stiffness in middle-aged and older adults.

Comparison of patient-reported outcomes of physical activity and accelerometry in people with multiple sclerosis and ambulatory impairment: A cross-sectional study

BackgroundAccelerometers and patient-reported outcomes (PRO) are used to assess physical activity (PA) in people with multiple sclerosis (pwMS). So far it is unknown, however, whether these assessments represent mobility limitations in pwMS with mild and moderate to severe disability alike. The primary aim of the study was to assess the correlation between accelerometry and International Physical Activity Questionnaire (IPAQ) scores in pwMS with different degrees of ambulatory impairment. Taken its frequent use into account, the Godin Leisure Time Exercise Questionnaire (GLTEQ) was investigated as additional PRO. MethodsIn a prospective cohort of pwMS, correlational analyses were performed between the number of daily steps, time spent in light, moderate to vigorous PA (MVPA) and time spent sitting as assessed using accelerometry (ActiGraph®-GT3X), and the respective IPAQ and GLTEQ scores. Additionally, associations of PA with disease-specific characteristics, aerobic capacity (VO2peak), walking assessments (Timed 25-Foot Walk, T25FW; 2-Minute Walk Test, 2MWT) and walking perception (Multiple Sclerosis Walking Scale-12; MSWS-12) were explored. Patient subgroups with mild (Expanded Disability Status Scale; EDSS score <4.0) and moderate to severe disability (EDSS ≥4.0) were analysed for the impact of ambulatory impairment on PA. Multiple linear regression was used to determine predictors of PA. ResultsA total of 56 pwMS completed the study, with a mean (standard deviation, SD) age of 48.4 (10.3) years, disease duration of 14.8 (9.6) years and median (interquartile range) EDSS score of 3.5 (2.0 – 4.4). Moderate to weak correlations were found between daily step count and IPAQ total metabolic equivalent (MET) minutes/week (p < 0.001; r = 0.506), MVPA MET-minutes/week (p < 0.01; r = 0.479) and walking MET-minutes/week (p < 0.05; r = 0.372) in the total cohort. Time spent sitting was inversely correlated with total MET-minutes/week and MVPA MET-minutes/week (p < 0.05; r = -0.358 and r = -0.365). Subgroup analysis revealed, that daily step count was significantly correlated with total MET-minutes/week, MVPA MET-minutes/week and walking MET-minutes/week (p < 0.01, r = 0.569; p < 0.01, r = 0.531 and p < 0.05, r = 0.480, respectively) in the “mild disability” subgroup only, whereas time spent sitting was inversely correlated with total MET-minutes/week (p < 0.05; r = -0.582) in the “moderate to severe disability” subgroup. There was no association between objectively assessed PA and GLTEQ scores in any group. In the total cohort, moderate to weak correlations were found between daily step count and walking assessments (T25FW: p < 0.01, ρ = -0.508; 2MWT: p < 0.01, ρ=0.463) and MSWS-12 (p < 0.001; ρ = -0.609). Moderate to weak correlations were also observed between VO2peak and walking assessments (T25FW: p < 0.01; ρ = -0.516; 2MWT: p < 0.01, ρ=0.480). Multiple linear regression analysis identified disability and VO2peak as predictors of PA (p = 0.045; β=0.25 and p < 0.001; β=0.49). ConclusionSignificant associations of objective PA measurements using accelerometry with IPAQ were found only in pwMS with “mild disability”. In pwMS with “moderate to severe disability”, IPAQ did not reflect the objectively assessed amount of PA. In our cohort, GLTEQ showed no association with objectively assessed PA. Thus, an MS-specific self-reported questionnaire for assessing PA is warranted.

Creating healthy eating and active environments in early learning settings: protocol of the CHEERS eHealth intervention study.

Early childhood educators through their daily interactions with children, play a central role in shaping young children's health behaviors. Given their influential role, early childhood educators are often targeted in interventions aiming at enhancing their nutrition and physical activity practices. This paper presents the design of the CHEERS eHealth program to improve nutrition and physical activity practices within Early Childhood Education and Care (ECEC) centers. The study has a longitudinal quasi-experimental design with recruitment of ECECs across Alberta Canada. ECEC intervention group educators complete 12 weekly online nutrition and physical activity modules and participate in weekly communities of practice sessions to discuss practical applications within their centers. Outcome assessments are scheduled at baseline (T1), mid-point at 5 months (T2), and end of program after 10 months (T3). Outcome measures include the Creating Healthy Eating and Active Environments survey (CHEERS), Mindful Eating Questionnaire (MEQ), Canadian Behavior, Attitude and Nutrition Knowledge Survey (C-BANKS 2.0), Physical Literacy Knowledge, Attitude, Self-Efficacy, and Behavior (PLKASB-ECE), the Environment and Policy Assessment and Observation (EPAO) derived variables, and an objective measure of children's physical activity using ActiGraph GT3X accelerometers. Linear mixed model analyses will be used to evaluate the effectiveness of the intervention. Qualitative assessments comprise exit interviews and open-response questions embedded within the educational modules. Preliminary baseline data from the 2019 cohort indicate no statistically significant differences between the intervention and control groups for the primary outcome variables, except age. Educators' personal nutrition-related knowledge, attitude and behaviors were positively associated with their self-assessments of the nutrition environment and practices in ECECs. A significant correlation was observed between educators' self-reported physical activity practices and observed activity practices. The CHEERS survey Food Served subscale showed a positive correlation with the objective measures of EPAO-Foods Provided and Nutrition Policy subdomains. We propose that this eHealth intervention would be an effective scaling up approach to enhancing the nutrition and physical activity environments of ECECs by fostering improved nutrition and physical activity-related knowledge, attitudes, and adherence to best practices which will potentially lead to improved outcomes for children in their care.

Cardiorespiratory Fitness and Physical Activity in Pediatric Diabetes

It is unclear whether cardiorespiratory fitness (CRF) and physical activity are lower among youths with type 1 diabetes (T1D) and type 2 diabetes (T2D) compared with youths without diabetes. To describe the magnitude, precision, and constancy of the differences in CRF and physical activity among youths with and without diabetes. MEDLINE, Embase, CINAHL, and SPORTDiscus were searched from January 1, 2000, to May 1, 2022, for eligible studies. Observational studies with measures of CRF and physical activity in children and adolescents aged 18 years or younger with T1D or T2D and a control group were included. Data extraction was completed by 2 independent reviewers. A random-effects meta-analysis model was used to estimate differences in main outcomes. The pooled effect estimate was measured as standardized mean differences (SMDs) with 95% CIs. The Preferred Reporting Items for Systematic Review and Meta-Analyses guideline was followed. The main outcomes were objectively measured CRF obtained from a graded maximal exercise test and subjective or objective measures of physical activity. Subgroup analyses were performed for weight status and measurement type for outcome measures. Of 7857 unique citations retrieved, 9 studies (755 participants) with measures of CRF and 9 studies (1233 participants) with measures of physical activity for youths with T2D were included; for youths with T1D, 23 studies with measures of CRF (2082 participants) and 36 studies with measures of PA (12 196 participants) were included. Random-effects models revealed that directly measured CRF was lower in youths with T2D (SMD, -1.06; 95% CI, -1.57 to -0.56; I2 = 84%; 9 studies; 755 participants) and in youths with T1D (SMD, -0.39; 95% CI, -0.70 to -0.09; I2 = 89%; 22 studies; 2082 participants) compared with controls. Random-effects models revealed that daily physical activity was marginally lower in youths with T1D (SMD, -0.29; 95% CI, -0.46 to -0.11; I2 = 89%; 31 studies; 12 196 participants) but not different among youths with T2D (SMD, -0.56; 95% CI, -1.28 to 0.16; I2 = 91%; 9 studies; 1233 participants) compared with controls. When analyses were restricted to studies with objective measures, physical activity was significantly lower in youths with T2D (SMD, -0.71; 95% CI, -1.36 to -0.05; I2 = 23%; 3 studies; 332 participants) and T1D (SMD, -0.67; 95% CI, -1.17 to -0.17; I2 = 93%; 12 studies; 1357 participants) compared with controls. These findings suggest that deficits in CRF may be larger and more consistent in youths with T2D compared with youths with T1D, suggesting an increased risk for cardiovascular disease-related morbidity in adolescents with diabetes, particularly among those with T2D. The findings reinforce calls for novel interventions to empower youths living with diabetes to engage in regular physical activity and increase their CRF.

Feasibility of wearable activity tracking devices to measure physical activity and sleep change among adolescents with chronic pain—a pilot nonrandomized treatment study

PurposePersonal informatics devices are being used to measure engagement in health behaviors in adults with chronic pain and may be appropriate for adolescent use. The aim of this study was to evaluate the utilization of a wearable activity tracking device to measure physical activity and sleep among adolescents attending a three-week, intensive interdisciplinary pain treatment (IIPT) program. We also assessed changes in physical activity and sleep from baseline to the treatment phase.MethodsParticipants (57.1% female, average age 15.88, SD = 1.27) wore an activity tracking device three weeks prior to starting and during the treatment program.ResultsOf 129 participants contacted, 47 (36.4%) agreed to participate. However, only 30 (64%) complied with the instructions for using the device prior to programming and during program participation. Preliminary analyses comparing averages from 3-weeks pre-treatment to 3-weeks during treatment indicated increases in daily overall activity minutes, daily step counts, and minutes of moderate to vigorous physical activity (by 353%), as well as a corresponding decrease in sedentary minutes. There was more missing data for sleep than anticipated.ConclusionsWearable activity tracking devices can be successfully used to measure adolescent physical activity in-person, with more difficulty obtaining this information remotely. Adolescents with chronic pain experience improvements in objective measurements of physical activity over the course of a 3-week IIPT program. Future studies may want to spend more time working with pediatric patients on their understanding of how to use trackers for sleep and physical activity.

Household Food Insecurity Is Associated With Physical Activity in Youth and Young Adults With Diabetes: A Cross-Sectional Study.

Physical activity (PA) is essential for optimal diabetes management. Household food insecurity (HFI) may negatively affect diabetes management behaviors. The purpose of this study was to cross-sectionally examine the association between HFI and PA in youth and young adults (YYA) with type 1 (N = 1998) and type 2 (N = 391) diabetes from the SEARCH for Diabetes in Youth Study. HFI was measured with the US Household Food Security Survey Module. PA was measured with the International Physical Activity Questionnaire Short Form. Walking, moderate-intensity PA (excluding walking), vigorous-intensity PA, moderate- to vigorous-intensity PA, and total PA were estimated as minutes per week, while time spent sitting was assessed in minutes per day. All were modeled with median regression. Meeting PA guidelines or not was modeled using logistic regression. YYA with type 1 diabetes who experienced HFI spent more time walking than those who were food secure. YYA with type 2 diabetes who experienced HFI spent more time sitting than those who were food secure. Future research should examine walking for leisure versus other domains of walking in relation to HFI and use objective PA measures to corroborate associations between HFI and PA in YYA with diabetes.

The Quantitative Impact of Visual Function on Accelerometer-measured Physical Activity in Older United States Adults: A Nationwide Cross-sectional Analysis

ObjectiveTo explore the impact of objective vision measures on novel metrics of objectively-measured physical activity (PA) in a nationally representative sample of US older adults. DesignCross-sectional analysis using data from the National Health and Aging Trends Study. ParticipantsAdults had their distance and near visual acuity (VA) and contrast sensitivity (CS) tested. Any objective vision impairment (VI), defined as any VI in distance VA, near VA, or CS, was the primary exposure. PA data were collected using the Actigraph CentrePoint Insight Watch worn for 7 days. MethodsMultivariable regression models were used to investigate the association between vision and PA measures. All analyses accounted for the survey design and models were adjusted for age, sex, race, living arrangement, education, and comorbidities. Main OutcomesPA metrics included: 1) total daily activity (active minutes per day, number of active bouts, and mean length of active bouts), 2) activity fragmentation, and 3) time until 75% activity. An active bout was defined as ≥1 consecutive active minute. Activity fragmentation was defined as the probability of an active minute being followed by a sedentary minute, with higher values indicating more fragmented activity. Time until 75% activity was defined as the time taken to complete 75% of daily PA starting from their first active bout. ResultsAmong 723 participants, sampled from 10,443,338 older adults in the US, 30% had any objective VI. Any objective VI was significantly associated with lower number of active minutes per day (7.8% fewer, [95% CI: −1.7% to −13.6%]), shorter active bouts (7.0 % shorter, [95% CI: −1.4% to −12.3%]), and greater activity fragmentation (β= 2.5%, [95% CI: 0.8% to 4.2%]), while no associations were found with number of active bouts. Time until 75% activity did not significantly differ between adults with any objective VI and those without (p=0.34). ConclusionsOlder US adults with any objective VI displayed lower total daily activity, as well as more fragmented, shorter periods of PA, despite having a similar number of active bouts compared to their normally sighted counterparts. Implementing interventions that increase bout duration may help promote physical activity in adults with VI.

THE LIFESTYLE OF MEN AND WOMEN AGED 50+ IN TAIWAN: ACTIVITY ON TIME OF DAY AND THEIR CIRCADIAN RHYTHMS

Abstract Background To identify the association of circadian rhythms and objective measures of physical activity at a different time-zone of day, as well as examine gender differences. Methods Participants aged 50 years and older living in the community in Taiwan were recruited. Those who suffer from dementia (cognitive dysfunction), mental illness, depression, or those taking drugs that affect daytime activities (e.g. sleeping pills, central nervous system drugs) were excluded. Activity was measured by wearable actigraphy device for at least seven days, diary, and self-reported questionnaires. Results Among the 55 participants enrolled in the beginning, 34 (62%) meet the criteria that wore a wearable actigraphy device at least 7 completed days were analyzed. The activity in the morning was the highest among the participants. Although not statistically significant, as the age increases, the amount of activity decreases (H value=4.943, P=0.084); the amount of activity in the evening is higher for people with higher education(Z=-1.776, P=0.076); people with lower loneliness had higher activity levels (Z=-1.622, P=0.105); people with better nutritional status had higher activity (Z=-1.656, P=0.098). The amount of activity in women is positively and significantly related to regular exercise (Z=-2.653, P=0.007**), and nutrition(Z=-2.367, P=0.017*), while men are significantly related to retired status(Z=-2.132, P=0.033*). Conclusions People who are older, retired, with chronic disease, more depression, and loneliness were associated with lower objective measure of physical activity. Determinant factors exist gender differences: while men are predicted by social variables (retired status), women are predicted by physiological variables (regular physical activity and nutritional status).

Abstract 15700: What Role Does Physical Activity Play in Health-Related Quality of Life (HRQoL) of Children With Hypertrophic Cardiomyopathy (HCM)?

Introduction: Higher levels of physical activity (PA) have been associated with better health-related quality of life (HRQoL). Whether this is true for patients counselled to restrict PA is unknown. Our aim was to examine the relationship between objective measures of PA and HRQoL in children with hypertrophic cardiomyopathy (HCM). Methods: A cross-sectional study of children with HCM, aged 10-18 yrs, enrolled from 10 Canadian sites (2016-23), who underwent cardiovascular screening including the PedsQL Quality of Life Inventory to measure HRQoL. PA measured via Fitbit occurred over 2 weeks. PA parameters included average daily heart rate, step-count, distance covered (Kms), stair flights, activity and sedentary time. Multiple linear regressions were performed to assess the relationship between PA and HRQoL. Covariate adjustments included: age/sex/BMI class at enrollment, cardiac arrest history, previous ICD implant and PA restriction. Correlation matrix was calculated for PA parameters. Results: Fifty-six patients were enrolled [71.4% male; median age 15.5 yrs (IQR 13.8-16.8), median BMI 23.3 Kg/m 2 (IQR 19.5-27.7)], including 11% with a cardiac arrest, 30.4% with an ICD implant and 75% PA restricted. The Fitbit was worn for a median of 14 days (IQR 13.3-14). Average daily steps were 7586 (IQR 5333-10324]) and median sedentary time 893 mins (IQR 735-1141). After adjustment, the average daily steps were associated with Physical HRQoL scores [mean difference (MD) of 2.0 [95% CI 0.3-3.0] per each 1000 steps increase (p=0.02)]. Similar effect sizes were seen for Physical HRQoL with average daily Kms, active time and stairs flights. These outcomes were highly correlated with daily steps (Spearman's ρ; 0.97, 0.81 and 0.6). Conversely, 100 mins of sedentary time decreased mean Physical HRQoL scores by 2 (MD = -2 [-4--0.4]; p=0.02). Only average daily stair flights (MD = 0.6 [0.1-1.1]; p=0.02) was associated with a higher Total HRQoL. Conclusions: Increasing PA in children with HCM, especially with flights of stairs, was associated with higher Physical/Total HRQoL, whereas sedentary time had a determinantal effect on Physical HRQoL. Efforts to decrease sedentary time and improve PA may improve the overall physical and total HRQoL of children with HCM.

Very brief intervention for physical activity behaviour change in cardiac rehabilitation: protocol for the ‘Measure It!’ effectiveness–implementation hybrid trial

IntroductionPhysical inactivity is a risk factor for repeat cardiac events and all-cause mortality in coronary heart disease (CHD). Cardiac rehabilitation, a secondary prevention programme, aims to increase physical activity levels...

Objective physical activity measures to predict hospitalizations among patients with cancer receiving chemoradiotherapy.

592 Background: There is an unmet need for measures that predict adverse events during cancer therapy. Previous studies have demonstrated feasibility and value of collecting physical activity (PA) data from cancer patients undergoing curative-intent chemoradiotherapy using a commercial fitness tracker. Here, we compare the predictive value of objective PA measures versus standard clinical information and assess whether additional PA measures beyond daily step count help predict hospitalization events during treatment. Methods: Cancer patients enrolled in NCT03115398 (2017-2020) wore a Garmin VivoFit continuously during chemoradiotherapy for a variety of solid tumors. ECOG performance status (PS) was assessed at baseline. PA measures tabulated for each day included total step count, peak 30-minute cadence (average step rate over the 30 most active minutes [not necessarily consecutive]), and sedentary time. Baseline PA measures were defined as mean values from up to 14 days prior to start of chemoradiotherapy. Cox regression models with time-dependent (3-day rolling averages of PA measures and recent EORTC QLQ-C30 quality of life [QoL] score) and time-fixed (baseline PA variables, age, sex, cancer diagnosis) covariates were used to identify predictors of hospitalization. The incremental predictive value of PA measures was assessed using logistic regression models and five-fold cross validation. Results: One hundred thirty-eight patients were included in the analysis. The most common cancer diagnoses were gastrointestinal cancer (29%) and head and neck cancer (28%). Twenty-four patients (17%) were hospitalized during chemoradiotherapy. Univariate Cox regression models identified baseline step count (hazard ratio [HR] = 0.85 per 1000 steps, 95% confidence interval [CI]: 0.74-0.98, p = 0.03) and baseline peak 30-minute cadence (HR = 0.86 per 10 steps/min, 95% CI: 0.74-0.99, p = 0.03) as the most significant prognostic predictors of hospitalization. Neither baseline PS nor baseline sedentary time were significant predictors of hospitalization risk. Including both baseline step count and peak 30-minute cadence in multivariable models did not improve predictive power. During treatment, peak 30-minute cadence was identified as the most powerful dynamic predictor of short-term hospitalization risk (HR = 0.83 per 10 steps/min, 95% CI: 0.75-0.92, p &lt; 0.001). Conclusions: This study examined the relationship between a suite of PA measures and risk of hospitalization during concurrent chemoradiotherapy. Our results suggest that before treatment, daily step count average can serve as a simple and effective metric for identifying high-risk patients. During treatment, measures of peak activity may have additional utility for identifying patients in need of enhanced supportive care.

Measuring objective physical activity in people with chronic low back pain using accelerometers: a scoping review.

Accelerometers can be used to objectively measure physical activity. They could be offered to people with chronic low back pain (CLBP) who are encouraged to maintain an active lifestyle. The aim of this study was to examine the use of accelerometers in studies of people with CLBP and to synthesize the main results regarding the measurement of objective physical activity. A scoping review was conducted following Arksey and O'Malley's framework. Relevant studies were collected from 4 electronic databases (PubMed, Embase, CINHAL, Web of Science) between January 2000 and July 2023. Two reviewers independently screened all studies and extracted data. 40 publications out of 810 citations were included for analysis. The use of accelerometers in people with CLBP differed across studies; the duration of measurement, physical activity outcomes and models varied, and several limitations of accelerometry were reported. The main results of objective physical activity measures varied and were sometimes contradictory. Thus, they question the validity of measurement methods and provide the opportunity to discuss the objective physical activity of people with CLBP. Accelerometers have the potential to monitor physical performance in people with CLBP; however, important technical limitations must be overcome.

Associations Between Adverse Childhood Experiences and Early Adolescent Physical Activity in the United States

ObjectiveTo determine the associations between the number of adverse childhood experiences (ACEs) and objectively-measured physical activity (PA) in a population-based, demographically diverse cohort of 9–14-year-olds and to determine which subtypes of ACEs were associated with physical activity levels. MethodsWe analyzed data (n = 7046) from the Adolescent Brain Cognitive Development (ABCD) Study 4.0 release at baseline and year 2 follow-up. ACE (cumulative score and subtypes) and physical activity (average Fitbit daily steps assessed at Year 2) were analyzed using linear regression analyses. Covariates included race and ethnicity, sex, household income, parent education, body mass index, study site, twins/siblings, and data collection period. ResultsAdjusted models suggest an inverse association between number of ACEs and Fitbit daily steps, with ≥4 (compared to 0) ACEs associated with 567 fewer daily steps (95% CI -902.2, -232.2). Of the ACEs subtypes, emotional abuse (B = −719.3, 95% CI −1430.8, −7.9), physical neglect (B = −423.7, 95% CI −752.8, −94.6), household mental illness (B = −317.1, 95% CI −488.3, −145.9), and household divorce or separation (B = −275.4, 95% CI −521.5, −29.2) were inversely and statistically significant associated with Fitbit daily steps after adjusting for confounders. ConclusionsOur results suggest that there is an inverse, dose-dependent relationship between cumulative number of ACEs and physical activity as measured by daily steps. This work highlights the importance of screening for ACEs among young people at an early age to help identify those who could benefit from interventions or community programs that support increased physical activity.

How Could Sensor-Based Measurement of Physical Activity Be Used in Cardiovascular Healthcare?

Physical activity and cardiovascular disease (CVD) are intimately linked. Low levels of physical activity increase the risk of CVDs, including myocardial infarction and stroke. Conversely, when CVD develops, it often reduces the ability to be physically active. Despite these largely understood relationships, the objective measurement of physical activity is rarely performed in routine healthcare. The ability to use sensor-based approaches to accurately measure aspects of physical activity has the potential to improve many aspects of cardiovascular healthcare across the spectrum of healthcare, from prediction, prevention, diagnosis, and treatment to disease monitoring. This review discusses the potential of sensor-based measurement of physical activity to augment current cardiovascular healthcare. We highlight many factors that should be considered to maximise the benefit and reduce the risks of such an approach. Because the widespread use of such devices in society is already a reality, it is important that scientists, clinicians, and healthcare providers are aware of these considerations.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a ‘clock’ of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal ‘clock’ of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman’s), indicating that our model assigns a more advanced GA when an individual’s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Adverse Childhood Experiences and Accelerometer-Measured Physical Activity and Sleep in Preadolescents

ObjectiveTo assess the relationship between adverse childhood experiences (ACEs) and objective measures of physical activity and sleep. MethodsData from the baseline and 2-year follow-up of the Adolescent Brain and Cognitive Development study were analyzed (n = 6227 for physical activity; n = 4151 for sleep). ACEs were assessed by parent report at baseline (mean age 9.9 years) with 3 levels: none, exposure to 1 ACE, and exposure to 2 or more ACEs. Objective measures of physical activity and sleep were assessed with an accelerometer at 2-year follow-up (mean age 11.9 years). Multivariate linear regression analyses were used to examine the relationship between ACEs and physical activity as well as sleep, adjusting for family income and sex. ResultsCompared to children with no ACEs, children with ACEs had fewer daily steps: 1 ACE (β = −323 (95% CI: −508 to −138), P < .001) and 2 or more ACEs (β = −417 (95% CI: −624 to −209), P < .001). ACEs were also associated with shorter sleep duration (minutes), although only for participants with 2 or more ACEs (1 ACE: β = −2.2 (−5.3 to 0.8), P = .16; 2 or more ACEs: β = −6.2 (95% CI: −9.6 to −2.7), P < .001). Rapid eye movement (REM) sleep specifically was reduced in participants with ACEs (1 ACE (β = −1.4 (−2.7 to −0.01), P = .05) and 2 or more ACEs (β = −2.3 (−3.8 to −0.8), P = .003). ConclusionsThere is a dose-response relationship between ACEs and reduced daily steps, total sleep duration, and REM sleep in preadolescents.