Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors.

BACKGROUND: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). METHODS: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. RESULTS: At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized ß ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. CONCLUSION: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. TRIAL REGISTRATION: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.

Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE).

BACKGROUND/OBJECTIVES: Sedentary behavior (SB) has both movement and postural components, but most SB research has only assessed low movement, especially in children. The purpose of this study was to compare estimates and health associations of SB when derived from a standard accelerometer cut-point, a novel sitting detection technique (CNN Hip Accelerometer Posture for Children; CHAP-Child), and both combined. METHODS: Data were from the International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE). Participants were 6103 children (mean ± SD age 10.4 ± 0.56 years) from 12 countries who wore an ActiGraph GT3X+ accelerometer on the right hip for approximately one week. We calculated SB time, mean SB bout duration, and SB breaks using a cut-point (SBmovement), CHAP-Child (SBposture), and both methods combined (SBcombined). Mixed effects regression was used to test associations of SB variables with pediatric obesity variables (waist circumference, body fat percentage, and body mass index z-score). RESULTS: After adjusting for MVPA, SBposture showed several significant obesity associations favoring lower mean SB bout duration (b = 0.251-0.449; all p < 0.001) and higher SB breaks (b = -0.005--0.052; all p < 0.001). Lower total SB was unexpectedly related to greater obesity (b = -0.077--0.649; p from <0.001-0.02). For mean SB bout duration and SB breaks, more associations were observed for SBposture (n = 5) than for SBmovement (n = 3) or SBcombined (n = 1), and tended to have larger magnitude as well. CONCLUSIONS: Using traditional measures of low movement as a surrogate for SB may lead to underestimated or undetected adverse associations between SB and obesity. CHAP-Child allows assessment of sitting posture using hip-worn accelerometers. Ongoing work is needed to understand how low movement and posture are related to one another, as well as their potential health implications.

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.

Community-Dwelling Older Adults and Physical Activity Recommendations: Patterns of Aerobic, Strengthening, and Balance Activities.

Though it is known that most older adults do not meet the recommended physical activity (PA) guidelines, little is known regarding their participation in balance activities or the full guidelines. Therefore, we sought to describe PA patterns among 1,352 community-dwelling older adult participants of the Adult Changes in Thought study, a longitudinal cohort study exploring dementia-related risk factors. We used a modified version of the Community Healthy Activities Model Program for Seniors questionnaire to explore PA performed and classify participants as meeting or not meeting the full guidelines or any component of the guidelines. Logistic regression was used to identify factors associated with meeting PA guidelines. Despite performing 10 hr of weekly PA, only 11% of participants met the full guidelines. Older age, greater body mass index, needing assistance with instrumental daily activities, and heart disease were associated with decreased odds of meeting PA guidelines. These results can guide interventions that address PA among older adults.

Associations Between Perceived Neighborhood Walkability and Device-Based Physical Activity and Sedentary Behavior Patterns in Older Adults.

Neighborhood walkability has been associated with self-reported sedentary behavior (SB) and self-reported and objective physical activity. However, self-reported measures of SB are inaccurate and can lead to biased estimates, and few studies have examined how associations differ by gender and age. The authors examined the relationships between perceived neighborhood walkability measured with the Physical Activity Neighborhood Environment Scale (scored 1.0-4.0) and device-based SB and physical activity in a cohort of community-dwelling older adults (N = 1,077). The authors fit linear regression models adjusting for device wear time, demographics, self-rated health, and accounting for probability of participation. The Higher Physical Activity Neighborhood Environment Scale was associated with higher steps (+676 steps/point on the Physical Activity Neighborhood Environment Scale, p = .001) and sit-to-stand transitions (+2.4 transitions/point, p = .018). Though not statistically significant, stratified analyses suggest an attenuation of effect for those aged 85 years and older and for women. Consistent with previous literature, neighborhood walkability was associated with more steps, though not with physical activity time. The neighborhood environment may also influence SB.

Patient and caregiver perspectives on a tool to increase recognition of undiagnosed dementia: a qualitative study.

BACKGROUND: Early detection of dementia may improve patient care and quality of life, yet up to half of people with dementia are undiagnosed. Electronic health record (EHR) data could be used to help identify individuals at risk of having undiagnosed dementia for outreach and assessment, but acceptability to people with dementia and caregivers is unknown. METHODS: We conducted five focus groups at Kaiser Permanente Washington (KPWA), an integrated healthcare system in Washington State, to explore people's feelings about timing of dementia diagnosis, use of EHR-based tools to predict risk of undiagnosed dementia, and communication about risk. We recruited people enrolled in KPWA who had dementia or mild cognitive impairment, people enrolled in KPWA who had neither diagnosis, and caregivers (i.e., loved ones of people with dementia who assist with various tasks of daily life). People who were non-white or Hispanic were oversampled. Two team members analyzed transcripts using thematic coding. RESULTS: Forty people (63% women; 59% non-white or Hispanic) participated in the focus groups. Themes that arose included: perceived pros and cons of early dementia diagnosis; questions and concerns about a potential tool to assess risk of undiagnosed dementia; and preferences related to patient-provider conversations disclosing that a person was at high risk to have undiagnosed dementia. Participants supported early diagnosis, describing benefits such as time to adjust to the disease, plan, involve caregivers, and identify resources. They also acknowledged the possible psychosocial toll of receiving the diagnosis. Participants supported use of an EHR-based tool, but some people worried about accuracy and privacy. Participants emphasized that information about risk of undiagnosed dementia should be communicated thoughtfully by a trusted provider and that the conversation should include advice about prognosis, treatment options and other resources when a new dementia diagnosis was made. CONCLUSION: People with dementia or mild cognitive impairment, people with neither diagnosis, and caregivers of people with dementia supported using EHR-based tools to help identify individuals at risk of having undiagnosed dementia. Such tools must be implemented carefully to address concerns and ensure that people living with dementia and their caregivers are adequately supported.

Associations between physical function and device-based measures of physical activity and sedentary behavior patterns in older adults: moving beyond moderate-to-vigorous intensity physical activity.

BACKGROUND: Research supports that moderate-to-vigorous intensity physical activity (MVPA) is key to prolonged health and function. Among older adults, substantial changes to MVPA may be infeasible, thus a growing literature suggests a shift in focus to whole-day activity patterns. METHODS: With data from 795 older adults aged 65-100 in the Adult Changes in Thought Activity Monitoring study, we used linear regression to estimate associations between ActiGraph and activPAL measured activity patterns - including light intensity physical activity, steps, standing, and sedentary behaviors - and physical function as measured by a short Performance-based Physical Function (sPPF) score (range 0-12), a composite score based on three standardized physical performance tasks: gait speed, timed chair stands, and grip strength. We examined whether relationships persisted when controlling for MVPA or differed across age, gender, or quartiles of MVPA. RESULTS: In models unadjusted for MVPA, a 1-standard deviation (SD) increment of daily sitting (1.9 h more), mean sitting bout duration (8 min longer average), or time spent in sedentary activity (1.6 h more) was associated with ~ 0.3-0.4 points lower mean sPPF score (all p < 0.05). A 1-SD increment in daily steps (~ 3500 more steps) was associated with ~ 0.5 points higher mean sPPF score (95% CI: 0.22 to 0.73). MVPA adjustment attenuated all relationships. The association between physical function and steps was strongest among adults aged 75+; associations of worse function with greater sedentary behavior were more pronounced in participants with the lowest levels of MVPA. CONCLUSIONS: We found associations between function and activity metrics other than MVPA in key subgroups, findings that support research on broader activity patterns and may offer ideas regarding practical intervention opportunities for improving function in older adults.

Device-assessed physical activity and sedentary behavior in a community-based cohort of older adults.

BACKGROUND: Few studies characterize older adult physical activity and sitting patterns using accurate accelerometer and concurrent posture measures. In this descriptive paper, we report accelerometer data collection protocols, consent rates, and physical behavior measures from a population-based cohort study (Adult Changes in Thought, ACT). METHODS: The ACT study holds enrollment steady at approximately 2000 members of Kaiser Permanente Washington aged 65+ without dementia undergoing detailed biennial assessments. In 2016 the ACT-Activity Monitor (ACT-AM) sub-study was initiated to obtain data from wearing activPAL and ActiGraph devices for 7 days following regular biennial visits. We describe the methods protocol of ACT-AM and present characteristics of people who did and did not consent to wear devices. We compute inverse probability of response weights and incorporate these weights in linear regression models to estimate means and 95% confidence intervals (CI) of device-based pattern metrics, adjusted for wear time and demographic factors, and weighted to account for potential selection bias due to device-wear consent. RESULTS: Among 1885 eligible ACT participants, 56% agreed to wear both devices (mean age 77 years, 56% female, 89% non-Hispanic white, 91% with post-secondary education). On average, those who agreed to wear devices were younger and healthier. Estimated mean (95% CI) activPAL-derived sitting, standing, and stepping times were 10.2 h/day (603-618 min/day), 3.9 h/day (226-239 min/day), and 1.4 h/day (79-84 min/day), respectively. Estimated mean ActiGraph derived sedentary (Vector Magnitude [VM] < =18 counts/15 s), light intensity (VM 19-518 counts/15 s), and moderate-to-vigorous intensity (VM > 518 counts/15 s) physical activity durations were 9.5 h/day (565-577 min/day), 4.5 h/day (267-276 min/day), and 1.0 h/day (59-64 min/day). Participants who were older, had chronic conditions, and were unable to walk a half-mile had higher sedentary time and less physical activity. CONCLUSIONS: Our recruitment rate demonstrates the feasibility of cohort participants to wear two devices that measure sedentary time and physical activity. Data indicate high levels of sitting time in older adults but also high levels of physical activity using cut-points developed for older adults. These data will help researchers test hypotheses related to physical behavior and health in older adults in the future.

Reducing Sitting Time in Obese Older Adults: The I-STAND Randomized Controlled Trial.

BACKGROUND: The authors tested the efficacy of the "I-STAND" intervention for reducing sitting time, a novel and potentially health-promoting approach, in older adults with obesity. METHODS: The authors recruited 60 people (mean age = 68 ± 4.9 years, 68% female, 86% White; mean body mass index = 35.4). The participants were randomized to receive the I-STAND sitting reduction intervention (n = 29) or healthy living control group (n = 31) for 12 weeks. At baseline and at 12 weeks, the participants wore activPAL devices to assess sitting time (primary outcome). Secondary outcomes included fasting glucose, blood pressure, and weight. Linear regression models assessed between-group differences in the outcomes. RESULTS: The I-STAND participants significantly reduced their sitting time compared with the controls (-58 min per day; 95% confidence interval [-100.3, -15.6]; p = .007). There were no statistically significant changes in the secondary outcomes. CONCLUSION: I-STAND was efficacious in reducing sitting time, but not in changing health outcomes in older adults with obesity.

Patterns of Surveillance Advanced Imaging and Serum Tumor Biomarker Testing Following Launch of the Choosing Wisely Initiative.

BACKGROUND: The purpose of this study was to assess advanced imaging (bone scan, CT, or PET/CT) and serum tumor biomarker use in asymptomatic breast cancer survivors during the surveillance period. PATIENTS AND METHODS: Cancer registry records for 2,923 women diagnosed with primary breast cancer in Washington State between January 1, 2007, and December 31, 2014, were linked with claims data from 2 regional commercial insurance plans. Clinical data including demographic and tumor characteristics were collected. Evaluation and management codes from claims data were used to determine advanced imaging and serum tumor biomarker testing during the peridiagnostic and surveillance phases of care. Multivariable logistic regression models were used to identify clinical factors and patterns of peridiagnostic imaging and biomarker testing associated with surveillance advanced imaging. RESULTS: Of 2,923 eligible women, 16.5% (n=480) underwent surveillance advanced imaging and 31.8% (n=930) received surveillance serum tumor biomarker testing. Compared with women diagnosed before the launch of the Choosing Wisely campaign in 2012, later diagnosis was associated with lower use of surveillance advanced imaging (odds ratio [OR], 0.68; 95% CI, 0.52-0.89). Factors significantly associated with use of surveillance advanced imaging included increasing disease stage (stage III: OR, 3.65; 95% CI, 2.48-5.38), peridiagnostic advanced imaging use (OR, 1.76; 95% CI, 1.33-2.31), and peridiagnostic serum tumor biomarker testing (OR, 1.35; 95% CI, 1.01-1.80). CONCLUSIONS: Although use of surveillance advanced imaging in asymptomatic breast cancer survivors has declined since the launch of the Choosing Wisely campaign, frequent use of surveillance serum tumor biomarker testing remains prevalent, representing a potential target for further efforts to reduce low-value practices.

Performance of digital screening mammography in a population-based cohort of black and white women.

PURPOSE: There is scarce information on whether digital screening mammography performance differs between black and white women. METHODS: We examined 256,470 digital screening mammograms performed from 2005 to 2010 among 31,654 black and 133,152 white Carolina Mammography Registry participants aged ≥40 years. We compared recall rate, sensitivity, specificity, and positive predictive value (PPV1) between black and white women, adjusting for potential confounders using random effects logistic regression. RESULTS: Breast cancer was diagnosed in 276 black and 1,095 white women. Recall rates were similar for blacks and whites (8.6 vs. 8.5 %), as were sensitivity (83.7 vs. 82.4 %), specificity (91.8 vs. 91.9 %), and PPV1 (4.8 vs. 5.3 %) (all p values >0.05). Stratified and adjusted models showed similar results. Despite comparable mammography performance, tumors diagnosed in black women were more commonly poorly differentiated and hormone receptor negative. CONCLUSION: Equivalent performance of digital screening mammography by race suggests that efforts to understand tumor disparities should focus on etiologic factors that influence tumor biology.

Participation in Older Adult Physical Activity Programs and Risk for Falls Requiring Medical Care, Washington State, 2005-2011.

INTRODUCTION: Physical activity is known to prevent falls; however, use of widely available exercise programs for older adults, including EnhanceFitness and Silver Sneakers, has not been examined in relation to effects on falls among program participants. We aimed to determine whether participation in EnhanceFitness or Silver Sneakers is associated with a reduced risk of falls resulting in medical care. METHODS: A retrospective cohort study examined a demographically representative sample from a Washington State integrated health system. Health plan members aged 65 or older, including 2,095 EnhanceFitness users, 13,576 Silver Sneakers users, and 55,127 nonusers from 2005 through 2011, were classified as consistent users (used a program ≥2 times in all years they were enrolled in the health plan during the study period); intermittent users (used a program ≥2 times in 1 or more years enrolled but not all years), or nonusers of EnhanceFitness or Silver Sneakers. The main outcome was measured as time-to-first-fall requiring inpatient or out-of-hospital medical treatment based on the International Classification of Diseases, 9th Revision, Clinical Modification, Sixth Edition and E-codes. RESULTS: In fully adjusted Cox proportional hazards models, consistent (hazard ratio [HR], 0.74; 95% confidence interval [CI], 0.63-0.88) and intermittent (HR, 0.87; 95% CI, 0.8-0.94) EnhanceFitness participation were both associated with a reduced risk of falls resulting in medical care. Intermittent Silver Sneakers participation showed a reduced risk (HR, 0.93; 95% CI, 0.90-0.97). CONCLUSION: Participation in widely available community-based exercise programs geared toward older adults (but not specific to fall prevention) reduced the risk of medical falls. Structured programs that include balance and strength exercise, as EnhanceFitness does, may be effective in reducing fall risk.

Historic Cognitive Function Trajectories as Predictors of Sedentary Behavior and Physical Activity in Older Adults.

BACKGROUND: We examined whether trajectories of cognitive function over 10 years predict later-life physical activity (PA), sedentary time (ST), and sleep. METHODS: Participants were from the Adult Changes in Thought (ACT) cohort study. We included 611 ACT participants who wore accelerometers and had 3+ measures of cognition in the 10 years prior to accelerometer wear. The Cognitive Assessment Screening Instrument (CASI) measured cognition and was scored using item-response theory (IRT). activPAL and ActiGraph accelerometers worn over 7 days measured ST and PA outcomes. Self-reported time in bed and sleep quality measured sleep outcomes. Analyses used growth mixture modeling to classify CASI-IRT scores into latent groups and examine associations with PA, ST, and sleep including demographic and health covariates. RESULTS: Participants (Mean age = 80.3 (6.5) years, 90.3% White, 57.1% female, 29.3% had less than 16 years of education) fell into 3 latent trajectory groups: average stable CASI (56.1%), high stable CASI (34.0%), and declining CASI (9.8%). The declining group had 16 minutes less stepping time (95% confidence interval [95% CI]: 0.6, 31.4), 1 517 fewer steps per day (95% CI: 138, 2 896), and 16.3 minutes per day less moderate-to-vigorous PA (95% CI: 1.3, 31.3) compared to the average stable group. There were no associations between CASI trajectory and sedentary or sleep outcomes. CONCLUSIONS: Declining cognition predicted lower PA providing some evidence of a reverse relationship between PA and cognition in older adults. However, this conclusion is limited by having outcomes at only one time point, a nonrepresentative sample, self-reported sleep outcomes, and using a global cognition measure.

Sitting Time Reduction and Blood Pressure in Older Adults: A Randomized Clinical Trial.

Importance: Practical health promotion strategies for improving cardiometabolic health in older adults are needed. Objective: To examine the efficacy of a sedentary behavior reduction intervention for reducing sitting time and improving blood pressure in older adults. Design, Setting, and Participants: This parallel-group randomized clinical trial was conducted in adults aged 60 to 89 years with high sitting time and body mass index of 30 to 50 from January 1, 2019, to November 31, 2022, at a health care system in Washington State. Intervention: Participants were randomized 1:1 to the sitting reduction intervention or a healthy living attention control condition for 6 months. Intervention participants received 10 health coaching contacts, sitting reduction goals, and a standing desk and fitness tracker to prompt sitting breaks. The attention control group received 10 health coaching contacts to set general healthy living goals, excluding physical activity or sedentary behavior. Main Outcomes and Measures: The primary outcome, measured at baseline, 3 months, and 6 months, was sitting time assessed using accelerometers worn for 7 days at each time point. Coprimary outcomes were systolic and diastolic blood pressure measured at baseline and 6 months. Results: A total of 283 participants (140 intervention and 143 control) were randomized (baseline mean [SD] age, 68.8 [6.2] years; 186 [65.7%] female; mean [SD] body mass index, 34.9 [4.7]). At baseline, 147 (51.9%) had a hypertension diagnosis and 97 (69.3%) took at least 1 antihypertensive medication. Sitting time was reduced, favoring the intervention arm, with a difference in the mean change of -31.44 min/d at 3 months (95% CI, -48.69 to -14.19 min/d; P < .001) and -31.85 min/d at 6 months (95% CI, -52.91 to -10.79 min/d; P = .003). Systolic blood pressure change was lower by 3.48 mm Hg, favoring the intervention arm at 6 months (95% CI, -6.68 to -0.28 mm Hg; P = .03). There were 6 serious adverse events in each arm and none were study related. Conclusions and Relevance: In this study of a 6-month sitting reduction intervention, older adults in the intervention reduced sedentary time by more than 30 min/d and reduced systolic blood pressure. Sitting reduction could be a promising approach to improve health in older adults. Trial Registration: ClinicalTrials.gov Identifier: NCT03739762.

Analysis of the 24-h activity cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought study.

The 24-h activity cycle (24HAC) is a new paradigm for studying activity behaviors in relation to health outcomes. This approach inherently captures the interrelatedness of the daily time spent in physical activity (PA), sedentary behavior (SB), and sleep. We describe three popular approaches for modeling outcome associations with the 24HAC exposure. We apply these approaches to assess an association with a cognitive outcome in a cohort of older adults, discuss statistical challenges, and provide guidance on interpretation and selecting an appropriate approach. We compare the use of the isotemporal substitution model (ISM), compositional data analysis (CoDA), and latent profile analysis (LPA) to analyze 24HAC. We illustrate each method by exploring cross-sectional associations with cognition in 1,034 older adults (Mean age = 77; Age range = 65-100; 55.8% female; 90% White) who were part of the Adult Changes in Thought (ACT) Activity Monitoring (ACT-AM) sub-study. PA and SB were assessed with thigh-worn activPAL accelerometers for 7-days. For each method, we fit a multivariable regression model to examine the cross-sectional association between the 24HAC and Cognitive Abilities Screening Instrument item response theory (CASI-IRT) score, adjusting for baseline characteristics. We highlight differences in assumptions and the scientific questions addressable by each approach. ISM is easiest to apply and interpret; however, the typical ISM assumes a linear association. CoDA uses an isometric log-ratio transformation to directly model the compositional exposure but can be more challenging to apply and interpret. LPA can serve as an exploratory analysis tool to classify individuals into groups with similar time-use patterns. Inference on associations of latent profiles with health outcomes need to account for the uncertainty of the LPA classifications, which is often ignored. Analyses using the three methods did not suggest that less time spent on SB and more in PA was associated with better cognitive function. The three standard analytical approaches for 24HAC each have advantages and limitations, and selection of the most appropriate method should be guided by the scientific questions of interest and applicability of each model's assumptions. Further research is needed into the health implications of the distinct 24HAC patterns identified in this cohort.

The electronic health record Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) Brain Health Trial: Protocol for an embedded, pragmatic clinical trial of a low-cost dementia detection algorithm.

BACKGROUND: About half of people living with dementia have not received a diagnosis, delaying access to treatment, education, and support. We previously developed a tool, eRADAR, which uses information in the electronic health record (EHR) to identify patients who may have undiagnosed dementia. This paper provides the protocol for an embedded, pragmatic clinical trial (ePCT) implementing eRADAR in two healthcare systems to determine whether an intervention using eRADAR increases dementia diagnosis rates and to examine the benefits and harms experienced by patients and other stakeholders. METHODS: We will conduct an ePCT within an integrated healthcare system and replicate it in an urban academic medical center. At primary care clinics serving about 27,000 patients age 65 and above, we will randomize primary care providers (PCPs) to have their patients with high eRADAR scores receive targeted outreach (intervention) or usual care. Intervention patients will be offered a "brain health" assessment visit with a clinical research interventionist mirroring existing roles within the healthcare systems. The interventionist will make follow-up recommendations to PCPs and offer support to newly-diagnosed patients. Patients with high eRADAR scores in both study arms will be followed to identify new diagnoses of dementia in the EHR (primary outcome). Secondary outcomes include healthcare utilization from the EHR and patient, family member and clinician satisfaction assessed through surveys and interviews. CONCLUSION: If this pragmatic trial is successful, the eRADAR tool and intervention could be adopted by other healthcare systems, potentially improving dementia detection, patient care and quality of life.

Exploring Differences in Older Adult Accelerometer-Measured Sedentary Behavior and Resting Blood Pressure Before and During the COVID-19 Pandemic.

Older adults have higher sedentary behavior (SB), lower physical activity, and are particularly susceptible to negative impacts from the COVID-19 pandemic and associated public health restrictions. Pandemic impacts to SB and health, particularly via objective assessment, are not well documented in the literature. Here we described differences in SB, physical activity, and blood pressure (BP) for older adults before and during the pandemic. Baseline thigh-worn activPAL accelerometer and BP measurements from 95 participants enrolled in a SB intervention trial pre-pandemic were compared to 60 enrolled post-pandemic. We used linear regression models adjusted for demographic and health factors to estimate differences in sample means of SB measures and BP. The post-COVID sample was older (age 67 vs. 70), more female (60% vs. 72%), and included more individuals of color (21% vs. 32%). In fully adjusted models, systolic BP was statistically significantly higher in the post-COVID group (6.8, 95% CI: [0.3,13.3]). After adjustment, activPAL-measured and self-reported activity were non-significant but trended towards greater total sitting (0.4 hours [-0.3, 1.1]), fewer daily steps (-270 [-1078, 538]), and greater self-reported TV time (0.4 hours, [-0.3, 1.1]) post-COVID. Future analyses are warranted to better quantify these impacts and guide clinical care and future interventions.

Agreement of Step-Based Metrics From ActiGraph and ActivPAL Accelerometers Worn Concurrently Among Older Adults.

Purpose: Our study evaluated the agreement of mean daily step counts, peak 1-min cadence, and peak 30-min cadence between the hip-worn ActiGraph GT3X+ accelerometer, using the normal filter (AGN) and the low frequency extension (AGLFE), and the thigh-worn activPAL3 micro (AP) accelerometer among older adults. Methods: Nine-hundred and fifty-three older adults (≥65 years) were recruited to wear the ActiGraph device concurrently with the AP for 4-7 days beginning in 2016. Using the AP as the reference measure, device agreement for each step-based metric was assessed using mean differences (AGN - AP and AGLFE - AP), mean absolute percentage error (MAPE), and Pearson and concordance correlation coefficients. Results: For AGN - AP, the mean differences and MAPE were: daily steps -1,851 steps/day and 27.2%, peak 1-min cadence -16.2 steps/min and 16.3%, and peak 30-min cadence -17.7 steps/min and 24.0%. Pearson coefficients were .94, .85, and .91 and concordance coefficients were .81, .65, and .73, respectively. For AGLFE - AP, the mean differences and MAPE were: daily steps 4,968 steps/day and 72.7%, peak 1-min cadence -1.4 steps/min and 4.7%, and peak 30-min cadence 1.4 steps/min and 7.0%. Pearson coefficients were .91, .91, and .95 and concordance coefficients were .49, .91, and .94, respectively. Conclusions: Compared with estimates from the AP, the AGN underestimated daily step counts by approximately 1,800 steps/day, while the AGLFE overestimated by approximately 5,000 steps/day. However, peak step cadence estimates generated from the AGLFE and AP had high agreement (MAPE ≤ 7.0%). Additional convergent validation studies of step-based metrics from concurrently worn accelerometers are needed for improved understanding of between-device agreement.

CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35.

Background: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35-99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results: Mean errors (activPAL - CHAP-Adult) and 95% limits of agreement were: sedentary time -10.5 (-63.0, 42.0) min/day, breaks in sedentary time 1.9 (-9.2, 12.9) breaks/day, mean bout duration -0.6 (-4.0, 2.7) min, usual bout duration -1.4 (-8.3, 5.4) min, alpha .00 (-.04, .04), and time in ≥30-min bouts -15.1 (-84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: -2.0% (4.0%), -4.7% (12.2%), 4.1% (11.6%), -4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson's correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.

"They're Going to Zoom It": A Qualitative Investigation of Impacts and Coping Strategies During the COVID-19 Pandemic Among Older Adults.

Introduction: Older adults, who already have higher levels of social isolation, loneliness, and sedentary behavior, are particularly susceptible to negative impacts from social distancing mandates meant to control the spread of COVID-19. We sought to explore the physical, mental, and social health impacts of the pandemic on older adults and their coping techniques. Materials and Methods: We conducted 25 semi-structured interviews with a sub-sample of participants in an ongoing sedentary behavior reduction intervention. Interviews were recorded and transcribed, and iterative coding was used to extract key themes. Results: Most participants reported an increase in sedentary behavior due to limitations on leaving their home and increased free time to pursue seated hobbies (e.g., reading, knitting, tv). However, many participants also reported increased levels of intentional physical activity and exercise, particularly outdoors or online. Participants also reported high levels of stress and a large decrease in in-person social connection. Virtual connection with others through phone and video was commonly used to stay connected with friends and family, engage in community groups and activities, and cope with stress and social isolation. Maintenance of a positive attitude and perspective gained from past hardships was also an important coping strategy for many participants. Discussion: The COVID-19 pandemic and associated social distancing measures have impacted older adults' perceived levels of activity, stress, and social isolation, but many leveraged technology and prior life experiences to cope. These themes could inform future interventions for older adults dealing with chronic stress and isolation.