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.

Feasibility of a Health Coach Intervention to Reduce Sitting Time and Improve Physical Functioning Among Breast Cancer Survivors: Pilot Intervention Study.

BACKGROUND: Sedentary behavior among breast cancer survivors is associated with increased risk of poor physical function and worse quality of life. While moderate to vigorous physical activity can improve outcomes for cancer survivors, many are unable to engage in that intensity of physical activity. Decreasing sitting time may be a more feasible behavioral target to potentially mitigate the impact of cancer and its treatments. OBJECTIVE: The purpose of this study was to investigate the feasibility and preliminary impact of an intervention to reduce sitting time on changes to physical function and quality of life in breast cancer survivors, from baseline to a 3-month follow-up. METHODS: Female breast cancer survivors with self-reported difficulties with physical function received one-on-one, in-person personalized health coaching sessions aimed at reducing sitting time. At baseline and follow-up, participants wore the activPAL (thigh-worn accelerometer; PAL Technologies) for 3 months and completed physical function tests (4-Meter Walk Test, Timed Up and Go, and 30-Second Chair Stand) and Patient-Reported Outcomes Measurement Information System (PROMIS) self-reported outcomes. Changes in physical function and sedentary behavior outcomes were assessed by linear mixed models. RESULTS: On average, participants (n=20) were aged 64.5 (SD 9.4) years; had a BMI of 30.4 (SD 4.5) kg/m2; and identified as Black or African American (n=3, 15%), Hispanic or Latina (n=4, 20%), and non-Hispanic White (n=14, 55%). Average time since diagnosis was 5.8 (SD 2.2) years with participants receiving chemotherapy (n=8, 40%), radiotherapy (n=18, 90%), or endocrine therapy (n=17, 85%). The intervention led to significant reductions in sitting time: activPAL average daily sitting time decreased from 645.7 (SD 72.4) to 532.7 (SD 142.1; ß=-112.9; P=.001) minutes and average daily long sitting bouts (bout length ≥20 min) decreased from 468.3 (SD 94.9) to 366.9 (SD 150.4; ß=-101.4; P=.002) minutes. All physical function tests had significant improvements: on average, 4-Meter Walk Test performance decreased from 4.23 (SD 0.95) to 3.61 (SD 2.53; ß=-.63; P=.002) seconds, Timed Up and Go performance decreased from 10.30 (SD 3.32) to 8.84 (SD 1.58; ß=-1.46; P=.003) seconds, and 30-Second Chair Stand performance increased from 9.75 (SD 2.81) to 13.20 completions (SD 2.53; ß=3.45; P<.001). PROMIS self-reported physical function score improved from 44.59 (SD 4.40) to 47.12 (SD 5.68; ß=2.53; P=.05) and average fatigue decreased from 52.51 (SD 10.38) to 47.73 (SD 8.43; ß=-4.78; P=.02). CONCLUSIONS: This 3-month pilot study suggests that decreasing time spent sitting may be helpful for breast cancer survivors experiencing difficulties with physical function and fatigue. Reducing sitting time is a novel and potentially more feasible approach to improving health and quality of life in cancer survivors.

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.

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.

A SIMPLE, CONSISTENT ESTIMATOR OF SNP HERITABILITY FROM GENOME-WIDE ASSOCIATION STUDIES.

Analysis of genome-wide association studies (GWAS) is characterized by a large number of univariate regressions where a quantitative trait is regressed on hundreds of thousands to millions of single-nucleotide polymorphism (SNP) allele counts, one at a time. This article proposes an estimator of the SNP heritability of the trait, defined here as the fraction of the variance of the trait explained by the SNPs in the study. The proposed GWAS heritability (GWASH) estimator is easy to compute, highly interpretable, and is consistent as the number of SNPs and the sample size increase. More importantly, it can be computed from summary statistics typically reported in GWAS, not requiring access to the original data. The estimator takes full account of the linkage disequilibrium (LD) or correlation between the SNPs in the study through moments of the LD matrix, estimable from auxiliary datasets. Unlike other proposed estimators in the literature, we establish the theoretical properties of the GWASH estimator and obtain analytical estimates of the precision, allowing for power and sample size calculations for SNP heritability estimates, and forming a firm foundation for future methodological development.

Smokers who report smoking but do not consider themselves smokers: a phenomenon in need of further attention.

BACKGROUND: Heightened stigma surrounding the action of smoking may decrease the likelihood that individuals who engage in smoking identify with the label 'smoker'. Non-identifying smokers (NIS) may undermine accurate smoking prevalence estimates and can be overlooked by tobacco control efforts. OBJECTIVE: We sought to characterise NIS in a cross-sectional study using a sample representative of the population of adults (>18 years) in California who reported smoking at least 100 cigarettes in their lifetime, smoking at least some days and at least once in the last 30 days (n=1698). Individuals were considered NIS if they met the above criteria and answered 'no' when asked if they 'considered themselves a smoker'. RESULTS: We estimate that 395 928 (SD=54 126) NIS were living in California in 2011 (a prevalence of 12.3% of all smokers in California). The odds of being NIS were higher among non-daily smokers who were previously daily smokers (adjusted OR (AOR)=7.63, 95% CI 2.67 to 21.8) or were never previously daily smokers (AOR=7.14, CI 2.78 to 18.3) compared with daily smokers. The odds of being an NIS were also higher among those who did not believe they were addicted to cigarettes (AOR=3.84, CI 1.68 to 9.22), were older than 65 years (vs less than 45 years) (AOR=3.35, CI 1.16 to 9.75) or were from ethnic minorities including Black and Asian (vs non-Hispanic white) (AOR=3.16, CI 1.19 to 8.49). CONCLUSIONS: Smoking surveillance should restructure selection criteria to more accurately account for NIS in areas with high stigma toward smokers. Targeted interventions may be needed for NIS including educating healthcare providers to enquire more deeply into smoking habits.

Covariate-modulated local false discovery rate for genome-wide association studies.

MOTIVATION: Genome-wide association studies (GWAS) have largely failed to identify most of the genetic basis of highly heritable diseases and complex traits. Recent work has suggested this could be because many genetic variants, each with individually small effects, compose their genetic architecture, limiting the power of GWAS, given currently obtainable sample sizes. In this scenario, Bonferroni-derived thresholds are severely underpowered to detect the vast majority of associations. Local false discovery rate (fdr) methods provide more power to detect non-null associations, but implicit assumptions about the exchangeability of single nucleotide polymorphisms (SNPs) limit their ability to discover non-null loci. METHODS: We propose a novel covariate-modulated local false discovery rate (cmfdr) that incorporates prior information about gene element-based functional annotations of SNPs, so that SNPs from categories enriched for non-null associations have a lower fdr for a given value of a test statistic than SNPs in unenriched categories. This readjustment of fdr based on functional annotations is achieved empirically by fitting a covariate-modulated parametric two-group mixture model. The proposed cmfdr methodology is applied to a large Crohn's disease GWAS. RESULTS: Use of cmfdr dramatically improves power, e.g. increasing the number of loci declared significant at the 0.05 fdr level by a factor of 5.4. We also demonstrate that SNPs were declared significant using cmfdr compared with usual fdr replicate in much higher numbers, while maintaining similar replication rates for a given fdr cutoff in de novo samples, using the eight Crohn's disease substudies as independent training and test datasets. Availability an implementation: https://sites.google.com/site/covmodfdr/ CONTACT: : wes.stat@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Smoking ban policies and their influence on smoking behaviors among current California smokers: a population-based study.

OBJECTIVE: To assess whether smoking ban policies are associated with smoking reduction and quit attempts among California smokers. METHODS: Data were examined for 1718 current smokers from follow-up telephone interviews conducted in 2011 of persons previously identified as smokers in a representative sample of the adult population of California. Population weighted logistic regressions controlling for demographic and other variables were used to evaluate the association between smoking ban policies (home, work, and town) and changes in tobacco use (past year quit attempt or reduction in smoking rate). RESULTS: Living in a home with a total ban was significantly associated with smoking reduction (adjusted odds ratio, AOR: 2.4, 95% CI: 1.4-4.2) and making a quit attempt (AOR: 2.3, 95% CI: 1.3-3.9) compared to living in a home with no home ban. Self-reported perception of an outdoor ban in one's city/town was associated with smoking reduction (AOR: 1.7, 95% CI: 1.02-2.7) and making a quit attempt (AOR: 1.8, 95% CI: 1.05-2.9). CONCLUSION: These results indicate that smoking bans not only protect nonsmokers from the harms of secondhand smoke, but are also associated with smoking reduction and cessation.

Development of responder definitions for fibromyalgia clinical trials.

OBJECTIVE: To develop responder definitions for fibromyalgia (FM) clinical trials using key symptom and function domains. METHODS: Twenty-four candidate responder definitions were developed by expert consensus and were evaluated in 12 randomized, placebo-controlled trials of 4 medications for the treatment of FM. For each definition, the treatment effects of the medication compared with placebo were analyzed using Cochran-Mantel-Haenszel tests or chi-square tests. A meta-analysis of the pooled results for the 4 medications established risk ratios to determine the definitions that best favored medication over placebo. RESULTS: Two definitions performed best in the analyses. Both definitions included ≥30% reduction in pain and ≥10% improvement in physical function. The definitions differed in that one (≥30% improvement in FM [FM30] short version) included ≥30% improvement in sleep or fatigue, and the other (FM30 long version) required ≥30% improvement in 2 of the following symptoms: sleep, fatigue, depression, anxiety, or cognition. In the analysis of both versions, the response rate was ≥15% for each medication and was significantly greater compared with placebo. The risk ratio favoring drug over placebo in the pooled analysis for FM30 version 3 (short version) was 1.50 (95% confidence interval [95% CI] 1.24-1.82; P ≤ 0.0001); the risk ratio for FM30 version 6 (long version) was 1.60 (95% CI 1.31-1.96; P ≤ 0.00001). CONCLUSION: Among the 24 responder definitions tested, 2 were identified as most sensitive in identifying response to treatment. The identification of responder definitions for FM clinical trials that include assessments of key symptom and function domains may improve the sensitivity of clinical trials to identify meaningful improvements, leading to improved management of FM.

Red blood cell methotrexate polyglutamates emerge as a function of dosage intensity and route of administration during pulse methotrexate therapy in rheumatoid arthritis.

OBJECTIVE: MTX is a prodrug producing anti-arthritic effects through a folylpolyglutamate synthase-mediated activation to MTX polyglutamates (MTXPGs). Our objective was to characterize the pharmacokinetics of intracellular MTXPGs and the factors associated with their accumulation in adult RA patients treated with MTX weekly. METHODS: MTX pharmacokinetics were evaluated in 47 MTX-naïve patients enrolled in an MTX dose-escalation study for an average of 20 weeks and 223 patients enrolled in a cross-sectional study under long-term MTX therapy. Short-chain (MTXPG1-2), long-chain (MTXPG3) and very long-chain (MTXPG4-5) concentrations were measured in circulating red blood cells using liquid chromatography. Statistical analyses consisted of non-linear mixed models, multivariate regression analyses and Wilcoxon signed-rank test. RESULTS: The accumulation of MTXPG1-5 was sigmoidal and steady-state concentrations were achieved after 7 weeks of therapy. However, additional exposure and MTX dosage escalation produced a selective redistribution towards longer chain MTXPGs at the expense of shorter chain MTXPGs. Age, glomerular filtration rate and route of MTX administration were the most important predictors of MTXPG accumulation. In 10 patients, a switch from oral to parenteral MTX was associated with a 37% increase in long-chain MTXPGs, a 132% increase in very long-chain MTXPGs and a concomitant 31% reduction in disease activity (P<0.02). CONCLUSION: The selective emergence of long-chain MTXPGs is function of dose, time of exposure and hence dosage intensity. Switching from oral to parenteral MTX produces a selective accumulation of longer chain MTXPGs that are known to be more potent inhibitors of de novo purine biosynthesis than shorter chain MTXPGs.