Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10-100 Hz), research has mainly focused on summarized ...metrics provided by accelerometers manufactures, such as the activity count (AC) by ActiGraph or Actical. Such measures do not have a publicly available formula, lack a straightforward interpretation, and can vary by software implementation or hardware type. To address these problems, we propose the physical activity index (AI), a new metric for summarizing raw tri-axial accelerometry data. We compared this metric with the AC and another recently proposed metric for raw data, Euclidean Norm Minus One (ENMO), against energy expenditure. The comparison was conducted using data from the Objective Physical Activity and Cardiovascular Health Study, in which 194 women 60-91 years performed 9 lifestyle activities in the laboratory, wearing a tri-axial accelerometer (ActiGraph GT3X+) on the hip set to 30 Hz and an Oxycon portable calorimeter, to record both tri-axial acceleration time series (converted into AI, AC, and ENMO) and oxygen uptake during each activity (converted into metabolic equivalents (METs)) at the same time. Receiver operating characteristic analyses indicated that both AI and ENMO were more sensitive to moderate and vigorous physical activities than AC, while AI was more sensitive to sedentary and light activities than ENMO. AI had the highest coefficients of determination for METs (0.72) and was a better classifier of physical activity intensity than both AC (for all intensity levels) and ENMO (for sedentary and light intensity). The proposed AI provides a novel and transparent way to summarize densely sampled raw accelerometry data, and may serve as an alternative to AC. The AI's largely improved sensitivity on sedentary and light activities over AC and ENMO further demonstrate its advantage in studies with older adults.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Latent class analysis (LCA) identifies distinct groups within a heterogeneous population, but its application to accelerometry-assessed physical activity and sedentary behavior has not been ...systematically explored. We conducted a systematic scoping review to describe the application of LCA to accelerometry.
Comprehensive searches in PubMed, Web of Science, CINHAL, SPORTDiscus, and Embase identified studies published through December 31, 2021. Using Covidence, two researchers independently evaluated inclusion criteria and discrepancies were resolved by consensus. Studies with LCA applied to accelerometry or combined accelerometry/self-reported measures were selected. Data extracted included study characteristics and both accelerometry and LCA methods.
Of 2555 papers found, 66 full-text papers were screened, and 12 papers (11 cross-sectional, 1 cohort) from 8 unique studies were included. Study sample sizes ranged from 217-7931 (mean 2249, standard deviation 2780). Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%). About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes. The accelerometer-based variables in the LCA model included measures by day of the week (38%), weekday vs. weekend (13%), weekly average (13%), dichotomized minutes/day (13%), sex specific z-scores (13%), and hour-by-hour (13%). The criteria to guide the selection of the final number of classes and model fit varied across studies, including Bayesian Information Criterion (63%), substantive knowledge (63%), entropy (50%), Akaike information criterion (50%), sample size (50%), Bootstrap likelihood ratio test (38%), and visual inspection (38%). The studies explored up to 5 (25%), 6 (38%), or 7+ (38%) classes, ending with 3 (50%), 4 (13%), or 5 (38%) final classes.
This review explored the application of LCA to physical activity and sedentary behavior and identified areas of improvement for future studies leveraging LCA. LCA was used to identify unique groupings as a data reduction tool, to combine self-report and accelerometry, and to combine different physical activity intensities and sedentary behavior in one LCA model or separate models.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes.
In the context of a randomized trial comparing 2 ...smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership.
Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app.
For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio OR 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app.
Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Background
Current estimates suggest that 75% of children diagnosed with a central nervous system (CNS) tumor will become 5‐year survivors. However, survivors of childhood CNS tumors are at increased ...risk for long‐term morbidity.
Methods
To determine long‐term neuropsychological and socioeconomic status (SES) outcomes, adult survivors of pediatric low‐grade gliomas (n = 181) in the Childhood Cancer Survivor Study and a sibling comparison group that was frequency‐matched by age and sex (n = 105) completed a comprehensive battery of standardized neuropsychological tests and an SES assessment. Multivariable regression models compared treatment‐specific groups for neuropsychological and SES outcomes and evaluated associations with tumor location, age at diagnosis, sex, and age at evaluation.
Results
In adjusted models, survivors treated with surgery and radiotherapy (surgery+RT; median age at diagnosis, 7 years; median age at assessment, 41 years) scored lower on estimated IQ than survivors treated with surgery only, who scored lower than siblings (surgery+RT, 93.9; surgery only, 101.2; siblings, 108.5; all P values <.0001). Survivors diagnosed at younger ages had low scores for all outcomes (P < .05) except for attention/processing speed. For SES outcomes, survivors treated with surgery+RT had lower occupation scores (odds ratio OR, 2.6; 95% confidence interval CI, 1.1‐5.9), lower income (OR, 2.6; 95% CI, 1.3‐5.0), and less education (OR, 2.1; 95% CI, 1.1‐4.0) than those treated with surgery only.
Conclusions
Decades after treatment, survivors treated with radiotherapy and at younger ages had poorer neuropsychological and SES outcomes. Lifelong surveillance of survivors of pediatric low‐grade gliomas may be warranted as life events, stages, and transitions (employment, family, and aging) present new challenges and risks.
Neuropsychological and socioeconomic status outcomes of 181 adult survivors of pediatric low‐grade gliomas were compared with the outcomes of 105 siblings. Decades after treatment, survivors treated with radiotherapy and at younger ages had poorer neuropsychological and socioeconomic status outcomes, and this supports the need for lifelong surveillance.
Objectives
To prospectively examine associations between accelerometer‐measured physical activity (PA) and mortality in older women, with an emphasis on light‐intensity PA.
Design
Prospective cohort ...study with baseline data collection between March 2012 and April 2014.
Setting
Women's Health Initiative cohort in the United States.
Participants
Community‐dwelling women aged 63 to 99 (N = 6,382).
Measurements
Minutes per day of usual PA measured using hip‐worn triaxial accelerometers, physical functioning measured using the Short Physical Performance Battery, mortality follow‐up for a mean 3.1 years through September 2016 (450 deaths).
Results
When adjusted for accelerometer wear time, age, race‐ethnicity, education, smoking, alcohol, self‐rated health, and comorbidities, relative risks (95% confidence intervals) for all‐cause mortality across PA tertiles were 1.00 (referent), 0.86 (0.69, 1.08), 0.80 (0.62, 1.03) trend P = .07, for low light; 1.00, 0.57 (0.45, 0.71), 0.47 (0.35, 0.61) trend P < .001, for high light; and, 1.00, 0.63 (0.50, 0.79), 0.42 (0.30, 0.57) trend P < .001, for moderate‐to‐vigorous PA (MVPA). Associations remained significant for high light‐intensity PA and MVPA (P < .001) after further adjustment for physical function. Each 30‐min/d increment in light‐intensity (low and high combined) PA and MVPA was associated, on average, with multivariable relative risk reductions of 12% and 39%, respectively (P < .01). After further simultaneous adjusting for light intensity and MVPA, the inverse associations remained significant (light‐intensity PA: RR = 0.93, 95% CI = 0.89–0.97; MVPA: RR = 0.67, 95% CI = 0.58–0.78). These relative risks did not differ between subgroups for age or race and ethnicity (interaction, P ≥ .14, all).
Conclusion
When measured using accelerometers, light‐intensity and MVPA are associated with lower mortality in older women. These findings suggest that replacing sedentary time with light‐intensity PA is a public health strategy that could benefit an aging society and warrants further investigation.
See related editorial by Barbara J. Nicklas.
Background/Objective
Falls cause significant problems for older adults. Sedentary time is associated with lower physical function and could increase the risk for falls.
Design
Prospective study.
...Setting
Sites across the United States.
Participants
Older women (N = 5,545, mean age 79 years) from the Women's Health Initiative Objective Physical Activity and Cardiovascular Health study.
Measurements
Accelerometers worn at the hip for up to 1 week collected measures of daily sedentary time and the mean sedentary bout duration, a commonly used metric for sedentary accumulation patterns. For up to 13 months after accelerometer wear, women reported daily whether they had fallen on monthly calendars.
Results
In fully adjusted models, the incident rate ratios (95% confidence interval) for quartiles 1 (lowest), 2, 3, and 4 of sedentary time respectively were 1.0 (ref.), 1.07 (0.93–1.24), 1.07 (0.91–1.25), and 1.14 (0.96–1.35; P‐trend = .65) and for mean sedentary bout duration was 1.0 (ref.), 1.05 (0.92–1.21), 1.02 (0.88–1.17), and 1.17 (1.01–1.37; P‐trend = .01), respectively. Women with a history of two or more falls had stronger associations between sedentary time and falls incidence compared with women with a history of no or one fall (P for interaction = .046).
Conclusions
Older women in the highest quartile of mean sedentary bout duration had a significantly increased risk of falling. Women with a history of frequent falling may be at higher risk for falling if they have high sedentary time. Interventions testing whether shortening total sedentary time and/or sedentary bouts lowers fall risk are needed to confirm these observational findings.
Maintaining high medication adherence is essential for achieving desired efficacy in clinical trials, especially prevention trials. However, adherence is traditionally measured by self-reports that ...are subject to reporting biases and measurement error. Recently, electronic medication dispenser devices have been adopted in several HIV pre-exposure prophylaxis prevention studies. These devices are capable of collecting objective, frequent, and timely drug adherence data. The device opening signals generated by such devices are often represented as regularly or irregularly spaced discrete functional data, which are challenging for statistical analysis. In this paper, we focus on clustering the adherence monitoring data from such devices. We first pre-process the raw discrete functional data into smoothed functional data. Parametric mixture models with change-points, as well as several non-parametric and semi-parametric functional clustering approaches, are adapted and applied to the smoothed adherence data. Simulation studies were conducted to evaluate finite sample performances, on the choices of tuning parameters in the pre-processing step as well as the relative performance of different clustering algorithms. We applied these methods to the HIV Prevention Trials Network 069 study for identifying subgroups with distinct adherence behavior over the study period.
To evaluate whether sedentary time (ST) and/or sedentary behavior patterns are related to incident diabetes in the U.S.'s oldest age-groups.
Women without physician-diagnosed diabetes (
= 4,839, mean ...± SD age = 79 ± 7 years) wore accelerometers for ≥4 days and were followed up to 6 years for self-reported newly diagnosed diabetes requiring treatment with medications. Hazard ratios (HRs) for incident diabetes were estimated across quartiles of accelerometer-measured ST and mean bout duration with use of Cox proportional hazards models. We conducted isotemporal substitution analyses using Cox regression and tested associations with risk for diabetes after statistically replacing ST with light physical activity (PA) or moderate-to-vigorous PA (MVPA) and after replacing light PA with MVPA.
During 20,949 person-years, 342 diabetes cases were identified. Women in ST quartile (Q)2, Q3, and Q4 (vs. Q1) had incident diabetes HR 1.20 (95% CI 0.87-1.65), 1.33 (0.97-1.82), and 1.21 (0.86-1.70);
= 0.04. Respective HRs following additional adjustment for BMI and MVPA were 1.04 (95% CI 0.74-1.47), 1.04 (0.72-1.50), and 0.85 (0.56-1.29);
= 0.90. Fully adjusted isotemporal substitution results indicated that each 30 min of ST replaced with MVPA (but not light PA) was associated with 15% lower risk for diabetes (HR 0.85 95% CI 0.75-0.96;
= 0.01); the HR for replacing 30 min of light PA with MVPA was 0.85 (95% CI 0.73-0.98);
= 0.03. Mean bout duration was not associated with incident diabetes.
Statistically replacing ST or light PA with MVPA was associated with lower diabetes risk in older women. While reducing ST is important for several health outcomes, results indicate that to reduce diabetes risk among older adults, the primary public health focus should be on increasing MVPA.
Background
Few studies have examined accelerometer‐measured physical activity and incident breast cancer (BC). Thus, this study examined associations between accelerometer‐measured vector magnitude ...counts per 15 seconds (VM/15s) and average daily minutes of light physical activity (LPA), moderate‐to‐vigorous PA (MVPA), and total PA (TPA) and BC risk among women in the Women's Health Accelerometry Collaboration (WHAC).
Methods
The WHAC comprised 21,089 postmenopausal women (15,375 from the Women's Health Study WHS; 5714 from the Women's Health Initiative Objective Physical Activity and Cardiovascular Health Study OPACH). Women wore an ActiGraph GT3X+ on the hip for ≥4 days and were followed for 7.4 average years to identify physician‐adjudicated in situ (n = 94) or invasive (n = 546) BCs. Multivariable stratified Cox regression estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for tertiles of physical activity measures in association with incident BC overall and by cohort. Effect measure modification was examined by age, race/ethnicity, and body mass index (BMI).
Results
In covariate‐adjusted models, the highest (vs. lowest) tertiles of VM/15s, TPA, LPA, and MVPA were associated with BC HRs of 0.80 (95% CI, 0.64–0.99), 0.84 (95% CI, 0.69–1.02), 0.89 (95% CI, 0.73–1.08), and 0.81 (95% CI, 0.64–1.01), respectively. Further adjustment for BMI or physical function attenuated these associations. Associations were more pronounced among OPACH than WHS women for VM/15s, MVPA, and TPA; younger than older women for MVPA; and women with BMI ≥30 than <30 kg/m2 for LPA.
Conclusion
Greater levels of accelerometer‐assessed PA were associated with lower BC risk. Associations varied by age and obesity and were not independent of BMI or physical function.
In a study of over 20,000 US postmenopausal women, higher levels of accelerometer‐measured physical activity were associated with lower risk of breast cancer. Associations varied by age and obesity and were not independent of body mass index or physical function.
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