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Purpose: To identify distinct trajectories of total moderate-to-vigorous physical activity (MVPA) and sedentary behavior following a breast cancer diagnosis and their correlates., Methods: The analysis examined 3000 female breast cancer survivors within Kaiser Permanente Northern California between 2006 and 2013. Self-reported time spent on total MVPA and sedentary behaviors were assessed at baseline (mean = 1.8 months post-diagnosis) and at 6 and 24 months follow up. Trajectory groups were identified using group-based trajectory modeling and K-means for longitudinal data analysis. Trajectory groups were named by baseline activity level (high, medium, or low) and direction of change (increaser, decreaser, or maintainer)., Results: Trajectory analyses identified three MVPA trajectories [high decreaser (7%), medium decreaser (35%), low maintainer (58%)] and four sedentary behavior trajectories [high maintainer (18%), high decreaser (27%), low increaser (24%), and low maintainer (31%)]. Women with higher education (ORs: 1.63-4.37), income (OR: 1.37), dispositional optimism (ORs: 1.60-1.86), and social support (OR: 1.33) were more likely to be high or medium decreasers of MVPA (all P < 0.05). High maintainers and high decreasers of sedentary behavior were more likely to have higher education (OR: 1.84) and social support (ORs: 1.42-1.86), but lower income (OR: 0.66; all P < 0.05)., Conclusions: In the 24 months following breast cancer diagnosis, 42% of survivors decreased MVPA and 73% maintained or increased time on sedentary behavior. Socioeconomic status and stress coping at diagnosis predicted subsequent PA trajectory., Implications for Cancer Survivors: It is important to prioritize exercise intervention and counseling during early stage of breast cancer survivorship, especially in survivors who are at high risk of becoming physically inactive post-diagnosis.

Owing to advances in sensor technologies on wearable devices, it is feasible to measure physical activity of an individual continuously over a long period. These devices afford opportunities to understand individual behaviors, which may then provide a basis for tailored behavior interventions. The large volume of data however poses challenges in data management and analysis. We propose a novel quantile coarsening analysis (QCA) of daily physical activity data, with a goal to reduce the volume of data while preserving key information. We applied QCA to a longitudinal study of 79 healthy participants whose step counts were monitored for up to 1 year by a Fitbit device, performed cluster analysis of daily activity, and identified individual activity signature or pattern in terms of the clusters identified. Using 21,393 time series of daily physical activity, we identified eight clusters. Employment and partner status were each associated with 5 of the 8 clusters. Using less than 2% of the original data, QCA provides accurate approximation of the mean physical activity, forms meaningful activity patterns associated with individual characteristics, and is a versatile tool for dimension reduction of densely sampled data.

Background and Purpose: Whether changes in leisure-time physical activity (LTPA) over time are associated with lower risk of stroke is not well established. We examined the association between changes in self-reported LTPA 10 years apart, with risk of incident stroke in the CTS (California Teachers Study). We hypothesized that the risk of stroke would be lowest among those who remained active., Methods: Sixty-one thousand two hundred and fifty-six CTS participants reported LTPA at 2 intensity levels (moderate and strenuous activity) at 2 time points (baseline 1995-96; 10-year follow-up 2005-2006). LTPA at each intensity level was categorized based on American Heart Association (AHA) recommendations (moderate, >150 minutes/week; strenuous, >75 minutes/week). Changes in LTPA were summarized as follows: (1) not meeting recommendations at both time points; (2) meeting recommendations only at follow-up; (3) meeting recommendations only at baseline; and (4) meeting recommendations at both time points. Incident strokes were identified through California state hospitalization records. Using multivariable Cox models, we examined the associations between changes in LTPA with incident stroke., Results: Nine hundred and eighty-seven women were diagnosed with stroke who completed both questionnaires. Meeting AHA recommendations at both the time points was associated with a lower risk of all stroke (adjusted hazard ratio, 0.84; 95% confidence interval, 0.72-0.98). The protective effects for stroke were driven by meeting AHA recommendations for moderate activity and largely observed for ischemic strokes (adjusted hazard ratio, 0.70; 95% confidence interval, 0.55-0.88)., Conclusions: Meeting AHA recommendations for moderate activity had a protective effect for reducing ischemic stroke risk. Participants who met AHA recommendations at baseline but not at follow-up, however, were not afforded reduced stroke risk., (© 2017 American Heart Association, Inc.)

Background: Previous studies of exercise have focused on measuring physical activity in totality using summary statistics such as metabolic equivalent score for total intensity or total energy count., Objective: We aimed to examine the multidimensionality of leisure-time physical activity (LTPA) and to identify the specific LTPA components that were associated with cardiovascular mortality in the elderly., Design and Participants: The Northern Manhattan Study (NOMAS) is a multiethnic prospective cohort of elderly stroke-free individuals consisting of a total of 3298 participants recruited between 1993 and 2001, with a median follow-up of 17 years., Main Measures: Physical activity questionnaire data were available in 3293 NOMAS participants, who were categorized into subgroups with similar exercise patterns by model-based cluster analysis. Three subgroup-defining LTPA features were identified and were considered as primary exposures in Cox proportional hazard models: frequency of activity, number of activity types (variety), and energy-to-duration ratio (EDR). We considered cardiovascular mortality and non-cardiovascular mortality as outcomes in Cox cause-specific proportional hazard models, and all-cause mortality as outcome in Cox models., Key Results: A high activity frequency was associated with reduced cardiovascular mortality (hazard ratio, HR = 0.93, P = 0.03), but demonstrated no effect on non-cardiovascular death. A high EDR was associated with increased risk of cardiovascular death (HR = 1.30, P = 0.01). A high number of activity types was beneficial in reducing all-cause mortality (HR = 0.87, P = 0.01)., Conclusions: Exercise frequency was protective against cardiovascular mortality, and a high variety of activity was protective against all-cause mortality. The performance of frequent and varied non-intense exercise in an elderly population such as ours is achievable and can reduce the risk of death., Competing Interests: The authors declare that they have no conflict of interest. Funding/Support This study was supported by the National Institutes of Health grants R01 HL111195 (Cheung/Elkind), NS K23073104 (Willey), and R01 NS029993 (Sacco/Elkind). Role of the Funder/Sponsor The funding source had no role in the design and conduct of the study with regard to the collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.

Objectives: The understanding of how stress influences health behavior can provide insights into developing healthy lifestyle interventions. This understanding is traditionally attained through observational studies that examine associations at a population level. This nomothetic approach, however, is fundamentally limited by the fact that the environment- person milieu that constitutes stress exposure and experience can vary substantially between individuals, and the modifiable elements of these exposures and experiences are individual-specific. With recent advances in smartphone and sensing technologies, it is now possible to conduct idiographic assessment in users' own environment, leveraging the full-range observations of actions and experiences that result in differential response to naturally occurring events. The aim of this paper is to explore the hypothesis that an ideographic N-of-1 model can better capture an individual's stress- behavior pathway (or the lack thereof) and provide useful person-specific predictors of exercise behavior., Methods: This paper used the data collected in an observational study in 79 participants who were followed for up to a 1-year period, wherein their physical activity was continuously and objectively monitored by actigraphy and their stress experience was recorded via ecological momentary assessment on a mobile app. In addition, our analyses considered exogenous and environmental variables retrieved from public archive such as day in a week, daylight time, temperature and precipitation. Leveraging the multiple data sources, we developed prediction algorithms for exercise behavior using random forest and classification tree techniques using a nomothetic approach and an N-of-1 approach. The two approaches were compared based on classification errors in predicting personalized exercise behavior., Results: Eight factors were selected by random forest for the nomothetic decision model, which was used to predict whether a participant would exercise on a particular day. The predictors included previous exercise behavior, emotional factors (e.g., midday stress), external factors such as weather (e.g., temperature), and self-determination factors (e.g., expectation of exercise). The nomothetic model yielded an average classification error of 36%. The ideographic N-of-1 models used on average about two predictors for each individual, and had an average classification error of 25%, which represented an improvement of 11 percentage points., Conclusions: Compared to the traditional one-size-fits-all, nomothetic model that generalizes population-evidence for individuals, the proposed N-of-1 model can better capture the individual difference in their stressbehavior pathways. In this paper, we demonstrate it is feasible to perform personalized exercise behavior prediction, mainly made possible by mobile health technology and machine learning analytics., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Schattauer GmbH.)

Objective: Because leisure-time physical activity (LTPA) is protective against incident dementia, we hypothesized that LTPA is protective against decline in domain-specific cognitive performance., Methods: As part of the Northern Manhattan Study, LTPA was ascertained at enrollment using a validated in-person questionnaire. We assessed cognition in participants in the Northern Manhattan Study MRI substudy using a standard neuropsychological examination (NPE) (n = 1,228), and a repeat examination was performed 5 years later (n = 876). LTPA was summarized as the maximum intensity of any activity performed, classified as none to light intensity (physical inactivity) (90%) vs moderate to heavy intensity (10%). The NPE was subcategorized using standardized z scores over validated domains: processing speed, semantic memory, episodic memory, and executive function. We used multivariable linear regression models to examine the association of LTPA with initial and change in cognitive performance. Analyses were adjusted for sociodemographics, cardiovascular disease risk factors, and MRI findings (white matter hyperintensity volume, silent brain infarcts, cerebral volume)., Results: No/low levels of LTPA were associated with worse executive function, semantic memory, and processing speed scores on the first NPE. The associations were slightly attenuated and no longer significant after adjusting for vascular risk factors. Cognitively unimpaired participants reporting no/low LTPA vs moderate/high levels declined more over time in processing speed (β = -0.231 ± 0.112, p = 0.040) and episodic memory (β = -0.223 ± 0.117, p = 0.057) adjusting for sociodemographic and vascular risk factors., Conclusions: A low level of LTPA is independently associated with greater decline in cognitive performance over time across domains., (© 2016 American Academy of Neurology.)

Purpose: Physical activity is currently commonly summarized by simple composite scores of total activity, such as total metabolic equivalent score (METS), without further information about the many specific aspects of activities. We sought to identify more comprehensive physical activity patterns, and their association with cardiovascular disease risk factors., Methods: The Northern Manhattan Study is a multiethnic cohort of stroke-free individuals. Questionnaires were used to capture multiple dimensions of leisure-time physical activity. Participants were grouped into METS categories and also into clusters by multivariate mixture modeling of activity frequency, duration, energy expenditure, and number of activity types. Associations between clusters and risk factors were assessed using χ(2) tests., Results: Using data available in 3293 participants, we identified six model-based clusters that were differentiated by frequency and diversity of activities, rather than activity duration. High activity clusters had lower prevalence of the risk factors compared with those with lower activity; associations with obesity and hypertension remained significant after adjusting for METS (P = .027, .043). METS and risk factors were not significantly associated after adjusting for the clusters., Conclusions: Data-driven clustering method is a principled, generalizable approach to depict physical activity and form subgroups associated with cardiovascular risk factors independently of METS., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: To examine whether the survival benefit of exercise is modified by obesity., Methods: In the Northern Manhattan Study, we collected baseline sociodemographics and cardiovascular disease risk factors. The primary exposure was leisure-time physical activity (LTPA) and the outcomes were total, vascular, and nonvascular deaths (non-VaD). LTPA was defined as any versus none and metabolic equivalent score category (total activity weighted by intensity). We used Cox models to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs)., Results: A total of 3298 participants (mean age 69 years, 52% Hispanic, 63% women) were followed over a mean of 11.8 years with 1589 total deaths (641 vascular, 819 nonvascular). Any activity (adjusted HR: 0.84, 95% CI: 0.75-0.94) was associated with reduced risk of all-cause mortality and non-VaD, but not VaD. We found an interaction (P < .05) of LTPA with body mass index (BMI) less than 30 for all-cause and vascular mortality. Any LTPA was associated with reduced all-cause mortality (adjusted HR: 0.77, 95% CI: 0.68-0.87) and VaD (adjusted HR: 0.79, 95% CI: 0.65-0.97) only among those with BMI less than 30., Conclusions: We found no evidence of an independent survival benefit of LTPA among those with BMI more than 30. The health benefits of exercise should be considered in the context of obesity., (Copyright © 2015 Elsevier Inc. All rights reserved.)