In recent years, researchers have begun applying a trajectory approach to identify homogeneous subgroups of physical activity (PA) in heterogeneous populations. This study systematically reviewed the articles identifying longitudinal PA trajectory classes and the related factors (e.g., determinants, predictors, and outcomes) in the general population during different life phases. The included studies used finite mixture models for identifying trajectories of PA, exercise, or sport participation. Three electronic databases, PubMed (Medline), Web of Science, and CINAHL, were searched from the year 2000 to 13 February 2018. The study was conducted according to the PRISMA recommendations. Twenty-seven articles were included and organized into three age group: youngest (eleven articles), middle (eight articles), and oldest (eight articles). The youngest group consisted mainly of youth, the middle group of adults and the oldest group of late middle-aged and older adults. Most commonly, three or four trajectory classes were reported. Several trajectories describing a decline in PA were reported, especially in the youngest group, whereas trajectories of consistently increasing PA were observed in the middle and oldest group. While the proportion of persistently physically inactive individuals increased with age, the proportion was relatively high at all ages. Generally, male gender, being Caucasian, non-smoking, having low television viewing time, higher socioeconomic status, no chronic illnesses, and family support for PA were associated either with persistent or increasing PA. The reviewed articles identified various PA subgroups, indicating that finite mixture modeling can yield new information on the complexity of PA behavior compared to studying population mean PA level only. The studies also provided novel information how different factors relate to changes in PA during life course. The recognition of the PA subgroups and their determinants is important for the more precise targeting of PA promotion and PA interventions. PROSPERO registration number: CRD42018088120 .

Sed using a self-report questionnaire completed in connection with a medical examination at two consecutive measurements in 1992 and 2001. By summing the LTPA items, a physical activity index (PAI) was formed for both measurement points according to which the participants were divided into tracking groups: persistently active, increasingly active, decreasingly active, and persistently inactive. MetS in 2001 was defined by the guidelines of the European Group for the Study of Insulin Resistance (EGIR), the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III), and the International Diabetes Federation (IDF). A continuous metabolic risk score was also calculated by summing the z-scores for the metabolic risk factors. Results: There was a significant linear relationship between MetS and LTPA at baseline in men and at follow-up in both sexes according to all three definitions. Persistent physical activity during 9 yr was associated with a lower prevalence of MetS than persistent physical inactivity on all definitions (all P < 0.05). Similar results were found for increasingly active women (all P < 0.05). All of theseassociations remained significant after adjustment for potential confounders. In both men and women, the 9-yr change in LTPA was related to the metabolic risk score after adjustments for baseline LTPA, age, smoking, and education. Conclusions: A physically active lifestyle across the lifespan may prevent or delay the onset of metabolic syndrome in young male and female adults....

Introduction: Despite substantial interest in the development of health behaviors, there is limited research that has examined the longitudinal relationship between physical activity (PA) and smoking trajectories from youth to adulthood in a Finnish population. This study aimed to identify trajectories of smoking and PA for males and females, and study the relationship between these trajectories from youth to adulthood. Methods: Latent profile analysis (LPA) was used to identify trajectories of smoking and PA separately for males and females among 3355 Finnish adults (52.1% females). Participants’ smoking and PA were assessed five to eight times over a 31-year period (3–18 years old at the baseline, 34–49 years at last follow-up). Multinomial logistic regression analysis was used to study the relationship between the trajectories of smoking and PA. Results: Five smoking trajectories and four to five PA trajectories were identified for males and females. Of the PA trajectory groups, the persistently active group were least likely to follow the trajectories of regular smoking and the inactive and low active groups were least likely to follow non-smoking trajectory group. Likewise, inactive (women only) and low active groups were less likely to belong to the non-smokers group. Conclusions: The study suggests that those who are persistently active or increasingly active have substantially reduced probabilities of being in the highest-risk smoking categories.

Background The long-term effects of leisure-time physical activity (LTPA) on job strain have not been assessed in a large prospective population-based cohort study.