Emerging evidence suggests that physical activity and sedentary behavior [reflected in physical inactivity (PI)], might be two different phenotypes that may have distinct underlying physiological mechanisms. The purpose of this review is to summarize the existing literature on the genetic determinants of PA and PI phenotypes in humans, considering them as distinct behaviors. Completed in January 2011, this review includes family studies, twin studies, association studies, genome-wide linkage studies and genome-wide association scan (GWAs) reporting different physical activity/inactivity-related phenotypes. In regards to PA, familial aggregation studies resulted in heritability estimates ranging from 0 to 60 %, and twin studies yielded heritability estimates (a²) and shared environment (c²) scores for PA phenotypes ranging from 0.00 to 0.85 and 0.00 to 0.84, respectively. Unique environmental (e²) results are well dispersed from 0.12 to 0.72. Suggestive linkages were found with markers nearby different activity-related genes: EDNRB, MC4R, UCP1, FABP2, CASR, SLC9A9. Significant associations with PA phenotypes were found for Ace, Gln223ARrg, MC4R and DRD2 genes. We found one GWAs that reported novel SNPs in the PAPSS2 gene on chromosome 10q23.2 and in two intergenic regions on chromosomes 2q33.1 and 18p11.32. Heritability estimates for PI ranged from 25 to 60 % and linkage studies recorded higher LOD scores for PI versus PA. The ACE genotype was strongly associated with PI. There are potentially different genetic influences on PA versus PI phenotypes. Future studies should focus on the different genetic influences on PA and PI to improve our understanding of underlying determinants of these behaviors.

Obesity in children is partly due to unhealthy lifestyle behaviours, e.g., sedentary activity and poor dietary choices. This trend has been seen globally. To determine the extent of these behaviours in a Portuguese population of children, 686 children 9.5 to 10.5 years of age were studied. Our aims were to: (1) describe profiles of children’s lifestyle behaviours; (2) identify behaviour pattern classes; and (3) estimate combined effects of individual/ socio-demographic characteristics in predicting class membership. Physical activity and sleep time were estimated by 24-h accelerometry. Nutritional habits, screen time and socio-demographics were obtained. Latent Class Analysis was used to determine unhealthy lifestyle behaviours. Logistic regression analysis predicted class membership. About 78% of children had three or more unhealthy lifestyle behaviours, while 0.2% presented no risk. Two classes were identified: Class 1-Sedentary, poorer diet quality; and Class 2-Insufficiently active, better diet quality, 35% and 65% of the population, respectively. More mature children (Odds Ratio (OR) = 6.75; 95%CI = 4.74–10.41), and boys (OR = 3.06; 95% CI = 1.98–4.72) were more likely to be overweight/obese. However, those belonging to Class 2 were less likely to be overweight/obese (OR = 0.60; 95% CI = 0.43–0.84). Maternal education level and household income did not significantly predict weight status (p ≥ 0.05).

Sedentary behaviour (SB) has been implicated as a potential risk factor for chronic disease. Since children spend most of their awake time in schools, this study aimed to identify individual- and school-level correlates of sedentary time using a multilevel approach, and to determine if these correlates have a similar effect in normal weight (NW) and overweight/obese (O/O) children. Sample comprised 686 Portuguese children aged 9-10 years from 23 schools that took part in the ISCOLE project. Actigraph GT3X + accelerometers were used 24 hours/day for 7 days to assess sedentary time (daily minutes <100 counts/min); BMI was computed and WHO cut-points were used to classify subjects as NW or O/O. Sex, BMI, number of siblings, family income, computer use on school days, and sleep time on school days were used as individual-level correlates. At the school level, school size (number of students), percentage of students involved in sports or physical activity (PA) clubs, school promotion of active transportation, and students’ access to equipment outside school hours were used. All multilevel modelling analysis was done in SPSS, WINPEPI, and HLM. School-level correlates explain ≈ 6.0% of the total variance in sedentary time. Results (β ± SE) showed that boys (-30.85 ± 5.23), children with more siblings (-8.56 ± 2.71) and those who sleep more (-17.78 ± 3.06) were less sedentary, while children with higher family income were more sedentary (4.32 ± 1.68). At the school level, no variable was significantly correlated with sedentary time. Among weight groups, variables related to sedentary time in NW were sex, sleep time and family income, while in O/O sex, number of siblings and sleep time were significant correlates. No school-level predictors were significantly associated in either of the weight groups. Notwithstanding the relevance of the school environment in the reduction of children’s sedentary time, individual and family characteristics played a more relevant role than the school context in this study.

Sedentary behavior (SB) expression and its underlying causal factors have been progressively studied, as it is a major determinant of decreased health quality. In the present study we applied Genotype x Age (GxAge) and Genotype x Sex (GxSex) interaction methods to determine if the phenotypic expression of different SB traits is influenced by an interaction between genetic architecture and both age and sex. A total of 1345 subjects, comprising 249 fathers, 327 mothers, 334 sons and 325 daughters, from 339 families of The Portuguese Healthy Family Study were included in the analysis. SB traits were assessed by means of a 3-d physical activity recall, the Baecke and IPAQ questionnaires. GxAge and GxSex interactions were analyzed using SOLAR 4.0 software. Sedentary behaviour heritability estimates were not always statistically significant (p>0.05) and ranged from 3% to 27%. The GxSex and GxAge interaction models were significantly better than the single polygenic models for TV (min/day), EEsed (kcal/day), personal computer (PC) usage and physical activty (PA) tertiles. The GxAge model is also significantly better than the polygenic model for Sed (min/day). For EEsed, PA tertiles, PC and Sed, the GxAge interaction was significant because the genetic correlation between SB environments was significantly different from 1. Further, PC and Sed variance heterogeneity among distinct ages were observed. The GxSex interaction was significant for EEsed due to genetic variance heterogeneity between genders and for PC due to a genetic correlation less than 1 across both sexes. Our results suggest that SB expression may be influenced by the interactions between genotype with both sex and age. Further, different sedentary behaviors seem to have distinct genetic architectures and are differentially affected by age and sex.