ABSTRACT
OBJECTIVES: The objectives of this study are to examine the association of physical activity in parents with physical activity in their adult offspring and explore if the offspring's genetic liability (ie, polygenic risk score) to physical activity influences this association. METHODS: The Trøndelag Health Study cohort is a population-based longitudinal study with data collected in 1984-1986, 1995-1997, 2006-2008 and 2017-2019. We calculated the odds ratio for being physically active and mean difference in physical activity levels according to parental physical activity (device-measured and self-reported) and own polygenic risk score. RESULTS: Compared with offspring with mothers in the lowest third of metabolic equivalent of task (MET)-min/day accumulated by vigorous physical activities, offspring with mothers in the upper third had an OR of 1.93 (95% CI 1.65 to 2.27) for accumulating ≥900 MET-min/week of vigorous physical activity. The OR for the corresponding father-offspring association was 1.78 (95% CI 1.48 to 2.14). Compared with offspring of parents not accumulating ≥900 MET-min/week, we found an OR of 1.89 (95% CI 1.45 to 2.44) for offspring to meet the same threshold if both parents accumulated ≥900 MET-min/week. Offspring with higher polygenic risk score to bephysically active and having physically active parents did more weekly physical activity, but we found no strong evidence of multiplicative synergistic effects between these two factors (all p values ≥0.01). CONCLUSION: Both parental physical activity and offspring's polygenic risk score were positively associated with physical activity levels in the adult offspring, but there was no evidence of effect modification between these factors. A family-based approach to promote physical activity may be effective from a public health perspective.
ABSTRACT
BACKGROUND: Despite apparent shortcomings such as measurement error and low precision, self-reported sedentary time is still widely used in surveillance and research. The aim of this study was threefold; (i) to examine the agreement between self-reported and device-measured sitting time in a general adult population; (ii), to examine to what extent demographics, lifestyle factors, long-term health conditions, physical work demands, and educational level is associated with measurement bias; and (iii), to explore whether correcting for factors associated with bias improves the prediction of device-measured sitting time based on self-reported sitting time. METHODS: A statistical validation model study based on data from 23 993 adults in the Trøndelag Health Study (HUNT4), Norway. Participants reported usual sitting time on weekdays using a single-item questionnaire and wore two AX3 tri-axial accelerometers on the thigh and low back for an average of 3.8 (standard deviation [SD] 0.7, range 1-5) weekdays to determine their sitting time. Statistical validation was performed by iteratively adding all possible combinations of factors associated with bias between self-reported and device-measured sitting time in a multivariate linear regression. We randomly selected 2/3 of the data (n = 15 995) for model development and used the remaining 1/3 (n = 7 998) to evaluate the model. RESULTS: Mean (SD) self-reported and device-measured sitting time were 6.8 (2.9) h/day and 8.6 (2.2) h/day, respectively, corresponding to a mean difference of 1.8 (3.1) h/day. Limits of agreement ranged from - 8.0 h/day to 4.4 h/day. The discrepancy between the measurements was characterized by a proportional bias with participants device-measured to sit less overestimating their sitting time and participants device-measured to sit more underestimating their sitting time. The crude explained variance of device-measured sitting time based on self-reported sitting time was 10%. This improved to 24% when adding age, body mass index and physical work demands to the model. Adding sex, lifestyle factors, educational level, and long-term health conditions to the model did not improve the explained variance. CONCLUSIONS: Self-reported sitting time had low validity and including a range of factors associated with bias in self-reported sitting time only marginally improved the prediction of device-measured sitting time.