Participants’ characteristics were described using median and interquartile ranges (IQR) or percentages. Differences between the UTIB, STIB, and RTIB were compared using ANOVA for continuous variables and chi-square tests for categorical variables. Kruskal-Wallis test was used for the same purpose for non-normally distributed variables. Bonferroni adjustment were applied to all Post Hoc Tests.

Two multinominal regression models were used to calculate the relative risk ratios (RRR) of being RTIB and STIB with UTIB as the reference group. Model I was adjusted for age, sex, average daily nap length and SPPB total score and was used to compare the highest and the middle tertile of three measures of physical activity to the lowest tertile: (1) average CPM; (2) % of time in intensity 2303–4999 cpm/day; (3) and % of time in intensity ≥5000 cpm/day. Besides all the covariates of Model I, Model II was additionally adjusted for % in intensity ≥5000 cpm/day to calculate the RRR of being RTIB and STIB for those who were highly sedentary (daily sedentary behavior ≥65% of wear time) compared to those who were not (daily sedentary behavior < 65% of wear time). From the same multinominal regression models, probability of being RTIB, STIB and UTIB by daily levels of physical activity and sedentary behavior were calculated. The models met all assumptions of a multinominal regression model and there were no interactions between age, sex, and the other variables.

Sensitivity analysis (data shown in supplementary file) was conducted to compare the amount of daily physical activity and sedentary behavior after removing sleep duration with two different methods: by a fixed period (23:00–8:00) and by data from accelerometer wear-time diary.

Statistical significance was set at p <  0.05. Data were presented as mean ± standard deviation in the descriptive analysis and as relative risk ratios and probability with confidence intervals in the multinominal models. Analyses were conducted in Propero (custom-made in University of Southern Denmark), Microsoft excel, SPSS (version 24; IBM Corp., Armonk, NY, USA), and Stata 16 (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.).

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