https://orcid.org/0000-0001-8476-2093
Figures & data
Figure 1. Flow of study participants.
aHouseholds in our study area (Burnaby, New Westminister, North Vancouver, Richmond, Surrey, Vancouver, West Vancouver, White Rock) that receive a Shelter Aid for Elderly Renters rental subsidy from BC Housing, have a head of household aged ≥65 years, and a telephone number on file with BC Housing. bCould not be reached again after expression of interests in study participation. cAs measured by self-report diary, based on ≥3 days with ≥480 min/day valid wear time; non-wear time determined by self-report. dAs measured by accelerometry (ActiGraph GT3X+, 60 s epochs), based on ≥3 days with ≥480 min/day valid wear time; non wear time defined as ≥60 min of continuous zeroes, allowing for up to 2 min of counts ≤100 (Troiano et al., Citation2008). eAs measured by accelerometry (ActiGraph GT3X+, 60 s epochs), based on ≥3 days with ≥480 min/day valid wear time; non wear time defined as ≥90 min of consecutive zeroes, while allowing for up to 2 min of non-zero counts if the interruption was accompanied by 30 consecutive minutes of 0 counts either upstream or downstream (Choi et al., Citation2011). fAs measured by accelerometry (ActiGraph GT3X+, 60 s epochs), based on ≥3 days with ≥480 min/day valid wear time; non wear time defined as ≥90 min of continuous zeroes, without any allowance for interruptions. gAs measured by accelerometry (ActiGraph GT3X+, 60 s epochs), based on ≥3 days with ≥480 min/day valid wear time; non wear time defined as ≥90 min of continuous zeroes, while allowing for up to 2 min of counts <50 counts. hAs measured by accelerometry (ActiGraph GT3X+, 60 s epochs), based on ≥3 days with ≥480 min/day valid wear time; non wear time defined as ≥90 min of continuous zeroes, while allowing for up to 2 min of counts <100 counts.
Table 1. Estimates of sedentary behaviour and wear time (min/day), by non-wear time algorithm
Table 2. Agreement between non-wear time criteria for estimates of sedentary behaviour and wear time (min/day)
Figure 2. Bland Altman plots of non-wear time algorithm comparisons for estimates of sedentary behaviour (min/day).
Diary: We used participants’ self-report diaries to identify non-wear time (>10 min); Troiano: We considered ≥60 min of continuous zeroes, while allowing for up to 2 min of counts ≤100 counts as non-wear time (Troiano et al., Citation2008); Choi: We considered ≥90 min of consecutive zeroes, while allowing for up to 2 min of non-zero counts if the interruption was accompanied by 30 consecutive minutes of 0 counts either upstream or downstream (Choi et al., Citation2011); Algorithm 4: We considered ≥90 min of continuous zeroes, without any allowances for interruptions, as non-wear time; Algorithm 5: We considered ≥90 min of continuous zeroes, while allowing for up to 2 min of counts ≤50 counts as non-wear time; Algorithm 6: We considered ≥90 min of continuous zeroes, while allowing for up to 2 min of counts ≤100 counts as non-wear time.
Figure 3. Bland Altman plots of non-wear time algorithm comparisons for estimates of wear time (min/day).
Diary: We used participants’ self-report diaries to identify non-wear time (>10 min); Troiano: We considered ≥60 min of continuous zeroes, while allowing for up to 2 min of counts ≤100 counts as non-wear time (Troiano et al., Citation2008); Choi: We considered ≥90 min of consecutive zeroes, while allowing for up to 2 min of non-zero counts if the interruption was accompanied by 30 consecutive minutes of 0 counts either upstream or downstream (Choi et al., Citation2011); Algorithm 4: We considered ≥90 min of continuous zeroes, without any allowances for interruptions, as non-wear time; Algorithm 5: We considered ≥90 min of continuous zeroes, while allowing for up to 2 min of counts ≤50 counts as non-wear time; Algorithm 6: We considered ≥90 min of continuous zeroes, while allowing for up to 2 min of counts ≤100 counts as non-wear time.
