Daily physical activity patterns among aging workers: the Finnish Retirement and Aging Study (FIREA)

Abstract

ObjectivesPhysical activity is associated with the aging workers' ability to work and predicts working beyond retirement age. To better understand physical activity behaviour in this growing population group, we aimed at characterising 24-hour physical activity patterns among aging workers, and to describe the association between occupational category and total, occupational and leisure-time physical activities.MethodsWe included 878 workers (mean age 62.4 years, SD 1.1, 85% women) from the Finnish Retirement and Aging Study, who wore an accelerometer on their non-dominant wrist for 1 week. We plotted mean hourly activity counts per minute (CPM) for working days and days off. We also compared mean daily CPM between genders and occupations between working days and days off, and work and leisure time by using repeated measures analysis of variance.ResultsActivity patterns were different between genders, occupations and types of the day. Women (2580, 95% CI 2540 to 2620) had higher daily mean CPM than men (2110, 95% CI 2020 to 2000). Women in manual occupations were more active than women in non-manual occupations during working days. The differences among men were in the same direction but less pronounced than among women. We found no differences in activity levels between occupations during days off and leisure time on working days.ConclusionsIn aging workers, physical activity differs by gender and occupation during working time, but not during leisure time. As low physical activity is associated with increased risk of early exit from employment, physical activity should be promoted at workplaces, especially among men and people in non-manual occupations.

Cross-sectional associations of neighbourhood socioeconomic disadvantage and greenness with accelerometer-measured leisure-time physical activity in a cohort of ageing workers

“… 24 These findings are in line with ours, although in that study no information on the participants’ working schedules was available. Our results are in agreement also with the findings indicating that an individual’s level of PA on working days is strongly related to a person’s occupation 26 and mode of commuting. 51 …”

“…24 These findings are in line with ours, although in that study no information on the participants' working schedules was available. Our results are in agreement also with the findings indicating that an individual's level of PA on working days is strongly related to a person's occupation 26 and mode of commuting. 51 Findings for greenness, when assessed individually, are in line with the common hypothesis that greenness would increase PA. 10 Moreover, we observed an interaction for neighbourhood socioeconomic disadvantage with greenness in relation to PA on non-working days.…”

“…We used data from the FIREA activity substudy including 938 participants who were still working at the baseline of the substudy. 26 Of them, we excluded six participants whose accelerometer-assessed total PA measurements had technical errors, 29 who had no valid PA measurements for any day, 156 who did not have information on 1 working and 1 non-working days and 39 who had no linkage to both objective measures of neighbourhood characteristics based on coordinates of their home address. While many studies have used a minimum of 3–4 valid days of PA measurement, 16 18 some have used lower cutoffs.…”

Physical Activity across Retirement Transition by Occupation and Mode of Commute

“…These participants were sent an invitation letter to participate in the activity substudy, and of them, 908 participants (34% of the eligible) returned the informed consent. There were slightly more women and employees with nonmanual occupation, and fewer self-reported inactive people, among the participants who consented to the accelerometer measurement as compared with those who were eligible, but did not consent (13). We followed the participants up annually with questionnaires and accelerometer measurements and enquired about the timing of retirement in each phase of the data collection.…”

“…First, we used mean daily activity counts (mean of daily mean VM CPM [18]) to compare overall physical activity before and after retirement, and the change after retirement, by gender and occupational groups. Second, we used mean hourly activity counts (mean of hourly mean VM CPM of each hour of the day [13]) to visualize activity levels across the day before and after retirement by gender. Third, proportion of total daily activity counts (mean of daily sum of VM CPM [19]) accumulated at work, and during leisure and commuting time, and postretirement total daily activity counts were determined to examine activity accumulated at different preretirement domains between gender and occupational groups.…”