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

Abstract

ObjectiveNeighbourhood characteristics may affect the level of physical activity (PA) of the residents. Few studies have examined the combined effects of distinctive neighbourhood characteristics on PA using objective data or differentiated between activity during working or non-working days. We examined the associations of socioeconomic disadvantage and greenness with accelerometer-measured leisure-time PA during working and non-working days.DesignCross-sectional study.SettingFinnish Retirement and Aging (FIREA) study.Participants708 workers (604 women, mean age 62.4 ranging from 58 to 64 years,) participating in the FIREA study who provided PA measurement data for at least 1 working and non-working day.Primary and secondary outcomesPA was measured with wrist-worn accelerometer on average of 4 working and 2 non-working days. Outcomes were total PA, light PA (LPA) and moderate-to-vigorous PA (MVPA). These measurements were linked to data on neighbourhood socioeconomic disadvantage and greenness within the home neighbourhood (750×750 m). Generalised linear models were adjusted for possible confounders.ResultsOn non-working days, higher neighbourhood disadvantage associated with lower levels of total PA (p value=0.07) and higher level of neighbourhood greenness associated with higher level of total PA (p value=0.04). Neighbourhood disadvantage and greenness had an interaction (p value=0.02); in areas of low disadvantage higher greenness did not associate with the level of total PA. However, in areas of high disadvantage, 2 SD higher greenness associated with 46 min/day (95% CI 8.4 to 85) higher total PA. Slightly stronger interaction was observed for LPA (p=0.03) than for the MVPA (p=0.09). During working days, there were no associations between neighbourhood characteristics and leisure-time total PA.ConclusionsOf the disadvantaged neighbourhoods, those characterised by high levels of greenness seem to associate with higher levels of leisure-time PA during non-working days. These findings suggest that efforts to add greenness to socioeconomically disadvantaged neighbourhoods might reduce inequalities in PA.

Historical development of accelerometry measures and methods for physical activity and sedentary behavior research worldwide: A scoping review of observational studies of adults

“…Almost one-third (29.7%) of studies asked participants to keep a logbook of accelerometer wear time, nonwear time, and/or sleep time ( Table 1 ). A few studies also specified a protocol to capture workday activities (n = 5) [ 84 , 107 , 108 , 119 , 162 ], bicycling (n = 3) [ 63 , 110 ], swimming/water activities (n = 2) [ 32 , 63 ], and outdoor activities (n = 1) [ 141 ]. Most studies required wear of one accelerometer (94.2%); however, 8 studies (5.2%) asked participants to wear two accelerometers at the same time and 1 study (0.6%) asked participants to wear four accelerometers at the same time.…”

Residential green environments are associated with human milk oligosaccharide diversity and composition

“…We used Landsat TM images obtained over the summertime (June-August, greenest months in Finland), to minimize the seasonal variation of living vegetation and cloud cover as well as to better identify vegetation areas and maximise the contrast in our estimated exposure. In each selected Landsat TM 5 images, the cloud was masked out, and the Normalized Difference Vegetation Index (NDVI) 36 For the analyses, areas covered by water were removed and the value ranged from 0 to 1, to prevent negative values for underestimating the greenness values of the residential area like in some prior studies 60 . We assumed that summertime NDVI identified the green space and vegetation density well, but greenness intensity might vary seasonally.…”

Cross-sectional associations of different types of nature exposure with psychotropic, antihypertensive and asthma medication

“…Another aim was to assess potential effect modification by socioeconomic status (SES) because there is some evidence that the health benefits of nature exposure might be most evident among lowest socioeconomic groups—for example, due to the lack of access to other health-promoting resources. 23 24 …”

“…These medicine groups were chosen because mental health problems, insomnia, hypertension and asthma are major disease groups from a public health perspective, and information on the relationship between nature exposure and asthma is lacking. Another aim was to assess potential effect modification by socioeconomic status (SES) because there is some evidence that the health benefits of nature exposure might be most evident among lowest socioeconomic groups—for example, due to the lack of access to other health-promoting resources 23 24…”