Background: objectively measured population physical activity (PA) data from older persons is lacking. The aim of this study was to describe free-living PA patterns and sedentary behaviours in Icelandic older men and women using accelerometer. Methods: from April 2009 to June 2010, 579 AGESII-study participants aged 73–98 years wore an accelerometer (Actigraph GT3X) at the right hip for one complete week in the free-living settings. Results: in all subjects, sedentary time was the largest component of the total wear time, 75%, followed by low-light PA, 21%. Moderate-vigorous PA (MVPA) was <1%. Men had slightly higher average total PA (counts × day⁻¹) than women. The women spent more time in low-light PA but less time in sedentary PA and MVPA compared with men (P < 0.001). In persons <75 years of age, 60% of men and 34% of women had at least one bout ≥10 min of MVPA, which decreased with age, with only 25% of men and 9% of women 85 years and older reaching this. Conclusion: sedentary time is high in this Icelandic cohort, which has high life-expectancy and is living north of 60° northern latitude.

Ndependently identified. Purpose This study examines the prospective associations between a wide range of midlife determinants and objectively measured sedentary time in old age. Methods Data from 565 participants (age 73–92 yr) of the AGESII-Reykjavik Study were used. Participants wore an accelerometer (ActiGraph GT3X) on the right hip for seven consecutive days. On average, 31 yr earlier (during midlife), demographic, socioeconomic, lifestyle, and biomedical factors were collected. Linear regression models were used to examine prospective associations between midlife determinants and sedentary time (<100 counts per minute) in old age. Results After adjustment for sex, age, follow-up time, minutes of moderate to vigorous physical activity, body mass index, health status, mobility limitation, and joint pain in old age, the midlife determinants not being married, primary education, living in a duplex or living in an apartment (vs villa), being obese, and having a heart disease were associated with, on average, 15.3, 12.4, 13.5, 13.3, 21.8, and 38.9 sedentary minutes more per day in old age, respectively. Conclusions This study shows that demographic, socioeconomic, and biomedical determinants in midlife were associated with considerably more sedentary time per day in old age. These results can indicate the possibility of predicting sedentariness in old age, which could be used to identify target groups for prevention programs reducing sedentary time in older adults....

In Iceland, there is a large variation in daylight between summer and winter. The aim of the study was to identify how this large variation influences physical activity (PA) and sedentary behavior (SB). Free living PA was measured by a waist-worn accelerometer for one week during waking hours in 138 community-dwelling older adults (61.1% women, 80.3 ± 4.9 years) during summer and winter months. In general, SB occupied about 75% of the registered wear-time and was highly correlated with age (β = 0.36). Although the differences were small, more time was spent during the summer in all PA categories, except for the moderate-to-vigorous PA (MVPA), and SB was reduced. More lifestyle PA (LSPA) was accumulated in ≥5-min bouts during summer than winter, especially among highly active participants. This information could be important for policy makers and health professionals working with older adults. Accounting for seasonal difference is necessary in analyzing SB and PA data.

To identify cross-sectional and seasonal patterns of sleep and physical activity (PA) in community-dwelling, older Icelandic adults using accelerometers. A seven-day free-living protocol of 244 (110 female) adults aged 79.7 ± 4.9 years was conducted as part of a larger population-based longitudinal observational-cohort study in the greater Reykjavik area of Iceland. A subpopulation (n = 72) repeated the 7-day measurement during seasonal periods with greater (13.4 ± 1.4 h) and lesser (7.7 ± 1.8 h) daylight. Cross-sectional analyses using multiple linear regression models revealed that day length was a significant independent predictor of sleep duration, mid-sleep, and rise time (all p < 0.05). However, the actual within-individual differences in sleep patterns of the repeaters were rather subtle between periods of longer and shorter day-lengths. Compared to women, men had a shorter sleep duration (462 ± 80 vs. 487 ± 68 minutes, p = 0.008), earlier rise time, and a greater number of awakenings per night (46.5 ± 18.3 vs. 40.2 ± 15.7, p = 0.007), but sleep efficiency and onset latency were similar between the two sexes. Daily PA was also similar between men and women and between periods of longer and shorter day-lengths. BMI, age, gender, and overall PA all contributed to the variations in sleep parameters using multiple regression analysis. The sleep and PA characteristics of this unique population revealed some gender differences, but there was limited variation in response to significant daylight changes which may be due to long-term adaptation.

Dynamic sitting, such as fidgeting and desk work, might be associated with health, but remains difficult to identify out of accelerometry data. We examined, in a laboratory study, whether dynamic sitting can be identified out of triaxial activity counts. Among 18 participants (56% men, 27.3 ± 6.5 years), up to 236 counts per minute were recorded in the anteroposterior and mediolateral axes during dynamic sitting using a hip-worn accelerometer. Subsequently, we examined in 621 participants (38% men, 80.0 ± 4.7 years) from the AGES-Reykjavik Study whether dynamic sitting was associated with cardio-metabolic health. Compared to participants who recorded the fewest dynamic sitting minutes (Q₁), those with more dynamic sitting minutes had a lower BMI (Q₂ = −1.39 (95%CI = −2.33;–0.46); Q₃ = −1.87 (−2.82;–0.92); Q₄ = −3.38 (−4.32;–2.45)), a smaller waist circumference (Q₂ = −2.95 (−5.44;–0.46); Q₃ = −3.47 (−6.01;–0.93); Q₄ = −8.21 (−10.72;–5.71)), and a lower odds for the metabolic syndrome (Q₂ = 0.74 [0.45;1.20] Q₃ = 0.58 [0.36;0.95]; Q₄ = 0.36 [0.22;0.59]). Our findings suggest that dynamic sitting might be identified using accelerometry and that this behaviour was associated with health. This might be important given the large amounts of time people spend sitting. Future studies with a focus on validation, causation and physiological pathways are needed to further examine the possible relevance of dynamic sitting.