Summary Although populations around the world are rapidly ageing, evidence that increasing longevity is being accompanied by an extended period of good health is scarce. A coherent and focused public ...health response that spans multiple sectors and stakeholders is urgently needed. To guide this global response, WHO has released the first World report on ageing and health , reviewing current knowledge and gaps and providing a public health framework for action. The report is built around a redefinition of healthy ageing that centres on the notion of functional ability: the combination of the intrinsic capacity of the individual, relevant environmental characteristics, and the interactions between the individual and these characteristics. This Health Policy highlights key findings and recommendations from the report.
Abstract Objectives To determine the ActiGraph GT3X+ cut-points with the highest accuracy for estimating time spent in sedentary behaviour in older adults in free-living environments. ActivPAL3 ™ was ...used as the reference standard. Design Cross-sectional study. Methods 37 participants (13 males and 24 females, 73.5 ± 7.3 years old) wore an ActiGraph GT3X+ and an ActivPAL3 ™ for 7 consecutive days. For ActivPAL3 ™, variables were created based on posture. For ActiGraph GT3X+, sedentary behaviour was defined as (1) vector magnitude and (2) vertical axis counts for 1-s, 15-s and 1-min epochs, with cut-points for 1-s epochs of <1 to <10 counts, for 15-s epochs of <1 to <100 counts and for 1-min epochs of <1 to <400 counts. For each of the ActiGraph GT3X+ cut-points, area under the receiver operating characteristic curve (area under the curve), sensitivity, specificity, and percentage correctly classified were calculated. Bias and 95% limits of agreement were calculated using the Bland-Altman method. Results The highest areas under the curve were obtained for the vector magnitude cut-points: <1 count/s, <70 counts/15-s, and <200 counts/min; and for the vertical axis cut-points: <1 count/s, <10 counts/15-s and <25 counts/min. Mean biases ranged from −4.29 to 124.28 min/day. The 95% limits of agreement for these cut-points were ±2 h suggesting great inter-individual variation. Conclusions The results suggest that cut-points are dependent on unit of analyses (i.e. epoch length and axes); cut-points for a given epoch length and axis cannot simply be extrapolated to other epoch lengths. Limitations regarding inter-individual variability and misclassification of standing activity as sitting/lying must be considered.
Abstract Objective Sedentary behaviour (SB) is associated with a range of negative health outcomes, but little is known about the validity of self-report methods for measuring SB in older adults. ...Thus, the aim was to assess the reliability and validity of two instruments for measuring SB in older adults. Design Cross-sectional study. Methods 41 community-dwelling older adults (14/27 male/female, 74.5 ± 7.6 years) wore an ActivPAL3 ™ (AP) for 7 consecutive days, then completed (1) a single question (SQ) to assess sitting time on a usual weekday, weekend day and yesterday (i.e. the last day of monitoring), and (2) a computer-delivered 24-h recall (MARCA) for the last two days. Intraclass correlation (ICC) and standard error of measurement (SEM) were used to assess test–retest reliability; validity was examined using Spearman's correlation, mean bias and limits of agreement, and kappa for classifying tertiles of time in SB, with AP as the reference standard. Results For the SQ, the ICC ranged from 0.64 to 0.79, with SEM 1.03–1.42 h/day. ICC for the MARCA ranged from 0.72 to 0.96, with SEM 0.47–1.18 h/day. The SQ showed modest correlation with AP ( r = 0.13–0.33), with mean biases of about −3.5 h/day. The MARCA showed moderate correlation with AP ( r = 0.49–0.67), with mean biases of about 1.4 h/day. When categorised into tertiles, agreement was significant but fair for the SQ, and moderate for the MARCA. Conclusion Both measures have acceptable reliability, but the MARCA provides more valid estimates of SB than the SQ, which underestimates SB in this group of older adults.
Since coronavirus disease 2019 (COVID‐19) entered the Netherlands, the older adults (aged 70 or above) were recommended to isolate themselves, resulting in less social contact and possibly increased ...loneliness. The aim of this qualitative study was to explore independently living older adults’ perceptions of social and emotional well‐being during the COVID‐19‐related self‐isolation, and their motivation to expand their social network in the future. Semi‐structured phone interviews were held with 20 community‐dwelling adults (age range 56–87; 55% female) between April and June 2020 in the Netherlands. The interviews were audio recorded and transcribed verbatim. Open coding process was applied to identify categories and themes. Participants said to use more digital technologies to maintain contacts and adapt to the government measurements. Most participants missed the lack of social contacts, while some participants had no problems with the reduced social contacts. The emotional well‐being of most participants did not change. Some participants felt unpleasant or mentioned that the mood of other people had changed. Participants were not motivated to expand their social network because of existing strong networks. The relatively vital community‐dwelling older adults in this study were able to adapt to the government recommendations for self‐isolation with limited negative impact on their socio‐emotional well‐being.
The aim of this prospective cohort study was to compare changes in lifestyle behaviours over nine years in women who were and were not diagnosed with osteoarthritis (OA). Data were from the 1945-51 ...cohort of the Australian Longitudinal Study on Women's Health (aged 50-55 in 2001) who completed written surveys in 2001, 2004, 2007 and 2010. The sample included 610 women who were, and 3810 women who were not diagnosed with OA between 2004 and 2007. Descriptive statistics were used to assess changes in lifestyle behaviours (weight, sitting time, physical activity, alcohol and smoking) in the two groups, over three survey intervals: from 2001-2004 (prior to diagnosis); from 2004-2007 (around diagnosis); and from 2007-2010 (following diagnosis). Compared with women without OA (28%), a greater proportion of women with OA (38%) made at least one positive lifestyle change (p < 0.001). These included losing > 5 kg (9.8% vs. 14.4%, p < 0.001), and reducing sitting time by an hour (29.5% vs. 39.1%, p < 0.001) following diagnosis. However, women with OA also made negative lifestyle changes (35% vs. 29%, p < 0.001), for example, gaining > 5 kg around the time of diagnosis (21.4% vs. 14.5%, p < 0.001) and increasing sitting time by an hour following diagnosis (38.4% vs. 32.3%, p = 0.003). More women with OA also started smoking following diagnosis (8.9% vs. 0.8%, p < 0.001). While some women made positive changes in lifestyle behaviours during and following OA diagnosis, others made negative changes. Consistent support from clinicians for managing OA symptoms may enable patients to make more positive changes in lifestyle behaviours.
To examine the effects of overall level and timing of physical activity (PA) on changes from a healthy body mass index (BMI) category over 12 years in young adult women.
Participants in the ...Australian Longitudinal Study on Women's Health (younger cohort, born 1973-1978) completed surveys between 2000 (age 22-27 years) and 2012 (age 34-39 years). Physical activity was measured in 2000, 2003, 2006, and 2009 and was categorized as very low, low, active, or very active at each survey, and a cumulative PA score for this 9-year period was created. Logistic regression was used to examine relationships between PA accumulated across all surveys (cumulative PA model) and PA at each survey (critical periods PA model), with change in BMI category (from healthy to overweight or healthy to obese) from 2000 to 2012.
In women with a healthy BMI in 2000, there were clear dose-response relationships between accumulated PA and transition to overweight (P=.03) and obesity (P<.01) between 2000 and 2012. The critical periods analysis indicated that very active levels of PA at the 2006 survey (when the women were 28-33 years old) and active or very active PA at the 2009 survey (age 31-36 years) were most protective against transitioning to overweight and obesity.
These findings confirm that maintenance of very high PA levels throughout young adulthood will significantly reduce the risk of becoming overweight or obese. There seems to be a critical period for maintaining high levels of activity at the life stage when many women face competing demands of caring for infants and young children.
Abstract Objective To summarize the diagnostic accuracy of self-reported osteoarthritis (OA), rheumatoid arthritis (RA), and arthritis (i.e., unspecified) in the general adult population. Study ...Design and Setting A systematic literature search identified studies reporting diagnostic data on self-reported diagnosis of OA, RA, or arthritis in adults in population-based or primary care samples. Index tests included any form of participant-reported presence of the condition. Reference tests included rheumatologist, physician, or health professional examination; medical record review; physician interview; laboratory tests; or radiography. Relevant articles were scored using the QUADAS tool. Diagnostic values were summarized using pooled estimates for sensitivity and specificity. Results The search strategy identified 16 articles: 11 for OA, 5 for RA, and 4 for arthritis. Four of 16 articles scored high on quality. The pooled sensitivity and specificity were 0.75 95% confidence interval (CI): 0.56, 0.88 and 0.89 (95% CI: 0.77, 0.95) for OA, 0.88 (95% CI: 0.59, 0.97) and 0.93 (95% CI: 0.66, 0.99) for RA, and 0.71 (95% CI: 0.59, 0.80) and 0.79 (95% CI: 0.65, 0.89) for arthritis. There were not enough studies to conduct meta-analyses for joint-specific OA. Conclusion The accuracy of self-reported OA and RA is acceptable for large-scale studies in which rheumatologist examination is not feasible. More high-quality studies are required to confirm the accuracy of self-reported arthritis and joint-specific OA.
Accurate estimation of energy expenditure (EE) from accelerometer outputs remains a challenge in older adults. The aim of this study was to validate different ActiGraph (AG) equations for predicting ...EE in older adults. Forty older adults (age = 77.4 ± 8.1 yrs) completed a set of household/gardening activities in their residence, while wearing an AG at the hip (GT3X+) and a portable calorimeter (MetaMax 3B - criterion). Predicted EEs from AG were calculated using five equations (Freedson, refined Crouter, Sasaki and Santos-Lozano (vertical-axis, vectormagnitude)). Accuracy of equations was assessed using root-mean-square error (RMSE) and mean bias. The Sasaki equation showed the lowest RMSE for all activities (0.47 METs) and across physical activity intensities (PAIs) (range 0.18-0.48 METs). The Freedson and Santos-Lozano equations tended to overestimate EE for sedentary activities (range: 0.48 to 0.97 METs), while EEs for moderate-to-vigorous activities (MVPA) were underestimated (range: −1.02 to −0.64 METs). The refined Crouter and Sasaki equations showed no systematic bias, but they respectively overestimated and underestimated EE across PAIs. In conclusion, none of the equations was completely accurate for predicting EE across the range of PAIs. However, the refined Crouter and Sasaki equations showed better overall accuracy and precision when compared with the other methods.
Recent global meta-analyses show that 40% of dementia cases can be attributed to twelve modifiable risk factors.
To investigate how health promotion strategies may differ in specific populations, ...this study estimated population attributable fractions (PAFs) of these risk factors for dementia in cognitively normal (CN) individuals and individuals with mild cognitive impairment (MCI) in United States and Greek cohorts.
We re-analyzed data from the National Alzheimer's Coordinating Centre (NACC, n = 16,147, mean age 75.2±6.9 years, 59.0% female) and the Hellenic Longitudinal Investigation of Aging and Diet (HELIAD, n = 1,141, mean age 72.9±5.0 years, 58.0% female). PAFs for the total samples and CN and MCI subgroups were calculated based on hazard ratios for the risk of dementia and risk factor prevalence in NACC (9 risk factors) and HELIAD (10 risk factors).
In NACC, 2,630 participants developed MCI (25.1%) and 3,333 developed dementia (20.7%) during a mean follow-up of 4.9±3.5 years. Weighted overall PAFs were 19.4% in the total sample, 15.9% in the CN subgroup, and 3.3% in the MCI subgroup. In HELIAD, 131 participants developed MCI (11.2%) and 68 developed dementia (5.9%) during an average follow-up of 3.1±0.86 years. Weighted overall PAFs were 65.5% in the total sample, 65.8% in the CN subgroup and 64.6% in the MCI subgroup.
Translation of global meta-analysis data on modifiable risk factors should be carefully carried out per population. The PAFs of risk factors differ substantially across populations, directing health policy making to tailored risk factor modification plans.
Studies of mid-aged adults provide evidence of a relationship between sitting-time and all-cause mortality, but evidence in older adults is limited. The aim is to examine the relationship between ...total sitting-time and all-cause mortality in older women.
The prospective cohort design involved 6656 participants in the Australian Longitudinal Study on Women's Health who were followed for up to 9 years (2002, age 76-81, to 2011, age 85-90). Self-reported total sitting-time was linked to all-cause mortality data from the National Death Index from 2002 to 2011. Cox proportional hazard models were used to examine the relationship between sitting-time and all-cause mortality, with adjustment for potential sociodemographic, behavioural and health confounders.
There were 2003 (30.1%) deaths during a median follow-up of 6 years. Compared with participants who sat <4 h/day, those who sat 8-11 h/day had a 1.45 times higher risk of death and those who sat ≥11 h/day had a 1.65 times higher risk of death. These risks remained after adding sociodemographic and behavioural covariates, but were attenuated after adjustment for health covariates. A significant interaction (p=0.02) was found between sitting-time and physical activity (PA), with increased mortality risk for prolonged sitting only among participants not meeting PA guidelines (HR for sitting ≥8 h/day: 1.31, 95% CI 1.07 to 1.61); HR for sitting ≥11 h/day: 1.47, CI 1.15 to 1.93).
Prolonged sitting-time was positively associated with all-cause mortality. Women who reported sitting for more than 8 h/day and did not meet PA guidelines had an increased risk of dying within the next 9 years.
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