Characterising activity and diet compositions for dementia prevention: protocol for the ACTIVate prospective longitudinal cohort study

Physical activity, sedentary behaviour, and cognitive function among older adults: A bibliometric analysis from 2004 to 2024

Because of population ageing, there will be a vast increase in the prevalence of cognitive decline and dementia. Physical activity and sedentary behaviour have been identified as modifiable lifestyle behaviours associated with these cognitive conditions. Therefore, the aim of this bibliometric analysis is to reveal the knowledge structure of the field of physical activity, sedentary behaviour, and cognitive function among older adults from 2004 to 2024, and to predict emerging research trends. A total of 1290 publications were retrieved from the Web of Science Core Collection. CiteSpace and VOSviewer were applied to conduct performance analysis, science mapping, and enrichment. T. Liu-Ambrose was the most prolific author (39 publications), and the University of British Columbia was the most prolific institution (48 publications). The USA, China, and Canada were the three most productive countries with 392, 174, and 136 publications respectively. Two research trends revealed the knowledge structure of this field, including the shift from evaluating the effectiveness of interventions on cognitive function to evaluating the effectiveness of interventions on other health-related outcomes, as well as an expansion of research on the role of physical activity and sedentary behaviour in the context of healthy ageing. Sleep, sedentary behaviour, and virtual reality may be emerging research trends and may predict directions for future research. Collectively, this bibliometric analysis provides a one-step overview of the knowledge structure in this field for researchers and other stakeholders, as well as a reference for future research.

Cross-sectional associations between 24-hour time-use composition, grey matter volume and cognitive function in healthy older adults

BACKGROUND

Increasing physical activity (PA) is an effective strategy to slow reductions in cortical volume and maintain cognitive function in older adulthood. However, PA does not exist in isolation, but coexists with sleep and sedentary behaviour to make up the 24-hour day. We investigated how the balance of all three behaviours (24-hour time-use composition) is associated with grey matter volume in healthy older adults, and whether grey matter volume influences the relationship between 24-hour time-use composition and cognitive function.

METHODS

This cross-sectional study included 378 older adults (65.6 ± 3.0 years old, 123 male) from the ACTIVate study across two Australian sites (Adelaide and Newcastle). Time-use composition was captured using 7-day accelerometry, and T1-weighted magnetic resonance imaging was used to measure grey matter volume both globally and across regions of interest (ROI: frontal lobe, temporal lobe, hippocampi, and lateral ventricles). Pairwise correlations were used to explore univariate associations between time-use variables, grey matter volumes and cognitive outcomes. Compositional data analysis linear regression models were used to quantify associations between ROI volumes and time-use composition, and explore potential associations between the interaction between ROI volumes and time-use composition with cognitive outcomes.

RESULTS

After adjusting for covariates (age, sex, education), there were no significant associations between time-use composition and any volumetric outcomes. There were significant interactions between time-use composition and frontal lobe volume for long-term memory (p = 0.018) and executive function (p = 0.018), and between time-use composition and total grey matter volume for executive function (p = 0.028). Spending more time in moderate-vigorous PA was associated with better long-term memory scores, but only for those with smaller frontal lobe volume (below the sample mean). Conversely, spending more time in sleep and less time in sedentary behaviour was associated with better executive function in those with smaller total grey matter volume.

CONCLUSIONS

Although 24-hour time use was not associated with total or regional grey matter independently, total grey matter and frontal lobe grey matter volume moderated the relationship between time-use composition and several cognitive outcomes. Future studies should investigate these relationships longitudinally to assess whether changes in time-use composition correspond to changes in grey matter volume and cognition.

Twenty-four-hour time-use composition and cognitive function in older adults: Cross-sectional findings of the ACTIVate study

Introduction

Physical activity, sedentary behaviour and sleep are associated with cognitive function in older adults. However, these behaviours are not independent, but instead make up exclusive and exhaustive components of the 24-h day. Few studies have investigated associations between 24-h time-use composition and cognitive function in older adults. Of these, none have considered how the quality of sleep, or the context of physical activity and sedentary behaviour may impact these relationships. This study aims to understand how 24-h time-use composition is associated with cognitive function across a range of domains in healthy older adults, and whether the level of recreational physical activity, amount of television (TV) watching, or the quality of sleep impact these potential associations.

Methods

384 healthy older adults (age 65.5 ± 3.0 years, 68% female, 63% non-smokers, mean education = 16.5 ± 3.2 years) participated in this study across two Australian sites (Adelaide, n = 207; Newcastle, n = 177). Twenty-four-hour time-use composition was captured using triaxial accelerometry, measured continuously across 7 days. Total time spent watching TV per day was used to capture the context of sedentary behaviours, whilst total time spent in recreational physical activity was used to capture the context of physical activity (i.e., recreational accumulation of physical activity vs. other contexts). Sleep quality was measured using a single item extracted from the Pittsburgh Sleep Quality Index. Cognitive function was measured using a global cognition index (Addenbrooke's Cognitive Examination III) and four cognitive domain composite scores (derived from five tests of the Cambridge Neuropsychological Test Automated Battery: Paired Associates Learning; One Touch Stockings of Cambridge; Multitasking; Reaction Time; Verbal Recognition Memory). Pairwise correlations were used to describe independent relationships between time use variables and cognitive outcomes. Then, compositional data analysis regression methods were used to quantify associations between cognition and 24-h time-use composition.

Results

After adjusting for covariates and false discovery rate there were no significant associations between time-use composition and global cognition, long-term memory, short-term memory, executive function, or processing speed outcomes, and no significant interactions between TV watching time, recreational physical activity engagement or sleep quality and time-use composition for any cognitive outcomes.

Discussion

The findings highlight the importance of considering all activities across the 24-h day against cognitive function in older adults. Future studies should consider investigating these relationships longitudinally to uncover temporal effects.

The perceived mental effort of everyday activities in older adults

People's perceptions of the mental effort required for everyday activities may drive variation in the relationships between lifestyles and cognitive ability. We asked n = 259 healthy older adults aged 60 to 70 years (90 males, 169 females) to provide a rating of the Perceived Mental Effort (PME) for each activity instance they recalled over a 48-h period as part of a time-use recall. PME was rated on a 9-point scale from "very, very low" (score of 1) to "very, very high" (score of 9). Across the entire sample, participants rated a total of 196 different activities and 17,433 activity instances. The mean PME for individual activities was 3.50 ± 1.58. PMEs varied significantly by activity domain, with highest ratings being for Work (5.48 ± 1.72) and the lowest for Self-Care (2.89 ± 0.98). In multivariate analyses, PME ratings were higher in males than females (+0.30), PMEs were higher later in the day, increased with task duration, and decreased with age (all p < 0.0001). Time-weighted average individual PMEs across the two days of recall ranged from 1.86 to 6.50, and were 0.3 units higher for males, but unrelated to age. Repeated intra-individual PME ratings for the same activity were very reliable (ICC = 0.995, mean absolute difference = 0.03 ± 0.17). PMEs show promise as a reliable measure of mental effort.

Brain age predicts long-term recovery in post-stroke aphasia

The association between age and language recovery in stroke remains unclear. Here, we used neuroimaging data to estimate brain age, a measure of structural integrity, and examined the extent to which brain age at stroke onset is associated with (i) cross-sectional language performance, and (ii) longitudinal recovery of language function, beyond chronological age alone. A total of 49 participants (age: 65.2 ± 12.2 years, 25 female) underwent routine clinical neuroimaging (T1) and a bedside evaluation of language performance (Bedside Evaluation Screening Test-2) at onset of left hemisphere stroke. Brain age was estimated from enantiomorphically reconstructed brain scans using a machine learning algorithm trained on a large sample of healthy adults. A subsample of 30 participants returned for follow-up language assessments at least 2 years after stroke onset. To account for variability in age at stroke, we calculated proportional brain age difference, i.e. the proportional difference between brain age and chronological age. Multiple regression models were constructed to test the effects of proportional brain age difference on language outcomes. Lesion volume and chronological age were included as covariates in all models. Accelerated brain age compared with age was associated with worse overall aphasia severity (F(1, 48) = 5.65, P = 0.022), naming (F(1, 48) = 5.13, P = 0.028), and speech repetition (F(1, 48) = 8.49, P = 0.006) at stroke onset. Follow-up assessments were carried out ≥2 years after onset; decelerated brain age relative to age was significantly associated with reduced overall aphasia severity (F(1, 26) = 5.45, P = 0.028) and marginally failed to reach statistical significance for auditory comprehension (F(1, 26) = 2.87, P = 0.103). Proportional brain age difference was not found to be associated with changes in naming (F(1, 26) = 0.23, P = 0.880) and speech repetition (F(1, 26) = 0.00, P = 0.978). Chronological age was only associated with naming performance at stroke onset (F(1, 48) = 4.18, P = 0.047). These results indicate that brain age as estimated based on routine clinical brain scans may be a strong biomarker for language function and recovery after stroke.