Should we work smarter or harder for our health? A comparison of intensity and domain-based time-use compositions and their associations with cognitive and cardiometabolic health.

BACKGROUND: Each day is made up of a composition of 'time-use behaviours'. These can be classified by their intensity (e.g., light or moderate-vigorous physical activity (PA)) or domain (e.g., chores, socialising). Intensity-based time-use behaviours are linked with cognitive function and cardiometabolic health in older adults, but it is unknown whether these relationships differ depending on the domain (or type/context) of behaviour. METHODS: This study included 397 older adults (65.5 ± 3.0 years, 69% female, 16.0 ± 3.0 years education) from Adelaide and Newcastle, Australia. Time-use behaviours were recorded using the Multimedia Activity Recall for Children and Adults (MARCA), cognitive function was measured using the Addenbrooke's Cognitive Examination III (ACE-III) and Cambridge Neuropsychological Test Automated Battery (CANTAB), and systolic and diastolic blood pressure, total cholesterol and waist-hip ratio were also recorded. Two 24-hr time-use compositions were derived from each participant's MARCA, including a four-part intensity composition (sleep, sedentary behaviour, light and moderate-vigorous PA) and an eight-part domain composition (Sleep, Self-Care, Chores, Screen Time, Quiet Time, Household Administration, Sport/Exercise, and Social). RESULTS: Linear regressions found significant associations between the domain composition and both ACE-III (p=0.010) and waist-hip ratio (p=0.009), and between the intensity composition and waist-hip ratio (p=0.025). Isotemporal substitution modelling demonstrated that the domains of sedentary behaviours and PA impacted their associations with ACE-III, whilst any PA appeared beneficial for waist-hip ratio. CONCLUSIONS: Findings suggest the domain of behaviour should be considered when aiming to support cognitive function, whereas for cardiometabolic health, it appears sufficient to promote any type of PA.

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

[This corrects the article DOI: 10.3389/fnhum.2022.1051793.].

Jointly modeling behavioral and EEG measures of proactive control in task switching.

In this study, we implement joint modeling of behavioral and single-trial electroencephalography (EEG) data derived from a cued-trials task-switching paradigm to test the hypothesis that trial-by-trial adjustment of response criterion can be linked to changes in the event-related potentials (ERPs) elicited during the cue-target interval (CTI). Specifically, we assess whether ERP components associated with preparation to switch task and preparation of the relevant task are linked to a response criterion parameter derived from a simple diffusion decision model (DDM). Joint modeling frameworks characterize the brain-behavior link by simultaneously modeling behavioral and neural data and implementing a linking function to bind these two submodels. We examined three joint models: The first characterized the core link between EEG and criterion, the second added a switch preparation input parameter and the third also added a task preparation input parameter. The criterion-EEG link was strongest just before target onset. Inclusion of switch and task preparation parameters did not improve the performance of the criterion-EEG link but was necessary to accurately model the ERP waveform morphology. While we successfully jointly modeled latent model parameters and EEG data from a task-switching paradigm, these findings show that customized cognitive models are needed that are tailored to the multiple cognitive control processes underlying task-switching performance. This is the first paper to implement joint modeling of behavioral measures and single-trial electroencephalography (EEG) data derived from the cue-target interval in a cued-trials task-switching paradigm. Model hyperparameters showed a strong link between response criterion and the pre-target negativity amplitude. Additional parameters (switch preparation, task preparation) were necessary to model the cue-locked ERP waveform morphology. This is consistent with multiple cognitive control processes underlying proactive control and points to the need for more nuanced models of task-switching performance.

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.

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

INTRODUCTION: Approximately 40% of late-life dementia may be prevented by addressing modifiable risk factors, including physical activity and diet. Yet, it is currently unknown how multiple lifestyle factors interact to influence cognition. The ACTIVate Study aims to (1) explore associations between 24-hour time-use and diet compositions with changes in cognition and brain function; and (2) identify duration of time-use behaviours and the dietary compositions to optimise cognition and brain function. METHODS AND ANALYSIS: This 3-year prospective longitudinal cohort study will recruit 448 adults aged 60-70 years across Adelaide and Newcastle, Australia. Time-use data will be collected through wrist-worn activity monitors and the Multimedia Activity Recall for Children and Adults. Dietary intake will be assessed using the Australian Eating Survey food frequency questionnaire. The primary outcome will be cognitive function, assessed using the Addenbrooke's Cognitive Examination-III. Secondary outcomes include structural and functional brain measures using MRI, cerebral arterial pulse measured with diffuse optical tomography, neuroplasticity using simultaneous transcranial magnetic stimulation and electroencephalography, and electrophysiological markers of cognitive control using event-related potential and time frequency analyses. Compositional data analysis, testing for interactions between time point and compositions, will assess longitudinal associations between dependent (cognition, brain function) and independent (time-use and diet compositions) variables. CONCLUSIONS: The ACTIVate Study will be the first to examine associations between time-use and diet compositions, cognition and brain function. Our findings will inform new avenues for multidomain interventions that may more effectively account for the co-dependence between activity and diet behaviours for dementia prevention. ETHICS AND DISSEMINATION: Ethics approval has been obtained from the University of South Australia's Human Research Ethics committee (202639). Findings will be disseminated through peer-reviewed manuscripts, conference presentations, targeted media releases and community engagement events. TRIAL REGISTRATION NUMBER: Australia New Zealand Clinical Trials Registry (ACTRN12619001659190).