A Wrinkle in Measuring Time Use for Cognitive Health: How should We Measure Physical Activity, Sedentary Behaviour and Sleep?

Isotemporal Substitution Effect of 24-h Movement Behaviors on Well-Being, Cognition, and BMI Among Older Adults

Background: This study investigated the interdependent relationships among older adults' daily engagement in physical activity (PA), sedentary time (ST), sleep, and their well-being, cognition, and body mass index (BMI). Method: Forty healthy older adults (31 females; Mean [age] = 70.8 ± 5.58) were included in the analysis. Participants wore a Fitbit tracker for an average of 23 h a day, five days a week, over six months. The Fitbit device tracked lightly active time, active time, ST, and sleep durations. Quality of life and cognitive flexibility were assessed using validated instruments. BMI was calculated using participants' self-reported height and weight. A compositional analysis (CODA) investigated the codependent associations among these variables and model time reallocation between behaviors. Results: Regression models utilizing CODA indicated significant associations between the outcomes of BMI (p = 0.05; Adj. R2 = 0.20), while cognitive flexibility and quality of life revealed no association (p > 0.05). Shifting 10 min from ST to active time is associated with a theoretical decrease of -0.76 (95% CI, -1.49 to -0.04) units in BMI. Similarly, reallocating 10 min from active time to ST is associated with a theoretical increase of 1.17 (95% CI, 0.03 to 2.3) units in BMI. Reallocating 10 min between other movement behaviors yielded no statistical significance. Conclusions: Our study highlights the importance of promoting active time to improve BMI in this population. Encouraging 10 min bouts of PA among older adults, in place of ST, is vital for improving national PA guideline adherence.

The use of sequential multiple assignment randomized trials (SMARTs) in physical activity interventions: a systematic review

BACKGROUND

Physical activity (PA) is often the cornerstone in risk-reduction interventions for the prevention and treatment of many chronic health conditions. PA interventions are inherently multi-dimensional and complex in nature. Thus, study designs used in the evaluation of PA interventions must be adaptive to intervention components and individual capacities. A Sequential Multiple Assignment Randomised Trial (SMART) is a factorial design in a sequential setting used to build effective adaptive interventions. SMARTs represent a relatively new design for PA intervention research. This systematic review aims to examine the state-of-the-art of SMARTs used to develop PA interventions, with a focus on study characteristics, design, and analyses.

METHODS

PubMed, Embase, PsychINFO, CENTRAL, and CinAHL were systematically searched through May 2023 for studies wherein PA SMARTs were conducted. Methodological quality was assessed using the Cochrane Risk of Bias 2 Tool.

RESULTS

Twenty studies across a variety of populations - e.g., obesity, chronic pain, and cardiovascular conditions, were included. All PA SMARTs involved two decision stages, with the majority including two initial treatment options. PA interventions most commonly consisted of individual aerobic exercise with strategies such as goal setting, wearable technology, and motivational interviewing also used to promote PA. Variation was observed across tailoring variables and timing of tailoring variables. Non-response strategies primarily involved augmenting and switching treatment options, and for responders to continue with initial treatment options. For analyses, most sample size estimations and outcome analyses accounted for the SMART aims specified. Techniques such as linear mixed models, weighted regressions, and Q-learning regression were frequently used. Risk of bias was high across the majority of included studies.

CONCLUSIONS

Individual-based aerobic exercise interventions supported by behaviour change techniques and wearable sensing technology may play a key role in the future development of SMARTs addressing PA intervention development. Clearer rationale for the selection of tailoring variables, timing of tailoring variables, and included measures is essential to advance PA SMART designs. Collaborative efforts from researchers, clinicians, and patients are needed in order to bridge the gap between adaptive research designs and personalised treatment pathways observed in clinical practice.

Validity and reliability of self-reported methods for assessment of 24-h movement behaviours: a systematic review

BACKGROUND

Time spent in sleep, sedentary behaviour (SB), and physical activity are exhaustive and mutually exclusive parts of a 24-h day that need to be considered in a combination. The aim of this study was to identify validated self-reported tools for assessment of movement behaviours across the whole 24-h day, and to review their attributes and measurement properties.

METHODS

The databases PubMed, Scopus, and SPORTDiscus were searched until September 2023. Inclusion criteria were: (i) published in English language, (ii) per-reviewed paper, (iii) assessment of self-reported time spent in sleep, SB, and physical activity, (iv) evaluation of measurement properties of all estimates across the full 24-h day, and (v) inclusion of adolescents, adults, or older adults. The methodological quality of included studies was assessed using the Consensus-based Standards for the selection of health Measurement Instruments checklist.

RESULTS

Our search returned 2064 records. After studies selection, we included 16 articles that reported construct validity and/or test-retest reliability of 12 unique self-reported tools - eight questionnaires, three time-use recalls, and one time-use diary. Most tools enable assessment of time spent in sleep, and domain-specific SB and physical activity, and account that sum of behaviours should be 24 h. Validity (and reliability) correlation coefficients for sleep ranged between 0.22 and 0.69 (0.41 and 0.92), for SB between 0.06 and 0.57 (0.33 and 0.91), for light-intensity physical activity between 0.18 and 0.46 (0.55 and 0.94), and for moderate- to vigorous-intensity physical activity between 0.38 and 0.56 (0.59 and 0.94). The quality of included studies being mostly fair-to-good.

CONCLUSIONS

This review found that only a limited number of validated self-reported tools for assessment of 24-h movement behaviours are currently available. Validity and reliability of most tools are generally adequate to be used in epidemiological studies and population surveillance, while little is known about adequacy for individual level assessments and responsiveness to behavioural change. To further support research, policy, and practice, there is a need to develop new tools that resonate with the emerging 24-h movement paradigm and to evaluate measurement properties by using compositional data analysis.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022330868.

The Association of the 24-Hour Activity Cycle Profiles With Cognition in Older Adults With Mild Cognitive Impairment: A Cross-Sectional Study

BACKGROUND

The relationship of cognition and the 24-h activity cycle (24-HAC), encompassing physical activity, sedentary behavior, and sleep, in older adults with mild cognitive impairment (MCI) remains uncertain. Distinct combinations of 24-HAC behaviors can characterize unique activity profiles and influence cognition. We aimed to characterize 24-HAC activity profiles in older adults with MCI and assess whether differences in cognition exist across profiles.

METHODS

We conducted a cross-sectional analysis utilizing baseline data from 3 randomized controlled trials involving 253 community-dwelling older adults (55 + years) with MCI (no functional impairment, dementia diagnosis, and Montreal Cognitive Assessment score <26/30). Using MotionWatch8© wrist-worn actigraphy (+5 days), we captured the 24-HAC. Cognition was indexed by the Alzheimer's Disease Assessment Scale Cognitive Plus (ADAS-Cog-Plus). Compositional data and latent profile analyses identified distinct 24-HAC activity profiles. Analysis of covariance examined whether 24-HAC activity profiles differed in cognition.

RESULTS

Four distinct activity profiles were identified. Profile 1 ("Average 24-HAC," n = 103) engaged in all 24-HAC behaviors around the sample average. Profile 2 ("Active Chillers," n = 70) depicted lower-than-average engagement in physical activity and higher-than-average sedentary behavior. Profile 3 ("Physical Activity Masters," n = 54) were the most active and the least sedentary. Profile 4 ("Sedentary Savants," n = 26) were the least active and the most sedentary. Sleep was similar across profiles. There were no significant differences in ADAS-Cog-Plus scores between 24-HAC activity profiles (p > .05).

CONCLUSIONS

Older adults with MCI exhibited four 24-HAC activity profiles conforming to recommended physical activity and sleep guidelines. Nonetheless, cognition was similar across these profiles.

Twenty-four hour physical activity, sedentary behaviour and sleep profiles in adults living with rheumatoid arthritis: a cross-sectional latent class analysis

BACKGROUND

Rheumatoid Arthritis (RA), an autoimmune systemic inflammatory disease, affects more than 17 million people globally. People with RA have higher risk of premature mortality; often experience chronic fatigue, pain and disrupted sleep; and are less physically active and more sedentary than healthy counterparts. It remains unclear how people with RA may balance sleep and awake movement activities over 24-hours, or how differences in 24-hour behaviours may be associated with determinants of health, or alignment with published activity guidelines.

METHODS

Cross-sectional exploration of objective measures of 24-hour sleep-wake activities in 203 people with RA. Latent Class Analysis (LCA) derived classes from time, by tertile, in six sleep-awake activities over 24 h. Comparisons of model fit statistics, class separation and interpretability defined best fit for number of classes. Variations in sleep-awake behaviour across classes and association of profile allocation with determinants of health, quality metrics for sleep, sitting and walking and alignment with published guidelines were explored. Multinomial logistic regression identified factors associated with likelihood of profile allocation.

RESULTS

LCA identified 2 to 6 classes and a 4-class model was determined as best fit for 24-hour sleep-awake behaviour profiles. One profile (26%) presented with more balanced 24-hour sleep, sitting and walking behaviours. The other three profiles demonstrated progressively less balanced 24-hour behaviours including: having low (< 7 h), high (> 8 h), or recommended (7-8 h) sleep duration in respective combination with high sitting (> 10 h), limited walking (< 3 h) or both when awake. Age, existing sitting and walking habit strength and fatigue were associated with likelihood of belonging to different profiles. More balanced 24-hour behaviour was aligned with better quality metrics for sleep, sitting and walking and published guidelines.

DISCUSSION

For people living with RA it is important to understand the 'whole person' and their 'whole day' to define who may benefit from support to modify 24-hour sleep-awake behaviours and which behaviours to modify. Supports should be informed by an understanding of personal or health-related factors that could act as barriers or facilitators for behavioural change, including exploring existing habitual sitting and walking behaviours.

TRIAL REGISTRATIONS

ClinicalTrials.gov ID: NCT02554474 (2015-09-16) and ClinicalTrials.gov ID: NCT03404245 (2018-01-11).

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights

BACKGROUND

The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored.

OBJECTIVE

This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies.

METHODS

Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights.

RESULTS

The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events.

CONCLUSIONS

This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

The influence of baseline sleep on exercise-induced cognitive change in cognitively unimpaired older adults: A randomised clinical trial

OBJECTIVES

Observational studies consistently demonstrate that physical activity is associated with elevated cognitive function, however, there remains significant heterogeneity in cognitive outcomes from randomized exercise interventions. Individual variation in sleep behaviours may be a source of variability in the effectiveness of exercise-induced cognitive change, however this has not yet been investigated. The current study aimed to (1) investigate the influence of a 6-month exercise intervention on sleep, assessed pre- and post-intervention and, (2) investigate whether baseline sleep measures moderate exercise-induced cognitive changes.

METHODS

We utilised data from the Intense Physical Activity and Cognition (IPAC) study (n = 89), a 6-month moderate intensity and high intensity exercise intervention, in cognitively unimpaired community-dwelling older adults aged 60-80 (68.76 ± 5.32). Exercise was supervised and completed on a stationary exercise bicycle, and cognitive function was measured using a comprehensive neuropsychological battery administered pre- and post-intervention. Sleep was measured using the Pittsburgh sleep quality index. There was no effect of the exercise intervention on any sleep outcomes from pre- to post-intervention.

RESULTS

There was a significant moderating effect of baseline sleep efficiency on both episodic memory and global cognition within the moderate intensity exercise group, such that those with poorer sleep efficiency at baseline showed greater exercise-induced improvements in episodic memory.

CONCLUSIONS

These results suggest that those with poorer sleep may have the greatest exercise-induced cognitive benefits and that baseline sleep behaviours may be an important source of heterogeneity in previous exercise interventions targeting cognitive outcomes.

Development of a 24-hour movement behaviors questionnaire (24HMBQ) for Chinese college students: validity and reliability testing

BACKGROUND

Physical activity (PA), sedentary behaviors (SB), and sleep are interrelated behavior components of a 24-hour day. Research interests continue to increase in examining the inter-relationship of three behaviors and their combined effects on health. The purpose of this study was to develop a comprehensive instrument to measure 24-hour movement behaviors for Chinese college students.

METHODS

The 24-hour movement behaviors questionnaire (24HMBQ) was developed based on a literature review and expert review. The target population (Chinese college students) and an expert panel assessed the face and content validity. After the final revision of the questionnaire, the participants (n = 229) were asked to complete the 24HMBQ twice to examine test-retest reliability. Convergent validity was evaluated using Spearman's rho, by comparing the 24HMBQ estimates of sleep, SB, and PA with results derived from the Pittsburgh Sleep Quality Index (PSQI), the Adult Sedentary Behaviors Questionnaire in China (ASBQC), and the International Physical Activity Questionnaire - Short Form (IPAQ-SF).

RESULTS

The 24HMBQ exhibited good face validity and high acceptability to respondents. Regarding content validity, the S-CVI/UA and S-CVI/Ave were 0.88 and 0.97, respectively. As indicated by ICC, the test-retest reliability was considered moderate to excellent, ranging from 0.68 to 0.97 (P < 0.01). Regarding the convergent validity, correlations were 0.32 for the duration of sleep per day, 0.33 for total time of physical activity per day, and 0.43 for the duration of sedentary behaviors per day.

CONCLUSION

The 24HMBQ is a feasible questionnaire with suitable validity and moderate to excellent test-retest reliability of all items. It is a promising tool to investigate 24-hour movement behaviors of Chinese college students. The 24HMBQ can be administrated in epidemiological studies.

Method for Activity Sleep Harmonization (MASH): a novel method for harmonizing data from two wearable devices to estimate 24-h sleep-wake cycles

Background

Daily 24-h sleep-wake cycles have important implications for health, however researcher preferences in choice and location of wearable devices for behavior measurement can make 24-h cycles difficult to estimate. Further, missing data due to device malfunction, improper initialization, and/or the participant forgetting to wear one or both devices can complicate construction of daily behavioral compositions. The Method for Activity Sleep Harmonization (MASH) is a process that harmonizes data from two different devices using data from women who concurrently wore hip (waking) and wrist (sleep) devices for ≥ 4 days.

Methods

MASH was developed using data from 1285 older community-dwelling women (ages: 60-72 years) who concurrently wore a hip-worn ActiGraph GT3X + accelerometer (waking activity) and a wrist-worn Actiwatch 2 device (sleep) for ≥ 4 days (N = 10,123 days) at the same time. MASH is a two-tiered process using (1) scored sleep data (from Actiwatch) or (2) one-dimensional convolutional neural networks (1D CNN) to create predicted wake intervals, reconcile sleep and activity data disagreement, and create day-level night-day-night pairings. MASH chooses between two different 1D CNN models based on data availability (ActiGraph + Actiwatch or ActiGraph-only). MASH was evaluated using Receiver Operating Characteristic (ROC) and Precision-Recall curves and sleep-wake intervals are compared before (pre-harmonization) and after MASH application.

Results

MASH 1D CNNs had excellent performance (ActiGraph + Actiwatch ROC-AUC = 0.991 and ActiGraph-only ROC-AUC = 0.983). After exclusions (partial wear [n = 1285], missing sleep data proceeding activity data [n = 269], and < 60 min sleep [n = 9]), 8560 days were used to show the utility of MASH. Of the 8560 days, 46.0% had ≥ 1-min disagreement between the devices or used the 1D CNN for sleep estimates. The MASH waking intervals were corrected (median minutes [IQR]: -27.0 [-115.0, 8.0]) relative to their pre-harmonization estimates. Most correction (-18.0 [-93.0, 2.0] minutes) was due to reducing sedentary behavior. The other waking behaviors were reduced a median (IQR) of -1.0 (-4.0, 1.0) minutes.

Conclusions

Implementing MASH to harmonize concurrently worn hip and wrist devices can minimizes data loss and correct for disagreement between devices, ultimately improving accuracy of 24-h compositions necessary for time-use epidemiology.

Improvement in Quality of Life with Use of Ambient-Assisted Living: Clinical Trial with Older Persons in the Chilean Population

In Chile, 18% of the population is over 60 years old and is projected to reach 31% in three decades. An aging population demands the development of strategies to improve quality of life (QoL). In this randomized trial, we present the implementation and evaluation of the Quida platform, which consists of a network of unintrusive sensors installed in the houses of elderly participants to monitor their activities and provide assistance. Sixty-nine elderly participants were included. A significant increase in overall QoL was observed amongst participants allocated to the interventional arm (p < 0.02). While some studies point out difficulties monitoring users at home, Quida demonstrates that it is possible to detect presence and movement to identify patterns of behavior in the sample studied, allowing us to visualize the behavior of older adults at different time intervals to support their medical evaluation.

Do the relationships of physical activity and total sleep time with cognitive function vary by age and biological sex? A cross-sectional analysis of the Canadian Longitudinal Study on Aging

OBJECTIVES

Physical activity (PA) and total sleep time (TST) are each associated with cognition; however, whether these relationships vary by age and biological sex is unclear. We examined the relationships of PA or TST with cognition, and whether age and sex moderated these relationships, using baseline data from the Canadian Longitudinal Study on Aging (CLSA; 2010-2015).

STUDY DESIGN

A cross-sectional analysis of participants from the Comprehensive cohort of the CLSA with complete PA and sleep data (n = 20,307; age range 45-86 years).

MAIN OUTCOME MEASURES

PA and TST were measured using the Physical Activity Scale for the Elderly (PASE) and self-reported TST over the past month. Cognition was indexed using a three-factor structural equation model (i.e., memory, executive function, and verbal fluency).

RESULTS

Non-linear restricted cubic spline models indicated that PA and TST explained statistically significant (p < 0.01) but modest variance of each cognitive domain (<1 % of 23-24 % variance). Age and sex did not moderate associations of PA with any cognitive domain. However, age and sex moderated relationships of TST with cognition, whereby: 1) associations of TST with memory decreased with age for males and females; and 2) males and females had different age-associated relationships of TST with executive function and verbal fluency.

CONCLUSIONS

PA and TST modestly contribute to multiple domains of cognition across middle and older adulthood. Importantly, the association of PA with cognition does not appear to vary across middle or older adulthood, nor does it vary by biological sex; however, TST appears to have a complex relationship with multiple domains of cognition which is both age- and sex-dependent.

Daily and hourly patterns of physical activity and sedentary behavior of older adults: Atherosclerosis risk in communities (ARIC) study

This cross-sectional study of older adults ≥ 65 years describes daily and hourly patterns of accelerometer-derived steps, sedentary, and physical activity behaviors and examines differences by day of the week and sociodemographic and health-related factors to identify time-use patterns. Data were from 459 Atherosclerosis Risk in Communities (ARIC) study participants (60% female; mean ± SD age = 78.3 ± 4.6 years; 20% Black) who wore a hip accelerometer ≥ 4 of 7 days, for ≥ 10 h/day in 2016. We used linear mixed models to examine daily patterns of steps, sedentary, low light, high light, and moderate-to-vigorous intensity physical activity (MVPA). Differences by sex, median age ( Collapse

Key Words

• Accelerometer
• Elderly
• Movement
• Steps
• Time-use

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Affiliation(s)

Erin E. Dooley The University of Alabama at Birmingham, Birmingham, AL, USA
Lisa A. Pompeii Baylor College of Medicine, Houston, TX, USA
Priya Palta Columbia University Irving Medical Center, New York, NY, USA
Pablo Martinez-Amezcua Columbia University Irving Medical Center, New York, NY, USA
Bjoern Hornikel The University of Alabama at Birmingham, Birmingham, AL, USA
Kelly R. Evenson Gillings School of Global Public Health, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jennifer A. Schrack Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Kelley Pettee Gabriel The University of Alabama at Birmingham, Birmingham, AL, USA

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Going digital - a commentary on the terminology used at the intersection of physical activity and digital health

In recent years digital technologies have become a major means for providing health-related services and this trend was strongly reinforced by the current Coronavirus disease 2019 (COVID-19) pandemic. As it is well-known that regular physical activity has positive effects on individual physical and mental health and thus is an important prerequisite for healthy aging, digital technologies are also increasingly used to promote unstructured and structured forms of physical activity. However, in the course of this development, several terms (e.g., Digital Health, Electronic Health, Mobile Health, Telehealth, Telemedicine, and Telerehabilitation) have been introduced to refer to the application of digital technologies to provide health-related services such as physical interventions. Unfortunately, the above-mentioned terms are often used in several different ways, but also relatively interchangeably. Given that ambiguous terminology is a major source of difficulty in scientific communication which can impede the progress of theoretical and empirical research, this article aims to make the reader aware of the subtle differences between the relevant terms which are applied at the intersection of physical activity and Digital Health and to provide state-of-art definitions for them.

Can exercise training promote better sleep and reduced fatigue in people with chronic stroke? A systematic review

Poor sleep and chronic fatigue are common in people with chronic stroke (i.e. ≥ 6 months post-stroke). Exercise training is a viable, low-cost therapy for promoting sleep and reducing fatigue; however, the effects of exercise on sleep and fatigue in people with chronic stroke are unclear. Thus, we conducted a systematic review ascertaining the effects of exercise on sleep and fatigue in people with chronic stroke. We systematically searched EMBASE, MEDLINE, AgeLine, the Cochrane Database of Systematic Reviews, CINAHL, SPORTDiscus, SCOPUS, and reference lists of relevant reviews for articles that examined the effects of exercise on sleep or fatigue in chronic stroke. Search results were limited to adults ≥ 18 years, randomized controlled trials, non-randomized trials, and pre-post studies, which were published in English and examined the effects of exercise on sleep or fatigue in people with chronic stroke. We extracted study characteristics and information on the measurement of sleep and fatigue, and assessed study quality and risk of bias using the CONSORT criteria and Cochrane risk-of-bias tool, respectively. We found two studies that examined the effects of exercise on sleep, and two that examined the effects of exercise on fatigue. All studies reported positive effects of exercise training on sleep and fatigue; however, there were concerns of bias and study quality in all studies. There is preliminary evidence that exercise promotes sleep and reduces fatigue in people with chronic stroke; however, the extent to which exercise impacts these health parameters is unclear.

How are combinations of physical activity, sedentary behaviour and sleep related to cognitive function in older adults? A systematic review

The relationships between cognitive function and each of physical activity, sleep and sedentary behaviour in older adults are well documented. However, these three "time use" behaviours are co-dependent parts of the 24-hour day (spending time in one leaves less time for the others), and their best balance for cognitive function in older adults is still largely unknown. This systematic review summarises the existing evidence on the associations between combinations of two or more time-use behaviours and cognitive function in older adults. Embase, Pubmed, PsycInfo, Medline and Emcare databases were searched in March 2020 and updated in May 2021, returning a total of 25,289 papers for screening. A total of 23 studies were included in the synthesis, spanning >23,000 participants (mean age 71 years). Findings support previous evidence that spending more time in physical activity and limiting sedentary behaviour is broadly associated with better cognitive outcomes in older adults. Higher proportions of moderate-vigorous physical activity in the day were most frequently associated with better cognitive function. Some evidence suggests that certain types of sedentary behaviour may be positively associated with cognitive function, such as reading or computer use. Sleep duration appears to share an inverted U-shaped relationship with cognition, as too much or too little sleep is negatively associated with cognitive function. This review highlights considerable heterogeneity in methodological and statistical approaches, and encourages a more standardised, transparent approach to capturing important daily behaviours in older adults. Investigating all three time-use behaviours together against cognitive function using suitable statistical methodology is strongly recommended to further our understanding of optimal 24-hour time-use for brain function in aging.

Relationships between physical activity, sleep and cognitive function: A narrative review

Physical activity and exercise can improve cognitive function and reduce the risk for dementia. Other lifestyle factors, including sleep, are associated with cognitive function and dementia risk, and exercise is an effective therapeutic strategy for improving sleep. Based on these associations, it has been hypothesised that sleep might be an important mediator for the effects of exercise on cognition. Here, we review the current literature to evaluate whether sleep and physical activity are independently or jointly associated with cognitive function. The extant literature in this area is minimal, and the causal relationships between physical activity, sleep and cognition have not been examined. A small number of cross-sectional studies in this area suggest that physical activity may attenuate some of the negative impact that poor sleep has on cognition, and also that sleep may be a mechanism through which physical activity improves cognitive abilities. Further research may enable the development of individually tailored intervention programs to result in the greatest cognitive benefit, ultimately delaying the onset of Alzheimer's disease.