Sage Journals: Discover world-class research

Abstract

Many older adults spend more than half of their time in sedentary behaviors that can lead to poor health outcomes, including declines in cognitive health. However, sedentary behaviors are diverse and can lead to different health outcomes. To determine the relationships between types of sedentary behaviors and cognitive health in older adults, we analyzed associations between sedentary behaviors and cognitive health among adults 50 years and older in the United States who participated in the Health and Retirement Study. We categorized sedentary behaviors based on the four domains of the Taylor taxonomy of sedentary behaviors. We found that certain categories of sedentary behaviors were positively associated with cognitive health in this population. These results support that the Taylor taxonomy can be used to identify sedentary behaviors that promote cognitive health and inform interventions that improve the quality of life of older adults.

Keywords

cognitive health sedentary behavior older adults taxonomy health promotion

Introduction

Many adults spend up to 60% of their waking hours in sedentary behaviors (e.g. sitting, reclining, lying down; Dempsey et al., 2020; Department of Health and Human Services, 2018; Katzmarzyk et al., 2019; Zou et al., 2024). These behaviors expend little energy (Tremblay et al., 2017) and are a risk factor for poor health, even independent of physical activity (Gibbs et al., 2015). As a result, international and national guidelines for physical activity recommend limiting sedentary behaviors (Dempsey et al., 2020; Department of Health and Human Services, 2018; Katzmarzyk et al., 2019). However, sedentary behavior is a lifestyle factor in many domains of daily living, including transportation, occupation, and leisure time based on Ecological Models of Health Behavior (Durand and Jáuregui, 2024; Sallis and Owen, 2015) and affect all ages (Zou et al., 2024). Although sedentary behaviors affect all ages, older adults have more sedentary behaviors than other age groups (Bauman et al., 2018; Harvey et al., 2013, 2015), so they have a greater risk for poor health outcomes.

Lifestyle factors, including sedentary behavior, have different associations with cognitive health in adults. Cognitive health and function encompass a range of abilities, including memory, attention, language, reasoning, problem-solving, and decision-making (Anderson and McConnell, 2007; Chen et al., 2022; Jedrziewski et al., 2007). Lifestyle factors that have positive associations with cognitive health include physical activity (Blondell et al., 2014; Larson et al., 2006; Ludyga et al., 2020; Northey et al., 2018), a Mediterranean diet (Lourida et al., 2013; Singh et al., 2014), social interaction and contact (Fung et al., 2011; James et al., 2011; Sommerlad et al., 2019), meditation (Gard et al., 2014), cognitive activity and mental stimulation (Vemuri et al., 2014; Wilson et al., 2012), and sleeping 7–8 hours per day (Wu et al., 2018). Conversely, lifestyle factors that have negative associations with cognitive health include tobacco smoking (Anstey et al., 2007; Peters et al., 2008; Sabia et al., 2012), chronic stress (Korten et al., 2017), sleep disturbances (Blackwell et al., 2014), poor social interaction (Kuiper et al., 2015), and perceived loneliness (Sundström et al., 2019). This dichotomous relationship between lifestyle factors and cognitive health is particularly important for reducing age-related cognitive decline and the risk of mortality later in life (Buckner, 2004; Ludyga et al., 2020), performing everyday activities (Hillman et al., 2008; Ludyga et al., 2020; Pinto et al., 2023; Raichlen et al., 2022, 2023; Stillman et al., 2020; Zou et al., 2024), and supporting overall well-being and independence (Kimura et al., 2023; Ren et al., 2024). Thus, a public health priority is to identify modifiable lifestyle factors that promote healthy cognitive aging (Huang et al., 2020).

Although the relationships among physical activity, brain health, and cognitive health have been extensively studied (Hillman et al., 2008; Ludyga et al., 2020; Stillman et al., 2020), the relationships among sedentary behaviors, brain health, and cognitive health are less well understood (Pinto et al., 2023; Raichlen et al., 2022, 2023; Zou et al., 2024). However, researchers have connected the type of sedentary behavior to changes in cognitive health, albeit with mixed results (Raichlen et al., 2022; Taylor et al., 2020; Zou et al., 2024). For example, television viewing for more than 3.5 hours per day was associated with cognitive decline (Fancourt and Steptoe, 2019). Also, among adults 60 years and older, regardless of physical activity, cognitively passive sedentary behaviors (i.e. television time) were associated with a greater risk of dementia, whereas cognitively active sedentary behaviors (i.e. computer time, board games, crafting, reading) were associated with a lower risk of dementia (de Rezende et al., 2014; Raichlen et al., 2022). Also, television viewing was associated with greater depression symptoms, whereas internet use was associated with lower depression symptoms (Loprinzi, 2019). These mixed associations between sedentary behavior and cognitive health and function were summarized in a systematic review: “The current body of evidence suggests sedentary behavior is negatively associated with cognitive function; however, the associations between sedentary behavior and cognitive function are complex and largely dependent on the exposure variable and outcomes assessed” (Falck et al., 2017: 809). This complexity contributes to challenges in understanding what sedentary behaviors account for different health outcomes.

To address these challenges, we developed the Taylor taxonomy of sedentary behaviors (Taylor, 2022). This taxonomy classifies sedentary behavior by four domains known as SNCC: (1) social interaction (i.e. not solitary, companionship, interacting, and connecting with others); (2) novelty (i.e. refreshingly new, unusual, or different); (3) choice (i.e. volition, preferred option, power, freedom, or decision to choose); and (4) cognition (i.e. mentally stimulating and engaging). Based on the classification, ratings are assigned to each behavior that indicate the degree (low, moderate, or high) to which the domain is observed. Based on the Taylor taxonomy, sedentary behaviors that rate high on the four domains would theoretically confer the greatest health benefits, whereas sedentary behaviors that rate low on the four domains would theoretically yield the least health benefits and could even be harmful. Thus, this new taxonomy established a comprehensive, integrated, and parsimonious conceptual framework to reconcile different health outcomes of sedentary behaviors.

Initially, the Taylor taxonomy of sedentary behaviors was developed for older adults. This population has more sedentary behaviors (e.g. television viewing consumes most non-working time) and concerns about psychological health (i.e. cognitive decline and dementia) than younger adults (Bauman et al., 2018; Harvey et al., 2013, 2015; Zou et al., 2024). Also, older adults are one of the fastest-growing demographic groups worldwide (Ortman et al., 2014). Thus, the Taylor taxonomy is a practical tool to classify, differentiate, and elucidate relationships between sedentary behaviors and cognitive health in older adults. In this study, we aimed to determine the relationship between sedentary behaviors and cognitive health among adults 50 years and older using the four domains of the Taylor taxonomy. We hypothesized that certain categories of sedentary behaviors would be negatively or positively associated with cognitive health in this population.

Materials and methods

Source of data

We obtained data from the Health and Retirement Study (HRS), a longitudinal panel study that surveys people older than 50 years in the United States (Fisher and Ryan, 2018). The “Core” survey is conducted biennially, with the first wave fielded in 1992, and a new cohort of people aged 51–56 years added every 6 years. In between Core interview waves, the HRS conducts off-year or supplemental studies. We obtained data from the HRS because (1) the data were collected from older adults and (2) the dataset has variables to test our hypotheses related to cognitive health and sedentary behaviors.

Transparency and openness

Data obtained from the HRS were de-identified and did not require approval from an institutional review board.

Demographics and basic health conditions

Our study used data from both the 2019 Consumption and Activities Mail Survey (CAMS) and the 2020 Core survey of the HRS. The 2020 Core survey included data from 15,723 records. Of these records, 721 used proxy interviews without self-rated memory, word recall, and serial sevens tests. These records were excluded from the analyses. Of the remaining 15,002 records, 4105 contributed data to the 2019 CAMS. Of those 4105 records, 185 were assigned zero 2019 CAMS weights and did not contribute to any analyses. Thus, our analytic sample included 3920 records.

Basic demographics were obtained from the Tracker file (2020 Tracker Early Version 3.0) of the HRS. Work for pay (yes/no at the time of the survey) was obtained from Section J of the 2020 Core survey, and basic health conditions were obtained from Section C of the 2020 Core survey. Table 1 presents the demographic and basic health conditions of the sample population, as well as descriptive statistics of hours per week of sedentary behaviors and physical activity.

Table 1.

Characteristics of the sample.

Variable	Value
Sex (%)
Female	54.6
Male	45.4
Race (%)
Black/African American	11.1
Non-Hispanic White	80.1
Other	8.8
Education (%)
High school or less	39.4
Some college or associate degree	27.9
College graduate	16.4
Post-college	16.3
Overall health (%)
Excellent	8.6
Very good	34.0
Good	34.1
Fair	19.0
Poor	4.4
Health compared to previous wave (%)
Better	10.9
About the same	68.9
Worse	20.2
Trouble falling asleep (%)
Most of the time	15.4
Sometimes	36.1
Rarely or never	48.5
Health conditions (%)
Depression in the past year (yes)^a	16.6
High blood pressure (yes)^b	58.5
Diabetes (yes)^b	25.1
Dementia (yes)^b	1.7
Psychiatric illness (yes)^b	21.4
Age (mean)	67.8
Sedentary behaviors (hours/week, mean)
Category 1	23.0
Category 2	7.7
Category 3	8.6
Physical activity (hrs/week, mean)
Low intensity	2.2
Moderate intensity	7.6
High intensity	2.7

Participants reported whether they felt depressed for two or more weeks in a row during the past 12 months.

Dichotomous variables were used to capture whether an individual had a history of high blood pressure, diabetes, dementia, or psychiatric problems.

Cognitive health variables

Measures of cognitive health were obtained from Section D of the 2020 Core survey. These measures included self-rated memory, word recall, and serial sevens test results. For self-rated memory, participants were asked to rate their memory at the present time as excellent, very good, good, fair, or poor. For the word-recall task, participants were shown a list of 10 nouns. Then they were asked to recall as many words as possible from the list in any order as part of the immediate word-recall task. Approximately 5 minutes later, participants were asked to recall the same nouns again as part of the delayed word-recall task. For the serial sevens test, participants were asked to subtract 7 from 100 and continue subtracting 7 from each subsequent number 5 times.

Physical activity variables

Measures of physical activities were obtained from Section A of the 2019 CAMS. For physical activities, yard work or gardening were used to capture low-intensity physical activity, walking was used to capture moderate-intensity physical activity, and sports or other exercises were used to capture high-intensity physical activity. Physical activities were measured in hours per week.

Sedentary behavior variables

In the 2019 CAMS instructions for Section A, the questions did not specifically ask about sitting. For example, none of the following question stems were part of the HRS: “how often do you perform the following activities while sitting or lying down?” and “about how much time do you spend sitting down” (Kurita et al., 2022; León-Muñoz et al., 2013; Major et al., 2023). Because sitting was not explicitly measured in the HRS, two investigators independently reviewed the list of 33 activities in the HRS to identify sedentary behaviors (e.g. behaviors in a sitting or lying posture while awake). The investigators agreed on five sedentary behaviors and then classified those behaviors into three categories based on the Taylor taxonomy (Taylor, 2022) and previous research (Compernolle et al., 2021; Kesse-Guyot et al., 2012; Kikuchi et al., 2014; Leask et al., 2015; Loprinzi, 2019; Mitsutake et al., 2020; Palmer et al., 2019; Raichlen et al., 2022, 2023; Saunders et al., 2020; Taylor et al., 2020; Toepoel, 2013; Zou et al., 2024). In the Taylor taxonomy, category 1 sedentary behaviors are repetitive and tedious, category 2 behaviors are chosen and provide moderate novelty and mental stimulation, and category 3 behaviors are socially interactive, mentally stimulating, and involve novelty (Figure 1).

Figure 1.

Categories of the Taylor taxonomy of sedentary behaviors. The four domains of the Taylor taxonomy are social interaction, novelty, choice, and cognition (SNCC). For each domain, sedentary behaviors are rated (low, moderate, and high) and then categorized (1, 2, and 3).

The two investigators rated each sedentary behavior on four domains from the Taylor taxonomy of sedentary behaviors (Table 2). The rating scale for each domain was high (10 points), moderate (5 points), and low (1 point). Then the means across the four domains were computed for each investigator/rater and averaged among the investigators (Table 2). Based on the average means, the investigators classified the five sedentary behaviors into three categories. Category 1 was rated low and included watching programs or movies/videos on TV and using computers (one sedentary behavior; average mean, 1.56). Category 2 was rated moderate and included reading newspapers, magazines, or books; and doing arts and crafts projects, including knitting, embroidery, or painting (two sedentary behaviors; average means, 4.31 and 5.93). Category 3 was rated high and included playing cards or games or solving puzzles; and communicating by telephone, letters, email, Facebook, Skype, or other media with friends, neighbors, or relatives (two sedentary behaviors; average means, 8.43 and 8.74; Table 2). Category 3 has a distinct emphasis on social interaction (Sommerlad et al., 2019; Toepoel, 2013). All these behaviors are engaged in while sitting or lying while awake and were measured in hours per week.

Table 2.

Ratings of sedentary behaviors from the health and retirement study using the Taylor taxonomy of sedentary behaviors.

Sedentary behaviors	Socially engaging	Novelty engaging	Choice	Cognitively engaging	Mean per rater	Average mean
Watching programs or movies/videos on TV, computers, etc.
Rater one	Low/moderate + 2.5	Low + 1.0	Low/ moderate + 2.5	Low + 1.0	1.75	1.56
Rater two	Low + 1.0	Low + 1.0	Low/ moderate + 2.5	Low + 1.0	1.37	1.56
Reading newspapers, magazines, or books
Rater one	Low/moderate + 2.5	Moderate + 5.0	Moderate + 5.0	Moderate + 5.0	4.37	4.31
Rater two	Low + 1.0	Low + 1.0	High + 10.0	Moderate + 5.0	4.25	4.31
Doing arts and crafts projects, including knitting, embroidery, or painting
Rater one	Low/moderate + 2.5	Moderate/high + 7.5	Moderate/high + 7.5	Moderate/high + 7.5	6.25	5.93
Rater two	Low/moderate + 2.5	Moderate + 5.0	High + 10.0	Moderate + 5.0	5.62	5.93
Communicating by telephone, letters, e-mail, Facebook, Skype, or other media with friends, neighbors, or relatives
Rater one	High + 10	Moderate/high + 7.5	High + 10	High + 10	9.37	8.74
Rater two	High + 10	Moderate/high + 7.5	High + 10	Moderate + 5	8.12	8.74
Playing cards or games or solving puzzles
Rater one	Moderate/high + 7.5	High + 10	Moderate/high + 7.5	High + 10	8.75	8.43
Rater two	Moderate/high + 7.5	Moderate + 5	High + 10	High + 10	8.12	8.43

Statistical analysis

The dependent variables were measures of cognitive health, including self-rated memory, word recall, and serial sevens test. The independent variable was sedentary behaviors placed in three categories. Ordinal logistic regression was used to examine the association between sedentary behaviors and self-rated memory. A positive estimated coefficient implied that a larger value in the explanatory variable was associated with a higher probability of being in a lower category rather than in a higher category. Because self-rated memory was reverse-coded (i.e. excellent is 1, poor is 5), being in a lower category implies better health. Linear regression was used to examine the association between sedentary behaviors and word recall. The total number of words recalled in immediate recall and delayed recall were the dependent variable. In each trial of the serial sevens test, participants received a 1 if the subtraction was correct and a 0 otherwise. The number of correct subtractions were summed over five trials. The serial sevens trials were treated as Bernoulli trials. Logistic regression was used to examine the association between sedentary behaviors and accuracy on the serial sevens test (i.e. the odds of making a correct subtraction).

All analyses included the same set of covariates, including physical activities, demographics, and basic health conditions. In SAS/STAT 15.1, the SURVEYREG procedure was used for linear regression, the SURVEYLOGISTIC procedure was used for binary logistic and ordinal logistic regression, and the SURVEYFREQ and SURVEYMEANS procedures were used to generate descriptive statistics. All analyses used the 2019 CAMS weights.

Results

To determine the relationship between sedentary behavior and cognitive health, we analyzed three categories of sedentary behaviors and three measures of cognitive health. For self-rated memory, we found a positive association between categories 2 and 3 sedentary behaviors and self-rated memory (category 2: p = 0.018; category 3: p = 0.005). More hours of category 2 or 3 sedentary behaviors were associated with greater self-rated memory. The association between category 1 sedentary behaviors and self-rated memory was not significant (p = 0.156; Table 3).

Table 3.

Associations between sedentary behaviors and self-rated memory and word recall.

Covariate analyses	Self-rated memory			Word recall
Variables	β	SE	p	β	SE	p
Category 1 sedentary behaviors	−0.003	0.002	0.156	−0.006	0.005	0.171
Category 2 sedentary behaviors	0.010	0.004	0.018	0.016	0.008	0.047
Category 3 sedentary behaviors	0.008	0.003	0.005	0.014	0.007	0.034
Low-intensity physical activity	−0.012	0.007	0.068	0.016	0.017	0.345
Moderate-intensity physical activity	−0.005	0.003	0.167	−0.013	0.009	0.167
High-intensity physical activity	0.000	0.007	0.971	0.020	0.014	0.155
Age	−0.025	0.005	<0.001	−0.087	0.009	<0.001
Work (yes vs no)	0.365	0.110	0.001	0.097	0.183	0.597
Education	0.286	0.043	<0.001	0.692	0.065	<0.001
Overall health	−0.739	0.070	<0.001	−0.391	0.097	0.000
Trouble sleeping (sometime vs most of the time)	0.252	0.136	0.068	0.522	0.234	0.029
Trouble sleeping (never vs most of the time)	0.317	0.132	0.018	0.605	0.214	0.006
Psychiatric (yes vs no)	−0.057	0.102	0.577	−0.428	0.191	0.028
Depression (yes vs no)	−0.342	0.144	0.020	0.161	0.228	0.481
Dementia (yes vs no)	−1.005	0.373	0.009	−2.359	0.532	<0.001
High blood pressure (yes vs no)	0.285	0.102	0.007	0.116	0.168	0.492
Diabetes (yes vs no)	−0.101	0.084	0.234	0.143	0.171	0.404

For word recall, we found that more hours of category 2 or 3 sedentary behaviors were associated with more words recalled (category 2: p = 0.047; category 3: p = 0.034). The association between category 1 sedentary behaviors and word recall was not significant (p = 0.171; Table 3). For subtraction, more hours of category 2 sedentary behaviors were associated with greater odds of performing a correct subtraction (p = 0.029). The association between category 1 and 3 sedentary behaviors and the odds of performing a correct subtraction were not significant (category 1: p = 0.209; category 3: p = 0.239; Table 4).

Table 4.

Associations between sedentary behaviors and accuracy in the serial sevens task.

Covariate analyses					95% CI
Variables	β	SE	p	OR	Lower	Upper
Category 1 sedentary behaviors	0.002	0.002	0.209	1.002	0.999	1.006
Category 2 sedentary behaviors	0.010	0.004	0.029	1.010	1.001	1.019
Category 3 sedentary behaviors	0.004	0.004	0.239	1.004	0.997	1.011
Low-intensity physical activity	−0.009	0.009	0.354	0.991	0.973	1.010
Moderate-intensity physical activity	−0.005	0.003	0.107	0.995	0.990	1.001
High-intensity physical activity	0.001	0.008	0.886	1.001	0.985	1.017
Age	−0.022	0.005	<0.001	0.979	0.970	0.988
Work (yes vs no)	0.251	0.083	0.003	1.286	1.090	1.517
Education	0.470	0.040	<0.001	1.600	1.478	1.732
Overall health	−0.142	0.043	0.001	0.868	0.797	0.944
Trouble sleeping (sometime vs most of the time)	−0.116	0.097	0.232	0.890	0.734	1.079
Trouble sleeping (never vs most of the time)	−0.147	0.094	0.121	0.863	0.716	1.041
Psychiatric (yes vs no)	−0.188	0.095	0.053	0.829	0.685	1.002
Depression (yes vs no)	−0.344	0.085	0.000	0.709	0.599	0.840
Dementia (yes vs no)	−0.012	0.274	0.965	0.988	0.573	1.703
High blood pressure (yes vs no)	−0.004	0.077	0.961	0.996	0.854	1.162
Diabetes (yes vs no)	0.002	0.094	0.980	1.002	0.832	1.208

OR: odds ratio.

Several covariates reached significance. Worse self-rated health was associated with worse self-rated memory (p < 0.001), less words recalled (p < 0.001), and lower odds of performing a correct subtraction (p = 0.001). Participants who “rarely or never” had trouble sleeping tended to self-report better memory (p = 0.018) and word recall (p = 0.006) than participants who had trouble sleeping “most of the time.” Older adults who reported depression in the past 12 years were less likely to perform a correct subtraction (p < 0.001) and tended to rate their memory as worse (p = 0.024). Older adults who were working for pay were more likely to perform a subtraction correctly (p = 0.003) and tended to rate their memory better (p = 0.001; Tables 3 and 4).

We performed separate analyses with uncategorized sedentary behaviors and the same model specifications (including the same set of covariates). Total hours of uncategorized sedentary behaviors were not significantly associated with self-rated memory (p = 0.303) or word recall (p = 0.406). In contrast, total hours of uncategorized sedentary behaviors were positively associated with the odds of a correct subtraction (p = 0.005). A possibility is that (1) uncategorized sedentary behaviors masked the association between sedentary behaviors and subtraction; and (2) category 2 sedentary behaviors (p = 0.029) drove the association between uncategorized sedentary hours and accuracy on the subtraction measure (Supplemental Table 1).

Discussion

In this study, we analyzed the relationship between categories of sedentary behavior (based on the four domains of the Taylor taxonomy) and cognitive health. We found no associations between category 1 sedentary behaviors and cognitive health. Conversely, we found positive associations between categories 2 and 3 sedentary behaviors and cognitive health. These results support that certain categories of sedentary behaviors based on the Taylor taxonomy of sedentary behaviors are associated with cognitive health.

Other studies also found that diverse sedentary behaviors had different associations with cognitive health. Among adults aged 70–79 years in the United States, longer reading times were positively associated with cognitive health, but television viewing had no association (Major et al., 2023). Also, among healthy adults aged 45–60 years in France, computer use was positively associated with better performance on cognitive measures (Kesse-Guyot et al., 2012). Furthermore, among adults 60 years and older in Scotland, England, and Wales, television time was associated with a greater risk of all-cause dementia, whereas computer time was associated with a lower risk of all-cause dementia (Raichlen et al., 2022). Conversely, among adults aged 61–70 years in the Netherlands, television, reading, or creative time was not associated with cognitive health (Wanders et al., 2021). These contrasting findings may be due to (1) differences in measures of cognitive health among studies or (2) cultural differences among countries that influence cognitive health and sedentary behaviors (Kesse-Guyot et al., 2012; Major et al., 2023; Raichlen et al., 2022; Wanders et al., 2021).

These differences also highlight that the relationships between sedentary behaviors and cognitive health in older adults are complex and inconsistent (Major et al., 2023; Olanrewaju et al., 2020; Taylor et al., 2020; Wanders et al., 2021). These challenges were summarized in a recent review of sedentary behavior and lifespan brain health in middle-aged (40–65 years old) and older (>65 years old) adults (Zou et al., 2024). To overcome these challenges, we can use the Taylor taxonomy of sedentary behaviors. This taxonomy is more comprehensive than previous approaches that emphasize cognitively active and passive sedentary behaviors (Hallgren et al., 2020; Raichlen et al., 2022; Saunders et al., 2020; Wingood et al., 2024; Zhou et al., 2022; Zou et al., 2024).

The physiological effects of sedentary behaviors are well documented and consistent (Pinto et al., 2023). In contrast, sedentary behaviors are not associated with consistent cognitive, mental, psychological, and emotional health outcomes. These different outcomes may be due to the diversity of sedentary behaviors. To distinguish between health-promoting and health-compromising sedentary behaviors, guidelines are needed to inform health policy, intervention development, and physician recommendations. These guidelines would help to increase health-promoting and decrease health-compromising sedentary behaviors (Aunger et al., 2018; Chase et al., 2020; Chastin et al., 2021).

The lifestyles of older adults are diverse and include working status (e.g. full-time, part-time, retired, caring for grandchildren or great-grandchildren) and living arrangements (in retirement communities, with adult children, in independent or memory care facilities). These lifestyle factors impact the types, amounts, and ways that sedentary behaviors accrue (Aunger et al., 2018). Also, lifestyle factors can influence older adults’ independence and quality of life, which can promote healthy lifestyles and reduce burdensome healthcare costs (Ortman et al., 2014). These considerations have global implications that affect health outcomes, as well as families, businesses, public health professionals, healthcare providers, clinicians, employers, policy makers, and the public (Ortman et al., 2014). With the Taylor taxonomy, we can better understand how diverse lifestyle factors in older adults affect cognitive health and other health outcomes (Taylor, 2022).

Although physical activity improves health (Department of Health and Human Services, 2018; El-Kotob et al., 2020; McLaughlin et al., 2020), these opportunities are not always available or possible for older adults with physical activity restrictions. These adults would benefit from understanding how distinct types of sedentary behaviors influence different health outcomes so they can choose behaviors that best support their health. For example, in our study, categories 2 and 3 sedentary behaviors were positively associated with cognitive health. Thus, older adults who want to optimize their cognitive health could participate in more categories 2 and 3 sedentary behaviors.

A fundamental premise of the Taylor taxonomy of sedentary behaviors is that no sedentary behavior is intrinsically healthy or harmful—context matters. For example, sitting while watching television may be associated with lower muscle activity and energy expenditure than sitting while using a computer (Raichlen et al., 2022). Also, television viewing often occurs in the evening, and this timing can be detrimental for cardiometabolic physiology (Raichlen et al., 2022). However, television viewing for more than 3 hours per day protected against impaired memory over 6 years (Wingood et al., 2024). Also, television viewing can provide relaxation, entertainment, and an escape from difficult life circumstances (Fancourt and Steptoe, 2019). These differences may be due to television viewing being both an alert (i.e. watching fast-paced changes in images) and passive interaction (i.e. consuming content without active engagement; Fancourt and Steptoe, 2019). To document the context of sedentary behaviors, the Taylor taxonomy addresses the five essential questions: what (behavior), why (purpose), where (location), when (time), and with whom (social setting; Chastin et al., 2013).

Based on the Taylor taxonomy of sedentary behaviors, any sedentary behavior can be reconfigured into a healthier engagement. For example, viewing television programs that are educational (i.e. cognition), promote understanding of others (i.e. novelty), and include discussions about the program content (i.e. social interaction and choice) can be a healthier sedentary behavior (Taylor, 2022; Wingood et al., 2024) than television viewing that is primarily passive and contributes to social isolation (Toepoel, 2013).

Sedentary lifestyles are ubiquitous, particularly among older adults who are sedentary 65%–80% of their waking time (Bauman et al., 2018; Harvey et al., 2013, 2015; Wullems et al., 2016). Also, sedentary behavior has more deleterious effects for people who are physically inactive (Department of Health and Human Services, 2018; Katzmarzyk et al., 2019). However, more research is needed to better understand the relationships among sedentary behaviors and light-intensity, moderate-intensity, and vigorous-intensity physical activity (Dempsey et al., 2020; Department of Health and Human Services, 2018; Katzmarzyk et al., 2019; Kikuchi et al., 2014). For example, one question is how to “balance” these behaviors for better health (Dempsey et al., 2020). One recommendation is to reduce discretionary sedentary time to fewer than 2 hours per day. Concomitantly, individuals should engage in moderate-intensity to vigorous-intensity physical activity 150 minutes per week (Falck et al., 2017). Another recommendation is to replace sedentary behavior with light-intensity physical activity (e.g. moving during television commercials), which reduces all-cause mortality (Dohrn et al., 2018).

Sedentary behavior and physical activity also have different challenges with correlates (i.e. factors and conditions that enable or impede) and measurements (Bauman et al., 2018; Chastin et al., 2015). For example, different sedentary behaviors have unique psychosocial, behavioral, environmental, sociocultural, and socio-economic correlates that can inform intervention development. To improve correlates and intervention research, the four domains of the Taylor taxonomy can be helpful (Taylor, 2022). Also, questionnaires are one measure of sedentary behavior (Bauman et al., 2018). However, an alternative measure is video recording a person’s sedentary behavior (e.g. a wearable camera could measure pattern-specific sedentary behaviors). This approach documents important characteristics and context (Kurita et al., 2022), and verifies social interaction (face-to-face or virtual), novelty, and choice (domains of the Taylor taxonomy). This approach can lead to greater understanding of sedentary behaviors and cognitive health.

Our study has several strengths. First, we empirically evaluated the Taylor taxonomy of sedentary behaviors (Taylor, 2022) among a large, representative sample of people 50 years and older in the United Sates. This older group spends more time in sedentary behaviors than younger groups (Bauman et al., 2018). Second, our study used three validated measures of cognitive health. Third, we adjusted for several covariates, including low-intensity, moderate-intensity, and high-intensity physical activity. This approach ensures that results are not biased by extraneous factors, reduces the chances of incorrect conclusions about causal relationships, and increases precision and statistical power. This approach also improves comparability of groups, more accurately isolates the effect of the independent variable on the dependent variable, and increases generalizability of the findings to different populations and contexts (Andrade, 2024; Van Lancker et al., 2024). Fourth, our findings are based on questionnaires completed by interviews, which overcome challenges that older adults may have with paper-based and digital questionnaires (Kutschar and Weichbold, 2019; Reichstadt et al., 2010). For example, the personal interview provided greater insights into older adults’ subjective experiences and views on successful aging than surveys and focus groups (Reichstadt et al., 2010). Also, the use of survey research explicitly faces unique methodological challenges in older adult populations (Quinn, 2010). Fifth, we disaggregated overall sitting time into conceptually or analytically determined categories of behavior, consistent with previous research (Compernolle et al., 2020; Kikuchi et al., 2014; Russell and Chase, 2019; Wingood et al., 2024).

Our study has limitations. First, sedentary behaviors were self-reported and may be inaccurate due to self-presentation bias, comprehension, uncertainty, recall errors, misestimates, social-desirability bias, influences of mood and depression, and other factors. Indeed, objective measures revealed more sedentary behaviors than self-reports (Harvey et al., 2015). Second, the posture during each sedentary behavior was unknown. Older adults may have stood, walked, or completed upper body exercises while engaging in behaviors classified as sedentary (e.g. standing while using the computer; Olanrewaju et al., 2020). However, sitting is the most prevalent posture for behaviors classified as sedentary in our study (Compernolle et al., 2020; Mitsutake et al., 2020), and older adults reported that performing habitual sedentary behaviors in a standing position is extremely difficult (Compernolle et al., 2020). Third, the full context of each sedentary behavior (e.g. alone or with others) was not provided. This context is needed to precisely and accurately rate the four domains of the Taylor taxonomy of sedentary behaviors. However, even without the full context, we identified three distinct categories of sedentary behaviors with different mean ratings. Finally, although we included a range of covariates, unmeasured confounders (e.g. snacking and drinking) may have affected our results.

Our study informs future work. First, future studies can use more precise and accurate survey questions. The following question stems are recommended: “while sitting” or “how often do you perform the following activities while sitting or lying down?” or “about how much time do you spend sitting” (Kurita et al., 2022; León-Muñoz et al., 2013; Major et al., 2023). Second, future studies should include objective measures of sedentary behaviors (e.g. inclinometers, accelerometers, sensors, activPAL monitors; Heesch et al., 2018; Leask et al., 2015; Mañas et al., 2017; Webster et al., 2021; Zou et al., 2024). These objective measures, when combined with self-reports and pictures (e.g. wearable camera), can provide a full context of sedentary behaviors (Leask et al., 2015). Third, future studies can include a comprehensive list of sedentary behaviors (e.g. writing, transportation in a vehicle as a driver or passenger) for more robust analyses. Fourth, future studies can address the direction of causality by including reverse and bidirectional causation. Reverse causation occurs when healthier individuals engage in more health-promoting sedentary behaviors versus individuals becoming healthier from participating in health-promoting behaviors. Bidirectional causality occurs when both conditions are true (Pedisic et al., 2014; Rebar et al., 2014). To evaluate causality, changes in sitting time during longitudinal studies, intervention studies, or randomized controlled trials are needed. Also, Mendelian randomization (i.e. a statistical method using genetic variation) can assist in examining potential causal effects on different outcomes, minimizing confounding, and addressing reverse causation (Pinto et al., 2023; Zou et al., 2024). Fifth, future research can replicate and extend our findings with other datasets (e.g. National Health and Aging Trends Study) that include different measures of cognitive health and a broader range of health outcomes (e.g. mental health, brain health, psychological health, physical function). Finally, future work can consider a global perspective among various countries representing diverse cultures, income levels, and perspectives of aging.

Conclusions

In our study, specific categories of sedentary behaviors were positively associated with cognitive health in older adults. These findings support efforts to encourage health-promoting sedentary behaviors and discourage health-compromising sedentary behaviors in this population. These findings also support that the Taylor taxonomy of sedentary behaviors can reduce complexity, promote understanding, and clarify relationships among sedentary behaviors and cognitive health. Thus, the Taylor taxonomy is a useful, practical, and valuable framework for understanding sedentary behaviors and informing interventions that improve the health and quality of life for older adults.

Supplemental Material

sj-docx-1-hpq-10.1177_13591053261419219 – Supplemental material for Associations between sedentary behaviors and cognitive health in older adults based on the Taylor taxonomy of sedentary behaviors

Supplemental material, sj-docx-1-hpq-10.1177_13591053261419219 for Associations between sedentary behaviors and cognitive health in older adults based on the Taylor taxonomy of sedentary behaviors by Wendell C. Taylor and Chuong Bui in Journal of Health Psychology

Footnotes

Acknowledgements

We thank Kelly Schrank of Bookworm Editing Services, LLC and Crystal Herron, PhD, ELS(D), CMPP, of Redwood Ink for editing the manuscript. Also, we thank Kelly Schrank for creating the Taylor taxonomy image.

ORCID iDs

Wendell C. Taylor

Chuong Bui

Ethical considerations

The Health and Retirement Study is a longitudinal project sponsored and approved by the National Institute on Aging (NIA U01AG009740) and the Social Security Administration.

Informed consent

There are no human participants in this article and informed consent was not required.

Consent to participate

For The Health and Retirement Study, participation in the study is completely voluntary. Prior to each interview and collection of biomarkers and physical measures, participants are provided with a written informed consent information document. At the start of each interview and collection of biomarkers and physical measures, all respondents are read a confidentiality statement, and give oral consent by agreeing to do the interview and collection of biomarkers and physical measures.

Consent for publication

Consent for publication is not applicable to this article as it does not contain any identifiable data

Author contributions

WCT: Conceptualization, Supervision, Writing - original draft, Writing - review & editing. CB: Data curation, Writing - original draft, Writing - review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The datasets generated during and/or analyzed during the current study are available in the Health and Retirement Study repository:

Supplemental material

Supplemental material for this article is available online.

References

Anderson

McConnell

(2007) Cognitive health: An emerging public health issue. Alzheimer’s & Dementia 3(2s): S70–S73.

Andrade

(2024) Regression: Understanding what covariates and confounds do in adjusted analyses. Journal of Clinical Psychiatry 85(4): 24f15573.

Anstey

von Sanden

Salim

, et al. (2007) Smoking as a risk factor for dementia and cognitive decline: A meta-analysis of prospective studies. American Journal of Epidemiology 166(4): 367–378.

Aunger

Doody

Greig

(2018) Interventions targeting sedentary behavior in non-working older adults: A systematic review. Maturitas 116: 89–99.

Bauman

Petersen

Blond

, et al. (2018) The descriptive epidemiology of sedentary behaviour. In: Leitzmann

Jochem

Schmid

(eds) Sedentary Behaviour Epidemiology. Springer International Publishing, pp.73–106.

Blackwell

Yaffe

Laffan

, et al. (2014) Associations of objectively and subjectively measured sleep quality with subsequent cognitive decline in older community-dwelling men: The MrOS sleep study. Sleep 37(4): 655–663.

Blondell

Hammersley-Mather

Veerman

(2014) Does physical activity prevent cognitive decline and dementia? A systematic review and meta-analysis of longitudinal studies. BMC Public Health 14(1): 12.

Buckner

(2004) Memory and executive function in aging and AD: Multiple factors that cause decline and reserve factors that compensate. Neuron 44(1): 195–208.

Chase

Otmanowski

Rowland

, et al. (2020) A systematic review and meta-analysis of interventions to reduce sedentary behavior among older adults. Translational Behavioral Medicine 10(5): 1078–1085.

10.

Chastin

Gardiner

Harvey

, et al. (2021) Interventions for reducing sedentary behaviour in community-dwelling older adults. Cochrane Database of Systematic Reviews (6). Art. No.: CD012784, 1–67.

11.

Chastin

Schwarz

Skelton

(2013) Development of a consensus taxonomy of sedentary behaviors (SIT): Report of Delphi round 1. PLoS One 8(12): e82313.

12.

Chastin

SFM

Buck

Freiberger

, et al. (2015) Systematic literature review of determinants of sedentary behaviour in older adults: A DEDIPAC study. International Journal of Behavioral Nutrition and Physical Activity 12(1): 12.

13.

Chen

Demnitz

Yamamoto

, et al. (2022) Defining brain health: A concept analysis. International Journal of Geriatric Psychiatry 37(1): 1–14.

14.

Compernolle

De Bourdeaudhuij

Cardon

, et al. (2021) Sex-specific typologies of older adults’ sedentary behaviors and their associations with health-related and socio-demographic factors: A latent profile analysis. BMC Geriatrics 21(1): 66.

15.

Compernolle

De Cocker

Cardon

, et al. (2020) Older adults’ perceptions of sedentary behavior: A systematic review and thematic synthesis of qualitative studies. Gerontologist 60(8): 572–e582.

16.

Dempsey

Biddle

SJH

Buman

, et al. (2020) New global guidelines on sedentary behaviour and health for adults: Broadening the behavioural targets. International Journal of Behavioral Nutrition and Physical Activity 17(1): 151.

17.

Department of Health and Human Services (2018) Physical activity guidelines advisory committee scientific report (2028) Available at: https://health.gov/sites/default/files/2019-09/PAG_Advisory_Committee_Report.pdf (accessed 6 April 2025).

18.

de Rezende

Rey-López

Matsudo

, et al. (2014) Sedentary behavior and health outcomes among older adults: A systematic review. BMC Public Health 14(1): 1–9.

19.

Dohrn

Kwak

Oja

, et al. (2018) Replacing sedentary time with physical activity: A 15-year follow-up of mortality in a national cohort. Clinical Epidemiology 10: 179–186.

20.

Durand

Jáuregui

(2024) Ecological models of health behavior. In: Glanz

Rimer

Viswanath

KKK

(eds) Health Behavior: Theory, Research, and Practice, 6th edn. Jossey-Bass Public Health, pp.22–36.

21.

El-Kotob

Ponzano

Chaput

, et al. (2020) Resistance training and health in adults: An overview of systematic reviews. Applied Physiology Nutrition and Metabolism 45(2): S165–S179.

22.

Falck

Davis

Liu-Ambrose

(2017) What is the association between sedentary behaviour and cognitive function? A systematic review. British Journal of Sports Medicine 51(10): 800–811.

23.

Fancourt

Steptoe

(2019) Television viewing and cognitive decline in older age: Findings from the English longitudinal study of ageing. Scientific Reports 9(1): 2851–2858.

24.

Fisher

Ryan

(2018) Overview of the health and retirement study and introduction to the special issue. Work Aging and Retirement 4(1): 1–9.

25.

Fung

AWT

Leung

GTY

Lam

LCW

(2011) Modulating factors that preserve cognitive function in healthy ageing. East Asian Archives of Psychiatry 21(4) 152–156.

26.

Gard

Hölzel

Lazar

(2014) The potential effects of meditation on age-related cognitive decline: A systematic review. Annals of the New York Academy of Sciences 1307(1): 89–103.

27.

Gibbs

Hergenroeder

Katzmarzyk

, et al. (2015) Definition, measurement, and health risks associated with sedentary behavior. Medicine and Science in Sports and Exercise 47(6): 1295–1300.

28.

Hallgren

Dunstan

Owen

(2020) Passive versus mentally active sedentary behaviors and depression. Exercise and Sport Sciences Reviews 48(1): 20–27.

29.

Harvey

Chastin

Skelton

(2013) Prevalence of sedentary behavior in older adults: A systematic review. International Journal of Environmental Research and Public Health 10(12): 6645–6661.

30.

Harvey

Chastin

Skelton

(2015) How sedentary are older people? A systematic review of the amount of sedentary behavior. Journal of Aging and Physical Activity 23(3): 471–487.

31.

Heesch

Hill

Aguilar-Farias

, et al. (2018) Validity of objective methods for measuring sedentary behaviour in older adults: A systematic review. International Journal of Behavioral Nutrition and Physical Activity 15(1): 119–217.

32.

Hillman

Erickson

Kramer

(2008) Be smart, exercise your heart: Exercise effects on brain and cognition. Nature Reviews Neuroscience 9(1): 58–65.

33.

Huang

Guo

Ruan

, et al. (2020) Associations of lifestyle factors with cognition in community-dwelling adults aged 50 and older: A longitudinal cohort study. Frontiers in Aging Neuroscience 12: 601487.

34.

James

Wilson

Barnes

, et al. (2011) Late-life social activity and cognitive decline in old age. Journal of the International Neuropsychological Society 17(6): 998–1005.

35.

Jedrziewski

Lee

Trojanowski

(2007) Physical activity and cognitive health. Alzheimer’s & Dementia 3(2): 98–108.

36.

Katzmarzyk

Powell

Jakicic

, et al. (2019) Sedentary behavior and health: Update from the 2018 physical activity guidelines advisory committee. Medicine and Science in Sports and Exercise 51(6): 1227–1241.

37.

Kesse-Guyot

Charreire

Andreeva

, et al. (2012) Cross-sectional and longitudinal associations of different sedentary behaviors with cognitive performance in older adults. PLoS One 7(10): e47831.

38.

Kikuchi

Inoue

Sugiyama

, et al. (2014) Distinct associations of different sedentary behaviors with health-related attributes among older adults. Preventive medicine 67: 335–339.

39.

Kimura

Sasaki

Masuda

, et al. (2023) Lifestyle factors that affect cognitive function-a longitudinal objective analysis. Frontiers in Public Health 11: 1215419.

40.

Korten

Comijs

Penninx

, et al. (2017) Perceived stress and cognitive function in older adults: Which aspect of perceived stress is important? International Journal of Geriatric Psychiatry 32(4): 439–445.

41.

Kuiper

Zuidersma

Oude Voshaar

, et al. (2015) Social relationships and risk of dementia: A systematic review and meta-analysis of longitudinal cohort studies. Ageing Research Reviews 22: 39–57.

42.

Kurita

Doi

Tsutsumimoto

, et al. (2022) Development of a questionnaire to evaluate older adults’ total sedentary time and sedentary time with cognitive activity. Journal of Geriatric Psychiatry and Neurology 35(3): 392–399.

43.

Kutschar

Weichbold

(2019) Interviewing elderly in nursing homes: Respondent and survey characteristics as predictors of item nonresponse. Survey Methods: Insights from the Field. Epub ahead of print 2 April 2019. DOI:10.13094/SMIF-2019-00015

44.

Larson

Wang

Bowen

, et al. (2006) Exercise is associated with reduced risk for incident dementia among persons 65 years of age and older. Annals of Internal Medicine 144(2): 73–81.

45.

Leask

Harvey

Skelton

, et al. (2015) Exploring the context of sedentary behaviour in older adults (what, where, why, when and with whom). European Review of Aging and Physical Activity 12(1): 1–8.

46.

León-Muñoz

Martínez-Gómez

Balboa-Castillo

, et al. (2013) Continued sedentariness, change in sitting time, and mortality in older adults. Medicine and Science in Sports and Exercise 45(8): 1501–1507.

47.

Loprinzi

(2019) The effects of sedentary behavior on memory and markers of memory function: A systematic review. Physician and Sportsmedicine 47(4): 387–394.

48.

Lourida

Soni

Thompson-Coon

, et al. (2013) Mediterranean diet, cognitive function, and dementia: A systematic review. Epidemiology 24(4): 479–489.

49.

Ludyga

Gerber

Pühse

, et al. (2020) Systematic review and meta-analysis investigating moderators of long-term effects of exercise on cognition in healthy individuals. Nature Human Behaviour 4(6): 603–612.

50.

Major

Simonsick

Napolitano

, et al. (2023) Domains of sedentary behavior and cognitive function: The health, aging, and body composition study, 1999/2000 to 2006/2007. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 78(11): 2035–2041.

51.

Mañas

del Pozo-Cruz

García-García

, et al. (2017) Role of objectively measured sedentary behaviour in physical performance, frailty and mortality among older adults: A short systematic review. European Journal of Sport Science 17(7): 940–953.

52.

McLaughlin

El-Kotob

Chaput

, et al. (2020) Balance and functional training and health in adults: An overview of systematic reviews. Applied Physiology Nutrition and Metabolism 45(10 (s2)): S180–S196.

53.

Mitsutake

Shibata

Ishii

, et al. (2020) Clustering of domain-specific sedentary behaviors and their association with physical function among community-dwelling older adults. Journal of Physical Activity and Health 17(7): 709–714.

54.

Northey

Cherbuin

Pumpa

, et al. (2018) Exercise interventions for cognitive function in adults older than 50: A systematic review with meta-analysis. British Journal of Sports Medicine 52(3): 154–160.

55.

Olanrewaju

Stockwell

Stubbs

, et al. (2020) Sedentary behaviours, cognitive function, and possible mechanisms in older adults: A systematic review. Aging Clinical and Experimental Research 32: 969–984.

56.

Ortman

Velkoff

Hogan

(2014) An Aging Nation: The Older Population in the United States, Current Population Reports. U.S. Department of Commerce Economics and Statistics Administration U.S. CENSUS BUREAU, pp.25–1140.

57.

Palmer

Gray

Fitzsimons

, et al. (2019) What do older people do when sitting and why? Implications for decreasing sedentary behavior. Gerontologist 59(4): 686–697.

58.

Pedisic

Grunseit

Ding

, et al. (2014) High sitting time or obesity: Which came first? Bidirectional association in a longitudinal study of 31,787 Australian adults. Obesity 22(10): 2126–2130.

59.

Peters

Poulter

Warner

, et al. (2008) Smoking, dementia and cognitive decline in the elderly, a systematic review. BMC Geriatrics 8: 1–7.

60.

Pinto

Bergouignan

Dempsey

, et al. (2023) Physiology of sedentary behavior. Physiological Reviews 103(4): 2561–2622.

61.

Quinn

(2010) Methodological considerations in surveys of older adults: Technology matters. International Journal of Emerging Technologies & Society 8(2): 114–133.

62.

Raichlen

Aslan

Sayre

, et al. (2023) Sedentary behavior and incident dementia among older adults. Journal of the American Medical Association 330(10): 934–940.

63.

Raichlen

Klimentidis

Sayre

, et al. (2022) Leisure-time sedentary behaviors are differentially associated with all-cause dementia regardless of engagement in physical activity. Proceedings of the National Academy of Sciences 119(35): e2206931119.

64.

Rebar

Vandelanotte

van Uffelen

, et al. (2014) Associations of overall sitting time and sitting time in different contexts with depression, anxiety, and stress symptoms. Mental Health and Physical Activity 7(2): 105–110.

65.

Reichstadt

Sengupta

Depp

, et al. (2010) Older adults’ perspectives on successful aging: Qualitative interviews. American Journal of Geriatric Psychiatry 18(7): 567–575.

66.

Ren

Cui

Liu

, et al. (2024) Longitudinal relationship between healthy lifestyle and cognitive function mediated by activities of daily living among middle-aged and older Chinese adults. Current Psychology 43(30): 24930–24940.

67.

Russell

Chase

(2019) The social context of sedentary behaviors and their relationships with health in later life. Journal of Aging and Physical Activity 27(4): 797–806.

68.

Sabia

Elbaz

Dugravot

, et al. (2012) Impact of smoking on cognitive decline in early old age: The Whitehall II cohort study. Archives of General Psychiatry 69(6): 627–635.

69.

Sallis

Owen

(2015) Ecological models of health behavior. In: Glanz

Rimer

Viswanath

(eds) Health Behavior: Theory, Research, and Practice, 5th edn. John Wiley & Sons, Inc, pp.43–64.

70.

Saunders

McIsaac

Douillette

, et al. (2020) Sedentary behaviour and health in adults: An overview of systematic reviews. Applied Physiology Nutrition and Metabolism 45(10 (s2)): S197–S217.

71.

Singh

Parsaik

Mielke

, et al. (2014) Association of mediterranean diet with mild cognitive impairment and Alzheimer’s disease: A systematic review and meta-analysis. Journal of Alzheimer’s Disease 39(2): 271–282.

72.

Sommerlad

Sabia

Singh-Manoux

, et al. (2019) Association of social contact with dementia and cognition: 28-year follow-up of the Whitehall II cohort study. Plos Medicine 16(8): e1002862.

73.

Stillman

Esteban-Cornejo

Brown

, et al. (2020) Effects of exercise on brain and cognition across age groups and health states. Trends in Neurosciences 43(7): 533–543.

74.

Sundström

Adolfsson

Nordin

, et al. (2019) Loneliness increases the risk of all-cause dementia and Alzheimer’s disease. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences 75(5): 919–926.

75.

Taylor

(2022) Understanding variations in the health consequences of sedentary behavior: A taxonomy of social interaction, novelty, choice, and cognition. Journal of Aging and Physical Activity 30(1): 153–161.

76.

Taylor

Rix

Gibson

, et al. (2020) Sedentary behavior and health outcomes in older adults: A systematic review. AIMS Medical Science 7(1): 10–39.

77.

Toepoel

(2013) Ageing, leisure, and social connectedness: How could leisure help reduce social isolation of older people? Social Indicators Research 113(1): 355–372.

78.

Tremblay

Aubert

Barnes

, et al. (2017) Sedentary behavior research network (SBRN) – Terminology consensus project process and outcome. International Journal of Behavioral Nutrition and Physical Activity 14(1): 17.

79.

Van Lancker

Bretz

Dukes

(2024) Covariate adjustment in randomized controlled trials: General concepts and practical considerations. Clinical Trials 21(4): 399–411.

80.

Vemuri

Lesnick

Przybelski

, et al. (2014) Association of lifetime intellectual enrichment with cognitive decline in the older population. JAMA Neurology 71(8): 1017–1024.

81.

Wanders

Bakker

van Hout

HPJ

, et al. (2021) Association between sedentary time and cognitive function: A focus on different domains of sedentary behavior. Preventive medicine 153: 106731.

82.

Webster

Zhou

Gallagher

, et al. (2021) Device-measured sedentary behavior in oldest old adults: A systematic review and meta-analysis. Preventive Medicine Reports 23: 101405.

83.

Wilson

Segawa

Boyle

, et al. (2012) Influence of late-life cognitive activity on cognitive health. Neurology 78(15): 1123–1129.

84.

Wingood

Gell

Rosenberg

, et al. (2024) Associations of cognitively active versus passive sedentary behaviors and cognition in older adults. Journal of Physical Activity and Health 21(9): 928–938.

85.

Sun

Tan

(2018) A systematic review and dose-response meta-analysis of sleep duration and the occurrence of cognitive disorders. Schlaf & Atmung 22: 805–814.

86.

Wullems

Verschueren

Degens

, et al. (2016) A review of the assessment and prevalence of sedentarism in older adults, its physiology/health impact and non-exercise mobility countermeasures. Biogerontology 17: 547–565.

87.

Zhou

Webster

Veliz

, et al. (2022) Profiles of sedentary behaviors in the oldest old: Findings from the national health and aging trends study. Aging Clinical and Experimental Research 34(9): 2071–2079.

88.

Zou

Herold

Cheval

, et al. (2024) Sedentary behavior and lifespan brain health. Trends in Cognitive Sciences 28(4): 369–382.