The Association of Sedentary Behaviour and Cognitive Function in People Without Dementia: A Coordinated Analysis Across Five Cohort Studies from COSMIC

The rising number of persons with dementia and the lack of effective treatments highlight the urgency for modifiable risk factors to be better understood, as these might account for around 30% of the population risk for dementia and Alzheimer’s disease [1‐3]. Physical activity (PA) is one of these factors that has been shown in previous research [4], including a systematic review with meta-analysis in older adults, to have beneficial effects on cognition [5]. Traditionally, physical inactivity is mainly conceptualized as a lack of exercise. However, recent work suggested that sedentary behaviour (SB; low-intensity activities with a Metabolic Equivalent of Task (MET) of < 1.5) [6], which can be regarded as the other key component of physical inactivity, might be a target in its own right, due to its strong association with cardiovascular disease risk [7, 8]. Whilst most research focused on PA examined moderate-to-vigorous intensity PAs (MVPA; activities with a MET of ≥ 3.0, different from light PA which are activities with a MET of 1.5–2.9 [6]), older adults only spend 0.2 h per day on such activities, whereas SB is more prevalent in older adults with an average of 9.2 h per day [9‐11]. Moreover, the Western population in general is becoming more sedentary, and physical inactivity represents the leading cause for all-cause mortality worldwide [12‐14], making SB a potentially very important modifiable risk factor for dementia to target.

Several studies found aspects of SB to be associated, mostly after long-term, to various cardiovascular risk factors [15‐19] and diseases [20]. Since these cardiovascular risk factors are associated with dementia as well, it raises the question of whether SB might also have an effect on cognition. In their review, Voss et al. endorse the hypothesis of an inverse relationship between SB and cognition, while acknowledging that conclusive research is currently lacking [21]. Besides these cardiovascular risk factors, that might underlie an association between SB and cognitive decline, also acute effects of SB related to vascular health and cerebral blood flow might be involved [22]. However, to date, experimental mechanistic studies on these acute effects of SB on cognition show conflicting findings [23‐25]. For example, in an experiment with 9 overweight adults, reduced cognitive performance was seen after 6 h of uninterrupted sitting compared to intermittent bouts of standing [25]. In contrast, no beneficial effects were found on cognitive performance by either interrupting an 8-h sitting period among 6 healthy males [24], or 7-h sitting period among 19 overweight adults [23]. In their review of observational studies examining the long-term effects of SB on cognition, Falck et al. found associations between higher SB levels and lower cognition levels in six of the eight studies they reviewed [26]. In these studies, 8 different SB measures and 13 different cognitive measures were used [26]. This diversity in measurement types, together with other factors such as diverse study designs, different types of confounders corrected for, and small sample sizes previously mentioned, may have contributed to these contradictory results. This complexity emphasizes the need to further explore this association before SB can be regarded as a potential target to reduce the risk on cognitive impairment.

For this purpose, we performed a coordinated analysis examining the association between SB and cognition, which combined five different cohort datasets across the world. This enabled us to apply a comparable analysis model across large, compatible datasets, while consistently controlling for confounding variables thus enabling the interpretation of effects on a similar outcome measure. Specifically, we evaluated whether there was a cross-sectional relationship between higher baseline SB (duration of sitting per day) and lower baseline global cognitive function (Mini-Mental State Examination (MMSE) or Modified Mini-Mental State (3MS) Examination) in older adults without dementia, and whether baseline SB was related to prospective decline in cognitive function.

Together the imputed datasets included 10,450 participants. A flowchart of the complete cases can be seen in Figure S1. Table 2 shows the baseline characteristics of the participants per study. Mean age within the cohorts ranged between 66.7 and 75.1 years, with a majority of female participants in most studies.

Unadjusted analysis (Table 3) showed no cross-sectional associations between SB and cognition in three studies (PATH, SGS, SLAS2). In one study (HELIAD), higher levels of SB were associated with lower MMSE scores, where 1 h/day of TV watching was associated with a 0.121 lower MMSE score (B = − 0.121, P < 0.001). In contrast, data from another study (SALSA) showed an increase in 3MS levels of 0.330 with every extra hour of SB/day (B = 0.330, P = 0.03). When the sub-questions in the SALSA study were analysed separately higher cognition levels were associated with lower levels of sitting at home (B = − 0.080, P = 0.005), while it was associated with higher levels of sitting while driving (B = 0.298, P < 0.001, see Table S3) in the unadjusted model. Adjusting for confounders resulted in point estimates suggesting smaller effects and loss of significance of the association between SB and cognition in both studies (HELIAD and SALSA). A positive cross-sectional association in one study (SLAS2) became significant after controlling for confounders, suggesting that every extra hour of SB/day was associated with a 0.118 higher cognition score (B = 0.118, P < 0.001). Stratified analysis by MVPA, however, showed that this was stronger for the high-PA group (Table S4). The stratified results in the SALSA study showed that higher SB levels were associated with worse cognition scores for the low-PA group, and with better cognition scores for the high-PA group. Stratification showed different unadjusted cross-sectional associations of SB with cognition by gender in SALSA, SGS, SLAS2 (Table S5).

Over a mean follow-up time of 2.7, 8.1, 2.0, and 3.8 years, cognitive function (SD) changed by − 0.94 (3.53, P < 0.001), − 7.56 (16.79, P < 0.001), 0.10 (2.48, P = 0.22), and − 0.49 (1.98, P < 0.001) for the studies HELIAD, SALSA, SGS, and SLAS2, respectively. No associations between SB and decline in MMSE/3MS were found in both the unadjusted and adjusted models. However, the stratified results from the longitudinal analysis showed positive associations between SB and MMSE in one study (HELIAD) in the low-PA group only (B = 0.072, P = 0.04), see Table S4. For the stratified analysis by gender, no differences were seen (Table S5). The secondary complete case analysis showed the same results (see Table S2) as the primary analysis using the imputed datasets.

Correlations between SB and covariates can be seen per study in Fig. 1. Inverse associations were found between SB and PA in all studies (three weak, two moderate associations, see Table S6). The evaluation of the model can be found in the S1 Results.

This is the first study reporting on the association between SB and cognition in multiple population-based cohorts combining large samples of individual participant data. The coordinated analysis design enabled us to preserve detailed information on all main variables and covariates, as well as to test the replicability of the results across studies [45]. Longitudinally, no associations between SB and the rate of cognitive decline were found. This was unexpected given recent studies reported short-term and long-term detrimental effects of SB on the brain [25, 46], although other studies could not establish an association between SB and cognition [24, 47].

What can explain the observed absence of a relationship between SB and cognition found in this coordinated analysis of five cohort studies? First of all, in our study, SB was operationalised as total sedentary time, as commonly done in this field of research. However, based on the current findings, we hypothesize the type of SB is important. As a result, it is possible that considering them together might have limited our capacity to detect their individual effects, since it could be that only specific types of SB have a negative influence on cognition. Take, for example, TV watching for which a cross-sectional association was seen in our unadjusted HELIAD results and previous studies [21, 48, 49]. TV viewing may require lower cognitive engagement than sitting while reading or making puzzles. In turn, different cognitive engagement is likely to be associated with differences in cerebral blood flow and oxygenated haemoglobin levels [50, 51]. We, therefore, speculate that the type of mental activity engaged in while seated moderates the effect SB has on the brain and cognition. This may also explain why in one study (SALSA), the results of the unadjusted cross-sectional analysis revealed a positive association, while when the cross-sectional relations were analysed separately sitting while driving was associated with higher cognition levels compared to sitting at home which was associated with lower cognition levels. Additionally, there are indications that it is not the total time of SB that is of influence but rather the extent to which a sitting bout is broken up by periods of standing or walking [52]. All in all, this suggests that potential effects of SB on the brain work via more specific ways than via total sitting time. The operationalisation of SB as total undifferentiated sedentary time might, therefore, in retrospect have resulted in the lack of a clear association. It is, therefore, necessary in future studies in this area to assess SB with multiple domain-specific questions on types of behaviour, reducing the risk of underestimation and improving validity [53].

The measures of our primary independent variable, SB, that were used in this study need to be critically assessed, as previous studies pointed to the potential problems with validity of SB questionnaires, and poor correlation of for example TV-time with total sitting time [7, 54, 55]. Especially single-item questions are known to result in an underestimation of total sitting time [56]. Additionally, since cognitive function is a factor that explains discrepancies between objective and subjective activity measures, recall bias makes subjective assessments in the older population less reliable [57]. Although this population concerned participants without cognitive impairment at baseline, we cannot rule out an information bias, which highlights the fact that research regarding SB is still in its infancy. For these reasons, we evaluated the associations of the SB measures with measures of BMI, age, and morbidity count, that were expected to result in positive significant correlations [11, 43, 44]. The expected correlations with SB, although small, were indeed verified in most studies. Furthermore, the lack of floor effects of the measures used indicate the SB measures had the capacity to discriminate between varying levels of SB, further reinforcing that the SB measures retained sufficient study-specific validity to serve as a marker of total SB. However, domain-specific assessment of SB is likely to both improve the assessment of total SB as well as add the possibility of studying outcomes in relation to different types of SB.

The same critical approach should be used to assess the study-specific validity of the outcome measures used. Although commonly used, the MMSE is criticized for not being sensitive enough to distinguish deterioration [58], and is affected by strong ceiling effects [59]. Especially in adults without cognitive impairment, it may not be sensitive to individual differences. However, the evaluation we performed on our model showed the analytical approach was sensitive enough to detect the association between known (risk) factors and MMSE scores [41, 42]. For age, education, and depression, significant associations were found in all of the five cohort studies in the adjusted models. This strengthens the validity of the model used in our study and lends further support to the rigour of our null findings for the association of SB and global cognition. Future research should nonetheless examine associations between SB and performance on specific cognitive domains [48]. Based on the improvements seen in executive functioning measures after aerobic training [60], this domain might be particularly prone to the negative effects of SB.

Moreover, the lack of an association between SB and cognition found in our study could be because we adjusted more adequately for confounding compared to some of the earlier studies. Previous research also reported that significant associations dissipated after correcting for confounders [47]. The existing literature and the correlations we observed between SB and each of the covariates (Fig. 1) show that multiple variables influence both SB and cognition, and may thus confound the association between SB and cognition. This can even differ for the different types of SB. TV viewing is known, for example, to be related to high caloric intake [61, 62], and the degree of social engagement can differ between varying SBs. Potentially, certain risk factors thus cluster with different types of SBs. This clustering might be one of the underlying reasons relationships between SB and cognition can potentially vary depending on the type of SB. Due to the thorough way in which the confounders were selected, it is reasonable to assume that the majority of potential confounders were included in our analysis and that residual confounding is not significantly impacting our results. The inclusion of these covariates may have had the effect of attenuating associations between SB and cognition; however, no clear directional association between SB and cognition was seen in models where the covariates were not adjusted for either. However, related to confounders, performing stratified analyses on MVPA revealed different effects of SB for the low-PA group versus the high-PA group in two studies (SALSA and SLAS2). This effect modification by PA has previously been shown for the relation between SB and mortality [63]. Therefore, this suggests that MVPA can, to some extent, counteract a potential detrimental effect of SB on cognition, creating a need for more comprehensive studies examining both factors simultaneously. Stratification did reveal gender differences in the unadjusted cross-sectional associations of SB and cognition in some studies, but no pattern of effect modification by gender consistently in the same direction could be replicated across studies.

Lastly, it is possible that historical SB (either accumulated SB or SB during another life phase) is of greater influence on cognitive function in older adults compared to current SB as investigated here. A similar mechanism is seen for smoking, in which cumulative smoking in pack-years is of greater influence on current mortality risk [64]. Relatedly, there is a possibility that one’s current SB is not an accurate reflection of one’s history of SB. Particularly around the age of retirement, it is expected that the amount of SB changes due to a changing daily routine [65]. This, in combination with the relative short time interval used for the longitudinal analysis, may have contributed to the lack of an association found between baseline SB and cognition decline. Therefore, while our findings diminish the likelihood of total undifferentiated sedentary time in late life as a risk factor for cognitive decline, the impact of cumulative or earlier life SB on cognitive decline remains to be elucidated. This would also give the opportunity to study the concept of reverse causation that could bias results.

The impact of the limitations scrutinized above should be balanced against the strengths of the current study. In addition to the aforementioned rigour of the confounder selection and the evaluation we performed to show the study specific validity of both the measures of total SB and global cognition, the main strength lies in the applied coordinated analysis approach. High-quality cohort study data were used that were made available through the COSMIC initiative. The coordinated analysis approach allowed for replication across the five data sets using a comparable analysis model and adds further rigour to the null finding for the association between total SB and global cognition. Finally, the generalizability of our findings benefitted strongly from the population-based character of the cohorts included and their geographical spread across the world.

Across the five population cohorts examined, this study did not find support for an association between total undifferentiated sedentary time and lower global cognition, at baseline or over time. We hypothesize that specific types of SB may differentially influence cognition depending on what a person is doing while sitting. Future research should investigate this further using sensitive neuropsychological tests and investigating mechanisms underlying the potential relationship between SB and cognition. For now, it is, however, too early to establish undifferentiated sedentary time as a potential preventable risk-factor for dementia prevention.