Conscientiousness Versus Executive Function as Predictors of Health Behaviors and Health Trajectories
Bibliographic record
Abstract
We welcome the paper by Bogg and Roberts [1] describing the potential role of conscientiousness in explaining health-related outcomes. However, we think that this discussion could be more focused by examining conceptually similar constructs that have documented patterns of connection with these same outcomes. Executive function (EF), for example, is a set of cognitive processes—subsuming behavioral inhibition, working memory, and set shifting—that assist in goal-directed behavior, temporal organization of responses, and future-oriented actions in general [2]. Prior studies have found that individual differences in EF predict medication adherence, health behavioral trajectories, and longevity [3–5]. In a recent study utilizing an age-stratified community sample collected from an urban region in western Canada (N = 208; age range 19–89), we assessed EF and frequency of fatty food consumption and found that stronger EF (whether measured by Stroop performance or Go–No Go performance) predicted less frequent consumption of such foods, an effect that was independent of demographics, IQ, and BMI [6]. Given the potential conceptual overlap between conscientiousness and EF, we undertook a reanalysis of this dataset, which also included a measure of the Big Five dimensions of personality (the BFI), as well as accelerometer-assessed physical activity. When entering the Big Five variables as a single block in a linear regression analysis, conscientiousness was indeed a significant predictor of physical activity behavior (β = .156, p = .045), but not fatty food consumption frequency (β = −.118, p = .132). Interestingly, conscientiousness was not the most important personality predictor of these outcomes; openness the strongest predictor in absolute terms. More importantly, when conscientiousness and EF were entered in a competitive test, EF was the only significant predictor of unique variability in each behavior (Tables 1 and 2). Finally, when predicting a composite index of both health behaviors combined, EF was a significantly stronger predictor (β = .368, p<.001) than was conscientiousness (β = .187, p=.011; z = 1.868, p = .031). Executive function versus conscientiousness as predictors of accelerometer-assessed physical activity N = 208; age stratified community sample; mean age = 45.21 years; executive function assessed using a composite of Stroop performance (% correct, incongruent trials) and Go-NoGo reaction times; physical activity assessed via tri-axial accelerometer worn for 7 days Executive function versus conscientiousness as predictors of accelerometer-assessed physical activity N = 208; age stratified community sample; mean age = 45.21 years; executive function assessed using a composite of Stroop performance (% correct, incongruent trials) and Go-NoGo reaction times; physical activity assessed via tri-axial accelerometer worn for 7 days Executive function versus conscientiousness as predictors of 2-week fatty food consumption N = 208; age stratified community sample; mean age = 45.21 years; executive function assessed using a composite of Stroop performance (% correct, incongruent trials) and Go-NoGo reaction times; fatty food consumption assessed via fatty food items from the NCI Fat Screener completed for two consecutive weeks Executive function versus conscientiousness as predictors of 2-week fatty food consumption N = 208; age stratified community sample; mean age = 45.21 years; executive function assessed using a composite of Stroop performance (% correct, incongruent trials) and Go-NoGo reaction times; fatty food consumption assessed via fatty food items from the NCI Fat Screener completed for two consecutive weeks We believe that although conscientiousness may be a potentially useful heuristic for thinking about health-related behaviors, risks, and outcomes, because there exists empirical overlap with EF—specifically, behavioral inhibition, the most “pure” facet of EF [2]—some of this overlap could be responsible for the association between conscientiousness and outcomes of interest (e.g., health behavior performance). On a theoretical level, this may suggest that some sub-facets of conscientiousness are more predictive of health outcomes than others partially because the global construct itself is not a necessary part of explanatory (or predictive) models. However, this aside, there are some advantages of EF on an epistemic level, in that its measurement does not require self-referencing. In order to score highly on a measure of conscientiousness, one must endorse being goal-oriented and following through on one's intentions. Consistent performance of health-related behaviors requires these same things, such that one could very well be considering such behaviors when deciding on a response to items contained in any self-report measure of conscientiousness. Executive function, however, does not suffer from these same measurement problems, and yet dovetails seamlessly with social–cognitive perspectives on self-regulatory process, and provides many avenues for intervention beyond personality change [7]. In summary, we think that the link between conscientiousness and health outcomes is possibly an important one. However, we suggest that there is overlap between conscientiousness and executive function, and that the latter may be a more parsimonious (and powerful) explanatory variable for many health-related phenomena of interest. Careful reconsideration of the conscientiousness dimension from a social neuroscience perspective may be a useful direction forward.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".