Causal Effects of Competing Obstetrical Interventions: Mediators of Placental Abruption and Perinatal Mortality.

Placental abruption, the premature placental separation, confers increased perinatal mortality risk with preterm delivery as an important pathway through which the risk appears mediated. While pregnancies complicated by abruption are often delivered through an obstetrical intervention, many deliver spontaneously. We examined the contributions of clinician-initiated (PTDIND) and spontaneous (PTDSPT) preterm delivery at <37 weeks as competing causal mediators of the abruption-perinatal mortality association. Using the Consortium for Safe Labor (2002-2008) data (n = 203,990; 1.6% with abruption), we applied a potential outcomes-based mediation analysis to decompose the total effect into direct and mediator-specific indirect effects through PTDIND and PTDSPT. Each mediated effect describes the reduction in the counterfactual mortality risk if that preterm delivery subtype was shifted from its distribution under abruption to without abruption. The total effect risk ratio (RR) of abruption on perinatal mortality was 5.4 (95% confidence interval [CI] 4.6, 6.3). The indirect effect RRs for PTDIND and PTDSPT were 1.5 (95% CI: 1.4, 1.6) and 1.5 (95% CI: 1.5, 1.6), respectively; these corresponded to mediated proportions of 25% each. These findings underscore that spontaneous and clinician-initiated preterm deliveries each play essential roles in shaping perinatal mortality risks associated with placental abruption.

Person-specific priorities in solitude.

OBJECTIVE: People value solitude in varying degrees. Theories and studies suggest that people's appreciation of solitude varies considerably across persons (e.g., an introverted person may value solitude more than an extraverted person), and solitude experiences (i.e., on average, people may value some functions of solitude, e.g., privacy, more than other functions, e.g., self-discovery). What are the unique contributions of these two sources? METHOD: We surveyed a quota-based sample of 501 US residents about their perceived importance of a diverse set of 22 solitude functions. RESULTS: Variance component analysis reveals that both sources contributed to the variability of perceived importance of solitude (person: 22%; solitude function: 15%). Crucially, individual idiosyncratic preferences (person-by-solitude function interaction) had a substantial impact (46%). Further analyses explored the role of personality traits, showing that different functions of solitude hold varying importance for different people. For example, neurotic individuals prioritize emotion regulation, introverted individuals value relaxation, and conscientious individuals find solitude important for productivity. CONCLUSIONS: People value solitude for idiosyncratic reasons. Scientific inquiries on solitude must consider the fit between a person's characteristics and the specific functions a solitary experience affords. This research suggests that crafting or enhancing positive solitude experiences requires a personalized approach.

Parametric g-formula for Testing Time-Varying Causal Effects: What It Is, Why It Matters, and How to Implement It in Lavaan.

Psychologists leverage longitudinal designs to examine the causal effects of a focal predictor (i.e., treatment or exposure) over time. But causal inference of naturally observed time-varying treatments is complicated by treatment-dependent confounding in which earlier treatments affect confounders of later treatments. In this tutorial article, we introduce psychologists to an established solution to this problem from the causal inference literature: the parametric g-computation formula. We explain why the g-formula is effective at handling treatment-dependent confounding. We demonstrate that the parametric g-formula is conceptually intuitive, easy to implement, and well-suited for psychological research. We first clarify that the parametric g-formula essentially utilizes a series of statistical models to estimate the joint distribution of all post-treatment variables. These statistical models can be readily specified as standard multiple linear regression functions. We leverage this insight to implement the parametric g-formula using lavaan, a widely adopted R package for structural equation modeling. Moreover, we describe how the parametric g-formula may be used to estimate a marginal structural model whose causal parameters parsimoniously encode time-varying treatment effects. We hope this accessible introduction to the parametric g-formula will equip psychologists with an analytic tool to address their causal inquiries using longitudinal data.

Understanding Etiologic Pathways Through Multiple Sequential Mediators: An Application in Perinatal Epidemiology.

BACKGROUND: Causal mediation analysis facilitates decomposing the total effect into a direct effect and an indirect effect that operates through an intermediate variable. Recent developments in causal mediation analysis have clarified the process of evaluating how-and to what extent-different pathways via multiple causally ordered mediators link the exposure to the outcome. METHODS: Through an application of natural effect models for multiple mediators, we show how placental abruption might affect perinatal mortality using small for gestational age (SGA) birth and preterm delivery as two sequential mediators. We describe methods to disentangle the total effect into the proportions mediated via each of the sequential mediators, when evaluating natural direct and natural indirect effects. RESULTS: Under the assumption that SGA births causally precedes preterm delivery, an analysis of 16.7 million singleton pregnancies is consistent with the hypothesis that abruption exerts powerful effects on perinatal mortality (adjusted risk ratio = 11.9; 95% confidence interval = 11.6, 12.1). The proportions of the estimated total effect mediated through SGA birth and preterm delivery were 2% and 58%, respectively. The proportion unmediated via either SGA or preterm delivery was 41%. CONCLUSIONS: Through an application of causal mediation analysis with sequential mediators, we uncovered new insights into the pathways along which abruption impacts perinatal mortality.

Nonlinear mediation analysis with high-dimensional mediators whose causal structure is unknown.

With multiple possible mediators on the causal pathway from a treatment to an outcome, we consider the problem of decomposing the effects along multiple possible causal path(s) through each distinct mediator. Under a path-specific effects framework, such fine-grained decompositions necessitate stringent assumptions, such as correctly specifying the causal structure among the mediators, and no unobserved confounding among the mediators. In contrast, interventional direct and indirect effects for multiple mediators can be identified under much weaker conditions, while providing scientifically relevant causal interpretations. Nonetheless, current estimation approaches require (correctly) specifying a model for the joint mediator distribution, which can be difficult when there is a high-dimensional set of possibly continuous and noncontinuous mediators. In this article, we avoid the need to model this distribution, by developing a definition of interventional effects previously suggested for longitudinal mediation. We propose a novel estimation strategy that uses nonparametric estimates of the (counterfactual) mediator distributions. Noncontinuous outcomes can be accommodated using nonlinear outcome models. Estimation proceeds via Monte Carlo integration. The procedure is illustrated using publicly available genomic data to assess the causal effect of a microRNA expression on the 3-month mortality of brain cancer patients that is potentially mediated by expression values of multiple genes.

Evaluating sensitivity to classification uncertainty in latent subgroup effect analyses.

BACKGROUND: Increasing attention is being given to assessing treatment effect heterogeneity among individuals belonging to qualitatively different latent subgroups. Inference routinely proceeds by first partitioning the individuals into subgroups, then estimating the subgroup-specific average treatment effects. However, because the subgroups are only latently associated with the observed variables, the actual individual subgroup memberships are rarely known with certainty in practice and thus have to be imputed. Ignoring the uncertainty in the imputed memberships precludes misclassification errors, potentially leading to biased results and incorrect conclusions. METHODS: We propose a strategy for assessing the sensitivity of inference to classification uncertainty when using such classify-analyze approaches for subgroup effect analyses. We exploit each individual's typically nonzero predictive or posterior subgroup membership probabilities to gauge the stability of the resultant subgroup-specific average causal effects estimates over different, carefully selected subsets of the individuals. Because the membership probabilities are subject to sampling variability, we propose Monte Carlo confidence intervals that explicitly acknowledge the imprecision in the estimated subgroup memberships via perturbations using a parametric bootstrap. The proposal is widely applicable and avoids stringent causal or structural assumptions that existing bias-adjustment or bias-correction methods rely on. RESULTS: Using two different publicly available real-world datasets, we illustrate how the proposed strategy supplements existing latent subgroup effect analyses to shed light on the potential impact of classification uncertainty on inference. First, individuals are partitioned into latent subgroups based on their medical and health history. Then within each fixed latent subgroup, the average treatment effect is assessed using an augmented inverse propensity score weighted estimator. Finally, utilizing the proposed sensitivity analysis reveals different subgroup-specific effects that are mostly insensitive to potential misclassification. CONCLUSIONS: Our proposed sensitivity analysis is straightforward to implement, provides both graphical and numerical summaries, and readily permits assessing the sensitivity of any machine learning-based causal effect estimator to classification uncertainty. We recommend making such sensitivity analyses more routine in latent subgroup effect analyses.

Confounder selection strategies targeting stable treatment effect estimators.

Inferring the causal effect of a treatment on an outcome in an observational study requires adjusting for observed baseline confounders to avoid bias. However, adjusting for all observed baseline covariates, when only a subset are confounders of the effect of interest, is known to yield potentially inefficient and unstable estimators of the treatment effect. Furthermore, it raises the risk of finite-sample bias and bias due to model misspecification. For these stated reasons, confounder (or covariate) selection is commonly used to determine a subset of the available covariates that is sufficient for confounding adjustment. In this article, we propose a confounder selection strategy that focuses on stable estimation of the treatment effect. In particular, when the propensity score (PS) model already includes covariates that are sufficient to adjust for confounding, then the addition of covariates that are associated with either treatment or outcome alone, but not both, should not systematically change the effect estimator. The proposal, therefore, entails first prioritizing covariates for inclusion in the PS model, then using a change-in-estimate approach to select the smallest adjustment set that yields a stable effect estimate. The ability of the proposal to correctly select confounders, and to ensure valid inference of the treatment effect following data-driven covariate selection, is assessed empirically and compared with existing methods using simulation studies. We demonstrate the procedure using three different publicly available datasets commonly used for causal inference.

Skin carotenoid status and plasma carotenoids: biomarkers of dietary carotenoids, fruits and vegetables for middle-aged and older Singaporean adults.

Skin carotenoid status (SCS) measured by resonance Raman spectroscopy (RRS) may serve as an emerging alternative measurement for dietary carotenoid, fruit and vegetable (FV) intake although its application had not been assessed in a middle-aged and older population in Asia. This cross-sectional study aims to concurrently examine the use of SCS and plasma carotenoids to measure FV and carotenoid intake in a middle-aged and older population, taking into consideration potential socio-demographic and nutritional confounders. The study recruited 103 middle-aged and older adults (mean age: 58 years) in Singapore. Dietary carotenoids and FV, plasma carotenoid concentration and SCS were measured using 3-d food records, HPLC and a biophotonic scanner which utilised RRS, respectively. Adjusted for statistically defined socio-demographic covariates sex, age, BMI, prescription medication and cigarette smoking, plasma carotenoids and SCS showed positive associations with dietary total carotenoids (ßplasma: 0·020 (95 % CI 0·000, 0·040) µmol/l/mg, P = 0·05; ßskin: 265 (95 % CI 23, 506) arbitrary units/mg, P = 0·03) and FV (ßplasma: 0·076 (95 % CI 0·021, 0·132) µmol/l per FV serving, P = 0·008; ßskin: 1036 (95 % CI 363, 1708) arbitrary units/FV serving, P = 0·003). The associations of SCS with dietary carotenoid and FV intake were null with the inclusion of dietary PUFA, fibre and vitamin C as nutritional covariates (P > 0·05). This suggests a potential influence of these nutritional factors on carotenoid circulation and deposition in the skin. In conclusion, SCS, similar to plasma carotenoids, may serve as a biomarker for both dietary carotenoid and FV intake in a middle-aged and older Singaporean population.

Skin carotenoids status as a potential surrogate marker for cardiovascular disease risk determination in middle-aged and older adults.

BACKGROUND AND AIMS: Upon consumption, carotenoids, which may attenuate cardiovascular disease (CVD) risk, diffuse from the blood and accumulate in the skin. This study aimed to assess the associations between dietary, plasma, and skin carotenoids with CVD risk indicators and to examine the mediational role of plasma carotenoids in the relationship between skin carotenoids status (SCS) and CVD risk. METHODS AND RESULTS: Dietary, plasma, and skin carotenoids were assessed in a cross-sectional study from a community in Singapore (n = 103) aged 50 to 75 y. Multiple linear regression and binary logistics regression models were used to examine the associations between the carotenoids status with classical CVD risk factors and composite CVD risk indicators. After controlling for covariates, SCS and plasma carotenoids were inversely associated with systolic blood pressure (skin: P < 0.001; plasma: P < 0.05) and diastolic blood pressure (skin: P < 0.001; plasma: P < 0.005). Additionally, each increment of 1000 in SCS was associated with an odds ratio of 0.924 (P < 0.01) for metabolic syndrome diagnosis and 0.945 (P < 0.05) for moderate to high CVD risk classification. Associations between SCS and composite CVD risk indicators were null when adjusted for the corresponding plasma carotenoids, indicating complete mediation. Dietary carotenoids, however, showed no relationship with the CVD risk indicators. CONCLUSION: Carotenoids bioavailability may be important for cardiovascular protection. SCS, driven by the corresponding plasma carotenoids, could be a potential noninvasive surrogate marker for CVD risk determination in middle-aged and older adults. CLINICAL TRIAL REGISTRATION: NCT03554954, https://clinicaltrials.gov/. TRIAL REGISTRATION DATE: 13 June 2018.

Randomization inference with general interference and censoring.

Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in the presence of interference has focused on the setting where it is a priori assumed that there is "partial interference," in the sense that individuals can be partitioned into groups wherein there is no interference between individuals in different groups. Bowers et al. (2012, Political Anal, 21, 97-124) and Bowers et al. (2016, Political Anal, 24, 395-403) consider randomization-based inferential methods that allow for more general interference structures in the context of randomized experiments. In this paper, extensions of Bowers et al. that allow for failure time outcomes subject to right censoring are proposed. Permitting right-censored outcomes is challenging because standard randomization-based tests of the null hypothesis of no treatment effect assume that whether an individual is censored does not depend on treatment. The proposed extension of Bowers et al. to allow for censoring entails adapting the method of Wang et al. (2010, Biostatistics, 11, 676-692) for two-sample survival comparisons in the presence of unequal censoring. The methods are examined via simulation studies and utilized to assess the effects of cholera vaccination in an individually randomized trial of 73 000 children and women in Matlab, Bangladesh.

Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention.

BACKGROUND: E- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it's not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this experimental study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB) against a background set of other BCTs. METHODS: In a 2 (action planning: present vs absent) × 2 (coping planning: present vs absent) × 2 (self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention 'MyPlan2.0' for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335, age = 35.8, 28.1% men) or SB (n = 138, age = 37.8, 37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB. RESULTS: First, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735, p = 0.007) and reduced SB (t = - 2.573, p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302, p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2 = 8849, p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2 = 3.918, p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x2 = 5.590, p = 0.014; x2 = 17.722, p < 0.001; x2 = 4.552, p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2 = 4.389, p = 0.031) and self-monitoring alone (x2 = 8.858, p = 003), respectively. CONCLUSIONS: This study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future. TRIAL REGISTRATION: This study was preregistered as a clinical trial (ID number: NCT03274271 ). Release date: 20 October 2017.

Estimation of Controlled Direct Effects in Longitudinal Mediation Analyses with Latent Variables in Randomized Studies.

In a randomized study with longitudinal data on a mediator and outcome, estimating the direct effect of treatment on the outcome at a particular time requires adjusting for confounding of the association between the outcome and all preceding instances of the mediator. When the confounders are themselves affected by treatment, standard regression adjustment is prone to severe bias. In contrast, G-estimation requires less stringent assumptions than path analysis using SEM to unbiasedly estimate the direct effect even in linear settings. In this article, we propose a G-estimation method to estimate the controlled direct effect of treatment on the outcome, by adapting existing G-estimation methods for time-varying treatments without mediators. The proposed method can accommodate continuous and noncontinuous mediators, and requires no models for the confounders. Unbiased estimation only requires correctly specifying a mean model for either the mediator or the outcome. The method is further extended to settings where the mediator or outcome, or both, are latent, and generalizes existing methods for single measurement occasions of the mediator and outcome to longitudinal data on the mediator and outcome. The methods are utilized to assess the effects of an intervention on physical activity that is possibly mediated by motivation to exercise in a randomized study.

Estimating curvilinear time-varying treatment effects: Combining g-estimation of structural nested mean models with time-varying effect models for longitudinal causal inference.

Longitudinal designs can fortify causal inquiries of a focal predictor (i.e., treatment) on an outcome. But valid causal inferences are complicated by causal feedback between confounders and treatment over time. G-estimation of a structural nested mean model (SNMM) is designed to handle the complexities beset by measured time-varying or treatment-dependent confounding in longitudinal data. But valid inference requires correctly specifying the functional form of the SNMM, such as how the effects stay constant or change over time. In this article, we develop a g-estimation strategy for linear structural nested mean models whose causal parameters adopt the form of time-varying coefficient functions. These time-varying coefficient functions are smooth semiparametric functions of time that permit probing how the treatment effects may change curvilinearly. Further effect modification by time-invariant and time-varying covariates can be readily postulated in the SNMM to test fine-grained effect heterogeneity. We then describe a g-estimation strategy for estimating such an SNMM. We utilize the established time-varying effect model (TVEM) approach from the prevention and psychotherapy research literature for modeling flexible changes in covariate-outcome associations over time. Moreover, we exploit a known benefit of g-estimation over routine regression methods: its double robustness conferring protection against biases induced by certain forms of model misspecification. We encourage psychology researchers seeking correct causal conclusions from longitudinal data to use an SNMM with time-varying coefficient functions to assess curvilinear causal effects over time, and to use g-estimation with TVEM to resolve measured treatment-dependent confounding. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Adjusting for Baseline Measurements of the Mediators and Outcome as a First Step Toward Eliminating Confounding Biases in Mediation Analysis.

Mediation analysis prevails for researchers probing the etiological mechanisms through which treatment affects an outcome. A central challenge of mediation analysis is justifying sufficient baseline covariates that meet the causal assumption of no unmeasured confounding. But current practices routinely overlook this assumption. In this article, we suggest a relatively easy way to mitigate the risks of incorrect inferences resulting from unmeasured confounding: include pretreatment measurements of the mediator(s) and the outcome as baseline covariates. We explain why adjusting for pretreatment baseline measurements is a necessary first step toward eliminating confounding biases. We hope that such a practice can encourage explication, justification, and reflection of the causal assumptions underpinning mediation analysis toward improving the validity of causal inferences in psychology research.

Understated gender disparities due to outcome-dependent selection: Commentary on Mackelprang et al. (2023).

What is the gender gap in invited publications in high-impact psychology journals? To answer this critical question, Mackelprang et al. (2023) examined invited publications in five high-impact psychology journals. They first calculated the share of women among authors of the invited publications (35.6%), then compared it with a "base rate" (42.3%; the share of women among associate and full psychology professors at R1 institutions). This comparison was presented as empirical evidence of women being underrepresented in the authorship of publications in these high-impact journals. In this commentary, we show that comparing these two descriptives-either using a difference or a ratio-provides little insight into the actual gender disparity of interest. A fundamental shortcoming of such a comparison is due to outcome-dependent selection. We explain what outcome-dependent selection is and why it is inappropriate. Crucially, we explain why, following such outcome-dependent selection, comparing the share of women in the selected sample with a "base rate" rules out drawing valid inferences about the actual gender gap. We urge researchers to recognize the perils of, and thus avoid, outcome-dependent selection. Finally, we suggest an alternative approach that permits a more accurate understanding of gender disparities in academia. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Estimating time-varying treatment effects in longitudinal studies.

Longitudinal study designs are frequently used to investigate the effects of a naturally observed predictor (treatment) on an outcome over time. Because the treatment at each time point or wave is not randomly assigned, valid inferences of its causal effects require adjusting for covariates that confound each treatment-outcome association. But adjusting for covariates which are inevitably time-varying is fraught with difficulties. On the one hand, standard regression adjustment for variables affected by treatment can lead to severe bias. On the other hand, omitting time-varying covariates from confounding adjustment precipitates spurious associations that can lead to severe bias. Thus, either including or omitting time-varying covariates for confounding adjustment can lead to incorrect inferences. In this article, we introduce an estimation strategy from the causal inference literature for evaluating the causal effects of time-varying treatments in the presence of time-varying confounding. G-estimation of the treatment effect at a particular wave proceeds by carefully adjusting for only pre-treatment instances of all variables while dispensing with any post-treatment instances. The introduced approach has various appealing features. Effect modification by time-varying covariates can be investigated using covariate-treatment interactions. Treatment may be either continuous or noncontinuous with any mean model permitted. Unbiased estimation requires correctly specifying a mean model for either the treatment or the outcome, but not necessarily both. The treatment and outcome models can be fitted with standard regression functions. In summary, g-estimation is effective, flexible, robust, and relatively straightforward to implement. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Data-driven covariate selection for confounding adjustment by focusing on the stability of the effect estimator.

Valid inference of cause-and-effect relations in observational studies necessitates adjusting for common causes of the focal predictor (i.e., treatment) and the outcome. When such common causes, henceforth termed confounders, remain unadjusted for, they generate spurious correlations that lead to biased causal effect estimates. But routine adjustment for all available covariates, when only a subset are truly confounders, is known to yield potentially inefficient and unstable estimators. In this article, we introduce a data-driven confounder selection strategy that focuses on stable estimation of the treatment effect. The approach exploits the causal knowledge that after adjusting for confounders to eliminate all confounding biases, adding any remaining non-confounding covariates associated with only treatment or outcome, but not both, should not systematically change the effect estimator. The strategy proceeds in two steps. First, we prioritize covariates for adjustment by probing how strongly each covariate is associated with treatment and outcome. Next, we gauge the stability of the effect estimator by evaluating its trajectory adjusting for different covariate subsets. The smallest subset that yields a stable effect estimate is then selected. Thus, the strategy offers direct insight into the (in)sensitivity of the effect estimator to the chosen covariates for adjustment. The ability to correctly select confounders and yield valid causal inferences following data-driven covariate selection is evaluated empirically using extensive simulation studies. Furthermore, we compare the introduced method empirically with routine variable selection methods. Finally, we demonstrate the procedure using two publicly available real-world datasets. A step-by-step practical guide with user-friendly R functions is included. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Small Sample Correction for Factor Score Regression.

Factor score regression (FSR) is widely used as a convenient alternative to traditional structural equation modeling (SEM) for assessing structural relations between latent variables. But when latent variables are simply replaced by factor scores, biases in the structural parameter estimates often have to be corrected, due to the measurement error in the factor scores. The method of Croon (MOC) is a well-known bias correction technique. However, its standard implementation can render poor quality estimates in small samples (e.g. less than 100). This article aims to develop a small sample correction (SSC) that integrates two different modifications to the standard MOC. We conducted a simulation study to compare the empirical performance of (a) standard SEM, (b) the standard MOC, (c) naive FSR, and (d) the MOC with the proposed SSC. In addition, we assessed the robustness of the performance of the SSC in various models with a different number of predictors and indicators. The results showed that the MOC with the proposed SSC yielded smaller mean squared errors than SEM and the standard MOC in small samples and performed similarly to naive FSR. However, naive FSR yielded more biased estimates than the proposed MOC with SSC, by failing to account for measurement error in the factor scores.

Reductions in kinesiophobia and distress after pain neuroscience education and exercise lead to favourable outcomes: a secondary mediation analysis of a randomized controlled trial in primary care.

ABSTRACT: Pain neuroscience education combined with exercise (PNE + exercise) is an effective treatment for patients with chronic spinal pain. Yet, however, little is known about its underlying therapeutic mechanisms. Thus, this study aimed to provide the first insights by performing a novel mediation analysis approach in a published randomized controlled trial in primary care where PNE + exercise was compared with standard physiotherapy. Four mediators (catastrophizing, kinesiophobia, central sensitization-related distress, and pain intensity) measured at postintervention and 3 outcomes (disability, health-related quality of life, and pain medication intake) measured at 6-month follow-up were included into the analysis. The postintervention measure of each outcome was also introduced as a competing candidate mediator in each respective model. In addition, we repeated the analysis by including all pairwise mediator-mediator interactions to allow the effect of each mediator to differ based on the other mediators' values. Postintervention improvements in disability, medication intake, and health-related quality of life strongly mediated PNE + exercise effects on each of these outcomes at 6-month follow-up, respectively. Reductions in disability and medication intake were also mediated by reductions in kinesiophobia and central sensitization-related distress. Reductions in kinesiophobia also mediated gains in the quality of life. Changes in catastrophizing and pain intensity did not mediate improvements in any outcome. The mediation analyses with mediator-mediator interactions suggested a potential effect modification rather than causal independence among the mediators. The current results, therefore, support the PNE framework to some extent as well as highlight the need for implementing the recent approaches for mediation analysis to accommodate dependencies among the mediators.

Do cues of infectious disease shape people's affective responses to social exclusion?

Social exclusion triggers aversive reactions (e.g., increased negative affect), but being excluded may bring substantial benefits by reducing pathogen exposure associated with social interactions. Is exclusion less aversive when cues of infectious diseases are salient in the environment? We conducted two preregistered experiments with a 2 (belonging status: included vs. excluded) × 2 (disease salience: low vs. high) design, using scenarios (Study 1, N = 347) and a well-validated exclusion paradigm, Cyberball (Study 2, N = 519). Positive affect and negative affect were measured as the key outcomes. Across the 2 studies, we found little evidence that disease salience moderated the effect of exclusion (vs. inclusion) on positive affect. At the same time, we observed consistent evidence that disease salience moderated the effect of exclusion (vs. inclusion) on the other affective component: negative affect. Concretely, disease salience increased participants' negative affect in inclusion conditions; in exclusion conditions, the effect of disease salience on negative affect was negligible or nearly zero. Using a novel and robust approach of mediation analysis (interventional indirect effects), we further showed that the motive of disease avoidance rivals the motive of affiliation in shaping people's experiences of social interactions. These findings suggest that cues of disease salience alter people's affective experience with inclusion but not exclusion. The current research represents an important step toward understanding people's affective responses to social exclusion and inclusion in complex social situations involving multiple, and potentially conflicting motives. (PsycInfo Database Record (c) 2023 APA, all rights reserved).