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.

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.

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).

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.

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).

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.

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.

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).

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.

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).

A structural after measurement approach to structural equation modeling.

In structural equation modeling (SEM), the measurement and structural parts of the model are usually estimated simultaneously. In this article, we revisit the long-standing idea that we should first estimate the measurement part, and then estimate the structural part. We call this the "structural-after-measurement" (SAM) approach to SEM. We describe a formal framework for the SAM approach under settings where the latent variables and their indicators are continuous. We review earlier SAM methods and establish how they are specific instances of the SAM framework. Decoupled estimation for the measurement and structural parts using SAM possesses three key advantages over simultaneous estimation in standard SEM. First, estimates are more robust against local model misspecifications. Second, estimation routines are less vulnerable to convergence issues in small samples. Third, estimates exhibit smaller finite sample biases under correctly specified models. We propose two variants of the SAM approach. "Local" SAM expresses the mean vector and variance-covariance matrix of the latent variables as a function of the observed summary statistics and the parameters of the measurement model. "Global" SAM holds the parameters of the measurement part fixed while estimating the parameters of the structural part. Our framework includes two-step corrected standard errors, and permits computing both local and global fit measures. Nonetheless, the SAM approach is an estimation strategy, and should not be regarded as a model-building tool. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

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.

Association Between Dietary Protein Intake and Sleep Quality in Middle-Aged and Older Adults in Singapore.

Poor sleep has been associated with the increased risk of developing detrimental health conditions. Diet and certain nutrients, such as dietary protein (PRO) may improve sleep. This cross-sectional study aimed to investigate the relationship between PRO intake, their amino acid components, and sources with sleep quality in middle-aged and older adults residing in Singapore. A dataset of 104 healthy subjects between the age of 50 and 75 years old were used. Collected data included 3-day food record and sleep quality [sleep duration, global sleep score (GSS), sleep latency (SL), and sleep efficiency (SE)]. The collected 3-day food records were extracted for PRO, tryptophan (Trp), and large neutral amino acid (LNAA) intake. PRO intake was further categorized into plant and animal PRO. A multivariate multiple linear regression (MLR) was performed to assess the association between PRO intake and sleep quality. Dietary Trp:LNAA ratio was positively associated with sleep duration (ßtotal: 108.234 h; p: 0.005) after multiple covariates adjustment. Similarly, plant Trp (ßplant: 2.653 h/g; p: 0.020) and plant Trp:LNAA (ßplant: 54.006 h; p: 0.008) was positively associated with sleep duration. No significant associations were observed for both SL and SE. Sleep duration in middle-aged and older Singaporean adults was positively associated with dietary Trp and Trp:LNAA, especially when obtained from plant sources.

Disentangling indirect effects through multiple mediators without assuming any causal structure among the mediators.

hen multiple mediators exist on the causal pathway from treatment to outcome, path analysis prevails for disentangling indirect effects along paths linking possibly several mediators. However, separately evaluating each indirect effect along different posited paths demands stringent assumptions, such as correctly specifying the mediators' causal structure, and no unobserved confounding among the mediators. These assumptions may be unfalsifiable in practice and, when they fail to hold, can result in misleading conclusions about the mediators. Nevertheless, these assumptions are avoidable when substantive interest is in inference about the indirect effects specific to each distinct mediator. In this article, we introduce a new definition of indirect effects called interventional indirect effects from the causal inference and epidemiology literature. Interventional indirect effects can be unbiasedly estimated without the assumptions above while retaining scientifically meaningful interpretations. We show that under a typical class of linear and additive mean models, estimators of interventional indirect effects adopt the same analytical form as prevalent product-of-coefficient estimators assuming a parallel mediator model. Prevalent estimators are therefore unbiased when estimating interventional indirect effects-even when there are unknown causal effects among the mediators-but require a different causal interpretation. When other mediators moderate the effect of each mediator on the outcome, and the mediators' covariance is affected by treatment, such an indirect effect due to the mediators' mutual dependence (on one another) cannot be attributed to any mediator alone. We exploit the proposed definitions of interventional indirect effects to develop novel estimators under such settings. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Estimating social influence in a social network using potential outcomes.

Social influence occurs when an individual's outcome is affected by another individual's actions. Current approaches in psychology that seek to examine social influence have focused on settings where individuals are nested in predefined groups and do not interact across groups. Such study designs permit using standard estimation methods such as multilevel models for estimating treatment effects but restrict social influence to originate only from individuals within the same group. In more general settings, such as social networks where an individual is free to interact with any other individual, the absence of discernible clusters or scientifically meaningful groups precludes existing estimation methods. In this article, we introduce a new class of methods for assessing social influence in social networks in the context of randomized experiments in psychology. Our proposal builds on the potential outcomes framework from the causal inference literature. In particular, we exploit the concept of (treatment) interference, which occurs between individuals when one individual's outcome is affected by other individuals' treatments. Estimation proceeds using randomization-based approaches that are established in other disciplines and guarantee valid inference by construction. We compared the proposed methods with standard methods empirically using Monte Carlo simulation studies. We illustrated the method using publicly available data from an experiment assessing the effects of an anticonflict intervention among students' peer networks. The R scripts used to implement the proposed methods in the simulation studies and the applied example are freely available online. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

Nonlinear effect of social interaction quantity on psychological well-being: Diminishing returns or inverted U?

Social contact is an important ingredient of a happy and satisfying life. But is more social contact necessarily better? Although it is well-established that increasing the quantity of social interactions on the low end of its spectrum promotes psychological well-being, the effect of interaction quantity on the high end remains largely unexplored. We propose that the effect of interaction quantity is nonlinear; specifically, at high levels of interaction quantity, its positive effects may be reduced (Diminishing Returns Hypothesis) or even reversed (Inverted U Hypothesis). To test these two competing hypotheses, we conducted a series of six studies involving a total of 161,836 participants using experimental (Study 1), cross-sectional (Studies 2 and 3), daily diary (Study 4), experience sampling (Study 5), and longitudinal survey designs (Study 6). Consistent evidence emerged across the studies supporting the Diminishing Returns Hypothesis. On the low end of the interaction quantity spectrum, increasing interaction quantity enhanced well-being as expected; whereas on the high end of the spectrum, the effect of interaction quantity was reduced or became nearly negligible, but did not turn negative. Taken together, the present research provides compelling evidence that the well-being benefits of social interactions are nearly negligible after moderate quantities of interactions are achieved. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The impact of tryptophan supplementation on sleep quality: a systematic review, meta-analysis, and meta-regression.

CONTEXT: L-tryptophan (Trp) has been documented to aid sleep, but a systematic compilation of its effect on sleep quality is still limited. OBJECTIVE: We assessed the effect of Trp supplementation on sleep quality via meta-analysis and meta-regression. The effects of daily Trp dose (<1 g and ≥1 g) were also assessed. DATA SOURCES: A database search was done in PubMed, Medline (Ovid), Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Cochrane and a total of 18 articles were collected. DATA EXTRACTION: Extracted data from 4 articles were also analyzed using random-effect meta-analysis and meta-regression. Standardized mean difference (SMD) was used in meta-analysis. DATA ANALYSIS: Results from the study suggested that Trp supplementation can shorten wake after sleep onset (-81.03 min/g, P = 0.017; SMD, -1.08 min [95%CI, -1.89 to -0.28]). In addition, the group receiving ≥1 g Trp supplementation had a shorter wake after sleep onset than the group with Trp < 1g supplementation (Trp <1 g vs Trp ≥1 g: 56.55 vs 28.91 min; P = 0.001). However, Trp supplementation did not affect other sleep components. CONCLUSION: Trp supplementation, especially at ≥1 g can help improve sleep quality.