An introduction to Bayesian statistics in health psychology

The paradox of the resident experiencing depression: Higher depression, less favorable help-seeking outcome expectations, and lower help-seeking intentions

BACKGROUND

Medical residents experiencing depression can cause life-threatening harm to themselves and their patients. Treatment is available, but many do not seek help.

METHODS

The current set of three studies investigated whether depressive symptomatology in and of itself served as a help-seeking barrier-and whether expectations of help-seeking benefits provided insight into why this occurred. Nine waves of cross-sectional data were collected from medical residents across several different hospitals in the United States.

RESULTS

There was a large negative association between levels of depressive symptomatology and help-seeking intentions (H1) in Studies 1 and 3. In Study 2, this association was significant for one of the two help-seeking measures. For all analyses, studies, and measures, there was a large negative association between residents' levels of depressive symptomatology and agreement that seeking help will lead to positive outcomes (H2). Likewise, there was a moderately large indirect effect for all analyses, studies, and measures such that the association between levels of depressive symptomatology and help-seeking intentions occurred through less favorable expectations of help-seeking benefits (H3). Lower agreement of the benefits associated with help-seeking explained between 43 and 65% of depressive symptomatology's negative association with help-seeking intentions across studies.

CONCLUSIONS

The current findings indicate that depressive symptomatology itself represents a help-seeking barrier and underscore the importance of help-seeking expectations in explaining why this occurs. If future studies reveal a causal relationship between the perceived benefits of help-seeking and help-seeking intentions, then increasing such expectations could offer a potential path for increasing resident help-seeking.

The Role of Religiosity and Personal Faith in Young People's Sexual Debut and Sexual Risk Taking: A Bayesian Approach

Using a Bayesian statistical approach, this study aimed to provide a robust assessment of associations between religiosity and personal faith, timing of sexual initiation and sexual risk taking in young people. To produce posterior probability distribution of the estimation of associations, this study combined two population-based surveys of emerging Croatian adults and meta-analytical estimates pertaining to international studies published between 2000 and 2020. Regardless of the prior used, age at sexual debut was delayed by both religiosity (correlations ranged from 0.10 to 0.13) and personal faith (r = 0.09-0.13). Apart from delaying sexual debut, the findings suggest a very limited role of religiosity and personal faith in the protection of sexual and reproductive health among young people.

Informing behaviour change intervention design using systematic review with Bayesian meta-analysis: physical activity in heart failure

Embracing the Bayesian approach, we aimed to synthesise evidence regarding barriers and enablers to physical activity in adults with heart failure (HF) to inform behaviour change intervention. This approach helps estimate and quantify the uncertainty in the evidence and facilitates the synthesis of qualitative and quantitative studies. Qualitative evidence was annotated using the Theoretical Domains Framework and represented as a prior distribution using an expert elicitation task. The maximum a posteriori probability (MAP) for the probability distribution for the log OR was used to estimate the relationship between physical activity and each determinant according to qualitative, quantitative, and qualitative and quantitative evidence combined. The probability distribution dispersion (SD) was used to evaluate uncertainty in the evidence. Three qualitative and 16 quantitative studies were included (N = 2739). High pro-b-type natriuretic peptide (MAP = -1.16; 95%CrI: [-1.21; -1.11]) and self-reported symptoms (MAP = - 0.48; 95%CrI: [ -0.40; -0.55]) were suggested as barriers to physical activity with low uncertainty (SD = 0.18 and 0.19, respectively). Modifiable barriers were symptom distress (MAP = -0.46; 95%CrI: [-0.68; -0.24], SD = 0.36), and negative attitude (MAP = -0.40; 95%CrI: [-0.49; -0.31], SD = 0.26). Modifiable enablers were social support (MAP = 0.56; 95%CrI: [0.48; 0.63], SD = 0.26), self-efficacy (MAP = 0.43; 95%CrI: [0.32; 0.54], SD = 0.37), positive physical activity attitude (MAP = 0.92; 95%CrI: [0.77; 1.06], SD = 0.36).

Building a nest in a storm: The impact of immigration-related stress on Latino mothers' parenting

Latina/o immigrant mothers in the United States (U.S.) often experience discrimination, which results in deleterious impacts on their parenting practices. Because of the cumulative impact of immigration-related stress, there is a need for research aimed at identifying specific contextual stressors that have the greatest impact on Latina/o immigrant parenting. Further, given significant barriers to access mental health services, there is an urgency to comprehend how pre-existing family strengths might counteract these parenting shortcomings in Latina/o families. Accordingly, we examined in these investigation-specific pathways through which immigration-related stress affects maternal parenting practices, and how emotional and relational processes within families might act as predecessors to positive parenting practices within a context of adversity. This study is embedded within a larger program of research aimed at culturally adapting evidence-based parenting programs for low-income Latina/o immigrants. Our sample consisted of 71 Mexican-origin Latina/o mothers, residents of an urban setting in the Midwest. According to Bayesian estimated path analysis, immigration-related stress was associated with parenting stress and emotional support, while emotional support, parenting stress, and the co-parenting alliance were associated with positive parenting practices. Indirect effects demonstrate that immigration-related stress is negatively associated with positive parenting practices when mediated by parenting stress and emotional support. Current findings highlight the need to carefully examine the impact of immigration-related stress on the parenting practices of Latina/o immigrant families and the need to inform parent training interventions accordingly.

Analysis of dynamic psychological processes to understand and promote physical activity behaviour using intensive longitudinal methods: a primer

Physical activity behaviour displays temporal variability, and is influenced by a range of dynamic psychological processes (e.g., affect) and shaped by various co-occurring events (e.g., social/environmental factors, interpersonal dynamics). Yet, most physical activity research tends not to examine the dynamic psychological processes implicated in adopting and maintaining physical activity. Intensive longitudinal methods (ILM) represent one particularly salient means of studying the complex psychological dynamics that underlie and result from physical activity behaviour. With the increased recent interest in using intensive longitudinal data to understand specific dynamic psychological processes, the field of exercise and health psychology is well-positioned to draw from state-of-the-art measurement and statistical approaches that have been developed and operationalised in other fields of enquiry. The purpose of this review is to provide an overview of some of the fundamental dynamic measurement and modelling approaches applicable to the study of physical activity behaviour change, as well as the dynamic psychological processes that contribute to such change.

Evaluation of an advanced critical care echocardiography program: a mixed methods study

PURPOSE

Limited data exist on advanced critical care echocardiography (CCE) training programs for intensivists. We sought to describe a longitudinal echocardiography program and investigate the effect of distributed conditional supervision vs predefined en-bloc supervision, as well as the effect of an optional echocardiography laboratory rotation, on learners' engagement.

METHODS

In this mixed methods study, we enrolled critical care fellows and faculty from five University of Toronto-affiliated intensive care units (ICU) between July 2015 and July 2018 in an advanced training program, comprising theoretical lectures and practical sessions. After the first year, the program was modified with changes to supervision model and inclusion of a rotation in the echo laboratory. We conducted semistructured interviews and investigated the effects of curricular changes on progress toward portfolio completion (150 transthoracic echocardiograms) using a Bayesian framework.

RESULTS

Sixty-five learners were enrolled and 18 were interviewed. Four (9%) learners completed the portfolio. Learners reported lack of time and supervision, and skill complexity as the main barriers to practicing independently. Conditional supervision was associated with a higher rate of submitting unsupervised echocardiograms than unconditional supervision (rate ratio, 1.11, 95% credible interval, 1.08 to 1.14). After rotation in the echocardiography laboratory, submission of unsupervised echocardiograms decreased.

CONCLUSION

Trainees perceived lack of time and limited access to supervision as major barriers to course completion. Nevertheless, successful portfolio completion was related to factors other than protected time in the echocardiography laboratory or unconditional direct supervision in ICU. Further research is needed to better understand the factors promoting success of CCE training programs.

Individual differences in postoperative recovery trajectories for adult symptomatic lumbar scoliosis

OBJECTIVE

The Adult Symptomatic Lumbar Scoliosis-1 (ASLS-1) trial demonstrated the benefit of adult symptomatic lumbar scoliosis (ASLS) surgery. However, the extent to which individuals differ in their postoperative recovery trajectories is unknown. This study's objective was to evaluate variability in and factors moderating recovery trajectories after ASLS surgery.

METHODS

The authors used longitudinal, multilevel models to analyze postoperative recovery trajectories following ASLS surgery. Study outcomes included the Oswestry Disability Index (ODI) score and Scoliosis Research Society-22 (SRS-22) subscore, which were measured every 3 months until 2 years postoperatively. The authors evaluated the influence of preoperative disability level, along with other potential trajectory moderators, including radiographic, comorbidity, pain/function, demographic, and surgical factors. The impact of different parameters was measured using the R2, which represented the amount of variability in ODI/SRS-22 explained by each model. The R2 ranged from 0 (no variability explained) to 1 (100% of variability explained).

RESULTS

Among 178 patients, there was substantial variability in recovery trajectories. Applying the average trajectory to each patient explained only 15% of the variability in ODI and 21% of the variability in SRS-22 subscore. Differences in preoperative disability (ODI/SRS-22) had the strongest influence on recovery trajectories, with patients having moderate disability experiencing the greatest and most rapid improvement after surgery. Reflecting this impact, accounting for the preoperative ODI/SRS-22 level explained an additional 56%-57% of variability in recovery trajectory, while differences in the rate of postoperative change explained another 7%-9%. Among the effect moderators tested, pain/function variables-such as visual analog scale back pain score-had the biggest impact, explaining 21%-25% of variability in trajectories. Radiographic parameters were the least influential, explaining only 3%-6% more variance than models with time alone. The authors identified several significant trajectory moderators in the final model, such as significant adverse events and the number of levels fused.

CONCLUSIONS

ASLS patients have highly variable postoperative recovery trajectories, although most reach steady state at 12 months. Preoperative disability was the most important influence, although other factors, such as number of levels fused, also impacted recovery.

Carbon dioxide effects on daytime sleepiness and EEG signal: A combinational approach using classical frequentist and Bayesian analyses

Environmental carbon dioxide (CO₂ ) could affect various mental and physiological activities in humans, but its effect on daytime sleepiness is still controversial. In a randomized and counterbalanced crossover study with twelve healthy volunteers, we applied a combinational approach using classical frequentist and Bayesian statistics to analyze the CO₂ exposure effect on daytime sleepiness and electroencephalogram (EEG) signals. Subjective sleepiness was measured by the Japanese Karolinska Sleepiness Scale (KSS-J) by recording EEG during CO₂ exposure at different concentrations: Normal (C), 4000 ppm (Moderately High: MH), and 40 000 ppm (high: H). The daytime sleepiness was significantly affected by the exposure time but not the CO₂ condition in the classical statistics. On the other hand, the Bayesian paired t-test revealed that the CO₂ exposure at the MH condition might induce daytime sleepiness at the 40-min point compared with the C condition. By contrast, EEG was significantly affected by a short exposure to the H condition but not exposure time. The Bayesian analysis of EEG was primarily consistent with results by the classical statistics but showed different credible levels in the Bayes' factor. Our result suggested that the EEG may not be suitable to detect objective sleepiness induced by CO₂ exposure because the EEG signal was highly sensitive to environmental CO₂ concentration. Our study would be helpful for researchers to revisit whether EEG is applicable as a judgment indicator of objective sleepiness.

Studying dietary intake in daily life through multilevel two-part modelling: a novel analytical approach and its practical application

BACKGROUND

Understanding which factors influence dietary intake, particularly in daily life, is crucial given the impact diet has on physical as well as mental health. However, a factor might influence whether but not how much an individual eats and vice versa or a factor's importance may differ across these two facets. Distinguishing between these two facets, hence, studying dietary intake as a dual process is conceptually promising and not only allows further insights, but also solves a statistical issue. When assessing the association between a predictor (e.g. momentary affect) and subsequent dietary intake in daily life through ecological momentary assessment (EMA), the outcome variable (e.g. energy intake within a predefined time-interval) is semicontinuous. That is, one part is equal to zero (i.e. no dietary intake occurred) and the other contains right-skewed positive values (i.e. dietary intake occurred, but often only small amounts are consumed). However, linear multilevel modelling which is commonly used for EMA data to account for repeated measures within individuals cannot be applied to semicontinuous outcomes. A highly informative statistical approach for semicontinuous outcomes is multilevel two-part modelling which treats the outcome as generated by a dual process, combining a multilevel logistic/probit regression for zeros and a multilevel (generalized) linear regression for nonzero values.

METHODS

A multilevel two-part model combining a multilevel logistic regression to predict whether an individual eats and a multilevel gamma regression to predict how much is eaten, if an individual eats, is proposed. Its general implementation in R, a widely used and freely available statistical software, using the R-package brms is described. To illustrate its practical application, the analytical approach is applied exemplary to data from the Eat2beNICE-APPetite-study.

RESULTS

Results highlight that the proposed multilevel two-part model reveals process-specific associations which cannot be detected through traditional multilevel modelling.

CONCLUSIONS

This paper is the first to introduce multilevel two-part modelling as a novel analytical approach to study dietary intake in daily life. Studying dietary intake through multilevel two-part modelling is conceptually as well as methodologically promising. Findings can be translated to tailored nutritional interventions targeting either the occurrence or the amount of dietary intake.

The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App

The current paper highlights a new, interactive Shiny App that can be used to aid in understanding and teaching the important task of conducting a prior sensitivity analysis when implementing Bayesian estimation methods. In this paper, we discuss the importance of examining prior distributions through a sensitivity analysis. We argue that conducting a prior sensitivity analysis is equally important when so-called diffuse priors are implemented as it is with subjective priors. As a proof of concept, we conducted a small simulation study, which illustrates the impact of priors on final model estimates. The findings from the simulation study highlight the importance of conducting a sensitivity analysis of priors. This concept is further extended through an interactive Shiny App that we developed. The Shiny App allows users to explore the impact of various forms of priors using empirical data. We introduce this Shiny App and thoroughly detail an example using a simple multiple regression model that users at all levels can understand. In this paper, we highlight how to determine the different settings for a prior sensitivity analysis, how to visually and statistically compare results obtained in the sensitivity analysis, and how to display findings and write up disparate results obtained across the sensitivity analysis. The goal is that novice users can follow the process outlined here and work within the interactive Shiny App to gain a deeper understanding of the role of prior distributions and the importance of a sensitivity analysis when implementing Bayesian methods. The intended audience is broad (e.g., undergraduate or graduate students, faculty, and other researchers) and can include those with limited exposure to Bayesian methods or the specific model presented here.

The association between challenging behaviour and symptoms of post-traumatic stress disorder in people with intellectual disabilities: a Bayesian mediation analysis approach

BACKGROUND

A preponderance of behavioural symptoms is assumed to be the main difference in the manifestation of symptoms of post-traumatic stress disorder (PTSD) in people with intellectual disability (ID). However, no study so far has assessed the relationship between challenging behaviour (CB) and PTSD. The present study aims to explore this relationship by exploring whether CB is directly related to trauma exposure or whether this relationship is mediated through core symptoms of PTSD.

METHODS

Trauma exposure and current symptoms of PTSD were assessed in 43 adults with mild to moderate ID. Parallel versions were administered to 43 caregivers, including the Aberrant Behaviour Checklist to measure CB. Bayesian mediation analyses were conducted using self-rated and informant-rated data.

RESULTS

The self-report data showed no associations of CB with trauma exposure or PTSD symptoms. The association between informant-rated trauma exposure and irritability was mediated by severity and frequency of PTSD symptoms. The associations between informant-reported trauma exposure and the Aberrant Behaviour Checklist subscales hyperactivity and inappropriate speech were mediated by PTSD symptom severity.

CONCLUSIONS

The relationship between trauma exposure and CB was mediated by PTSD symptoms. PTSD core symptoms should be considered as underlying causes of CB, highlighting the necessity to explore trauma biography and symptoms of PTSD. The improvement of self-report assessment in people with ID is an important task for future studies.

Predicting limiting 'free sugar' consumption using an integrated model of health behavior

Excess intake of 'free sugars' is a key predictor of chronic disease, obesity, and dental ill health. Given the importance of determining modifiable predictors of free sugar-related dietary behaviors, we applied the integrated behavior change model to predict free sugar limiting behaviors. The model includes constructs representing 'reasoned' or deliberative processes that lead to action (e.g., social cognition constructs, intentions), and constructs representing 'non-conscious' or implicit processes (e.g., implicit attitudes, behavioral automaticity) as predictors of behavior. Undergraduate students (N = 205) completed measures of autonomous and controlled motivation, the theory of planned behavior (TPB) measures of explicit attitude, subjective norms, perceived behavioral control (PBC), and intentions, past behavior, implicit attitude, and behavioral automaticity at an initial point in time, and free sugar limiting behavior and behavioral automaticity two weeks later. A Bayesian structural equation model indicated that explicit attitude, subjective norms, and PBC predicted behavior via intention. Autonomous motivation predicted behavior indirectly through all TPB variables, while controlled motivation predicted behavior only via subjective norms. Implicit attitudes and behavioral automaticity predicted behavior directly and independently. Past behavior predicted behavior directly and indirectly through behavioral automaticity and intentions, but not implicit attitudes. Current findings suggest pervasive effects of constructs representing both reasoned and non-conscious processes and signpost potential targets for behavioral interventions aimed at minimizing free sugar consumption.

An Information Theoretic Approach to Model Selection: A Tutorial with Monte Carlo Confirmation

A reliance on null hypothesis significance testing (NHST) and misinterpretations of its results are thought to contribute to the replication crisis while impeding the development of a cumulative science. One solution is a data-analytic approach called Information-Theoretic (I-T) Model Selection, which builds upon Maximum Likelihood estimates. In the I-T approach, the scientist examines a set of candidate models and determines for each one the probability that it is the closer to the truth than all others in the set. Although the theoretical development is subtle, the implementation of I-T analysis is straightforward. Models are sorted according to the probability that they are the best in light of the data collected. It encourages the examination of multiple models, something investigators desire and that NHST discourages. This article is structured to address two objectives. The first is to illustrate the application of I-T data analysis to data from a virtual experiment. A noisy delay-discounting data set is generated and seven quantitative models are examined. In the illustration, it is demonstrated that it is not necessary to know the "truth" is to identify the one that is closest to it and that the most likely models conform to the model that generated the data. Second, we examine claims made by advocates of the I-T approach using Monte Carlo simulations in which 10,000 different data sets are generated and analyzed. The simulations showed that 1) the probabilities associated with each model returned by the single virtual experiment approximated those that resulted from the simulations, 2) models that were deemed close to the truth produced the most precise parameter estimates, and 3) adding a single replicate sharpens the ability to identify the most probable model.

Social support from evaluative familiar persons - a buffer against stress? Preliminary evidence of neuroendocrine and psychological responses

Background and Objectives: Social support may have a stress-buffering effect when an individual is or could be negatively judged by others, but paradoxically may also exacerbate stress. The aim of our study was to examine these findings when social support was provided by a positive or negative evaluative audience composed of familiar and close others (teachers). Design and Methods: 84 men were confronted with the Trier Social Stress Test for Groups through a 3 (negative, positive, no-audience) x 2 (familiar, unfamiliar) experimental design with four measurement points of cortisol levels and state anxiety. We also tested whether closeness with the committee members predicted these variables for the participants in the familiar conditions. Results: Using both a frequentist and a Bayesian approach, familiarity and social support did not have stress-buffering effects (or merely anecdotal effects) on cortisol levels but buffered self-reported anxiety only for the participants faced with a supportive audience composed of familiar persons. Closeness with the experimenters was not a significant predictor of the stress responses. Conclusions: Because these results are preliminary evidence, further investigations into the relations between support provider and recipient during evaluative tasks would be worthwhile to better explain opposing findings found in this growing literature.

The mediating role of resilience and self-esteem between negative life events and positive social adjustment among left-behind adolescents in China: a cross-sectional study

BACKGROUND

In China, adolescents are frequently left behind by their parents. A great deal of scientific evidence demonstrates considerable psychological and social impacts that negative life events may have on adolescents who are left behind. While a direct relationship between negative life events and psychological and social effects has been observed, indirect effects have yet to be examined. Therefore, the objective of this study was to determine the association between negative life events and positive social adjustment and how resilience and self-esteem mediate this association.

METHODS

A cross-sectional study was carried out in the provinces of Shandong, Henan, and Sichuan in China. A questionnaire was distributed to 4716 left-behind adolescents in ten middle/high schools. We performed Bayesian estimations in structural equation modeling using the Markov Chain Monte Carlo algorithm to test our hypotheses.

RESULTS

Negative life events were significantly related to resilience (r_s = - 0.402), self-esteem (r_s = - 0.292), and positive social adjustment (r_s = - 0.239). Positive social adjustment was directly affected by resilience (β = 0.639) and self-esteem (β = 0.448). Negative life events were not only directly related to positive social adjustment (β = - 0.187, 95% credible interval: - 0.233 ~ - 0.139), but also showed an indirect effect on positive social adjustment (β = - 0.541, 95% credible interval: - 0.583 ~ - 0.501) through resilience (β = - 0.370) and self-esteem (β = - 0.171). The total effect of negative life events on positive social adjustment was - 0.728, where 74.31% was mediated by resilience and self-esteem. The indirect effect of negative life events on positive social adjustment through resilience and self-esteem was 2.893 times more than the direct effect.

CONCLUSIONS

Resilience and self-esteem mediated most of the effect of negative life events on positive social adjustment. Interventions should be developed to improve the social adjustment of adolescents who are left behind, particularly the enhancement of resilience and self-esteem.

Perceived addiction to smoking and associations with motivation to stop, quit attempts and quitting success: A prospective study of English smokers

AIMS

Some argue that perceived addiction to smoking (PAS) might undermine motivation to stop. We examined the association of PAS with motivation to stop in a population sample and assessed its association with past and future quit attempts and future quit success.

METHOD

12,700 smokers in England were surveyed between September 2009-March 2012 as part of the Smoking Toolkit Study. 2796 smokers were followed up after 6 months. PAS was assessed at baseline by a single self-report item. The outcome variables were ratings of motivation to stop and reports of past-year quit attempts at baseline, and quit attempts in the past 6 months and smoking status at follow-up. Baseline covariates were sex, age, social grade and daily cigarette consumption.

RESULTS

In adjusted analyses, PAS was positively associated with at least some degree of motivation to stop versus no motivation (ORs = 1.97-2.96, all p's < 0.001). PAS was also positively associated with past-year quit attempts (OR = 1.43, 95% CI = 1.32-1.55, p < 0.001), but not with future quit attempts (OR = 1.17, 95% CI = 0.99-1.39, p = 0.064) or quit success (OR = 1.04, 95% CI = 0.73-1.47, p = 0.83).

CONCLUSION

In smokers in England, perceived addiction to smoking is positively associated with motivation to stop and having recently made a quit attempt but is not clearly associated with future quit attempts or success. These findings provide no grounds for believing that increasing smokers' perceived addiction through promotion of stop-smoking support has undermined motivation to stop.

Bayesian evaluation of behavior change interventions: a brief introduction and a practical example

INTRODUCTION

Evaluating effects of behavior change interventions is a central interest in health psychology and behavioral medicine. Researchers in these fields routinely use frequentist statistical methods to evaluate the extent to which these interventions impact behavior and the hypothesized mediating processes in the population. However, calls to move beyond the exclusive use of frequentist reasoning are now widespread in psychology and allied fields. We suggest adding Bayesian statistical methods to the researcher's toolbox of statistical methods.

OBJECTIVES

We first present the basic principles of the Bayesian approach to statistics and why they are useful for researchers in health psychology. We then provide a practical example on how to evaluate intervention effects using Bayesian methods, with a focus on Bayesian hierarchical modeling. We provide the necessary materials for introductory-level readers to follow the tutorial.

CONCLUSION

Bayesian analytical methods are now available to researchers through easy-to-use software packages, and we recommend using them to evaluate the effectiveness of interventions for their conceptual and practical benefits.

Role of parental and environmental characteristics in toddlers' physical activity and screen time: Bayesian analysis of structural equation models

Background

Guided by the Socialization Model of Child Behavior (SMCB), this cross-sectional study examined direct and indirect associations of parental cognitions and behavior, the home and neighborhood environment, and toddlers’ personal attributes with toddlers’ physical activity and screen time.

Methods

Participants included 193 toddlers (1.6 ± 0.2 years) from the Parents’ Role in Establishing healthy Physical activity and Sedentary behavior habits (PREPS) project. Toddlers’ screen time and personal attributes, physical activity- or screen time-specific parental cognitions and behaviors, and the home and neighborhood environment were measured via parental-report using the PREPS questionnaire. Accelerometry-measured physical activity was available in 123 toddlers. Bayesian estimation in structural equation modeling (SEM) using the Markov Chain Monte Carlo algorithm was performed to test an SMCB hypothesized model. Covariates included toddlers’ age, sex, race/ethnicity, main type of childcare, and family household income.

Results

In the SMCB hypothesized screen time model, higher parental barrier self-efficacy for limiting toddlers’ screen time was associated with higher parental screen time limiting practices (β = 0.451), while higher parental negative outcome expectations for limiting toddlers’ screen time was associated with lower parental screen time limiting practices (β = − 0.147). In turn, higher parental screen time limiting practices was associated with lower screen time among toddlers (β = − 0.179). Parental modeling of higher screen time was associated with higher screen time among toddlers directly (β = 0.212) and indirectly through the home environment. Specifically, higher screen time among parents was associated with having at least one electronic device in toddlers’ bedrooms (β = 0.146) and, in turn, having electronics in the bedroom, compared to none, was associated with higher screen time among toddlers (β = 0.250). Neighborhood safety was not associated with toddlers’ screen time in the SEM analysis. No significant correlations were observed between the SMCB variables and toddlers’ physical activity; thus, no further analyses were performed for physical activity.

Conclusions

Parents and their interactions with the home environment may play an important role in shaping toddlers’ screen time. Findings can inform family-based interventions aiming to minimize toddlers’ screen time. Future research is needed to identify correlates of toddlers’ physical activity.

On Nomological Validity and Auxiliary Assumptions: The Importance of Simultaneously Testing Effects in Social Cognitive Theories Applied to Health Behavior and Some Guidelines

Tests of social cognitive theories provide informative data on the factors that relate to health behavior, and the processes and mechanisms involved. In the present article, we contend that tests of social cognitive theories should adhere to the principles of nomological validity, defined as the degree to which predictions in a formal theoretical network are confirmed. We highlight the importance of nomological validity tests to ensure theory predictions can be disconfirmed through observation. We argue that researchers should be explicit on the conditions that lead to theory disconfirmation, and identify any auxiliary assumptions on which theory effects may be conditional. We contend that few researchers formally test the nomological validity of theories, or outline conditions that lead to model rejection and the auxiliary assumptions that may explain findings that run counter to hypotheses, raising potential for ‘falsification evasion.’ We present a brief analysis of studies (k = 122) testing four key social cognitive theories in health behavior to illustrate deficiencies in reporting theory tests and evaluations of nomological validity. Our analysis revealed that few articles report explicit statements suggesting that their findings support or reject the hypotheses of the theories tested, even when findings point to rejection. We illustrate the importance of explicit a priori specification of fundamental theory hypotheses and associated auxiliary assumptions, and identification of the conditions which would lead to rejection of theory predictions. We also demonstrate the value of confirmatory analytic techniques, meta-analytic structural equation modeling, and Bayesian analyses in providing robust converging evidence for nomological validity. We provide a set of guidelines for researchers on how to adopt and apply the nomological validity approach to testing health behavior models.