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Mediation analysis is widely adopted by researchers to disentangle the causal pathways by which an intervention affects an outcome. This paper describes a model to estimate the direct and indirect effect from a single subject AB-design with repeated assessments of both the mediator and the outcome. We discuss the plausibility of the modeling assumptions and contrast different approaches to deal with the autocorrelation in the time series data. While there are only small differences between those approaches when the number of timepoints is small (T = 15), the Generalized Least Squares approach performs best in medium (T = 30) to large (T = 90) time series. We apply the proposed methodology to data from a single case AB-design that was conducted with a mother of an excessively crying baby. Daily crying and baby sleep during the night were assessed with online diaries during a baseline phase and intervention phase. Between both phases, the pediatrician instructed the mother how to apply a responsive soothing intervention, the happiest baby (THB) method. We find that the direct effect of THB on sleeping is positive. THB also reduces crying but decreased crying during the day is associated with decreased sleeping during the night and hence a negative indirect effect of THB on sleeping via crying is found.

Although researchers are increasingly paying attention to the dyadic nature of sexual desire and its relevance to sexual and relational outcomes, our understanding of how sexual desire operates on a couple level and how it may influence the occurrence of sexual activity in relationships remains limited. This study used ecological momentary assessments to explore to what extent similarity in levels of desire for sex with one's sexual partner, or dyadic sexual desire, was associated with sexual initiations, receptivity to one's partner's initiations, and partnered sexual activity, and how these associations were moderated by perceived partner support. Ninety-four cohabitating couples (M age = 26.30, SD = 7.60) provided six reports a day for 10 consecutive days. We used response surface analysis to examine the associations among both the degree and direction of (dis)similarity in partners' dyadic desire and each of the three outcome variables. Our results revealed that although partners tended to experience similar levels of desire on a moment-to-moment basis, similarity in desire levels was not associated with the three outcome variables. Rather, higher desire within couples predicted each partner's behavioral outcomes. At lower levels of perceived partner support, dyadic sexual desire was negatively associated with women's receptivity to sexual initiations by their partners, indicating high context-sensitivity of women's sexual responding. These findings offer new insights into the links between the similarity in partners' levels of dyadic desire and sexual behaviors in couples, suggesting the need for increased focus on maintaining desire and promoting perceived partner support in clinical practice. ispartof: JOURNAL OF SEX RESEARCH ispartof: location:United States status: Published online

During the initial outbreak of the global COVID-19 pandemic, many countries imposed a total lockdown (containment at home). Although it was still allowed in Belgium to be physically active or exercise with people from your household in the vicinity of your home, engaging in sports or physical activity in a group or club context was no longer permitted. To examine whether a lack of physical activity was potentially threatening to the mental well-being of citizens and vice versa, the present study examined concurrent and reciprocal relationships between physical activity and anxiety, depressive symptoms, and sleep quality during the COVID-19 lockdown in a 9-week longitudinal design. In a sample of 983 Belgian adults (75.1% female; Mage = 43.78, range = 18–82 years), we explored these relationships at both the between- and within-person levels through random intercept cross-lagged panel models. The findings indicate that more physical activity was associated with lower symptoms of anxiety and depression and better sleep quality, a finding observed both at the between-person (across weeks; βanxiety = − 0.25, βdepression = − 0.30, βsleep quality = 0.24, p < .001) and within-person level (within weeks; βanxiety = − 0.10, βdepression = − 0.14, βsleep quality = 0.11, p < .05). Moreover, at the within-person level, an increase in feelings of anxiety and depression at one moment predicted lower levels of physical activity one week later (βanxiety = − 0.04, βdepression = − 0.06, p < .05). Since poor mental health poses a threat to the maintenance of physical activity, the current findings suggest that it is critical to invest in the mental health of individuals during distressing times.

The transition from childhood to adolescence often elicits psychological problems, which provides great opportunities for organizing universal, school-based prevention programs. The present study aims to evaluate the effectiveness of Boost Camp, a new prevention program targeting young adolescents' emotion regulation skills. Junior high school adolescents (n = 347; 53% female, M-age = 11.92) from six schools were randomly allocated to the intervention condition (n = 139) or to the control condition (n = 208), using a clustered randomized controlled design. Program integrity was evaluated by adolescents, trainers, and independent coders. Questionnaires were used to assess primary outcomes (emotion regulation and emotional wellbeing), secondary outcomes (school achievement, bullying experiences, and psychological stigmatization), and moderators (gender, psychological problems, and executive functioning). Assessment of the main outcome variables was conducted at baseline, post intervention, and three as well as six months' follow-up. Qualitative analyses showed a good program adherence and positive evaluations of the program. Furthermore, main results demonstrated that Boost Camp had short-term effects on depressive symptoms, self-esteem, indirect bullying experiences, and psychological stigmatization. However, no significant effects on emotion regulation were found and all beneficial effects immediately after the intervention disappeared at follow-up. Program effects were not moderated by gender, executive functioning impairments, or symptoms of psychopathology. These findings demonstrate that Boost Camp is feasible as a universal, school-based program and that it has the potential to enhance wellbeing outcomes, at least short-term and when implemented by external trainers. Several suggestions to optimize the program in order to obtain long-term effects are included in the discussion section.

The longitudinal actor–partner interdependence model (L-APIM) is used to study actor and partner effects, both linear and curvilinear, in dyadic intensive longitudinal data. A burning question is how to conduct power analyses for different L-APIM variants. In this paper, we introduce an accessible power analysis application, called PowerLAPIM, and provide a hands-on tutorial for conducting simulation-based power analyses for 32 L-APIM variants. With PowerLAPIM, we target the number of dyads needed, but not the number of repeated measurements for both partners (which is often fixed in longitudinal studies). PowerLAPIM allows to study moderation of linear and quadratic actor and partner effects by incorporating time-varying covariates or a categorical dyad-level predictor to test group differences. We also provide the functionality to account for serial dependency in the outcome variable by including autoregressive effects. Building on existing study that can yield estimates and thus plausible values of relevant model parameters, we illustrate how to perform a power analysis for a future study. In this illustration, we also demonstrate how to run a sensitivity analysis, to assess the impact of uncertainty about the model parameters, and of changes in the number of repeated measurements.

INTRODUCTION: The presence or absence of clearly defined symptoms and underlying pathophysiology may be a crucial variable related to variability in well-being and stigmatization in individuals with chronic pain (ICPs). In the context of pain, absence of clearly defined symptoms and pathophysiology deviates from the widely endorsed biomedical model and as such, may lead to stigmatization, which in turn could be related to ICPs' well-being.OBJECTIVES: The present study compared physical, psychological, social well-being, and perceived stigmatization in individuals with clearly defined symptoms and underlying pathophysiology (rheumatoid arthritis, RA) and individuals with less well understood symptoms and pathophysiology (fibromyalgia, FM) using daily diaries. Furthermore, the association between daily perceived stigmatization and daily well-being was examined.MATERIALS AND METHODS: Seventy-nine participants with FM, 86 participants with RA, and 33 participants with both diagnoses completed a diary for 14 consecutive days.RESULTS: Compared to individuals with RA, individuals with FM and individuals with both diagnoses reported worse daily well-being. After controlling for age, pain duration, and daily pain, differences between FM and RA remained significant for social well-being and completion of plans. Differences between RA and the dual diagnosis group remained significant for completion of plans, negative affect, and isolation. Furthermore, results suggested more stigmatization in individuals with FM than in individuals with RA. Individuals with both diagnoses reported equal stigmatization as individuals with FM, but more stigmatization than individuals with RA. Finally, increased levels of perceived stigma were associated with lower well-being.DISCUSSION: Findings highlight that the absence of clearly defined symptoms and pathophysiology could be contributing to greater feelings of stigmatization, which may be detrimental for ICPs' well-being.

With multiple potential 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 Pearl's path-specific effects framework (Pearl, 2001; Avin et al., 2005), such fine-grained decompositions necessitate stringent assumptions, such as correctly specifying the causal structure among the mediators, and there being no unobserved confounding among the mediators. In contrast, interventional direct and indirect effects for multiple mediators (Vansteelandt and Daniel, 2017) 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 non-continuous mediators. In this article, we avoid the need to model this distribution, by developing a definition of interventional effects previously suggested by VanderWeele and Tchetgen Tchetgen (2017) for longitudinal mediation. We propose a novel estimation strategy that uses non-parametric estimates of the (counterfactual) mediator distributions. Non-continuous outcomes can be accommodated using non-linear outcome models. Estimation proceeds via Monte Carlo integration. The procedure is illustrated using publicly available genomic data (Huang and Pan, 2016) to assess the causal effect of a microRNA expression on the three-month mortality of brain cancer patients that is potentially mediated by expression values of multiple genes., 30 pages, 2 figures, 3 tables

The multilevel autoregressive model disentangles unobserved heterogeneity from state-dependence. Statistically, the random intercept accounts for the dependence of all measurements at different time points on an observed underlying factor, while the lagged dependent predictor allows the outcome to depend on the outcome at the previous time point. In this paper, we consider different implementations of the simplest multilevel autoregressive model, and explore how each of them deals with the endogeneity assumption and the initial conditions problem. We discuss the performance of the no centering approach, the manifest centering approach, and the latent centering approach in the setting where the number of time points is small. We find that some commonly used approaches show bias for the autoregressive parameter. When the outcome at the first time point is considered predetermined, the no centering approach assuming endogeneity performs best.

The conversational actions of reformulating and mirroring constitute some of the core intervention techniques of psychotherapy. The purpose of the present study was to investigate the way in which therapists in cognitive-behavioral (CBT) and psychodynamic therapy (PDT) use reformulating and mirroring strategies to return patients' prior talk and how their differential usage can be viewed in light of the respective manualized recommendations. A mixed methods approach was applied using qualitative data that derived from a RCT. The data collection consisted of 200 excerpts assembled from both treatment conditions. The method of Conversation Analysis was used to determine the practices that accomplished instances of reformulating and mirroring, and to examine their distinct implications for subsequent talk. The quantitative analysis revealed that cognitive-behavioral therapists are significantly more likely to use reformulations, which is in harmony with what is suggested in CBT's treatment manuals. Psychodynamic therapists' frequent use of transformative formulations is, by contrast, unexpected in regard to the suggestions of the treatment protocol, as these interventions steer toward topical closure. Compared to the CBT condition, psychodynamic therapists were still significantly more likely to rely on mirroring strategies, which are in line with PDT's theoretical preference. Our findings raise the question whether alleged differences in treatment styles, as they are imposed by RCT methodology, are actually tangible in manual-guided clinical practice.

Objective: To explore fathers' experiences as biological parents in father–stepmother families. Background: Biological parents play an important role in the formation and development of stepfamily relationships, but little is known about fathers in stepfamilies. Method: In-person interviews were conducted with 10 Belgian fathers. Interviews were analyzed using interpretative phenomenological analysis. Results: Three themes emerged from the data: (a) reflecting on children's experiences and transitions, (b) pursuing a shared family understanding, and (c) innovating new ways of doing fatherhood and family. The first theme centers on fathers' concerns about the impact family transitions may have on their children. The second theme centers on fathers' pursuit of a shared understanding that clarifies family roles and expectations within the stepfamily. The last theme addresses the creativity of these fathers as they co-construct new stepfamily and fatherhood identities in the absence of culturally ascribed norms. Conclusion: Given their connecting role as parent and partner, biological fathers in stepfamilies are key to the formation and development of a new family narrative. Findings contribute to family practitioners' understanding of how fathers experience stepfamily life.

In recent literature, researchers have put a lot of time and effort in expanding mediation to multilevel settings. Unfortunately, such extensions are often limited to continuous settings, whereas research on multilevel mediation with binary mediators and outcomes remains rather sparse. Additionally, in lower-level mediation, the effect of the lower-level mediator on the outcome may oftentimes be confounded by an (un)measured upper-level variable. When such confounding is left unaddressed, the effect of the mediator, and hence the causal mediation effects themselves, will be estimated with bias. In linear settings, bias due to unmeasured additive upper-level confounding is often remedied by separating the effect of the mediator into a within- and between-cluster component. However, this solution is no longer valid when considering binary outcome measures. To assess the severity of this transgression, we aim to tackle lower-level mediation in binary settings from a counterfactual point of view, with a special focus on small clusters. We do this by 1) providing non-parametrical identification assumptions of the direct and indirect effect, 2) parametrically identifying these effects based on appropriate modelling equations, 3) considering estimation models for the mediator and the outcome, and 4) estimating the causal effects through an imputation algorithm that samples counterfactuals. Since steps three and four can be completed in various ways, we compare the performance of three different estimation models (an uncentered and centred separate modelling method, and a joint approach), and two different ways of predicting random effects (marginally versus conditionally). Employing simulations, we observe that the joint approach combined with a marginal generation of random effects performs best when sample sizes are sufficiently large. Additionally, we illustrate our findings with data from a crossover study that assesses the impact of experimentally induced goal conflict on the helping behaviour of partners of individuals with chronic pain.

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 2 = 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.

Objective: The current study is part of a multiple case study that investigated emotional change in individuals who have committed sexual offenses (ISOs). This case study highlights how one client used sex as maladaptive coping strategy in order to suppress negative emotions. Method: A mixed methods design was used to track changes in the client’s Affect Regulation (AR) during 4 phases, including a baseline (phase A), treatment as usual (phase B), treatment with an Emotion-Focused Therapy (EFT) component added (phase C), and follow up (phase A). Results: The qualitative description with verbatim clinical vignettes, revealed deeper insight into some important steps and hindrances and the impact of specific EFT-interventions. Quantitative analysis of self-report and observational measures showed a significant improvement in the client’s AR across different phases of treatment. Conclusions: The study improves our knowledge of the emotional change in ISOs during treatment and illustrates some key interventions, steps, and hindrances. ispartof: Journal Of Clinical Psychology vol:76 issue:4 pages:1-12 ispartof: location:United States status: Published online

The current study examined the respective role of step- family members' individual characteristics and their dyadic relationships in stepfamily members' percep- tions of mattering to one another. Parents, stepparents, and adolescents from 86 stepfamilies participated. Applying social relations model analyses, several trends emerged regarding the relative importance of individ- ual and dyadic factors. Parents' and adolescents' reports of mattering to one another were mainly driven by individual factors, whereas relational factors were important to understand the reports of mattering in which the stepparent was one of the dyad members. These findings contribute to stepfamily research by highlighting that stepfamily members' sense of belong- ing is at least partly a function of the interpersonal adaptation between the stepparent and other family members.

In dyadic research, the actor–partner interdependence model (APIM) is widely used to model the effect of a predictor measured across dyad members on one’s own and one’s partner outcome. When such d...

When considering dyadic data, one of the questions is whether the roles of the two dyad members can be considered equal. This question may be answered empirically using indistinguishability tests in the actor-partner interdependence model. In this paper several issues related to such indistinguishability tests are discussed: the difference between maximum likelihood and restricted maximum likelihood based tests for equality in variance parameters; the choice between the structural equation modelling and multilevel modelling framework; and the use of sequential testing rather than one global test for a set of indistinguishability tests. Based on simulation studies, we provide guidelines for best practice. All different types of tests are illustrated with cross-sectional and longitudinal data, and corroborated with corresponding R code.

The present study investigated the effectiveness of different strategies to increase the willingness to taste, liking and consumption of a disliked vegetable in preschool children, and the moderating role of reward sensitivity. Kindergarten classes (n = 8; preschool children: n = 154, 46,8% boys, age: M = 5.08, SD = 0.61) were assigned to one of four different conditions (i.e. Repeated Neutral Exposure, Repeated Exposure + social reward, Repeated Exposure + token reward and control condition). The results demonstrated that children’s liking and consumption of the disliked vegetable significantly increased in the three active strategies (i.e. consumption and liking increases ranged from 10.1 to 20.6 g, and 34.1 to 51.8% respectively) compared to the control condition (i.e. consumption and liking increases ranged from 0.3 to 1.7 g, and 1.9 to 4.7% respectively). No significant differences were found between the three active strategies for all dependent variables. Little evidence was found for the moderating role of the individual characteristic reward sensitivity.

The actor-partner interdependence model (APIM) is widely used for analyzing dyadic data. Although dyadic research has become immensely popular, its statistical complexity might be a barrier. To remedy this, a free user-friendly web application, called APIM_SEM, has been developed. This app automatically performs the statistical analyses (i.e., structural equation modeling) of both simple and more complex APIMs. It allows the researcher to analyze distinguishable or indistinguishable dyads, to examine dyadic patterns, to estimate actor and partner effects of one or two predictors, and to control for covariates. Results are provided in software and text format, complemented by summary tables and figures. As an illustration, the effect of romantic appraisal on relational satisfaction is assessed by fitting APIMs with varying complexity.

The present review examines how stepfamily members without a shared history co-construct a shared family identity and what family processes are relevant in this stepfamily formation. Three databases (Web of Science, PsycInfo, and ProQuest) were systematically searched, resulting in 20 included qualitative studies. The meta-ethnography approach of Noblit and Hare allowed synthesizing these qualitative studies and constructing a comprehensive framework of stepfamilies doing family. Three interdependent family tasks were identified: (a) honoring the past, (b) marking the present, and (c) investing in the future. Stepfamily members' experiences of these family tasks are strongly affected by the dominant societal perspectives and characterized by an underlying dialectical tension between wanting to be like a first-time family and feeling the differences in their family structure at the same time. These findings clearly demonstrate the family work that all stepfamily members undertake and provide a broader context for interpreting stepfamilies' co-construction of a new family identity.

Analyses of randomised experiments frequently include attempts to decompose the intention-to-treat effect into a direct and indirect effect, mediated by given intermediaries, with the aim to shed light onto the treatment mechanism. Methods from causal mediation analysis have facilitated this by allowing for arbitrary models for the outcome and the mediator. They thereby generalise the traditional approach to direct and indirect effects, which is essentially limited to linear models. The default maximum likelihood methods make use of a model for the conditional distribution of the mediator, given treatment and baseline covariates, but are prone to bias when that model is misspecified. In randomised experiments, specification of such model can be easily avoided, but at the expense of a sometimes major efficiency loss when those baseline covariates are predictive of the mediator. In this article, we develop a compromise approach: it makes use of a model for the mediator to optimally extract information from the baseline covariate data but is insulated from the impact of misspecification of that model; it achieves this by exploiting the known randomisation probabilities. Simulation studies and the analysis of a randomised study show major efficiency gains and confirm our theoretical findings that the default methods from causal mediation analysis are sometimes, although not always, reasonably robust to model misspecification. Copyright © 2017 John Wiley & Sons, Ltd.

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.

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).

Background Parental behavior can influence how well adolescents cope with chronic pain. Previous research has largely focused on how parents negatively impact adolescent functioning. Yet more recent work suggests that parents - and particularly parental psychological flexibility - can foster better adolescent pain-related functioning. In this study we examined if parental protective responses and instructions to engage in activities in the presence of pain mediate the impact of parental psychological flexibility and acceptance of adolescent pain on adolescents' daily pain-related behavior. Method Fifty-six adolescents with chronic pain (M age = 14.5 years, 86% girls) and one of their parents (93% mothers) were recruited at initial evaluation at two pediatric pain clinics in the US. Parents completed baseline questionnaires assessing psychologically flexible parenting and acceptance of adolescent pain. Next, parents and adolescents completed a 14-day self-report diary assessing adolescent activity-avoidance and activity-engagement in the presence of pain (adolescent report), and parental protective responses and instructions for their adolescent to engage in activities (parent report). Results Psychologically flexible parenting and acceptance of adolescent pain in parents were indirectly related to lower daily adolescent activity-avoidance, via their negative association with daily parental protective responses. Positive associations also emerged between baseline psychologically flexible parenting and overall levels of adolescent activity-engagement via its negative association with overall levels of parental protectiveness across the 14-day period. Psychologically flexible parenting and parental acceptance of adolescent pain were also indirectly related to daily decreases in adolescent activity-avoidance via their association with daily increases in parental activity-engagement instructions. These baseline parental resilience factors were also positively related to overall levels of parental engagement instructions, a route via which an indirect association with both higher overall activity-engagement as well as higher overall activity-avoidance in the adolescent was observed. Conclusion Our findings suggest an (indirect) adaptive role of parental psychological flexibility on adolescent daily pain-related behavior via its impact on parental protective behavior. If our findings replicate, they would suggest that these parental behaviors could be targeted in pain treatments that include both adolescents and their parents. Future research could further examine the impact of parental instructions on pain-related behavior in adolescents with chronic pain.

ObjectiveThis study sets out to identify potential daily antecedents and consequences of pain-related activity avoidance and engagement behavior in adolescents with chronic pain.MethodsAdolescents (N = 65, Mage = 14.41) completed baseline self-reports and a diary for 14 days. Afternoon and evening reports were used to infer a network structure of within-day associations between pain intensity, pain-related fear, pain catastrophizing, affect, and pain-related activity avoidance and engagement behavior. Baseline psychological flexibility was examined as a potential resilience factor.ResultsActivity avoidance in the evening was predicted by pain-related fear and avoidance earlier that afternoon. Activity engagement was predicted by positive affect and activity engagement in the afternoon. Pain-related behavior in the afternoon was not related to subsequent changes in pain intensity, pain-related fear, pain catastrophizing, or affect. Pain-related fear in the afternoon was predictive of increased levels of pain and pain catastrophizing in the evening. Both pain-related fear and pain catastrophizing in the evening were predicted by negative affect in the afternoon. Psychological flexibility was associated with lower levels of daily activity avoidance and buffered the negative association between pain intensity and subsequent activity engagement.ConclusionsThis study provides insight into unique factors that trigger and maintain activity avoidance and engagement and into the role of psychological flexibility in pediatric pain. Future work should focus on both risk and resilience factors and examine the role of psychological flexibility in chronic pediatric pain in greater detail.

Decomposing an exposure effect on an outcome into separate natural indirect effects through multiple mediators requires strict assumptions, such as correctly postulating the causal structure of the mediators, and no unmeasured confounding among the mediators. In contrast, interventional indirect effects for multiple mediators can be identified even when – as often – the mediators either have an unknown causal structure, or share unmeasured common causes, or both. Existing estimation methods for interventional indirect effects require calculating each distinct indirect effect in turn. This can quickly become unwieldy or unfeasible, especially when investigating indirect effect measures that may be modified by observed baseline characteristics. In this article, we introduce simplified estimation procedures for such heterogeneous interventional indirect effects using interventional effect models. Interventional effect models are a class of marginal structural models that encode the interventional indirect effects as causal model parameters, thus readily permitting effect modification by baseline covariates using (statistical) interaction terms. The mediators and outcome can be continuous or noncontinuous. We propose two estimation procedures: one using inverse weighting by the counterfactual mediator density or mass functions, and another using Monte Carlo integration. The former has the advantage of not requiring an outcome model, but is susceptible to finite sample biases due to highly variable weights. The latter has the advantage of consistent estimation under a correctly specified (parametric) outcome model, but is susceptible to biases due to extrapolation. The estimators are illustrated using publicly available data assessing whether the indirect effects of self-efficacy on fatigue via self-reported post-traumatic stress disorder symptoms vary across different levels of negative coping among health care workers during the COVID-19 outbreak.

Mediation analysis is routinely adopted by researchers from a wide range of applied disciplines as a statistical tool to disentangle the causal pathways by which an exposure or treatment affects an outcome. The counterfactual framework provides a language for clearly defining path-specific effects of interest and has fostered a principled extension of mediation analysis beyond the context of linear models. This paper describes medflex, an R package that implements some recent developments in mediation analysis embedded within the counterfactual framework. The medflex package offers a set of ready-made functions for fitting natural effect models, a novel class of causal models which directly parameterize the path-specific effects of interest, thereby adding flexibility to existing software packages for mediation analysis, in particular with respect to hypothesis testing and parsimony. In this paper, we give a comprehensive overview of the functionalities of the medflex package.

Cognitive and behavioral treatment programs for individuals who have committed sexual offenses (ISOs) have shown significant but small effect sizes. A growing bodyof research points toward the importance of difficulties in affect regulation (AR) as a risk factor for sexual recidivism. On this basis, it seems important to target difficulties in AR in treatment. The current systematic case study investigates the potential contribution of emotion-focused therapy (EFT) to changing problematic AR in ISOs. Kevin was a high-risk offender with a traumatic history who met the diagnostic criteria of pedophilic and borderline disorders, with serious AR difficulties. Self-report outcome measures, observation measures, and a biomarker were used to track changes in AR, psychological symptoms, and distress during baseline (Phase A); treatment as usual (Phase B); treatment with an EFT component added (Phase C); and follow-up (Phase A). Statistically significant change was found in AR, psychological symptoms, and distress during treatment (Phase B + C); however, it is not possible to attribute these changes causally to EFT. An examination of the qualitative process data provides deeper insights into how the client reacted to specific EFT interventions. Verbatim clinical vignettes are included to clarify key interventions, hindrances, and mechanisms of change. This study provides preliminary support for the role of therapy to facilitate emotional change in ISOs. ispartof: International Journal of Offender Therapy and Comparative Criminology vol:63 issue:15-16 pages:1-25 ispartof: location:United States status: Published online

In 2–1–1 multilevel models for mediation, the effect of the mediator on the outcome is often decomposed into a between-cluster and a within-cluster effect. This can be achieved by either a manifest...

The social relations model (SRM) is typically used to identify sources of variance in interpersonal dispositions in families. Traditionally, it uses dyadic measurements that are obtained from a round-robin design, where each family member rates each other family member. Those dyadic measurements are mostly considered to be continuous, but we, however, will discuss how the SRM can be adapted to count dyadic measurements. Such SRM for count data can be formulated in the SEM-framework by viewing it as a confirmatory factor analysis (CFA), but it can also be defined in the multilevel framework. These two frameworks result in equivalent models of which the parameters can be estimated using maximum likelihood estimation or a Bayesian approach. We perform a simulation study to compare the performance of those two estimators. As an illustration, we consider intergenerational co-activity data from a block design and contrast family dynamics between non-divorced families and stepfamilies.

It is well known from the mediation analysis literature that the identification of direct and indirect effects relies on strong no unmeasured confounding assumptions of no unmeasured confounding. Even in randomized studies the mediator may still be correlated with unobserved prognostic variables that affect the outcome, in which case the mediator's role in the causal process may not be inferred without bias. In the behavioural and social science literature very little attention has been given so far to the causal assumptions required for moderated mediation analysis. In this paper we focus on the index for moderated mediation, which measures by how much the mediated effect is larger or smaller for varying levels of the moderator. We show that in linear models this index can be estimated without bias in the presence of unmeasured common causes of the moderator, mediator and outcome under certain conditions. Importantly, one can thus use the test for moderated mediation to support evidence for mediation under less stringent confounding conditions. We illustrate our findings with data from a randomized experiment assessing the impact of being primed with social deception upon observer responses to others' pain, and from an observational study of individuals who ended a romantic relationship assessing the effect of attachment anxiety during the relationship on mental distress 2 years after the break-up.

The present study investigated whether partners’ frustration of relational needs for autonomy, competence, and relatedness was associated with relationship dissatisfaction and conflict (frequency, topics, communication patterns). Self-report measures were completed by 230 committed heterosexual couples. APIM and regression analyses revealed that (1) need frustration is associated with how dissatisfied partners are with their relationship, how frequently they initiate conflict, and how they communicate during conflicts, but not with the number of conflict topics; (2) relatedness-frustration matters most, whereas only limited evidence was found for autonomy- and competence-frustration; (3) both one's own and one's partner's need frustration play a role in dissatisfaction and conflict; and (4) although most of the results were consistent across gender, gender differences were found for how need frustration affects couples’ conflict communication. Therapeutic implications are discussed. Practitioner points Addressing need frustration in couples may help to reduce relationship dissatisfaction, conflict, and destructive conflict communication Therapists should especially focus on relatedness-frustration of men and women, but also pay attention to their autonomy-frustration Destructive conflict communication patterns in couples reflect relatedness-frustration in both men and women and autonomy- and competence-frustration in men.