The dynamics in health-related quality of life of patients with stable coronary artery disease were revealed: a network analysis

Temporal network of experience sampling methodology identifies sleep disturbance as a central symptom in generalized anxiety disorder

BACKGROUND

A temporal network of generalized anxiety disorder (GAD) symptoms could provide valuable understanding of the occurrence and maintenance of GAD. We aim to obtain an exploratory conceptualization of temporal GAD network and identify the central symptom.

METHODS

A sample of participants (n = 115) with elevated GAD-7 scores (Generalized Anxiety Disorder 7-Item Questionnaire [GAD-7] ≥ 10) participated in an online daily diary study in which they reported their GAD symptoms based on DSM-5 diagnostic criteria (eight symptoms in total) for 50 consecutive days. We used a multilevel VAR model to obtain the temporal network.

RESULTS

In temporal network, a lot of lagged relationships exist among GAD symptoms and these lagged relationships are all positive. All symptoms have autocorrelations and there are also some interesting feedback loops in temporal network. Sleep disturbance has the highest Out-strength centrality.

CONCLUSIONS

This study indicates how GAD symptoms interact with each other and strengthen themselves over time, and particularly highlights the relationships between sleep disturbance and other GAD symptoms. Sleep disturbance may play an important role in the dynamic development and maintenance process of GAD. The present study may develop the knowledge of the theoretical model, diagnosis, prevention and intervention of GAD from a temporal symptoms network perspective.

Dynamic Individual Risk Networks: Personalized Network Modelling Based on Experience Sampling Data

Following a network perspective, risk of sexual reoffending can be understood as a construct that emerges from the interactions between risk factors. If these interrelationships are validly mapped out, this leads to an increased understanding of the risk and thus may contribute to more effective and/or more efficient interventions. This paper reports on personalized network modeling mapping the interrelationships of dynamic risk factors for an individual convicted of sexual offenses, using experience sampling (ESM) based on Stable-2007 items. The longitudinal character of ESM enables both the assessment of interrelations between risk factors within a timeframe and the relationships between risk factors over time. Networks are calculated and compared to the clinical assessment of interrelationships between the risk factors.

Clinical outcome assessment in patients with epilepsy: The value of health-related quality of life measurements

This narrative review provides an overview of the current knowledge on health-related quality of life (HRQOL), a relevant clinical outcome in patients with epilepsy. It shows that the most important factor determining HRQOL in this patient group is seizure frequency. In particular, seizure-freedom is associated with better HRQOL scores. Many other factors may impact perceived HRQOL aspects, but their interrelation is complex and requires further research. Novel analytical approaches, such as hierarchical cluster and symptom network analyses might shed further light on this, and may result in recommendations for interventions on the most 'central' factors influencing different aspects of HRQOL in patients with epilepsy. Next, an overview of the HRQOL tools and analytical methods currently used in epilepsy care, with a focus on clinical trials, is provided. The QOLIE-31 is the most frequently applied and best validated tool. Several other questionnaires focusing on specific aspects of HRQOL (e.g., mood, social impact) are less frequently used. We show some pitfalls that should be taken into account when designing study protocols including HRQOL endpoints. This includes standardized statistical analysis approaches and predefined reporting methods for HRQOL in epilepsy populations. It has been shown in other patient groups that the lack of such standardisation negatively impacts the quality and comparability of results. We conclude with a number of recommendations for future research.

Auditing the research practices and statistical analyses of the group-level temporal network approach to psychological constructs: A systematic scoping review

Network analyses have become increasingly common within the field of psychology, and temporal network analyses in particular are quickly gaining traction, with many of the initial articles earning substantial interest. However, substantial heterogeneity exists within the study designs and methodology, rendering it difficult to form a comprehensive view of its application in psychology research. Since the field is quickly growing and since there have been many study-to-study variations in terms of choices made by researchers when collecting, processing, and analyzing data, we saw the need to audit this field and formulate a comprehensive view of current temporal network analyses. To systematically chart researchers' practices when conducting temporal network analyses, we reviewed articles conducting temporal network analyses on psychological variables (published until March 2021) in the framework of a scoping review. We identified 43 articles and present the detailed results of how researchers are currently conducting temporal network analyses. A commonality across results concerns the wide variety of data collection and analytical practices, along with a lack of consistency between articles about what is reported. We use these results, along with relevant literature from the fields of ecological momentary assessment and network analysis, to formulate recommendations on what type of data is suited for temporal network analyses as well as optimal methods to preprocess and analyze data. As the field is new, we also discuss key future steps to help usher the field's progress forward and offer a reporting checklist to help researchers navigate conducting and reporting temporal network analyses.

Recovering Within-Person Dynamics from Psychological Time Series

Idiographic modeling is rapidly gaining popularity, promising to tap into the within-person dynamics underlying psychological phenomena. To gain theoretical understanding of these dynamics, we need to make inferences from time series models about the underlying system. Such inferences are subject to two challenges: first, time series models will arguably always be misspecified, meaning it is unclear how to make inferences to the underlying system; and second, the sampling frequency must be sufficient to capture the dynamics of interest. We discuss both problems with the following approach: we specify a toy model for emotion dynamics as the true system, generate time series data from it, and then try to recover that system with the most popular time series analysis tools. We show that making straightforward inferences from time series models about an underlying system is difficult. We also show that if the sampling frequency is insufficient, the dynamics of interest cannot be recovered. However, we also show that global characteristics of the system can be recovered reliably. We conclude by discussing the consequences of our findings for idiographic modeling and suggest a modeling methodology that goes beyond fitting time series models alone and puts formal theories at the center of theory development.

Effectiveness of Ergonomic Training to Decrease Awkward Postures during Dental Scaling Procedures: A Randomized Clinical Trial

Studies demonstrate that there is a lack of effective ergonomic principles for adopting a neutral posture during the execution of dental procedures. ISO 11.226:2000 Standard, Corr. 1:2006 has been thoroughly evaluated and adapted to the way that dentists work by the European Society of Dental Ergonomics (ESDE). However, after 15 years, no studies that showed strong evidence of effectiveness in reducing the prevalence of awkward posture in applying its parameters within the scope of dental practice were found. The aim of this study was to verify the effectiveness of applying the ergonomic parameters proposed by the European Society of Dental Ergonomics (ESDE) and ISO 11226 in reducing the prevalence of the main awkward postures adopted by female dental surgeons during the execution of dental scaling on a dental mannequin. A randomized clinical trial was carried out with sixty dental surgeons randomly assigned to two groups: the intervention group, who received instructions and theoretical and practical ergonomic training; and the control group, who received the same training only at the end of the study. For data analysis, Software IBM SPSS 27 and RStudio was used. Descriptive statistics were performed to verify the effectiveness of the intervention, and generalized linear models (specifically, generalized estimated equation models) were used. Poisson distribution was carried out with log link function and network analyses. Sixty female dental surgeons participated in the study. Twenty-two were distributed in the intervention group and thirty-eight in the control group. It was found that ergonomic training enabled a 63% reduction in the prevalence of awkward postures and that there was a statistically significant difference (p < 0.001) only in the intervention group. The analyses showed that the estimated marginal means of postures not recommended in the groups’ initial control, final control, initial intervention, and final intervention were 8.6, 8.2, 9.0, and 3.4, respectively. The relationship of networks analyses of the variables is shown with different profiles in the control and intervention groups, but the same pattern between the groups only vary in the strength and direction of the correlations. It was concluded that the ergonomic training based on the parameters of ISO 11226 and DIN EN 1005-4, and its adaptations to the dental practice provided by the European Society of Dental Ergonomics, as well as recent studies, contributed significantly to reducing the prevalence of awkward postures adopted by female dentists during the simulation of the basic periodontal procedures; however, it was not effective enough to improve the posture of the head and neck.

Dynamics in oral health-related factors of Indigenous Australian children: A network analysis of a randomized controlled trial

OBJECTIVES

Network analysis is an innovative, analytic approach that enables visual representation of variables as nodes and their corresponding statistical associations as edges. It also provides a new way of framing oral health-related questions as complex systems of variables. We aimed to generate networks of oral health variables using epidemiological data of Indigenous children, and to compare network structures of oral health variables among participants who received immediate or delayed delivery of an oral health intervention.

METHODS

Epidemiological data from 448 mother-child dyads enrolled in a randomized controlled trial of dental caries prevention in South Australia, Australia, were obtained. Networks were estimated with nodes representing study variables and edges representing partial correlation coefficients between variables. Data included dental caries, impact on quality of life, self-rated general health, self-rated oral health, dental service utilization, knowledge of oral health, fatalism and self-efficacy in three time points. Communities of nodes, centrality, clustering coefficient and network stability were estimated.

RESULTS

The oral health intervention interacted with the network through self-rated general health and knowledge of oral health. Networks depicting groups shortly after receiving the intervention presented higher clustering coefficients and a similar arrangement of nodes. Networks tended to return to a preintervention state.

CONCLUSION

The intervention resulted in increased connectivity and changes in the structure of communities of variables in both intervention groups. Our findings contribute to elucidating dynamics between variables depicting oral health networks over time.

Ecological momentary assessment versus retrospective assessment for measuring change in health-related quality of life following cardiac intervention

Background

Measuring change in health-related quality-of-life (HRQoL) is important to assess the impact of disease and/or treatment. Ecological momentary assessment (EMA) comprises the repeated assessment of momentary HRQoL in the natural environment and is particularly suited to capture daily experiences. Our objective was to study whether change in momentary measures or retrospective measures of HRQoL are more strongly associated with criterion measures of change in HRQoL.

Twenty-six coronary artery disease patients completed momentary and retrospective HRQoL questionnaires before and after coronary revascularization. Momentary HRQoL was assessed with 14 items which were repeatedly presented 9 times a day for 7 consecutive days. Each momentary assessment period was followed by a retrospective HRQoL questionnaire that used the same items, albeit phrased in the past tense and employing a one-week time frame. Criterion measures of change comprised the New York Heart Association functioning classification system and the Subjective Significance Change Questionnaire. Regression analysis was used to determine the association of momentary and retrospective HRQoL change with the criterion measures of change.

Results

Change according to momentary HRQoL items was more strongly associated with criterion measures of change than change according to retrospective HRQoL items. Five of 14 momentary items were significantly associated with the criterion measures. One association was found for the retrospective items, however, in the unexpected direction.

Conclusion

Momentary HRQoL measures better captured change in HRQoL after cardiac intervention than retrospective HRQoL measures. EMA is a valuable expansion of the armamentarium of psychometrically sound HRQoL measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41687-020-00261-2.

The current status of temporal network analysis for clinical science: Considerations as the paradigm shifts?

OBJECTIVE

Network analysis in psychology has ushered in a potentially revolutionary way of analyzing clinical data. One novel methodology is in the construction of temporal networks, models that examine directionality between symptoms over time. This paper provides context for how these models are applied to clinically-relevant longitudinal data.

METHODS

We provide a survey of statistical and methodological issues involved in temporal network analysis, providing a description of available estimation tools and applications for conducting such analyses. Further, we provide supplemental R code and discuss simulations examining temporal networks that vary in sample size, number of variables, and number of time points.

RESULTS

The following packages and software are reviewed: graphicalVAR, mlVAR, gimme, SparseTSCGM, mgm, psychonetrics, and the Mplus dynamic structural equation modeling module. We discuss the utility each procedure has for specific design considerations.

CONCLUSION

We conclude with notes on resources for estimating these models, emphasizing how temporal networks best approximate network theory.