Does stigmatization moderate the association between intention and implementation of learned prevention-strategies at work after a depressive episode? - a cross-sectional pilot study

Sustainable return to work after depression - A comparative study among occupational physicians and affected employees

Introduction

The number of sick days taken from work due to depression is steadily rising. A successful return to work (RTW) is essential for sustainable reintegration. This study aims to identify factors to optimize RTW and to investigate approaches for sustainable RTW (sRTW) after depressive episodes.

Methods

Semi-structured expert interviews with senior occupational physicians (OPs, N = 5) served to develop two surveys among OPs (N = 180) and employees after depressive episode (N = 192). Predictors of RTW rating, workplace-based RTW interventions and sRTW interventions were analyzed using multiple hierarchical regression, chi-square difference and t-tests.

Results

For OPs, employee training on mental illness prevention was found to be the strongest predictor of overall RTW rating, whereas understanding and appreciation in conversations and stigmatization were strongest predictors of overall RTW rating by the employees. Compared to the employees, OPs reported significantly more availability of workplace-based interventions. To prevent relapse, the employees prioritized sufficient time and financial security during the RTW process more than OPs.

Conclusions

The study identified facilitating and hindering factors that can inform further research and practice to improve RTW after depressive episodes. To redress the awareness gap about the availability of workplace-based interventions, regular contact between OPs and employees is crucial. Several factors were considered to be of varying importance for relapse prevention by the two groups. Multiple perceptions and needs ought to be taken into account during RTW.

The Moderating Power of Impulsivity: A Systematic Literature Review Examining the Theory of Planned Behavior

The theory of planned behavior (TPB) states that behavioral intention is the best predictor of actual behavior change. However, intention explains only a portion of the variance in behavior. Of specific interest is the question of which moderating or mediating variables can be leveraged to aid health promotion interventions utilizing the tenets of behavioral economics (delay discounting and commitment contracts) in the intention–behavior pathway. Impulsivity has been postulated to fill this role and may be applied to multiple behaviors. We aim to determine if impulsivity moderates the association between intention and actual behavior in the TPB, to discover what other variables may moderate or mediate this association, and to apply the findings to future studies in the field of behavioral economics. To this end, a systematic review was conducted using the PubMed, PsychINFO, and Embase online databases. Eligible studies in peer-reviewed journals published prior to November 2021 were selected. Thirty-three studies were included in the final review, examining physical activity, diet, preventive health, mental health, addiction, and medication adherence behaviors. Three main concepts emerged: (1) impulsivity moderates the association between intention and behavior change; (2) self-efficacy moderates the association between intention and behavior change; and (3) planning and self-efficacy contribute to moderated mediation. This review demonstrates a gap in the literature regarding the application of the TPB to the intention–behavior pathway for health behaviors. Future studies in behavioral economics may leverage the variables of impulsivity, self-efficacy, and planning to predict follow-through in this area and to develop targeted change initiatives.

Can machine learning be useful as a screening tool for depression in primary care?

Depression is a widespread disease with a high economic burden and a complex pathophysiology disease that is still not wholly clarified, not to mention it usually is associated as a risk factor for absenteeism at work and suicide. Just 50% of patients with depression are diagnosed in primary care, and only 15% receive treatment. Stigmatization, the coexistence of somatic symptoms, and the need to remember signs in the past two weeks can contribute to explaining this situation. In this context, tools that can serve as diagnostic screening are of great value, as they can reduce the number of undiagnosed patients. Besides, Artificial Intelligence (AI) has enabled several fruitful applications in medicine, particularly in psychiatry. This study aims to evaluate the performance of Machine Learning (ML) algorithms in the detection of depressive patients from the clinical, laboratory, and sociodemographic data obtained from the Brazilian National Network for Research on Cardiovascular Diseases from June 2016 to July 2018. The results obtained are promising. In one of them, Random Forests, the accuracy, sensibility, and area under the receiver operating characteristic curve were, respectively, 0.89, 0.90, and 0.87.