The Efficacy of a Web-Based Stress Management Intervention for Employees Experiencing Adverse Working Conditions and Occupational Self-efficacy as a Mediator: Randomized Controlled Trial

Nixon, Patricia; Ebert, David Daniel; Boß, Leif; Angerer, Peter; Dragano, Nico; Lehr, Dirk

doi:10.2196/40488

Cited by 10 publications

(8 citation statements)

References 65 publications

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“…Available trials were the ones by Ebert, Lehr, et al (2016, n = 264; Ebert, Heber, et al, 2016, n = 264; Ebert et al, 2021, n = 396), Heber et al (2016, n = 264), and Nixon et al (2021, n = 404; 2022, n = 264). Four (66.7%) of these trials only included participants with elevated levels of perceived stress (Perceived Stress Scale 10 ≥ 22; Cohen et al, 1983), one trial (Nixon et al, 2022; 16.7%) focused on individuals with unfavorable working conditions (effort–reward imbalance > 0.715), while another (Ebert et al, 2021) was a universal prevention trial with no symptom-specific inclusion criteria. All studies compared the effect of the intervention to a waitlist control group with full access to treatment as usual.…”

Section: Methodsmentioning

confidence: 99%

“…In the study conducted by Heber et al (2016), participants received content-focused feedback after each session. The trials conducted by Ebert, Lehr, et al (2016) and Nixon et al (2021, 2022) employed “adherence-focused” guidance with feedback on demand. This approach involved motivational prompts aimed at improving adherence, along with the option to contact trained personnel through an internal messaging system, who then provide personalized written feedback within 48 hr.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we extend IPD meta-analysis methods to develop and internally validate prognostic models that predict the long-term effects of an internet-based stress intervention when used as an “indirect” treatment for clinically relevant depressive symptoms. The intervention we focus on is “Get.On Fit im Stress,” which has been evaluated in multiple randomized controlled trials (RCTs; Ebert et al, 2021; Ebert, Heber, et al, 2016; Ebert, Lehr, et al, 2016; Heber et al, 2016; Nixon et al, 2021, 2022). This intervention was found to have a significant average treatment effect (ATE); but no extensive analysis has yet been conducted to assess if there are heterogenous treatment effects across patients.…”

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confidence: 99%

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Predicting effects of a digital stress intervention for patients with depressive symptoms: Development and validation of meta-analytic prognostic models using individual participant data.

Harrer,

Baumeister,

Cuijpers

et al. 2024

Journal of Consulting and Clinical Psychology

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Objective: Digital stress interventions could be helpful as an "indirect" treatment for depression, but it remains unclear for whom this is a viable option. In this study, we developed models predicting individualized benefits of a digital stress intervention on depressive symptoms at 6-month follow-up. Method: Data of N = 1,525 patients with depressive symptoms (Center for Epidemiological Studies' Depression Scale, CES-D ≥ 16) from k = 6 randomized trials (digital stress intervention vs. waitlist) were collected. Prognostic models were developed using multilevel least absolute shrinkage and selection operator and boosting algorithms, and were validated using bootstrap bias correction and internal-external cross-validation. Subsequently, expected effects among those with and without a treatment recommendation were estimated based on clinically derived treatment assignment cut points. Results: Performances ranged from R 2 = 21.0%-23.4%, decreasing only slightly after model optimism correction (R 2 = 17.0%-19.6%). Predictions were greatly improved by including an interim assessment of depressive symptoms (optimism-corrected R 2 = 32.6%-35.6%). Using a minimally important difference of d = −0.24 as assignment cut point, approximately 84.6%-93.3% of patients are helped by this type of intervention, while the remaining 6.7%-15.4% would experience clinically negligible benefits ( δ = −0.02 to −0.19). Using reliable change as cut point, a smaller subset (39.3%-46.2%) with substantial expected benefits ( δ = −0.68) receives a treatment recommendation. Conclusions: Metaanalytic prognostic models applied to individual participant data can be used to predict differential benefits of a digital stress intervention as an indirect treatment for depression. While most patients seem to benefit, the developed models could be helpful as a screening tool to identify those for whom a more intensive depression treatment is needed. What is the public health significance of this article?Recent evidence suggests that digital interventions targeting perceived stress could be used as a nonstigmatizing, "indirect" approach to address depressive symptoms in the general population; but it is unclear if this intervention strategy is useful and safe for all patients. Using intervention and control group data of six randomized controlled trials, we developed meta-analytic prognostic models that predict, based on each patient's baseline characteristics, the individualized benefits that a digital stress intervention is expected to have when used as an indirect treatment for depression. We found that the great majority of patients would be helped to some extent by the intervention but that the models also allow to identify a smaller subgroup experiencing no more than clinically negligible benefits. If corroborated further, such models could be used within digital stress interventions to determine if this format provides a suitable low-threshold treatment for patients' depressive symptoms or if more intensive care should be initiated.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting effects of a digital stress intervention for patients with depressive symptoms: Development and validation of meta-analytic prognostic models using individual participant data.

Harrer,

Baumeister,

Cuijpers

et al. 2024

Journal of Consulting and Clinical Psychology

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“…Many intervention studies taking place in the work environment are addressing these factors, and their results are promising, although there are many different ways of studying changes in psychosocial work-related factors, and a lot of gaps in knowledge still exist [46][47][48][49]. In addition, workplace interventions to improve employees' health and reduce sickness absence should look at men and women separately, in order to realize and maximize their effectiveness and cost-effectiveness.…”

Section: Diskusijamentioning

confidence: 99%

“…Mnoge interventne studije koje se sprovode u radnom okruženju bave se ovim faktorima, i njihovi rezultati obećavaju, mada postoji mnogo različitih načina za proučavanje promena u psihosocijalnim faktorima povezanim sa poslom, a još uvek postoji i mnogo praznina u saznanjima iz ove oblasti [46][47][48][49]. Takođe, intervencije na radnom mestu koje su usmerene na unapređivanje zdravlja zaposlenih i smanjivanje odsustvovanja sa posla zbog bolovanja, trebalo bi da analiziraju žene i muškarce odvojeno, kako bi bile maksimalno delotvorne i isplative.…”

Section: Diskusijaunclassified

Gender differences at the workplace: Sickness absence and productivity loss at work and their association with health and work-related factors

Đikanović¹,

Kouwenhoven-Pasmooij²,

Kraaijenhagen³

et al. 2023

Srpski medicinski časopis Lekarske komore

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Introduction: Differences in sickness absence and productivity loss at work between men and women are recognized but need to be better understood. Materials and methods: In a cross-sectional study, 10,407 employees from 37 companies in the Netherlands participated in a Web-based health risk assessment, between 2010 and 2014. Self-reported short-term (<9 days) and long-term (10 or more days) sickness absences during the preceding 12 months were assessed. The questionnaire also asked about productivity loss at work, physical and psychosocial work-related factors, and health problems. Log-linear models were used to estimate prevalence ratios (PR), with 95% confidence intervals (CI). Results: Women, more often than men, experienced short-term and long-term sickness absence (PR 1.06, 95% CI 1.01 - 1.11, and PR 1.33, 95% CI 1.21 - 1.46, respectively) but were less likely to have productivity loss at work (more than 30%), (PR 0.90, 95% CI 0.81 - 0.99). In short-term sickness absence, gender differences were reduced by 20%, after controlling for psychosocial work-related factors, and by 60%, after controlling for health problems, separately. None of the factors mentioned above could explain a large productivity loss at work among men. Conclusions: Higher prevalence of sickness absence among women can partly be explained by psychosocial work-related factors and health problems. Further studies are needed to explore large productivity loss at work among men.

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Finding the Best Match — a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions

Zantvoort,

Scharfenberger,

Boß

et al. 2023

J Healthc Inform Res

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With the need for psychological help long exceeding the supply, finding ways of scaling, and better allocating mental health support is a necessity. This paper contributes by investigating how to best predict intervention dropout and failure to allow for a need-based adaptation of treatment. We systematically compare the predictive power of different text representation methods (metadata, TF-IDF, sentiment and topic analysis, and word embeddings) in combination with supplementary numerical inputs (socio-demographic, evaluation, and closed-question data). Additionally, we address the research gap of which ML model types — ranging from linear to sophisticated deep learning models — are best suited for different features and outcome variables. To this end, we analyze nearly 16.000 open-text answers from 849 German-speaking users in a Digital Mental Health Intervention (DMHI) for stress. Our research proves that — contrary to previous findings — there is great promise in using neural network approaches on DMHI text data. We propose a task-specific LSTM-based model architecture to tackle the challenge of long input sequences and thereby demonstrate the potential of word embeddings (AUC scores of up to 0.7) for predictions in DMHIs. Despite the relatively small data set, sequential deep learning models, on average, outperform simpler features such as metadata and bag-of-words approaches when predicting dropout. The conclusion is that user-generated text of the first two sessions carries predictive power regarding patients’ dropout and intervention failure risk. Furthermore, the match between the sophistication of features and models needs to be closely considered to optimize results, and additional non-text features increase prediction results.

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The Efficacy of a Web-Based Stress Management Intervention for Employees Experiencing Adverse Working Conditions and Occupational Self-efficacy as a Mediator: Randomized Controlled Trial

Cited by 10 publications

References 65 publications

Predicting effects of a digital stress intervention for patients with depressive symptoms: Development and validation of meta-analytic prognostic models using individual participant data.

Predicting effects of a digital stress intervention for patients with depressive symptoms: Development and validation of meta-analytic prognostic models using individual participant data.

Gender differences at the workplace: Sickness absence and productivity loss at work and their association with health and work-related factors

Finding the Best Match — a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions

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