Editorial: Low-Intensity Interventions for Psychiatric Disorders.

Editorial

The effect of e-mental health interventions on academic performance in university and college students:A meta-analysis of randomized controlled trials

Background: Mental health symptoms are common among college and university students and these can affect their academic performance. E-mental health interventions have proven effective in addressing mental health complaints but their effect on academic performance has not been synthesized yet. Objectives: To synthesize the evidence from randomized controlled trials for the effectiveness of e-mental health interventions on academic performance in college and university students compared to inactive controls. Data sources and eligibility criteria: We searched six databases (PubMed, Cochrane library, CINAHL, ERIC, PsycINFO, Web of Science) during the period January 2000 until September 2019 for randomized controlled trials that reported on e-mental health interventions (guided or unguided) for college and university students and measured academic performance (e.g. grade point average). Study appraisal and synthesis methods: Study and participant characteristics and the academic performance measures at post-intervention were extracted. The latter were pooled and Hedges' g was calculated as the effect size. Heterogeneity and publication bias were investigated. Results: Six studies containing 2428 participants were included in the meta-analysis. These focussed on either mood and anxiety or alcohol and tobacco use. The pooling of data resulted in a small but non-significant effect of g = 0.26 (95% CI, −0.00, 0.52; p = .05) on academic performance, favouring e-mental health interventions over inactive controls. Interventions had positive effects on depression (g = −0.24) and anxiety (g = −0.2). Heterogeneity was high. Discussion: Despite the small and non-significant effect, our meta-analysis points to a promising direction for the effectiveness of e-mental health interventions on academic performance. Yet, these results must be interpreted with caution, as heterogeneity was high and few studies on the effectiveness of e-mental health interventions for students reported academic performance measures

Screening medical patients for distress and depression:does measurement in the clinic prior to the consultation overestimate distress measured at home?

BACKGROUND: Medical patients are often screened for distress in the clinic using a questionnaire such as the Hospital Anxiety and Depression Scale (HADS) while awaiting their consultation. However, might the context of the clinic artificially inflate the distress score ? To address this question we aimed to determine whether those who scored high on the HADS in the clinic remained high scorers when reassessed later at home. METHOD: We analysed data collected by a distress and depression screening service for cancer out-patients. All patients had completed the HADS in the clinic (on computer or on paper) prior to their consultation. For a period, patients with a high score (total of > or = 15) also completed the HADS again at home (over the telephone) 1 week later. We used these data to determine what proportion remained high scorers and the mean change in their scores. We estimated the effect of ‘ regression to the mean’ on the observed change. RESULTS: Of the 218 high scorers in the clinic, most [158 (72.5 %), 95% confidence interval (CI) 66.6–78.4] scored high at reassessment. The mean fall in the HADS total score was 1.74 (95% CI 1.09–2.39), much of which could be attributed to the estimated change over time (regression to the mean) rather than the context. CONCLUSIONS: Pre-consultation distress screening in clinic is widely used. Reassuringly, it only modestly overestimates distress measured later at home and consequently would result in a small proportion of unnecessary further assessments. We conclude it is a reasonable and convenient strategy

Core Depressive Symptoms In Depressed Cancer OutpatientsB

Objective: This study aimed to estimate the prevalence of core depressive symptoms among cancer outpatients diagnosed with depressive or adjustment disorders with depressed mood. We also aimed to detect potential differences between patient self-assessment and psychiatrist evaluation in classifying the severity of depression. Methods: Fifty-two outpatients diagnosed with solid tumor malignancy and depressive or adjustment disorder with depressed mood were assessed using the Hamilton Depression Rating Scale (HAMD-17) (and its shortened version the HAMD-7) and the Zung Self-Rating Depression Scale (ZSDS) (and its shortened version BZSDS). Results: Based on HAMD-7 results, the prevalence of moderate depression was low (7.7%); using the BZSDS moderate depression was absent. Mild depression was identified in 82.3% and 73% of our subjects using the HAMD-7 and the BZSDS, respectively. The strength of agreement between psychiatrist and patients' self-evaluation for mild depression was "slight", employing the original and the abbreviated versions of both scales. Conclusion: Our findings suggest that the prevalence of core depressive symptoms is very low in cancer patients diagnosed with depressive disorder. The lack of a strong agreement between psychiatrist and patient in classifying the severity of depression highlights the importance of factors such as well-being and functional status among depressed cancer patients in their self assessment of depression. © Massimo et al

Predictors of treatment dropout in self-guided web-based interventions for depression: an ‘individual patient data’ meta-analysis

Background. It is well known that web-based interventions can be effective treatments for depression. However, dropout rates in web-based interventions are typically high, especially in self-guided web-based interventions. Rigorous empirical evidence regarding factors influencing dropout in self-guided web-based interventions is lacking due to small study sample sizes. In this paper we examined predictors of dropout in an individual patient data meta-analysis to gain a better understanding of who may benefit from these interventions. Method. A comprehensive literature search for all randomized controlled trials (RCTs) of psychotherapy for adults with depression from 2006 to January 2013 was conducted. Next, we approached authors to collect the primary data of the selected studies. Predictors of dropout, such as socio-demographic, clinical, and intervention characteristics were examined. Results. Data from 2705 participants across ten RCTs of self-guided web-based interventions for depression were analysed. The multivariate analysis indicated that male gender [relative risk (RR) 1.08], lower educational level (primary education, RR 1.26) and co-morbid anxiety symptoms (RR 1.18) significantly increased the risk of dropping out, while for every additional 4 years of age, the risk of dropping out significantly decreased (RR 0.94). Conclusions. Dropout can be predicted by several variables and is not randomly distributed. This knowledge may inform tailoring of online self-help interventions to prevent dropout in identified groups at ris

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood

BackgroundAlthough major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare. MethodsEcological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1-10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period. ResultsOverall, four profiles were identified, which we labeled as: (1) "very negative and least variable mood" (n = 14) (2) "negative and moderate variable mood" (n = 204), (3) "positive and moderate variable mood" (n = 41), and (4) "negative and highest variable mood" (n = 28). The degree of emotional inertia was virtually identical across the profiles. ConclusionsThe real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia

Examining the Theoretical Framework of Behavioral Activation for Major Depressive Disorder: Smartphone-Based Ecological Momentary Assessment Study

Background: Behavioral activation (BA), either as a stand-alone treatment or as part of cognitive behavioral therapy, has been shown to be effective for treating depression. The theoretical underpinnings of BA derive from Lewinsohn et al's theory of depression. The central premise of BA is that having patients engage in more pleasant activities leads to them experiencing more pleasure and elevates their mood, which, in turn, leads to further (behavioral) activation. However, there is a dearth of empirical evidence about the theoretical framework of BA.Objective: This study aims to examine the assumed (temporal) associations of the 3 constructs in the theoretical framework of BA.Methods: Data were collected as part of the "European Comparative Effectiveness Research on Internet-based Depression Treatment versus treatment-as-usual" trial among patients who were randomly assigned to receive blended cognitive behavioral therapy (bCBT). As part of bCBT, patients completed weekly assessments of their level of engagement in pleasant activities, the pleasure they experienced as a result of these activities, and their mood over the course of the treatment using a smartphone-based ecological momentary assessment (EMA) application. Longitudinal cross-lagged and cross-sectional associations of 240 patients were examined using random intercept cross-lagged panel models.Results: The analyses did not reveal any statistically significant cross-lagged coefficients (all P>.05). Statistically significant cross-sectional positive associations between activities, pleasure, and mood levels were identified. Moreover, the levels of engagement in activities, pleasure, and mood slightly increased over the duration of the treatment. In addition, mood seemed to carry over, over time, while both levels of engagement in activities and pleasurable experiences did not.Conclusions: The results were partially in accordance with the theoretical framework of BA, insofar as the analyses revealed cross-sectional relationships between levels of engagement in activities, pleasurable experiences deriving from these activities, and enhanced mood. However, given that no statistically significant temporal relationships were revealed, no conclusions could be drawn about potential causality. A shorter measurement interval (eg, daily rather than weekly EMA reports) might be more attuned to detecting potential underlying temporal pathways. Future research should use an EMA methodology to further investigate temporal associations, based on theory and how treatments are presented to patients.</p

Process evaluation of a blended web-based intervention on return to work for sick-listed employees with common mental health problems in the occupational health setting

Purpose A blended web-based intervention, "eHealth module embedded in collaborative occupational health care" (ECO), aimed at return to work, was developed and found effective in sick-listed employees with common mental disorders. In order to establish the feasibility of ECO, a process evaluation was conducted. Methods Seven process components were investigated: recruitment, reach, dose delivered, dose received, fidelity, satisfaction and context. Quantitative and qualitative methods were used to collect data: an online questionnaire for the employees, website data, telephonic interviews with occupational physicians (OPs) and observations of the researchers. Results Recruitment was uncomplicated for the employees, but required several steps for the OPs. Reach was 100 % at the OP level and 76.3 % at the employee level. Dose delivered and received for OPs: 91.6 % received minimally one email message. Dose delivered and received for the employees: finishing of the different modules of ECO varied between 13 and 90 %. Fidelity: the support of the OP to the employee in ECO was lower than anticipated. Satisfaction: both employees and OPs were satisfied with the intervention. However, employees reported a need for more support in ECO. The context showed that OPs had limited time to support the employees and it was impossible for the employee to contact the OP outside their regular contacts. Conclusion Feasibility of ECO and satisfaction of employees and OPs with ECO were good. Fidelity of OPs was limited. For further implementation in the occupational health setting, especially contextual barriers regarding time limitation and accessibility of OPs for employees should be addressed