Who benefits from indirect prevention and treatment of depression using an online intervention for insomnia? Results from an individual-participant data meta-analysis.

BACKGROUND: Major depressive disorder (MDD) is highly prevalent and burdensome for individuals and society. While there are psychological interventions able to prevent and treat MDD, uptake remains low. To overcome structural and attitudinal barriers, an indirect approach of using online insomnia interventions seems promising because insomnia is less stigmatized, predicts MDD onset, is often comorbid and can outlast MDD treatment. This individual-participant-data meta-analysis evaluated the potential of the online insomnia intervention GET.ON Recovery as an indirect treatment to reduce depressive symptom severity (DSS) and potential MDD onset across a range of participant characteristics. METHODS: Efficacy on depressive symptom outcomes was evaluated using multilevel regression models controlling for baseline severity. To identify potential effect moderators, clinical, sociodemographic, and work-related variables were investigated using univariable moderation and random-forest methodology before developing a multivariable decision tree. RESULTS: IPD were obtained from four of seven eligible studies (N = 561); concentrating on workers with high work-stress. DSS was significantly lower in the intervention group both at post-assessment (d = -0.71 [95% CI-0.92 to -0.51]) and at follow-up (d = -0.84 [95% CI -1.11 to -0.57]). In the subsample (n = 121) without potential MDD at baseline, there were no significant group differences in onset of potential MDD. Moderation analyses revealed that effects on DSS differed significantly across baseline severity groups with effect sizes between d = -0.48 and -0.87 (post) and d = - 0.66 to -0.99 (follow-up), while no other sociodemographic, clinical, or work-related characteristics were significant moderators. CONCLUSIONS: An online insomnia intervention is a promising approach to effectively reduce DSS in a preventive and treatment setting.

Does outcome expectancy predict outcomes in online depression prevention? Secondary analysis of randomised-controlled trials.

BACKGROUND: Evidence shows that online interventions could prevent depression. However, to improve the effectiveness of preventive online interventions in individuals with subthreshold depression, it is worthwhile to study factors influencing intervention outcomes. Outcome expectancy has been shown to predict treatment outcomes in psychotherapy for depression. However, little is known about whether this also applies to depression prevention. The aim of this study was to investigate the role of participants' outcome expectancy in an online depression prevention intervention. METHODS: A secondary data analysis was conducted using data from two randomised-controlled trials (N = 304). Multilevel modelling was used to explore the effect of outcome expectancy on depressive symptoms and close-to-symptom-free status postintervention (6-7 weeks) and at follow-up (3-6 months). In a subsample (n = 102), Cox regression was applied to assess the effect on depression onset within 12 months. Explorative analyses included baseline characteristics as possible moderators. Outcome expectancy did not predict posttreatment outcomes or the onset of depression. RESULTS: Small effects were observed at follow-up for depressive symptoms (ß = -.39, 95% confidence interval [CI]: [-0.75, -0.03], p = .032, padjusted = .130) and close-to-symptom-free status (relative risk = 1.06, 95% CI: [1.01, 1.11], p = .013, padjusted = 0.064), but statistical significance was not maintained when controlling for multiple testing. Moderator analyses indicated that expectancy could be more influential for females and individuals with higher initial symptom severity. CONCLUSION: More thoroughly designed, predictive studies targeting outcome expectancy are necessary to assess the full impact of the construct for effective depression prevention. PATIENT OR PUBLIC CONTRIBUTION: This secondary analysis did not involve patients, service users, care-givers, people with lived experience or members of the public. However, the findings incorporate the expectations of participants using the preventive online intervention, and these exploratory findings may inform the future involvement of participants in the design of indicated depression prevention interventions for adults. CLINICAL TRIAL REGISTRATION: Original studies: DRKS00004709, DRKS00005973; secondary analysis: osf.io/9xj6a.

Allergic diseases as risk factors for Long-COVID symptoms: Systematic review of prospective cohort studies.

OBJECTIVE: The role of allergy as a risk factor for Long-COVID (LC) is unclear and has not been thoroughly examined yet. We aimed to systematically review and appraise the epidemiological evidence on allergic diseases as risk factors for LC. DESIGN: This is an initial systematic review. Two reviewers independently performed the study selection and data extraction using Covidence. Risk of bias (RoB) and certainty of evidence (GRADE) were assessed. Random effects meta-analyses were used to pool unadjusted ORs within homogeneous data subsets. DATA SOURCES: We retrieved articles published between January 1st, 2020 and January 19th, 2023 from MEDLINE via PubMed, Scopus, the WHO-COVID-19 database and the LOVE platform (Epistemonikos Foundation). In addition, citations and reference lists were searched. ELIGIBILITY CRITERIA: We included prospective cohort studies recruiting individuals of all ages with confirmed SARS-CoV-2 infection that were followed up for at least 12 months for LC symptoms where information on pre-existing allergic diseases was available. We excluded all study designs that were not prospective cohort studies and all publication types that were not original articles. RESULTS: We identified 13 studies (9967 participants, range 39-1950 per study), all assessed as high RoB, due to population selection and methods used to ascertain the exposures and the outcome. Four studies did not provide sufficient data to calculate Odds Ratios. The evidence supported a possible relationship between LC and allergy, but was very uncertain. For example, pre-existing asthma measured in hospital-based populations (6 studies, 4019 participants) may be associated with increased risk of LC (Odds Ratio 1.94, 95% CI 1.08, 3.50) and findings were similar for pre-existing rhinitis (3 studies, 1141 participants; Odds Ratio 1.96, 95% CI 1.61, 2.39), both very low certainty evidence. CONCLUSIONS: Pre-existing asthma or rhinitis may increase the risk of LC.

Timing of complementary feeding for early childhood allergy prevention: An overview of systematic reviews.

OBJECTIVE: To summarise and critically appraise systematic review (SR) evidence on the effects of timing of complementary feeding (CF) on the occurrence of allergic sensitisation and disease. DESIGN: Overview of SRs. AMSTAR-2 and ROBIS were used to assess methodological quality and risk of bias (RoB) of SRs. RoB 2 Tool was used to assess RoB of primary randomised controlled trials (RCTs) (or extracted). The certainty of evidence (CoE) was assessed using GRADE. Findings were synthesised narratively. DATA SOURCES: MEDLINE (via PubMed and Ovid), the Cochrane Library and Web of Science Core Collection (2010 to 27 February 2023). ELIGIBILITY CRITERIA: SRs investigating the effects of timing of CF in infants or young children (0-3 years) on risk of developing food allergy (FA), allergic sensitisation, asthma, allergic rhinitis, atopic eczema and adverse events based on RCT evidence. RESULTS: Eleven SRs were included. Only two SRs had low RoB; common issues were failure to report on funding of primary studies and failure to provide a list of excluded trials. Common limitations of included trials were lack of blinding of outcome assessment or detailed trial preregistration, and inadequate handling of high loss to follow up. Primary study overlap was very high for specific FA and slight to moderate for FA in general and other primary outcomes. Introducing specific foods (peanut, cooked egg) early probably reduces the risk of specific FA. Evidence for other allergic outcomes was mostly very uncertain and based on few primary studies. Trials varied regarding timing of CF, nature of complementary foods and population risk, which limited comparability between SRs. CONCLUSIONS: For developing guidelines to support decision-making on the timing of CF as a preventive strategy, early introduction of specific foods (i.e. egg and peanut) seems promising and safe, whereas more extensive research is required regarding other allergic outcomes and potential adverse events.

Predicting heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain: Secondary analysis of two randomized controlled trials.

Background: Depression is highly prevalent among individuals with chronic back pain. Internet-based interventions can be effective in treating and preventing depression in this patient group, but it is unclear who benefits most from this intervention format. Method: In an analysis of two randomized trials (N = 504), we explored ways to predict heterogeneous treatment effects of an Internet-based depression intervention for patients with chronic back pain. Univariate treatment-moderator interactions were explored in a first step. Multilevel model-based recursive partitioning was then applied to develop a decision tree model predicting individualized treatment benefits. Results: The average effect on depressive symptoms was d = -0.43 (95 % CI: -0.68 to -0.17; 9 weeks; PHQ-9). Using univariate models, only back pain medication intake was detected as an effect moderator, predicting higher effects. More complex interactions were found using recursive partitioning, resulting in a final decision tree with six terminal nodes. The model explained a large amount of variation (bootstrap-bias-corrected R2 = 45 %), with predicted subgroup-conditional effects ranging from di = 0.24 to -1.31. External validation in a pilot trial among patients on sick leave (N = 76; R2 = 33 %) pointed to the transportability of the model. Conclusions: The studied intervention is effective in reducing depressive symptoms, but not among all chronic back pain patients. Predictions of the multivariate tree learning model suggest a pattern in which patients with moderate depression and relatively low pain self-efficacy benefit most, while no benefits arise when patients' self-efficacy is already high. If corroborated in further studies, the developed tree algorithm could serve as a practical decision-making tool.