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Sökning: WFRF:(Knaevelsrud C)

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  • Furukawa, Toshi A., et al. (författare)
  • Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression : a systematic review and component network meta-analysis using individual data
  • 2021
  • Ingår i: Lancet psychiatry. - London, United Kingdom : Elsevier. - 2215-0374 .- 2215-0366. ; 8:6, s. 500-511
  • Forskningsöversikt (refereegranskat)abstract
    • Findings We identified 76 RCTs, including 48 trials contributing individual participant data (11 704 participants) and 28 trials with aggregate data (6474 participants). The participants' weighted mean age was 42.0 years and 12 406 (71%) of 17 521 reported were women. There was suggestive evidence that behavioural activation might be beneficial (iMD -1.83 [95% credible interval (CrI) -2.90 to -0.80]) and that relaxation might be harmful (1.20 [95% CrI 0.17 to 2.27]). Baseline severity emerged as the strongest prognostic factor for endpoint depression. Combining human and automated encouragement reduced dropouts from treatment (incremental odds ratio, 0.32 [95% CrI 0.13 to 0.93]). The risk of bias was low for the randomisation process, missing outcome data, or selection of reported results in most of the included studies, uncertain for deviation from intended interventions, and high for measurement of outcomes. There was moderate to high heterogeneity among the studies and their components. 511
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  • Karyotaki, Eirini, et al. (författare)
  • Internet-Based Cognitive Behavioral Therapy for Depression: A Systematic Review and Individual Patient Data Network Meta-analysis
  • 2021
  • Ingår i: JAMA Psychiatry. - 2168-622X .- 2168-6238. ; 78:54, s. 361-371
  • Forskningsöversikt (refereegranskat)abstract
    • Importance: Personalized treatment choices would increase the effectiveness of internet-based cognitive behavioral therapy (iCBT) for depression to the extent that patients differ in interventions that better suit them. Objective: To provide personalized estimates of short-term and long-term relative efficacy of guided and unguided iCBT for depression using patient-level information. Data Sources: We searched PubMed, Embase, PsycInfo, and Cochrane Library to identify randomized clinical trials (RCTs) published up to January 1, 2019. Study Selection: Eligible RCTs were those comparing guided or unguided iCBT against each other or against any control intervention in individuals with depression. Available individual patient data (IPD) was collected from all eligible studies. Depression symptom severity was assessed after treatment, 6 months, and 12 months after randomization. Data Extraction and Synthesis: We conducted a systematic review and IPD network meta-analysis and estimated relative treatment effect sizes across different patient characteristics through IPD network meta-regression. Main Outcomes and Measures: Patient Health Questionnaire-9 (PHQ-9) scores. Results: Of 42 eligible RCTs, 39 studies comprising 9751 participants with depression contributed IPD to the IPD network meta-analysis, of which 8107 IPD were synthesized. Overall, both guided and unguided iCBT were associated with more effectiveness as measured by PHQ-9 scores than control treatments over the short term and the long term. Guided iCBT was associated with more effectiveness than unguided iCBT (mean difference [MD] in posttreatment PHQ-9 scores, -0.8; 95% CI, -1.4 to -0.2), but we found no evidence of a difference at 6 or 12 months following randomization. Baseline depression was found to be the most important modifier of the relative association for efficacy of guided vs unguided iCBT. Differences between unguided and guided iCBT in people with baseline symptoms of subthreshold depression (PHQ-9 scores 5-9) were small, while guided iCBT was associated with overall better outcomes in patients with baseline PHQ-9 greater than 9. Conclusions and Relevance: In this network meta-analysis with IPD, guided iCBT was associated with more effectiveness than unguided iCBT for individuals with depression, benefits were more substantial in individuals with moderate to severe depression. Unguided iCBT was associated with similar effectiveness among individuals with symptoms of mild/subthreshold depression. Personalized treatment selection is entirely possible and necessary to ensure the best allocation of treatment resources for depression.
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4.
  • Rozental, Alexander, et al. (författare)
  • Consensus statement on defining and measuring negative effects of Internet interventions
  • 2014
  • Ingår i: Oral Abstracts from the 7th Scientific Meeting of the International Society for Research on Internet Interventions. ; , s. 39-39
  • Konferensbidrag (övrigt vetenskapligt)abstract
    • Purpose: Internet interventions provide a potential for promoting mental health and alleviating emotional distress. A large number of clinical trials have demonstrated their efficacy for several psychiatric conditions, and Internet interventions will likely become a common and valuable alternative within the regular health care. In the meantime, research has paid little attention to the possibility that some treatments might be associated with different types of negative effects. Evidence from face-to-face treatments suggests that 5-10% of all patients deteriorate despite receiving best available care. In addition, other forms of negative effects may exist as well, e.g., social stigmatization, interpersonal difficulties, and decreased self-esteem. However, a lack of agreement on how to define and measure negative effects has left researchers without practical guidelines for monitoring and reporting deterioration and adverse events in clinical trials, warranting a consensus for conducting research on negative effects.Method: The objective of the current paper is to provide recommendations that could promote the study of negative effects in Internet interventions. Ten leading experts in the field of Internet interventions were invited to participate and share their perspective on how to explore negative effects, using the Delphi technique to facilitate a dialogue and reach an agreement.Results: The importance of conducting further research on negative effects is discussed, and suggestions on how to classify and measure negative effects are provided, involving methods from quantitative and qualitative research. Potential mechanisms underlying negative effects are also presented, differentiating factors shared with face-to-face treatments from those unique to treatments delivered via the Internet.Conclusion: Negative effects should be expected and need to be recognized to a greater extent, and researchers are advised to systematically probe for negative effects whenever conducting clinical trials involving Internet interventions, as well as to share their findings in scientific journals.
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