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- Furukawa, Toshi A., et al.
(författare)
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Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression : a systematic review and component network meta-analysis using individual data
- 2021
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Ingår i: Lancet psychiatry. - London, United Kingdom : Elsevier. - 2215-0374 .- 2215-0366. ; 8:6, s. 500-511
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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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| 2. |
- Karyotaki, Eirini, et al.
(författare)
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Internet-Based Cognitive Behavioral Therapy for Depression : A Systematic Review and Individual Patient Data Network Meta-analysis
- 2021
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Ingår i: JAMA psychiatry. - : American Medical Association. - 2168-6238 .- 2168-622X. ; 78:4, s. 361-371
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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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| 3. |
- Meyer, Björn, et al.
(författare)
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Effects of an Internet intervention (Deprexis) on severe depression symptoms : Randomized controlled trial
- 2015
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Ingår i: Internet Interventions. - : Elsevier. - 2214-7829. ; 2:1, s. 48-59
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundStudies have shown that certain Internet interventions can help alleviate depression. However, many such interventions contain personal support elements, making it difficult to ascertain whether the program or the support drives the effects. Studies are needed to investigate whether Internet interventions contribute to symptom reduction even when they are delivered without personal support, and even among severely depressed individuals who often receive other forms of treatment.ObjectiveThis randomized controlled trial aimed to examine the effect of an Internet intervention that was deployed without personal support (“Deprexis”) among adults with initially severe depression symptoms.MethodsAdults recruited from a range of sources who had exceeded the threshold for severe depression (PHQ-9 ≥ 15) in a pre-screening assessment and met inclusion criteria were randomized (N = 163) to the intervention (3 months program access; n = 78) or care-as-usual/waitlist control (n = 85). A diagnostic screening interview was administered by telephone at baseline to all participants. Online assessments were administered at baseline, 3 months (post-treatment), and 6 months (follow-up). The main outcome was the Patient Health Questionnaire (PHQ-9) between baseline and post-treatment.ResultsEighty-two percent of randomized participants were reached for the post-treatment assessment. Results for the intention-to-treat (ITT) sample showed significant intervention effects on depression reduction between baseline and post-treatment (linear mixed model [MM], F1,155.6 = 9.00, p < .01, for the time by condition interaction), with a medium between-group effect size, Cohen's d = 0.57 (95% CI: 0.22–0.92). Group differences in depression severity at follow-up were marginally significant in the ITT sample, t (119) = 1.83, p = 0.07, and smaller than at post-treatment (PHQ-9, d = 0.33, 95% CI: − 0.03–0.69). The number needed to treat (NNT) at post-treatment was 5, with 38% of participants in the intervention group achieving response (at least 50% PHQ-9 symptom change, plus post-treatment score < 10), compared to 17% in the control group, p < 0.01. Effects on secondary outcomes, including anxiety, health-related quality of life, and somatic symptoms, were not significant, with the exception of significant effects on anxiety reduction in PP analyses. Early ratings of program helpfulness/alliance (after 3 weeks) predicted pre–post depression reduction, controlling for baseline severity and early symptom change.ConclusionsThese results replicate and extend previous findings by showing that Deprexis can facilitate symptomatic improvement over 3 months and, perhaps to a lesser degree, up until 6 months among adults with initially severe depression.
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| 4. |
- Shani, Reut, et al.
(författare)
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Personalized cognitive training : Protocol for individual-level meta-analysis implementing machine learning methods
- 2021
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Ingår i: Journal of Psychiatric Research. - : Elsevier BV. - 0022-3956 .- 1879-1379. ; 138, s. 342-348
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Tidskriftsartikel (refereegranskat)abstract
- Accumulating evidence suggests that cognitive training may enhance well-being. Yet, mixed findings imply that individual differences and training characteristics may interact to moderate training efficacy. To investigate this possibility, the current paper describes a protocol for a data-driven individual-level meta-analysis study aimed at developing personalized cognitive training. To facilitate comprehensive analysis, this protocol proposes criteria for data search, selection and pre-processing along with the rationale for each decision. Twenty-two cognitive training datasets comprising 1544 participants were collected. The datasets incorporated diverse training methods, all aimed at improving well-being. These training regimes differed in training characteristics such as targeted domain (e.g., working memory, attentional bias, interpretation bias, inhibitory control) and training duration, while participants differed in diagnostic status, age and sex. The planned analyses incorporate machine learning algorithms designed to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.
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