| 1. |
- 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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| 2. |
- Andersson, Evelyn, et al.
(författare)
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Genetics of response to cognitive behavior therapy in adults with major depression : a preliminary report
- 2019
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Ingår i: Molecular Psychiatry. - : Nature Publishing Group. - 1359-4184 .- 1476-5578. ; 24:4, s. 484-490
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Tidskriftsartikel (refereegranskat)abstract
- Major depressive disorder is heritable and a leading cause of disability. Cognitive behavior therapy is an effective treatment for major depression. By quantifying genetic risk scores based on common genetic variants, the aim of this report was to explore the utility of psychiatric and cognitive trait genetic risk scores, for predicting the response of 894 adults with major depressive disorder to cognitive behavior therapy. The participants were recruited in a psychiatric setting, and the primary outcome score was measured using the Montgomery Asberg Depression Rating Scale-Self Rated. Single-nucleotide polymorphism genotyping arrays were used to calculate the genomic risk scores based on large genetic studies of six phenotypes: major depressive disorder, bipolar disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, intelligence, and educational attainment. Linear mixed-effect models were used to test the relationships between the six genetic risk scores and cognitive behavior therapy outcome. Our analyses yielded one significant interaction effect (B = 0.09, p < 0.001): the autism spectrum disorder genetic risk score correlated with Montgomery Asberg Depression Rating Scale-Self Rated changes during treatment, and the higher the autism spectrum disorder genetic load, the less the depressive symptoms decreased over time. The genetic risk scores for the other psychiatric and cognitive traits were not related to depressive symptom severity or change over time. Our preliminary results indicated, as expected, that the genomics of the response of patients with major depression to cognitive behavior therapy were complex and that future efforts should aim to maximize sample size and limit subject heterogeneity in order to gain a better understanding of the use of genetic risk factors to predict treatment outcome.
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| 3. |
- Forsell, Erik, et al.
(författare)
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Internet delivered cognitive behavior therapy for antenatal depression : A randomised controlled trial
- 2017
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Ingår i: Journal of Affective Disorders. - Amsterdam, Netherlands : Elsevier. - 0165-0327 .- 1573-2517. ; 221, s. 56-64
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Tidskriftsartikel (refereegranskat)abstract
- Major depression occurs in 5-10% of pregnancies and is associated with many negative effects for mother and child, yet treatment options are scarce. To our knowledge, this is the first published randomised controlled trial on Internet delivered Cognitive Behavior Therapy (ICBT) for this group.Objective: To test the efficacy of a pregnancy adapted version of an existing 10-week ICBT-program for depression as well as assessing acceptability and adherence.Design: Randomised controlled trial.Setting: Online and telephone.Population or sample: Self-referred pregnant women (gestational week 10-28 at intake) currently suffering from major depressive disorder.Methods: 42 pregnant women (gestational week 12-28) with major depression were randomised to either treatment as usual (TAU) provided at their antenatal clinic or to ICBT as an add-on to usual care.Main outcome measures: The primary outcome was depressive symptoms measured with the Montgomery-Asberg depression rating scale-self report (MADRS-S). The Edinburgh Postnatal Depression Scale and measures of anxiety and sleep were used. Credibility, satisfaction, adherence and utilization were also assessed.Results: The ICBT group had significantly lower levels of depressive symptoms post treatment (p < 0.001, Hedges g = 1.21) and were more likely to be responders (i.e. achieve a statistically reliable improvement) (RR = 0.36; p = 0.004). Measures of treatment credibility, satisfaction, utilization, and adherence were comparable to implemented ICBT for depression.Limitations: Small sample size and no long-term evaluation.Conclusion: Pregnancy adapted ICBT for antenatal depression is feasible, acceptable and efficacious. These results need to be replicated in larger trials to validate these promising findings.
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| 4. |
- 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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| 5. |
- Rozental, Alexander, et al.
(författare)
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Overcoming procrastination : one-year follow-up and predictors of change in a randomized controlled trial of Internet-based cognitive behavior therapy
- 2017
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Ingår i: Cognitive Behaviour Therapy. - : Informa UK Limited. - 1650-6073 .- 1651-2316. ; 46:3, s. 177-195
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Tidskriftsartikel (refereegranskat)abstract
- Procrastination is a common self-regulatory failure that can have a negative impact on well-being and performance. However, few clinical trials have been conducted, and no follow-up has ever been performed. The current study therefore aimed to provide evidence for the long-term benefits and investigate predictors of a positive treatment outcome among patients receiving Internet-based cognitive behavior therapy (ICBT). A total of 150 self-recruited participants were randomized to guided or unguided ICBT. Self-report measures of procrastination, depression, anxiety, and quality of life were distributed at pre-treatment assessment, post-treatment assessment, and one-year follow-up. Mixed effects models were used to investigate the long-term gains, and multiple linear regression for predictors of a positive treatment outcome, using the change score on the Irrational Procrastination Scale as the dependent variable. Intention-to-treat was implemented for all statistical analyses. Large within-group effect sizes for guided and unguided ICBT, Cohen’s d = .97–1.64, were found for self-report measures of procrastination, together with d = .56–.66 for depression and anxiety. Gains were maintained, and, in some cases, improved at follow-up. Guided and unguided ICBT did not differ from each other, mean differences −.31–1.17, 95% CIs [−2.59–3.22], and none of the predictors were associated with a better result, bs −1.45–1.61, 95% CIs [−3.14–4.26]. In sum, ICBT could be useful and beneficial in relation to managing procrastination, yielding great benefits up to one year after the treatment period has ended, with comparable results between guided and unguided ICBT.
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| 6. |
- Rozental, Alexander, et al.
(författare)
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Psychometric evaluation of the Swedish version of the pure procrastination scale, the irrational procrastination scale, and the susceptibility to temptation scale in a clinical population
- 2014
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Ingår i: BMC Psychology. - : Springer Science and Business Media LLC. - 2050-7283. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- Background: Procrastination is a prevalent self-regulatory failure associated with stress and anxiety, decreased well-being, and poorer performance in school as well as work. One-fifth of the adult population and half of the student population describe themselves as chronic and severe procrastinators. However, despite the fact that it can become a debilitating condition, valid and reliable self-report measures for assessing the occurrence and severity of procrastination are lacking, particularly for use in a clinical context. The current study explored the usefulness of the Swedish version of three Internet-administered self-report measures for evaluating procrastination; the Pure Procrastination Scale, the Irrational Procrastination Scale, and the Susceptibility to Temptation Scale, all having good psychometric properties in English.Methods: In total, 710 participants were recruited for a clinical trial of Internet-based cognitive behavior therapy for procrastination. All of the participants completed the scales as well as self-report measures of depression, anxiety, and quality of life. Principal Component Analysis was performed to assess the factor validity of the scales, and internal consistency and correlations between the scales were also determined. Intraclass Correlation Coefficient, Minimal Detectable Change, and Standard Error of Measurement were calculated for the Irrational Procrastination Scale.Results: The Swedish version of the scales have a similar factor structure as the English version, generated good internal consistencies, with Cronbach’s α ranging between .76 to .87, and were moderately to highly intercorrelated. The Irrational Procrastination Scale had an Intraclass Correlation Coefficient of .83, indicating excellent reliability. Furthermore, Standard Error of Measurement was 1.61, and Minimal Detectable Change was 4.47, suggesting that a change of almost five points on the scale is necessary to determine a reliable change in self-reported procrastination severity.Conclusions: The current study revealed that the Pure Procrastination Scale, the Irrational Procrastination Scale, and the Susceptibility to Temptation Scale are both valid and reliable from a psychometric perspective, and that they might be used for assessing the occurrence and severity of procrastination via the Internet.
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| 7. |
- Blom, Kerstin, et al.
(författare)
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An investigation and replication of sleep-related cognitions, acceptance and behaviours as predictors of short- and long-term outcome in cognitive behavioural therapy for insomnia
- 2021
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Ingår i: Journal of Sleep Research. - : John Wiley & Sons. - 0962-1105 .- 1365-2869. ; 30:5
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Tidskriftsartikel (refereegranskat)abstract
- The objectives were to investigate the potential for sleep-related behaviours, acceptance and cognitions to predict outcome (insomnia severity) of cognitive behavioural therapy for insomnia (CBT-I). Baseline and outcome data from four randomised controlled trials (n = 276) were used. Predictors were the Dysfunctional Beliefs and Attitudes about Sleep-10 (DBAS-10), Sleep-Related Behaviours Questionnaire (SRBQ), and Sleep Problems Acceptance Questionnaire (SPAQ), and empirically derived factors from a factor analysis combining all items at baseline (n = 835). Baseline values were used to predict post-treatment outcome, and pre-post changes in the predictors were used to predict follow-up outcomes after 3-6 months, 1 year, or 3-10 years, measured both as insomnia severity and as better or worse long-term sleep patterns. A majority (29 of 52) of predictions of insomnia severity were significant, but when controlling for insomnia severity, only two (DBAS-10 at short-term and SRBQ at mid-term follow-up) of the 12 predictions using established scales, and three of the 40 predictions using empirically derived factors, remained significant. The strongest predictor of a long-term, stable sleep pattern was insomnia severity reduction during treatment. Using all available predictors in an overfitted model, 21.2% of short- and 58.9% of long-term outcomes could be predicted. We conclude that although the explored constructs may have important roles in CBT-I, the present study does not support that the DBAS-10, SRBQ, SPAQ, or factors derived from them, would be unique predictors of outcome.
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| 8. |
- Blom, Kerstin, et al.
(författare)
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Psychological Treatment of Comorbid Insomnia and Depression : A Double-Blind Randomized Placebo-Controlled Trial
- 2024
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Ingår i: Psychotherapy and Psychosomatics. - : S. Karger. - 0033-3190 .- 1423-0348. ; 93:2, s. 100-113
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Insomnia and depression are highly prevalent disorders and commonly occur together. Cognitive behavioral therapy for insomnia, CBT-I, has been shown to be effective in treating insomnia and also comorbid depression. However, it is unclear whether effects of CBT-I on depression are specific or nonspecific. Also, depressive symptoms often remain too high after CBT-I, indicating a need for improved treatments. The objective was to determine whether combining CBT-I with CBT for depression, without increasing treatment length, reduces both insomnia and depression more than CBT for depression with a placebo insomnia intervention. Methods: A 12-week double-blind randomized controlled trial with a 6-month follow-up in a psychiatric setting using therapist-guided internet-delivered treatments was conducted. Patients (N = 126) were diagnosed with insomnia disorder and major depression by physicians. Primary outcome measures were as follows: self-rating scales Insomnia Severity Index (ISI) and Montgomery-angstrom sberg Depression Rating Scale (MADRS-S). Results: The combined treatment showed specific effects on insomnia severity over the control treatment (p = 0.007) but was not more effective in reducing depression severity. Within-group effects (Cohen's d) at post and at 6 months were as follows: ISI 1.40 and 1.42 (combined treatment), 0.95 and 1.00 (control); MADRS-S 0.97 and 1.12 (combined), 0.88 and 0.89 (control). Conclusions: CBT-I shows large specific effects on insomnia severity and is superior to control in this regard. Both treatments had similar effects on depression severity, i.e., combining CBT-I with CBT for depression did not enhance outcomes on depression compared to control. We suggest CBT-I should always be offered to patients with insomnia and depression comorbidity, possibly as the first-hand choice. Combining it with a psychological treatment for depression could be too burdening and may not be beneficial.
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| 9. |
- Boman, Magnus, et al.
(författare)
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Learning machines in Internet-delivered psychological treatment
- 2019
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Ingår i: Progress in Artificial Intelligence. - : Springer Verlag. - 2192-6352 .- 2192-6360. ; 8:4, s. 475-485
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Tidskriftsartikel (refereegranskat)abstract
- A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.
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| 10. |
- Forsell, Erik, et al.
(författare)
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Clinically sufficient classification accuracy and key predictors of treatment failure in a randomized controlled trial of Internet-delivered Cognitive Behavior Therapy for Insomnia
- 2022
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Ingår i: Internet Interventions. - : Elsevier. - 2214-7829. ; 29
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Tidskriftsartikel (refereegranskat)abstract
- Background: In Adaptive Treatment Strategies, each patient's outcome is predicted early in treatment, and treatment is adapted for those at risk of failure. It is unclear what minimum accuracy is needed for a classifier to be clinically useful. This study aimed to establish a empirically supported benchmark accuracy for an Adaptive Treatment Strategy and explore the relative value of input predictors. Method: Predictions from 200 patients receiving Internet-delivered cognitive-behavioral therapy in an RCT was analyzed. Correlation and logistic regression was used to explore all included predictors and the predictive capacity of different models. Results: The classifier had a Balanced accuracy of 67 %. Eleven out of the 21 predictors correlated significantly with Failure. A model using all predictors explained 56 % of the outcome variance, and simpler models between 16 and 47 %. Important predictors were patient rated stress, treatment credibility, depression change, and insomnia symptoms at week 3 as well as clinician rated attitudes towards homework and sleep medication. Conclusions: The accuracy (67 %) found in this study sets a minimum benchmark for when prediction accuracy could be clinically useful. Key predictive factors were mainly related to insomnia, depression or treatment involvement. Simpler predictive models showed some promise and should be developed further, possibly using machine learning methods.
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