| 11. |
- Forsell, Erik
(författare)
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Adaptive treatment strategies in internet-delivered cognitive behavior therapy : predicting and avoiding treatment failures
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Background: Internet-delivered Cognitive Behavior Therapy (ICBT) is efficacious for a number of psychiatric disorders and can be successfully implemented in routine psychiatric care. Still, only about half of patients experience a good enough treatment outcome. Using data from the early part of treatment to identify patients with high risk of not benefitting from it, and target them with additional resources to prevent the predicted failure is a potential way forward. We call this an Adaptive Treatment Strategy, and a very important part of it is the ability to predict the outcome for a specific patient. Aims: To establish a proof of concept for an Adaptive Treatment Strategy in ICBT, and explore outcome prediction further by evaluating the accuracy of an empirically supported classification algorithm, the time point in treatment when acceptable accuracy can be reached, and the accuracy of ICBT-therapists’ own predictions. Preliminary benchmarks regarding the clinical usefulness of prediction will be established. Studies: Four studies were performed: Study I was a randomized controlled trial (RCT; n=251) where patients’ risk of treatment Failure (Red=high risk of failure, Green=low risk) was predicted during week 4 out of 9 in ICBT for Insomnia. Red patients (n=102) were then randomized to either continuing with standard treatment (n=51) or having their treatment individually adapted (n=51). In Study II, the classification algorithm from Study I was evaluated in terms of classification accuracy and the contribution of the different predictors used. In Study III, data from 4310 regular care ICBT-patients having received treatment for either Depression, Social anxiety disorder or Panic disorder were analyzed in a series of multiple regression models using weekly observations of the primary symptom measure as predictors to classify risk of Failure. As a contrast, Study IV examines ICBT therapists’ own predictions on both categorical and continuous treatment outcomes, as they made predictions for each of their patients (n=897) during week 4 in the same three treatments as in Study III. Results: The RCT was successful in that Red patients receiving Adapted treatment improved significantly more than Red patients receiving standard treatment, and their odds of failure were nearly cut in half. Green patients did better than Red patients, indicating that the accuracy of the classification algorithm was clinically useful. Study II showed that the balanced accuracy of the classifier was 67% and that only 11 of 21 predictors correlated significantly with Failure. Notable predictors were symptom levels as well as different markers of treatment engagement. Study III and IV showed that acceptable predictions could be made halfway through treatment using only symptom scores and basic statistics, and that ICBT-therapists predicted outcomes better than chance but on average 9.5 % less accurate than the statistical models. Therapist predictions reached the clinical acceptance benchmark only for remission in Social anxiety disorder. At treatment week four, therapist could predict on average 16% of the variance in continuous outcomes, compared to a statistical model explaining 39%. Conclusions: We find support for the clinical usefulness of an Adaptive Treatment Strategy in ICBT for insomnia, and establish a preliminary benchmark that a classification algorithm with at least 67% balanced accuracy should be sufficient for clinical purposes. Simple statistical models using only symptom scores can reach clinically acceptable levels of accuracy halfway through 12-week ICBT-programs. Previous findings that therapists’ predictions are less accurate than statistical models seem to hold also for therapists providing ICBT. However, it was also indicated that clinicians’ ratings of adherence and activity do add unique information to prediction algorithms. In line with previous findings, the vast majority of useful prediction variables were found during early treatment, rather than before treatment start.
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| 12. |
- 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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| 13. |
- Forsell, Erik, et al.
(författare)
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Development of a very brief scale for detecting and measuring panic disorder using two items from the Panic Disorder Severity Scale-Self Report
- 2019
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Ingår i: Journal of Affective Disorders. - : Elsevier. - 0165-0327 .- 1573-2517. ; 257, s. 615-622
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To minimize the burden in detecting and monitoring Panic Disorder and Agoraphobia by developing a very brief scale with selected items from the Panic Disorder Severity Scale-Self Report (PDSS-SR), and to investigate the proposed scale's psychometric properties in a comorbid sample. Methods: A sample of 5103 patients from the Internet Psychiatry Clinic in Sweden, diagnosed and treated with Internet-based cognitive behavioral therapy for panic disorder (n = 1390), social anxiety disorder (n = 1313) or depression (n = 2400), responded to the PDSS-SR. Six criteria related to factor structure, sensitivity to change and clinical representativeness were used to select items. Psychometric analyses for the selected very brief scale were performed. Results: Items 2 (distress during panic attacks) and 4 (agoraphobic avoidance), were selected to create the very brief PDSS-SR version. Correlations with the full scale were high at screening, pre and post, and for change (0.87-0.93). Categorical Omega was omega(c) = 0.74. With a cut-off of 3 points, the scale could detect panic disorder in a psychiatric sample with a sensitivity of 85% and a specificity of 66%. Limitations: Limitations include lack of healthy controls and lack of blinding on secondary outcome measures. Conclusion: The proposed 2-item PDSS-SR version is a good candidate for a very brief panic disorder questionnaire, both for detecting cases and for measuring change. This is especially useful in clinical settings when measuring more than one condition at a time.
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| 14. |
- 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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| 15. |
- Forsell, Erik, et al.
(författare)
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Predicting Treatment Failure in Regular Care Internet-Delivered Cognitive Behavior Therapy for Depression and Anxiety Using Only Weekly Symptom Measures
- 2020
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Ingår i: Journal of Consulting and Clinical Psychology. - : American Psychological Association (APA). - 0022-006X .- 1939-2117. ; 88:4, s. 311-321
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Therapist guided Internet-Delivered Cognitive Behavior Therapy (ICBT) is effective, but as in traditional CBT, not all patients improve, and clinicians generally fail to identify them early enough. We predict treatment failure in 12-week regular care ICBT for Depression, Panic disorder and Social anxiety disorder, using only patients' weekly symptom ratings to identify when the accuracy of predictions exceed 2 benchmarks: (a) chance, and (b) empirically derived clinician preferences for actionable predictions. Method: Screening, pretreatment and weekly symptom ratings from 4310 regular care ICBT-patients from the Internet Psychiatry Clinic in Stockholm, Sweden was analyzed in a series of regression models each adding 1 more week of data. Final score was predicted in a holdout test sample, which was then categorized into Success or Failure (failure defined as the absence of both remitter and responder status). Classification analyses with Balanced Accuracy and 95% Confidence intervals was then compared to predefined benchmarks. Results: Benchmark 1 (better than chance) was reached 1 week into all treatments. Social anxiety disorder reached Benchmark 2 (>65%) at week 5, whereas Depression and Panic Disorder reached it at week 6. Conclusions: For depression, social anxiety and panic disorder, prediction with only patient-rated symptom scores can detect treatment failure 6 weeks into ICBT, with enough accuracy for a clinician to take action. Early identification of failing treatment attempts may be a viable way to increase the overall success rate of existing psychological treatments by providing extra clinical resources to at-risk patients, within a so-called Adaptive Treatment Strategy.
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| 16. |
- Forsell, Erik, et al.
(författare)
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Proof of Concept for an Adaptive Treatment Strategy to Prevent Failures in Internet-Delivered CBT : A Single-Blind Randomized Clinical Trial With Insomnia Patients
- 2019
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Ingår i: American Journal of Psychiatry. - : American Psychiatric Association Publishing. - 0002-953X .- 1535-7228. ; 176:4, s. 315-323
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Tidskriftsartikel (refereegranskat)abstract
- Objective: This study aimed to demonstrate proof of concept for an adaptive treatment strategy in Internet-delivered cognitive-behavioral therapy (ICBT), where risk of treatment failure is assessed early in treatment and treatment for at-risk patients is adapted to prevent treatment failure. Methods: A semiautomated algorithm assessed risk of treatment failure early in treatment in 251 patients undergoing ICBT for insomnia with therapist guidance. At-risk patients were randomly assigned to continue standard ICBT or to receive adapted ICBT. The primary outcome was self-rated insomnia symptoms using the Insomnia Severity Index in a linear mixed-effects model. The main secondary outcome was treatment failure (having neither responded nor remitted at the posttreatment assessment). Results: A total of 102 patients were classified as at risk and randomly assigned to receive adapted ICBT (N=51) or standard ICBT (N=51); 149 patients were classified as not at risk. Patients not at risk had significantly greater score reductions on the Insomnia Severity Index than at-risk patients given standard ICBT. Adapted ICBT for at-risk patients was significantly more successful in reducing symptoms compared with standard ICBT, and it decreased the risk of failing treatment (odds ratio= 0.33). At-risk patients receiving adapted ICBT were not more likely to experience treatment failure than those not at risk (odds ratio= 0.51), though they were less likely to experience remission. Adapted treatment required, on average, 14 more minutes of therapist-patient time per remaining week. Conclusions: An adaptive treatment strategy can increase treatment effects for at-risk patients and reduce the number of failed treatments. Future studies should improve accuracy in classification algorithms and identify key factors that boost the effect of adapted treatments.
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| 17. |
- Forsell, Martti, et al.
(författare)
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Hardware and Software Support for NUMA Computing on Configurable Emulated Shared Memory Architectures
- 2013
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Ingår i: 2013 IEEE 27th International Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW). - : IEEE conference proceedings. - 9780769549798 ; , s. 640-647
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Konferensbidrag (refereegranskat)abstract
- The emulated shared memory (ESM) architectures are good candidates for future general purpose parallel computers due to their ability to provide easy-to-use explicitly parallel synchronous model of computation to programmers as well as avoid most performance bottlenecks present in current multicore architectures. In order to achieve full performance the applications must, however, have enough thread-level parallelism (TLP). To solve this problem, in our earlier work we have introduced a class of configurable emulated shared memory (CESM) machines that provides a special non-uniform memory access (NUMA) mode for situations where TLP is limited or for direct compatibility for legacy code sequential computing or NUMA mechanism. Unfortunately the earlier proposed CESM architecture does not integrate the different modes of the architecture well together e.g. by leaving the memories for different modes isolated and therefore the programming interface is non-integrated. In this paper we propose a number of hardware and software techniques to support NUMA computing in CESM architectures in a seamless way. The hardware techniques include three different NUMA-shared memory access mechanisms and the software ones provide a mechanism to integrate NUMA computation into the standard parallel random access machine (PRAM) operation of the CESM. The hardware techniques are evaluated on our REPLICA CESM architecture and compared to an ideal CESM machine making use of the proposed software techniques.
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| 18. |
- Forsell, Martti, et al.
(författare)
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NUMA Computing with Hardware and Software Co-Support on Configurable Emulated Shared Memory Architectures
- 2014
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Ingår i: International Journal of Networking and Computing. - 2185-2839 .- 2185-2847. ; 4:1, s. 189-206
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Tidskriftsartikel (refereegranskat)abstract
- The emulated shared memory (ESM) architectures are good candidates for future general purpose parallel computers due to their ability to provide an easy-to-use explicitly parallel synchronous model of computation to programmers as well as avoid most performance bottlenecks present in current multicore architectures. In order to achieve full performance the applications must, however, have enough thread-level parallelism (TLP). To solve this problem, in our earlier work we have introduced a class of configurable emulated shared memory (CESM) machines that provides a special non-uniform memory access (NUMA) mode for situations where TLP is limited or for direct compatibility for legacy code sequential computing and NUMA mechanism. Unfortunately the earlier proposed CESM architecture does not integrate the different modes of the architecture well together e.g. by leaving the memories for different modes isolated and therefore the programming interface is non-integrated. In this paper we propose a number of hardware and software techniques to support NUMA computing in CESM architectures in a seamless way. The hardware techniques include three different NUMA shared memory access mechanisms and the software ones provide a mechanism to integrate and optimize NUMA computation into the standard parallel random access machine (PRAM) operation of the CESM. The hardware techniques are evaluated on our REPLICA CESM architecture and compared to an ideal CESM machine making use of the proposed software techniques.
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| 19. |
- 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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| 20. |
- Heinonen, Essi, et al.
(författare)
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MAGDALENA : study protocol of a randomised, placebo-controlled trial on cognitive development at 2 years of age in children exposed to SSRI in utero
- 2018
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Ingår i: BMJ Open. - : BMJ Publishing Group Ltd. - 2044-6055. ; 8:8
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Ten per cent of all pregnant women are depressed. Standard therapy of pregnant women with moderate depression is selective serotonin reuptakeinhibitors (SSRI). Observational studies on neurodevelopment after fetal SSRI exposure show conflicting results. Our primary objective is to compare the cognitive development in children exposed to sertraline and maternal depression with those exposed to maternal depression and placebo in utero. We hypothesise that there is a significant neurodevelopmental difference between the groups. As a secondary objective, we study the add-on effect of sertraline to internet-based cognitive behavioural therapy (ICBT) to treat moderate depression during pregnancy. Methods and analysis MAGDALENA is a randomised, placebo-controlled, double-blinded trial in Stockholm Healthcare Region with 2.3 million inhabitants. The women are recruited in weeks 9-21 of pregnancy either through Antenatal Health Clinics or through social media. They are to be diagnosed with moderate depression without ongoing antidepressive therapy or any serious comorbidity. The women in the intervention arm receive sertraline combined with a 12-week period of ICBT; the control arm is treated with placebo and ICBT. We assess the cognitive development in the offspring at the age of 2 years using Bayley Scales of Infant and Toddler Development, third edition (BSID-III). We aim at recruiting 200 women, 100 women in each treatment arm, to ensure statistical power to detect a clinically relevant difference between the groups. Ethics and dissemination This randomised trial will provide long-sought evidence about the effects of SSRI and maternal depression during pregnancy on the neurodevelopment in the offspring. The study is approved by the Regional Ethical Review Board at Karolinska Institutet in Stockholm and the Swedish Medical Products Agency. It is registered with the European Clinical Trials Database (EudraCT), Number: 2013-004444-31. Results will be disseminated at scientific conferences, published in peer-reviewed journals and made available to the public.
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