Comparison of the Effectiveness of Cognitive Behavioral Therapy and Neurofeedback: Reducing Insomnia Symptoms
Bibliographic record
Abstract
INTRODUCTION: The term sleep disorder refers to difficulty in initiating sleep, maintaining it or a relaxing sleep despite having enough time to sleep. Cognitive behavioral therapy is a non-drug multi-dimensional treatment that targets behavioral and cognitive factors of this disorder. Some pieces of research have shown that psychiatric and neurological disorders can be distinguished from distinct EEG patterns and neuro-feedback can be used to make a change in these patterns. This study aimed to compare the cognitive behavioral therapy and neuro-feedback in the treatment of insomnia.METHODS: The sample included people, who had already been diagnosed insomnia by a psychiatrist in Isfahan, Iran. Random sampling was employed to choose the participants. Pittsburg sleep quality index (PSQI) was used for the selection of the participants, too. The sample included 40 patients who were randomly selected and interviewed and then diagnostic tests performed on the PSQI, and then they were divided into 3 groups. Data were analyzed using ANOVA. Following the implementation of the independent effect of the treatment was significant and one-way ANOVA with post hoc test L.S.D were carried out on CBT and controls (p = 0.001), CBT, neuro-feedback therapy (p = 0.003), neuro-feedback treatment and control (p = 0.001).RESULTS: It was shown that there was a significant difference between the groups. Based on the descriptive statistics of the 2 abovementioned treatments, neuro-feedback therapy in first position and cognitive-behavioral therapy were most effective in the second position, and the control group showed the lowest efficiency.CONCLUSIONS: Both treatments were significantly effective, and so we can use both neuro-feedback and CBT for the treatment of insomnia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".