Computerized cognitive training in attention-deficit/hyperactivity disorder (ADHD): a meta-analysis of randomized controlled trials with blinded and objective outcomes
Bibliographic record
Abstract
This meta-analysis investigated the effects of computerized cognitive training (CCT) on clinical, neuropsychological and academic outcomes in individuals with attention-deficit/hyperactivity disorder (ADHD). The authors searched PubMed, Ovid, and Web of Science until 19th January 2022 for parallel-arm randomized controlled trials (RCTs) using CCT in individuals with ADHD. Random-effects meta-analyses pooled standardized mean differences (SMD) between CCT and comparator arms. RCT quality was assessed with the Cochrane Risk of Bias 2.0 tool (PROSPERO: CRD42021229279). Thirty-six RCTs were meta-analysed, 17 of which evaluated working memory training (WMT). Analysis of outcomes measured immediately post-treatment and judged to be "probably blinded" (PBLIND; trial n = 14) showed no effect on ADHD total (SMD = 0.12, 95%CI[-0.01 to -0.25]) or hyperactivity/impulsivity symptoms (SMD = 0.12, 95%[-0.03 to-0.28]). These findings remained when analyses were restricted to trials (n: 5-13) with children/adolescents, low medication exposure, semi-active controls, or WMT or multiple process training. There was a small improvement in inattention symptoms (SMD = 0.17, 95%CI[0.02-0.31]), which remained when trials were restricted to semi-active controls (SMD = 0.20, 95%CI[0.04-0.37]), and doubled in size when assessed in the intervention delivery setting (n = 5, SMD = 0.40, 95%CI[0.09-0.71]), suggesting a setting-specific effect. CCT improved WM (verbal: n = 15, SMD = 0.38, 95%CI[0.24-0.53]; visual-spatial: n = 9, SMD = 0.49, 95%CI[0.31-0.67]), but not other neuropsychological (e.g., attention, inhibition) or academic outcomes (e.g., reading, arithmetic; analysed n: 5-15). Longer-term improvement (at ~6-months) in verbal WM, reading comprehension, and ratings of executive functions were observed but relevant trials were limited in number (n: 5-7). There was no evidence that multi-process training was superior to working memory training. In sum, CCT led to shorter-term improvements in WM, with some evidence that verbal WM effects persisted in the longer-term. Clinical effects were limited to small, setting specific, short-term effects on inattention symptoms.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.052 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".