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Transparency in Cognitive Training Meta-analyses: A Meta-review.
Alejandro Sandoval-Lentisco1, Rubén López-Nicolás2, Miriam Tortajada2
1Department Basic Psychology and Methodology, University of Murcia, Murcia, Spain. alejandro.sandovall@um.es.
Neuropsychology Review
|April 19, 2024
Summary
This meta-review found that while cognitive training meta-analyses report study searches well, they often omit details on statistical methods and primary data. Improved transparency is needed for future cognitive training reviews.
Area of Science:
- Cognitive science
- Psychology
- Medical research methodology
Background:
- Meta-analyses offer flexibility in design and analysis, necessitating transparent reporting of all factors influencing results.
- The field of cognitive training is marked by ongoing controversy, highlighting the need for rigorous and transparent meta-analytic reviews.
Purpose of the Study:
- To assess the transparency of meta-analyses examining the benefits of cognitive training.
- To identify areas where reporting practices in cognitive training meta-analyses can be improved.
Main Methods:
- A meta-review was conducted, including 97 meta-analytic reviews on cognitive training benefits across diverse populations.
- Included reviews covered various clinical conditions and age groups.
- Reporting transparency was evaluated across study selection, data collection, statistical parameters, and eligibility criteria.
Main Results:
- Most reviews transparently detailed study search, screening, and data collection procedures.
- Authors frequently failed to report specific meta-analytic parameters, effect size computation formulas, or primary study data used.
- Significant heterogeneity was observed in eligibility criteria (e.g., training duration, age), and preregistered protocols often lacked clarity on handling data multiplicity or publication bias.
Conclusions:
- While not questioning cognitive training benefits, this meta-review highlights critical transparency deficits in existing meta-analyses.
- Future meta-analyses must improve reporting of statistical methods, primary data, and adhere strictly to preregistered protocols and eligibility criteria for enhanced reliability.
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