ArticleTrials2022
Individualised computerised cognitive training for community-dwelling people with mild cognitive impairment: study protocol of a completely virtual, randomised, controlled trial.
Article in Trials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring the potential of artificial intelligence in individualized cognitive training: A systematic review.PloS one · 2025Pooled it
- User experience with two computerized cognitive intervention programs for people with mild cognitive impairment.BMC geriatrics · 2025Trial
- Trial
- Trial
- From Sensation to Action: Neuroplasticity, Cognitive-Motor Training, and Emerging Biomarkers of Adaptation.Brain sciences · 2026Review
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundPeople with mild cognitive impairment (MCI) are at increased risk of converting to dementia. Cognitive training can improve the cognitive abilities of people with MCI. Computerised cognitive training (CCT) offers several advantages over traditional paper-and-pencil cognitive training and has the potential to be more individualised by matching task difficulty with individual performance. Recent systematic reviews have reported promising effects of CCT on improving the cognitive capacities of people with MCI. However, the quality of existing studies has been limited, and it is still unclear whether CCT can influence the progression to dementia. We developed an 'individualised' CCT (MAKSCog) specialised for people with MCI that automatically matches task difficulty with individual performance and an active control training ('basic' CCT). The aims of the present study are (a) to evaluate MAKSCog and (b) to investigate whether it can be applied to maintain the cognitive abilities of people with MCI.
methodsThe present study investigates the effects of CCT on cognition in a randomised controlled intervention study in Germany. Participants are community-dwelling people with a psychometric diagnosis of MCI based on the Montreal Cognitive Assessment (MoCA) and Mini-Mental Status Test (MMSE). Screening and baseline testing are conducted via a videoconferencing assessment and telephone. Participants are randomly allocated. The treatment phase is 6 months with an open phase in which participants can freely decide to continue to use the CCTs. Additionally, both CCTs contain a monthly computerised cognitive assessment that measures different cognitive abilities: information processing speed, memory span, short term memory, and logical reasoning. DISCUSSION: This is the first study to investigate the effect of MAKSCog, an individualised CCT, specifically developed for people with different subtypes of MCI. A methodological strength is the double-blind, randomised, controlled design and the use of basic CCT as an active control group. The study is conducted entirely virtually with valid telehealth assessments for cognitive function. Methodological limitations might include a restriction to participants who feel comfortable with the use of technology and who own a computer, laptop, or tablet.
trial registrationISRCTN ISRCTN14437015 . Prospectively registered on 27 February 2020.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.