Evidence map›Paper›PMID 39963688›Full record

ArticleFrontiers in aging2025

Engagement in computerized cognitive training instructions by older people. A within-subject design to evaluate comprehension and acceptability of serious games instructions.

Christelle Nahas, Marc Gandit, Emmanuel Monfort

Abstract read
In one paragraph

Article in Frontiers in aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Christelle NahasTranslational Innovation in Medicine and Complexity (TIMC, UMR 5525), Université Grenoble Alpes, CNRS, Grenoble, France.
Marc GanditLaboratoire InterUniversitaire de Psychologie (LIP/PC2S, EA4145), Université Grenoble Alpes, Grenoble, France.
Emmanuel MonfortTranslational Innovation in Medicine and Complexity (TIMC, UMR 5525), Université Grenoble Alpes, CNRS, Grenoble, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article emphasizes the advantages of using a within-subject experimental design to assess the impact of salient visual cues on the comprehension and acceptability of computerized cognitive training (CCT) instructions among older adults. The study would involve participants aged 65 and above, who will engage in an online experiment presenting two sets of instructions for serious games: one with salient visual cues and one without. This within-subject design eliminates the need for random assignment, improves internal consistency, and enhances statistical power. Participants serve as their own controls, providing a more robust comparison of how visual cues affect instruction comprehension and software acceptance. The primary objective is to identify indicators of acceptability for CCT serious games and to evaluate how well participants comprehend the instructions, influencing their intention to use the software. The hypothesis suggests that salient visual cues will improve instruction comprehension and foster greater software acceptability. By focusing on this design method, the study aims to enhance the engagement of older adults in cognitive training programs, reducing dropout rates. This research offers valuable insights into methodological strengths that can be applied in future studies to improve the usability and acceptance of CCT tools for older adults without cognitive impairments.

Indexed as

computerized cognitive trainingengagementinstructional comprehensionolder adultssoftware acceptancevisual cueswithin-subject design

Identifiers

PMID39963688
PMCPMC11830709

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.