Alexa, let's train now! — A systematic review and classification approach to digital and home-based physical training interventions aiming to support healthy cognitive aging
Bibliographic record
Abstract
BACKGROUND: There is mounting evidence that regular physical activity is an important prerequisite for healthy cognitive aging. Consequently, the finding that almost one-third of the adult population does not reach the recommended level of regular physical activity calls for further public health actions. In this context, digital and home-based physical training interventions might be a promising alternative to center-based intervention programs. Thus, this systematic review aimed to summarize the current state of the literature on the effects of digital and home-based physical training interventions on adult cognitive performance. METHODS: In this pre-registered systematic review (PROSPERO; ID: CRD42022320031), 5 electronic databases (PubMed, Web of Science, PsycInfo, SPORTDiscus, and Cochrane Library) were searched by 2 independent researchers (FH and PT) to identify eligible studies investigating the effects of digital and home-based physical training interventions on cognitive performance in adults. The systematic literature search yielded 8258 records (extra 17 records from other sources), of which 27 controlled trials were considered relevant. Two reviewers (FH and PT) independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of Study qualiTy and reporting in EXercise (TESTEX scale). RESULTS: Of the 27 reviewed studies, 15 reported positive effects on cognitive and motor-cognitive outcomes (i.e., performance improvements in measures of executive functions, working memory, and choice stepping reaction test), and a considerable heterogeneity concerning study-related, population-related, and intervention-related characteristics was noticed. A more detailed analysis suggests that, in particular, interventions using online classes and technology-based exercise devices (i.e., step-based exergames) can improve cognitive performance in healthy older adults. Approximately one-half of the reviewed studies were rated as having a high risk of bias with respect to completion adherence (≤85%) and monitoring of the level of regular physical activity in the control group. CONCLUSION: The current state of evidence concerning the effectiveness of digital and home-based physical training interventions is mixed overall, though there is limited evidence that specific types of digital and home-based physical training interventions (e.g., online classes and step-based exergames) can be an effective strategy for improving cognitive performance in older adults. However, due to the limited number of available studies, future high-quality studies are needed to buttress this assumption empirically and to allow for more solid and nuanced conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.118 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.066 | 0.039 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".