ArticlePeerJ. Computer science2025
Impact of machine learning on dietary and exercise behaviors in type 2 diabetes self-management: a systematic literature review.
Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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
2 citing papers in PubMed.
- Machine Learning in Adapted Physical Activity: Clinical Applications, Monitoring, and Implementation Pathways for Personalized Exercise in Chronic Conditions: A Narrative Review.Journal of functional morphology and kinesiology · 2026Review
- Gene-Diet Interactions in Diabetes Mellitus: Current Insights and the Potential of Personalized Nutrition.Genes · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Self-awareness and self-management in diabetes are critical as they enhance patient well-being, decrease financial burden, and alleviate strain on healthcare systems by mitigating complications and promoting healthier life expectancy. Incomplete understanding persists regarding the synergistic effects of diet and exercise on diabetes management, as existing research often isolates these factors, creating a knowledge gap in comprehending their combined influence. Current diabetes research overlooks the interplay between diet and exercise in self-management. A holistic study is crucial to mitigate complications and healthcare burdens effectively. Multi-dimensional research questions covering complete diabetic management such as publication channels for diabetic research, existing machine learning solutions, physical activity tacking existing methods, and diabetic-associated datasets are included in this research. In this study, using a proper research protocol primary research articles related to diet, exercise, datasets, and blood analysis are selected and their quality is assessed for diabetic management. This study interrelates two major dimensions of diabetes management together that are diet and exercise.
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What Socratic holds
Registered trials
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 Socratic graph.