ArticleProceedings of the SIGCHI conference on human factors in computing systems. CHI Conference2021
From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations.
Article in Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled 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.
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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Digital nudging techniques for behaviour change in lifestyle medicine: a scoping review.Frontiers in digital health · 2026Pooled it
- Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review.Nutrients · 2024Pooled it
- Mapping Digital Nudges and Recommender Systems for Obesity Prevention: Scoping Review.Interactive journal of medical research · 2026Review
- Persuasive technologies for modifying food habits: a review of mindless, reflective, and social approaches to eating behavior change.Frontiers in digital health · 2026Review
- Maximizing Engagement, Trust, and Clinical Benefit of AI-Generated Recovery Support Messages for Alcohol Use Disorder: Protocol for an Optimization Study.JMIR research protocols · 2025Article
- Informing the Design of Individualized Self-Management Regimens from the Human, Data, and Machine Learning Perspectives.ACM transactions on computer-human interaction : a publication of the Association for Computing Machinery · 2025Article
- AI Applications for Chronic Condition Self-Management: Scoping Review.Journal of medical Internet research · 2025Article
- Alerts and Collections for Automating Patients' Sensemaking and Organizing of Their Electronic Health Record Data for Reflection, Planning, and Clinical Visits: Qualitative Research-Through-Design Study.JMIR human factors · 2023Article
- Who needs what (features) when? Personalizing engagement with data-driven self-management to improve health equity.Journal of biomedical informatics · 2023Article
- Machine Learning in Nutrition Research.Advances in nutrition (Bethesda, Md.) · 2022Article
- Examining AI Methods for Micro-Coaching Dialogs.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2022Article
- Something Similar: Exploring the Usefulness of an On-the-Spot Meal Recommendation System for Health Goal Attainment.Extended abstracts on Human factors in computing systems. CHI ConferenceArticle
- T2 Coach: A Qualitative Study of an Automated Health Coach for Diabetes Self-Management.Proceedings of the SIGCHI conference on human factors in computing systems. CHI ConferenceArticle
- Feasibility and Utility of Multimodal Micro Ecological Momentary Assessment on a Smartwatch.Proceedings of the SIGCHI conference on human factors in computing systems. CHI ConferenceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 7 institutions in 1 country.
Funding
Abstract
Self-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants' self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.