Evidence map›Paper›PMID 42645103›Full record

ReviewDiseases (Basel, Switzerland)2026

Artificial Intelligence, Wearable Technologies, and Virtual Reality in Precision Nutrition and Obesity Management: A Critical Narrative Review.

Yin Yin Bashir, Rahaf Al-Huneiti, Anfal Al-Dalaeen, Firas S Azzeh

Abstract readReview
In one paragraph

Review in Diseases (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Yin Yin BashirDepartment of Clinical Nutrition and Dietetics, Faculty of Allied Medical Sciences, Applied Science Private University, Amman 11937, Jordan.
Rahaf Al-HuneitiDepartment of Clinical Nutrition and Dietetics, Faculty of Allied Medical Sciences, Applied Science Private University, Amman 11937, Jordan.
Anfal Al-DalaeenDepartment of Clinical Nutrition and Dietetics, Faculty of Allied Medical Sciences, Applied Science Private University, Amman 11937, Jordan.ORCID 0000-0002-2685-7294
Firas S AzzehPreparatory Year Program, Department of Biology, Batterjee Medical College, Jeddah 21442, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundObesity is a chronic, multifactorial disease that demands personalized and sustainable management approaches. Digital health technologies, such as artificial intelligence, wearable devices, mobile health apps, and virtual reality (VR), may support obesity care by providing enhanced behavioral monitoring, personalized feedback, and patient engagement.

objectiveThis critical narrative review discusses the current evidence on artificial intelligence, wearable technologies, and VR in the context of precision nutrition and obesity management and their possible clinical applications and limitations.

methodA critical narrative review was conducted using peer-reviewed literature published between 2019 and 2026 and identified through PubMed and Google Scholar. Search terms included combinations of "precision nutrition," "personalized nutrition," "obesity," "weight management," "metabolic health," "digital health," "artificial intelligence," "machine learning," "mobile health," "wearable devices," and "omics" using Boolean operators. Evidence from randomized controlled trials, systematic reviews, meta-analyses, and key conceptual studies was critically synthesized due to substantial heterogeneity in interventions and outcomes.

resultWearables and mobile applications can enable continuous self-monitoring of physical activity, dietary intake, sleep, and physiological measures. Artificial intelligence may improve dietary personalization, risk prediction, glycemic control, and adaptive feedback. VR offers an immersive way to tackle behavioral and cognitive mechanisms related to overeating such as cravings, food cue reactivity, and inhibitory control. However, the evidence is heterogeneous, with many studies limited by short follow-up periods, small samples, variable adherence, and insufficient clinical validation.

conclusionsArtificial intelligence, wearable technologies, and VR are promising tools for precision obesity management, but their long-term clinical effectiveness remains uncertain. Future research should prioritize adequately powered trials, longer follow-up, standardized outcomes, transparent algorithms, ethical data governance, and integration with multidisciplinary nutrition and obesity care.

Indexed as

artificial intelligencedigital healthobesityvirtual realitywearable technologiesweight management

Identifiers

PMID42645103
PMCPMC13512059

What Socratic holds

Textmetadata
Read underepoch 390

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 Socratic graph.