Evidence map›Paper›PMID 42395931›Full record

ReviewAmerican journal of lifestyle medicine2026

Leveraging Wearable Technology to Support Behavior Change in Personalized Nutrition and Lifestyle Medicine.

Lara Zakaria

Abstract readReview
In one paragraph

Review in American journal of lifestyle medicine, 2026. 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. Shifting the Paradigm of Health Care Through Lifestyle Medicine.American journal of lifestyle medicine · 2026
    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

1 author.

Lara ZakariaRutgers University Ernest Mario School of Pharmacy, New Brunswick, NJ, USA (LZ).ORCID https://orcid.org/0009-0005-6005-4275

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sustained behavior change remains one of the greatest challenges in personalized nutrition and lifestyle medicine, despite well-established links between diet, physical activity, sleep, stress regulation, and chronic disease risk. Consumer health technologies-including activity and sleep trackers, heart rate (HR) and heart rate variability (HRV) monitors, continuous glucose monitors (CGM), and bioelectrical impedance-based body composition scales-provide objective, at-home metrics that translate lifestyle behaviors into measurable physiologic and metabolic feedback. Wearable-derived data can enhance self-awareness, reinforce learning, and support adherence by revealing patterns between lifestyle behaviors and outcomes such as glycemic variability, autonomic balance, energy expenditure, and changes in fat and lean mass. Evidence across domains such as sleep, stress regulation, physical activity, and glycemic response suggests that these tools are most effective when used to identify trends over time between clinical encounters and guide personalized adjustments, rather than as isolated metrics. Integrated within a clinician-guided, patient-centered framework, these technologies can reinforce self-regulation, refine individualized recommendations, and extend care between visits. As digital platforms evolve, integration with AI and emerging biologic insights-including nutrigenomics and the gut microbiome-may further enhance precision. Furthermore, when clinical oversight is maintained, patients may develop greater awareness and agency over the relationships between daily behaviors and physiological responses.

Indexed as

behavior changebody composition analysiscontinuous glucose monitoringpersonalized nutritionwearable technology

Identifiers

PMID42395931
PMCPMC13323037

What OpenQuestion 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 OpenQuestion graph.