Evidence map›Paper›PMID 41939285›Full record

ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2025

Engagements with Generative AI and Personal Health Informatics: Opportunities for Planning, Tracking, Reflecting, and Acting around Personal Health Data.

Shaan Chopra, Katherine Juarez, James Fogarty, Sean A Munson

Abstract read
In one paragraph

Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2025. 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.

Shaan ChopraUniversity of Washington, USA.
Katherine JuarezUniversity of Washington, USA.
James FogartyUniversity of Washington, USA.
Sean A MunsonUniversity of Washington, USA.

Funding

Transform Dissemination and Implementation Science in CTSA ProgramsUL1TR002319 · NCATS · UNIVERSITY OF WASHINGTON · PI John K. Amory · 2017 to 2026
$100.0M
Open Tools for Self-Tracking, Self Experimentation, and Patient-Provider Collaboration in Symptom Self-Management and Clinical CareR01LM012810 · NLM · UNIVERSITY OF WASHINGTON · PI FOGARTY, JAMES A · 2018 to 2021
$1.3M
NCATS NIH HHS UL1 TR002319NLM NIH HHS R01 LM012810
6 · The paper itself

Abstract

Personal informatics processes require navigating distinct challenges across stages of tracking, but the range of data, goals, expertise, and context that individuals bring to self-tracking often presents barriers that undermine those processes. We investigate the potential of Generative AI (GAI) to support people across stages of pursuing self-tracking for health. We conducted interview and observation sessions with 19 participants from the United States who self-track for health, examining how they interact with GAI around their personal health data. Participants formulated and refined queries, reflected on recommendations, and abandoned queries that did not meet their needs and health goals. They further identified opportunities for GAI support across stages of self-tracking, including in deciding what data to track and how, in defining and modifying tracking plans, and in interpreting data-driven insights. We discuss GAI opportunities in accounting for a range of health goals, in providing support for self-tracking processes across planning, reflection, and action, and in consideration of limitations of embedding GAI in health self-tracking tools.

Indexed as

actionabilityEmpirical studies in ubiquitous and mobile computinggenerative AIhealth trackingHuman-centered computing → Human computer interaction (HCI)personal health datapersonal informaticspersonalizationqualitative researchreflectionself-trackingsense-making

Identifiers

PMID41939285
PMCPMC13045739

What OpenQuestion holds

Textmetadata
LicenceTDM
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.