Evidence map›Paper›PMID 42847378›Full record

ArticleAnnals of behavioral medicine : a publication of the Society of Behavioral Medicine2026

Optimizing generative artificial intelligence prompts to engage urges for physical activity in middle-aged and older adults.

Yingjia Liu, Allyson Tabaczynski, Saeed Abdullah, Lizbeth Benson, David E Conroy

Abstract read
In one paragraph

Article in Annals of behavioral medicine : a publication of the Society of Behavioral Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

5 authors.

Yingjia LiuDepartment of Kinesiology, The Pennsylvania State University, University Park, PA 16802, United States.ORCID 0009-0001-6515-7936
Allyson TabaczynskiSchool of Kinesiology, University of Michigan, Ann Arbor, MI 48109, United States.ORCID 0000-0003-2209-5103
Saeed AbdullahCollege of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, United States.ORCID 0000-0002-4371-8173
Lizbeth BensonInstitute for Social Research, University of Michigan, Ann Arbor, MI 48106, United States.ORCID 0000-0001-9895-5283
David E ConroyDepartment of Kinesiology, The Pennsylvania State University, University Park, PA 16802, United States.ORCID 0000-0003-0204-4093

Funding

Roybal Center for Promoting Adherence to Behavior Change and Enhancing Cognitive FunctionP30AG086637 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DAVID E. CONROY · 2024 to 2026
$2.9M
National Institutes of AgingNational Institutes of Aging P30 AG086637NIA NIH HHS P30 AG086637
6 · The paper itself

Abstract

backgroundMost middle-aged and older adults do not engage in sufficient physical activity. Text messages have proven effective for promoting physical activity, but little is known about how message content can engage motivational mechanisms. PURPOSE: This study aimed to examine how generative artificial intelligence (AI) prompts could be engineered to create messages that engage physical activity urges.

methodsStudy 1 involved iterative prompt development and expert evaluations of messages, varying by physical activity behavioral context (preparation vs. execution), benefit horizon (short-term vs. long-term), temporal focus (recalled past vs. imagined future), and behavioral target (physical activity vs. sedentary behavior). Study 2 involved a web-based factorial experiment to assess how these factors affected urges among middle-aged and older adults.

resultsIn Study 1, ratings of confidence that text messages (nMessages = 16) would evoke urges were moderate yet heterogenous across the experts (nExperts = 15). Themes including physical activity cues and affective appeal emerged as key factors. In Study 2, 639 adults (aged 40-85 years, M = 57; 52% female) rated 80 messages. Men reported stronger urges than women, and participants with stronger baseline urges reported stronger urges after reading the messages. Age moderated the effect of context, such that the execution (vs. preparation) prompt advantage increased with age. Two significant 3-way interactions showed that execution-based prompts outperformed preparation prompts, except when prompts targeted (1) long-term future benefits and (2) future physical activity experiences.

conclusionsThis study identified how specific generative AI prompt features can evoke physical activity urges in middle-aged and older adults, supporting the development of motivationally targeted interventions.

Indexed as

ExerciseGenerative Artificial IntelligenceHealth PromotionMotivationText MessagingAdultAgedAged, 80 and overFemaleHumansMaleMiddle Agedcravinggenerative artificial intelligencemotivationtelemedicine

Identifiers

PMID42847378
PMCPMC13649150

What OpenQuestion holds

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Registered trials

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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.