Evidence map›Paper›PMID 42251424›Full record

ReviewJournal of health, population, and nutrition2026

PhysB: a framework for designing conversational agents to support physical activity in adolescents.

Richard Moore, Abdel-Karim Al-Tamimi, Lambros Lazuras

Abstract readReview
In one paragraph

Review in Journal of health, population, and nutrition, 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

3 authors.

Richard MooreSheffield Hallam University, Sheffield, UK. richie1moore@hotmail.com.
Abdel-Karim Al-TamimiSheffield Hallam University, Sheffield, UK.
Lambros LazurasUniversity of Lincoln, Lincoln, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conversational Agents (CAs) are a promising tool for enhancing adolescents' physical activity behaviours. To effectively fulfil their role, these agents must address the diverse range of barriers adolescents encounter and provide effective, evidence-based solutions. To achieve this, a theoretical framework titled PhysB is proposed, which is grounded in theory and evidence, allowing for the identification and categorisation of barriers through natural language input. The PhysB framework outlines the relationship between the process of identifying barriers and the cognitive system driving the CA's interaction with adolescents. It incorporates key constructs from various theoretical models and frameworks and draws from behaviour change theory. The PhysB framework's main function is to inform the design and evaluation of personalised, evidence-based conversational AI interventions to promote positive behaviour change in adolescents. The PhysB framework's broad applicability extends to practitioners, policymakers, researchers, and designers involved in designing conversational AI interventions.

Indexed as

Adolescent BehaviorCommunicationExerciseHealth PromotionAdolescentCognitionHumansAdolescentsBarriersCognitive systemsConversational agentsConversational AILarge Language ModelsPhysical activity

Identifiers

PMID42251424
PMCPMC13660111

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.