Evidence map›Paper›PMID 37771819›Full record

ArticleFrontiers in digital health2023

Feasibility of generating structured motivational messages for tailored physical activity coaching.

Ramya P Ghantasala, Nele Albers, Kristell M Penfornis, Milon H M van Vliet, Willem-Paul Brinkman

Abstract read
In one paragraph

Article in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

  1. Observational
  2. Review
  3. 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

5 authors.

Ramya P GhantasalaDepartment of Intelligent Systems, Delft University of Technology, Delft, Netherlands.
Nele AlbersDepartment of Intelligent Systems, Delft University of Technology, Delft, Netherlands.
Kristell M PenfornisUnit Health Medical and Neuropsychology, Institute of Psychology, Leiden University, Leiden, Netherlands.
Milon H M van VlietPublic Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands.
Willem-Paul BrinkmanDepartment of Intelligent Systems, Delft University of Technology, Delft, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tailored motivational messages are helpful to motivate people in eHealth applications for increasing physical activity, but it is not sufficiently clear how such messages can be effectively generated in advance. We, therefore, put forward a theory-driven approach to generating tailored motivational messages for eHealth applications for behavior change, and we examine its feasibility by assessing how motivating the resulting messages are perceived. For this, we designed motivational messages with a specific structure that was based on an adaptation of an existing ontology for tailoring motivational messages in the context of physical activity. To obtain tailored messages, experts in health psychology and coaching successfully wrote messages with this structure for personas in scenarios that differed with regard to the persona's mood, self-efficacy, and progress. Based on an experiment in which 60 participants each rated the perceived motivational impact of six generic and six tailored messages based on scenarios, we found credible support for our hypothesis that messages tailored to mood, self-efficacy, and progress are perceived as more motivating. A thematic analysis of people's free-text responses about what they found motivating and demotivating about motivational messages further supports the use of tailored messages, as well as messages that are encouraging and empathetic, give feedback about people's progress, and mention the benefits of physical activity. To aid future work on motivational messages, we make our motivational messages and corresponding scenarios publicly available.

Indexed as

behavior changeeHealthfeedbackmonitoringmotivationpersonalizationphysical activitytailoring

Identifiers

PMID37771819
PMCPMC10523307

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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.