Evidence map›Paper›PMID 40450111›Full record

ArticleNPJ digital medicine2025

Psychological, economic, and ethical factors in human feedback for a chatbot-based smoking cessation intervention.

Nele Albers, Francisco S Melo, Mark A Neerincx, Olya Kudina, Willem-Paul Brinkman

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. 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.

Nele AlbersDepartment of Intelligent Systems, Delft University of Technology, Delft, Netherlands. n.albers@tudelft.nl.
Francisco S MeloINESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
Mark A NeerincxDepartment of Intelligent Systems, Delft University of Technology, Delft, Netherlands.
Olya KudinaDepartment of Values, Technology and Innovation, Delft University of Technology, Delft, Netherlands.
Willem-Paul BrinkmanDepartment of Intelligent Systems, Delft University of Technology, Delft, Netherlands.

Funding

Nederlandse Organisatie voor Wetenschappelijk Onderzoek 628.011.211
6 · The paper itself

Abstract

Integrating human support with chatbot-based behavior change interventions raises three challenges: (1) attuning the support to an individual's state (e.g., motivation) for enhanced engagement, (2) limiting the use of the concerning human resources for enhanced efficiency, and (3) optimizing outcomes on ethical aspects (e.g., fairness). Therefore, we conducted a study in which 679 smokers and vapers had a 20% chance of receiving human feedback between five chatbot sessions. We find that having received feedback increases retention and effort spent on preparatory activities. However, analyzing a reinforcement learning (RL) model fit on the data shows there are also states where not providing feedback is better. Even this "standard" benefit-maximizing RL model is value-laden. It not only prioritizes people who would benefit most, but also those who are already doing well and want feedback. We show how four other ethical principles can be incorporated to favor other smoker subgroups, yet, interdependencies exist.

Identifiers

PMID40450111
PMCPMC12126561

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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.