Designing Persuasive Social Robots for Health Behavior Change: A Systematic Review of Behavior Change Strategies and Evaluation Methods
- URL: http://arxiv.org/abs/2601.15309v1
- Date: Tue, 13 Jan 2026 16:31:35 GMT
- Title: Designing Persuasive Social Robots for Health Behavior Change: A Systematic Review of Behavior Change Strategies and Evaluation Methods
- Abstract summary: Social robots are increasingly applied as health behavior change interventions, yet actionable knowledge to guide their design and evaluation remains limited.<n>This systematic review synthesizes (1) the behavior change strategies used in existing HRI studies employing social robots to promote health behavior change, and (2) the evaluation methods applied to assess behavior change outcomes.
- Score: 18.710588898648407
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Social robots are increasingly applied as health behavior change interventions, yet actionable knowledge to guide their design and evaluation remains limited. This systematic review synthesizes (1) the behavior change strategies used in existing HRI studies employing social robots to promote health behavior change, and (2) the evaluation methods applied to assess behavior change outcomes. Relevant literature was identified through systematic database searches and hand searches. Analysis of 39 studies revealed four overarching categories of behavior change strategies: coaching strategies, counseling strategies, social influence strategies, and persuasion-enhancing strategies. These strategies highlight the unique affordances of social robots as behavior change interventions and offer valuable design heuristics. The review also identified key characteristics of current evaluation practices, including study designs, settings, durations, and outcome measures, on the basis of which we propose several directions for future HRI research.
Related papers
- Social-spatial dependencies for learning visual navigation [1.0312968200748118]
Navigation for social organisms rarely is a fully independent activity.<n>Group structure and dynamics, as well as embodied interactions, critically influence useful behavior.<n>Our findings challenge the approach of only inspecting individual behavior for social organisms.
arXiv Detail & Related papers (2026-07-08T14:28:10Z) - Beyond Rational Illusion: Behaviorally Realistic Strategic Classification [56.458581740660236]
We propose the Prospect-Guided Strategic Framework (Pro-SF) to address the behaviorally realistic strategic classification problem.<n>Pro-SF is a principled framework grounded in prospect theory to model and learn under behaviorally realistic strategic responses.<n> Experiments on synthetic and real-world datasets establish Pro-SF as a behaviorally grounded approach to strategic classification.
arXiv Detail & Related papers (2026-05-19T11:04:55Z) - Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations [80.39504840645687]
Existing benchmarks measure ToM capability improvement through story-reading, multiple-choice questions from a third-person perspective.<n>This paper proposes a new paradigm of interactive ToM evaluation with both perspective and metric shifts.<n>Our findings reveal that improvements on static benchmarks do not always translate to better performance in dynamic HAI interactions.
arXiv Detail & Related papers (2026-04-28T15:38:31Z) - Propensity Inference: Environmental Contributors to LLM Behaviour [4.443585107770285]
Motivated by loss of control risks from misaligned AI systems, we develop and apply methods for measuring language models' propensity for unsanctioned behaviour.<n>We apply the methodology to measure the effects of 12 environmental factors (6 strategic in nature, 6 non-strategic) and thus the extent to which behaviour is explained by strategic aspects of the environment.<n>Across 23 language models and 11 evaluation environments, we find approximately equal contributions from strategic and non-strategic factors for explaining behaviour, do not find strategic factors becoming more influential as capabilities improve, and find some evidence for a trend for increased sensitivity to goal conflicts.
arXiv Detail & Related papers (2026-04-22T21:35:27Z) - Anticipating Gaming to Incentivize Improvement: Guiding Agents in (Fair) Strategic Classification [6.660458629649826]
We explore individuals' choice between genuinely improving their qualifications ('improvement'') vs. attempting to deceive the algorithm.<n>We formulate these interactions as a Stackelberg game, where a firm deploys a (fair) classifier, and individuals strategically respond.
arXiv Detail & Related papers (2025-05-08T18:47:23Z) - Exploring the Impact of Reflexivity Theory and Cognitive Social Structures on the Dynamics of Doctor-Patient Social System [0.0]
We create two different models for a doctor-patient system.
One retains the established assumptions, while the other incorporates principles of reflexivity theory and cognitive social structures.
We utilize a microbial genetic algorithm to optimize the behaviour of the physician and patient agents in both models.
arXiv Detail & Related papers (2024-11-08T23:23:13Z) - The Double-Edged Sword of Behavioral Responses in Strategic Classification: Theory and User Studies [7.695481260089599]
We propose a strategic classification model that considers behavioral biases in human responses to algorithms.
We show how misperceptions of a classifier can lead to different types of discrepancies between biased and rational agents' responses.
We show that strategic agents with behavioral biases can benefit or (perhaps, unexpectedly) harm the firm compared to fully rational strategic agents.
arXiv Detail & Related papers (2024-10-23T17:42:54Z) - LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly.
We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models.
It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
arXiv Detail & Related papers (2024-04-01T16:50:54Z) - PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
Psychological measurement is essential for mental health, self-understanding, and personal development.
PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
arXiv Detail & Related papers (2024-02-19T18:00:30Z) - Strategic Evaluation: Subjects, Evaluators, and Society [1.1606619391009658]
We argue that the design of an evaluation itself can be understood as furthering goals held by the evaluator.
We put forward a model that represents the process of evaluation using three interacting agents.
Treating evaluators as themselves strategic allows us to re-cast the scrutiny directed at decision subjects.
arXiv Detail & Related papers (2023-10-05T16:33:08Z) - On the Value of Myopic Behavior in Policy Reuse [67.37788288093299]
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence.
In this work, we present a framework called Selective Myopic bEhavior Control(SMEC)
SMEC adaptively aggregates the sharable short-term behaviors of prior policies and the long-term behaviors of the task policy, leading to coordinated decisions.
arXiv Detail & Related papers (2023-05-28T03:59:37Z) - POETREE: Interpretable Policy Learning with Adaptive Decision Trees [78.6363825307044]
POETREE is a novel framework for interpretable policy learning.
It builds probabilistic tree policies determining physician actions based on patients' observations and medical history.
It outperforms the state-of-the-art on real and synthetic medical datasets.
arXiv Detail & Related papers (2022-03-15T16:50:52Z) - The world seems different in a social context: a neural network analysis
of human experimental data [57.729312306803955]
We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals.
An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
arXiv Detail & Related papers (2022-03-03T17:19:12Z) - A Review of Computational Approaches for Evaluation of Rehabilitation
Exercises [58.720142291102135]
This paper reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems.
The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches.
arXiv Detail & Related papers (2020-02-29T22:18:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.