News Release

Why mental health advice often adds to your to-do list

Peer-Reviewed Publication

University of Bath

From “try yoga” to “start journaling,” most mental health advice piles on extra tasks. Rarely does it tell you to stop doing something harmful. New research from the University of Bath and University of Hong Kong shows that this “additive advice bias” appears everywhere: in conversations between people, posts on social media, and even recommendations from AI chatbots. The result? Well-intentioned tips that may leave people feeling more overwhelmed than helped.

With mental health problems rising worldwide and services under strain, friends, family, online communities and AI are often the first port of call. Understanding how we advise each other could be key to making that support more effective.

A collection of eight studies involving hundreds of participants, published in Communications Psychology, analysed experimental data, real-world Reddit advice, and tested ChatGPT’s responses. Participants advised strangers, friends, and themselves on scenarios involving both harmful habits, like gambling and missing beneficial activities, such as exercise.

Key findings:

  • Additive dominates - Across every context, people suggested adding activities far more than removing harmful activities.
  • Feasibility and benefit –  Doing more was seen to be easier and more beneficial than cutting harmful things out.
  • Advice varies by relationship – cutting harmful things out is viewed as easier for our close friends than for ourselves.
  • AI mirrors human bias – ChatGPT gave predominantly additive advice, reflecting patterns in online social media.

Senior author, Dr Tom Barry from the Department of Psychology at the University of Bath, England said.

“In theory, good advice should balance doing more with doing less. But we found a consistent tilt towards piling more onto people’s plates and even AI has learned to do it. While well-meaning, it can unintentionally make mental health feel like an endless list of chores.”

Co-author, Dr Nadia Adelina from the Department of Psychology at the University of Hong Kong, Hong Kong said:

“As AI chatbots become a major source of mental health guidance, they risk amplifying this bias. Building in prompts to explore what people might remove from their lives could make advice more balanced and less overwhelming.”

This research was supported by the Research Promotion Fund of the Department of Psychology, University of Bath, England.


ENDS

For more information, please contact:

Rebecca Tanswell
University of Bath Press Office
Tel: 01225 386319
Email: rlt54@bath.ac.uk   

Notes to editors

Link to the research paper People overlook subtractive solutions to mental health problems | Communications Psychology

https://doi.org/10.1038/s44271-025-00312-8

About the University of Bath


The University of Bath is one of the UK's leading universities, recognised for high-impact research, excellence in education, an outstanding student experience and strong graduate prospects.

  • We are ranked in the top 10 in all of the UK’s major university guides.
  • The University achieved a triple Gold award in the last Teaching Excellence Framework 2023, the highest awards possible, for both the overall assessment and for student outcomes and student experience. The Teaching Excellence Framework (TEF) is a national scheme run by the Office for Students (OfS).
  • We are also ranked among the top 10% of universities globally, placing 132nd in the QS World University Rankings 2026.

Research from Bath is helping to change the world for the better. Across the University’s three Faculties and School of Management, our research is making an impact in society, leading to low-carbon living, positive digital futures, and improved health and wellbeing. Find out all about our Research with Impact: https://www.bath.ac.uk/campaigns/research-with-impact/


Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.