New research from the University of Bath and University of Hong Kong reveals a pervasive bias in mental health advice, heavily favoring the addition of new tasks over stopping harmful behaviors.

Dubbed "additive advice bias" by researchers, this tendency is widespread across everyday conversations, social media posts, and even AI chatbot responses. Mental health support advice, such as "try yoga" or "start journaling," often involves piling on new tasks, potentially leaving individuals feeling more overwhelmed than helped.

With mental health issues rising globally and straining existing services, friends, family, online communities, and AI often become the first sources of support. Understanding how we advise each other could be key to making this support more effective. The research indicates an opportunity for companies to develop solutions focused on reducing stress or harmful habits, rather than simply adding more tasks.

Market Signals

Sector Impacts & Opportunities: There's a growing demand for mental health solutions focusing on reducing stress and harmful habits, presenting an opportunity for innovative companies to differentiate themselves from traditional models.
Competitor Positioning Changes: Companies adopting a balanced approach to advice-giving (additive and subtractive) or focusing on subtractive strategies will gain a competitive edge.
AI's Role: Mental health chatbots risk amplifying this bias, creating an opportunity for AI developers to build more balanced and effective solutions.

Winners & Losers

Winners: Individuals seeking mental health support will benefit from more effective, less overwhelming advice. Innovative startups/companies developing subtractive or balanced mental health solutions will gain market share and user trust. AI developers creating AI capable of providing balanced advice will have a competitive edge. Losers: Providers of purely additive advice may lose users to more effective solutions. Platforms that don't adapt their AI risk being seen as unhelpful or outdated.

Action Timeline

Immediate: Audit current content and chatbots to identify "additive advice bias" and brainstorm "subtractive" solutions.
Short-term: Develop AI models that offer balanced advice, focusing on creating solutions that reduce mental load.
Long-term: Integrate balanced advice strategies into mental health support models across platforms, reshaping user expectations.

Risk Assessment

Execution Risk: Developing effective "subtractive" advice requires a deep understanding of behavioral psychology and user needs; poorly implemented subtractive advice can be counterproductive.
Market Adoption: Convincing a market accustomed to "more is better" advice to embrace "less is better" may take time.
AI Development Challenges: Ensuring AI provides genuinely helpful "subtractive" advice, rather than merely removing positive suggestions, is a complex technical challenge.

Key Indicators

Adoption of "Subtractive" Advice: Monitor how quickly mental health apps, platforms, and content creators incorporate advice focused on removing harmful elements.
AI Model Updates: Track announcements from AI developers regarding their efforts to address "additive advice bias" in mental health applications.

Regional Perspective

While this research is global, its implications are particularly pertinent in markets seeing a rise in AI usage for mental health, such as Australia, where founder well-being is viewed as essential business infrastructure. The Australian mental health app market is projected to grow significantly by 2030, indicating a clear opportunity for solutions addressing "additive advice bias."

Immediate Implications

What Happens Next: AI-powered platforms are expected to begin reviewing their algorithms to provide more balanced advice.
Time-Sensitive Opportunities or Risks: Companies that can quickly develop "subtractive" solutions or integrate them into their existing offerings have an opportunity to gain an early competitive advantage.

What to Watch

Leading Indicators to Monitor: Adoption of "subtractive" advice by key players in the mental health space.
Expected Next Developments: AI model updates to address the bias.
Key Decision Points Ahead: Companies' decisions on restructuring their content strategies and user interfaces.

Analysis

The "additive advice bias" highlights a significant gap in the mental health support market. Individuals, especially younger generations, are seeking support that reduces their burden rather than increasing it. Companies that understand this shift and develop "subtractive" solutions, whether through content or AI tools, will be well-positioned for success. It is essential for companies to re-evaluate their approaches to ensure the support provided is truly effective and considers the psychological impact of added tasks.