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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Introduction to Stress and Lifestyle01:27

Introduction to Stress and Lifestyle

Stress is a multifaceted response to events perceived as challenging or threatening, highlighting physical, emotional, cognitive, and behavioral reactions. Physically, stress can lead to fatigue, sleep disruptions, and various health issues such as frequent colds, chest pains, and nausea. Emotionally, it can manifest as anxiety, depression, irritability, and anger triggered by both minor and major life events. Cognitively, it may result in difficulty in concentration, memory, and...
Lifestyle Factors and Health01:20

Lifestyle Factors and Health

Lifestyle factors play a critical role in maintaining overall health and preventing chronic diseases. Key elements, such as regular physical activity, a nutritious diet, and abstinence from smoking, can significantly enhance physical, mental, and emotional well-being while reducing the risk of several life-threatening conditions.
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Study Design in Statistics01:15

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
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Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
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Explainable AI for Well-Being Prediction From Lifestyle Data: 2-Study Design.

Flore Vancompernolle Vromman1, Corentin Vande Kerckhove1, Joël Gagnon2,3

  • 1Louvain Research Institute in Management and Organizations, UCLouvain, Place de l'Université 1, Louvain-la-Neuve, Wallonia, 1348, Belgium, 32 479251700.

JMIR Mental Health
|May 8, 2026
PubMed
Summary
This summary is machine-generated.

Explainable AI enhances user satisfaction with well-being assessments. Visual and interactive explanations are most effective, improving trust and actionable insights for public health.

Keywords:
XAIexplainable artificial intelligenceexplanationshuman-centered AIinterpretable modelslifestyle predictorswell-being

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Public Health
  • Behavioral Science

Background:

  • Well-being is crucial for public health and social progress, influenced by dynamic determinants.
  • Increasing behavioral data and AI prominence enable scalable well-being assessments.
  • Explainable AI (XAI) is vital for user trust and reflection in AI-driven well-being tools.

Purpose of the Study:

  • To assess the predictive power of modifiable lifestyle and contextual factors on subjective well-being.
  • To investigate how different explanation modalities impact user satisfaction with AI-generated well-being feedback.

Main Methods:

  • Developed a parsimonious regularized linear model for estimating individual well-being from lifestyle predictors.
  • Conducted an experiment comparing explanation modalities (visual, interactive, textual, quantitative, population-comparison) against a control group.
  • Evaluated user satisfaction with AI-generated assessments across different explanation formats with 1252 participants.

Main Results:

  • Any form of explanation significantly increased user satisfaction compared to no explanation.
  • Visual and interactive explanation modalities yielded the highest user satisfaction scores.
  • Population-comparison feedback was least preferred and least effective in explaining AI assessments.

Conclusions:

  • Well-being tools should integrate interpretable AI models with visual or interactive explanations.
  • Focusing on actionable behavioral levers is more effective than emphasizing population norms.
  • Findings provide design guidance for deploying XAI in well-being applications to boost user satisfaction.