Description

This study examines the analysis and use of data derived from mood lists. It focuses on collecting mood data from different users, analyzing that data to understand emotional trends, and exploring its applications in various areas such as mental health, personal development, and well-being enhancement strategies.

Mood List Data Analysis and Use

Mood list data analysis involves the collection and processing of data to monitor users’ emotional states. Both quantitative and qualitative methods are used to identify trends, patterns, and influences on users’ moods. Applications include:
Assessing mental health
Developing personal strategies for improving well-being
Creating personalized support programs

Objective

The aim of the study is to demonstrate how mood list data analysis can enhance the understanding of emotional states, promote personal growth, and support mental health through targeted interventions.

Calibration

To evaluate the outcomes of the analysis, various indicators are used, such as:
Accuracy of predictive models
Sensitivity and specificity of the analyses
User satisfaction with mood list–based applications
Calibration also includes qualitative assessments and user feedback for the continuous improvement of the analysis tools and their implementation.

References

Diener, E., & Seligman, M. E. (2004). “Beyond Money: Toward an Economy of Well-Being.” Psychological Science, 5(2), 160–164.
Fredrickson, B. L. (2001). “The Role of Positive Emotions in Positive Psychology: The Broaden-and-Build Theory of Positive Emotions.” American Psychologist, 56(3), 218–226.
Kabat-Zinn, J. (2013). Mindfulness for Beginners: Reclaiming the Present Moment—and Your Life. Sounds True.
Sirois, F. M. (2015). “The Role of Trait and State Mindfulness in Psychological Health: A Review of the Literature.” Mindfulness, 6(4), 692–708.