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eMindLog: Self-Measurement of Anxiety and Depression Using Mobile Technology
Thomas M Penders1, Karl L Wuensch2, Philip T Ninan3
1Brody School of Medicine, Department of Psychiatry and Behavioral Medicine, East Carolina University, Greenville, NC, United States.
JMIR Research Protocols
|May 26, 2017
Summary
The eMindLog tool accurately measures anxiety and depression in adults, showing excellent reliability and validity. This novel self-report measure aids in tracking symptoms and intervention effectiveness.
Area of Science:
- Psychological assessment
- Neuroscience-based mental health tools
Background:
- Quantifying anxiety and depressive experiences is crucial for self-monitoring and tracking intervention progress.
- eMindLog is a new self-report measure for anxiety and depression, integrating psychological principles with a neuroscience framework.
Purpose of the Study:
- To evaluate the psychometric properties of the eMindLog tool.
- To assess the reliability and validity of eMindLog in a nonclinical adult sample.
Main Methods:
- A cross-sectional study involving 198 adults who completed eMindLog and the Hospital Anxiety and Depression Scale (HADS).
- eMindLog utilizes mobile technology for daily emotion, thought, and behavior tracking, alongside weekly assessments of symptoms, quality of life, and functioning.
- The study employed factor analysis to support the theoretically derived index derivatives.
Main Results:
- eMindLog demonstrated excellent internal consistency (Cronbach alpha = .94).
- Strong correlations were found between eMindLog anxiety/sadness indexes and HADS subscales (r=.66 and r=.62, respectively).
- Factor analysis confirmed the theoretical structure of eMindLog for anxiety, anger, sadness, and anhedonia.
Conclusions:
- eMindLog is a reliable and valid self-measurement tool for anxiety and depression in nonclinical populations.
- Further research is needed to validate eMindLog's psychometric properties in clinical settings.
- Precise self-measurement of anxiety and depression offers benefits for case detection, treatment monitoring, and biomarker exploration.
