Open AccessA New Digital Assessment of Mental Health and Well-being in the Workplace: Development and Validation of the Unmind Index
Author(s) -
Anika Sierk,
Eoin Travers,
Marcos Economides,
Bao Sheng Loe,
Luning Sun,
Heather Bolton
Publication year - 2022
Publication title -
jmir mental health
Language(s) - English
Resource type - Journals
ISSN - 2368-7959
DOI - 10.2196/34103
Subject(s) - confirmatory factor analysis , discriminant validity , mental health , exploratory factor analysis , psychology , applied psychology , convergent validity , face validity , reliability (semiconductor) , happiness , clinical psychology , psychometrics , structural equation modeling , computer science , social psychology , internal consistency , psychiatry , machine learning , power (physics) , physics , quantum mechanics
Background Unmind is a workplace, digital, mental health platform with tools to help users track, maintain, and improve their mental health and well-being (MHWB). Psychological measurement plays a key role on this platform, providing users with insights on their current MHWB, the ability to track it over time, and personalized recommendations, while providing employers with aggregate information about the MHWB of their workforce. Objective Due to the limitations of existing measures for this purpose, we aimed to develop and validate a novel well-being index for digital use, to capture symptoms of common mental health problems and key aspects of positive well-being. Methods In Study 1A, questionnaire items were generated by clinicians and screened for face validity. In Study 1B, these items were presented to a large sample (n=1104) of UK adults, and exploratory factor analysis was used to reduce the item pool and identify coherent subscales. In Study 2, the final measure was presented to a new nationally representative UK sample (n=976), along with a battery of existing measures, with 238 participants retaking the Umind Index after 1 week. The factor structure and measurement invariance of the Unmind Index was evaluated using confirmatory factor analysis, convergent and discriminant validity by estimating correlations with existing measures, and reliability by examining internal consistency and test-retest intraclass correlations. Results Studies 1A and 1B yielded a 26-item measure with 7 subscales: Calmness, Connection, Coping, Happiness, Health, Fulfilment, and Sleep. Study 2 showed that the Unmind Index is fitted well by a second-order factor structure, where the 7 subscales all load onto an overall MHWB factor, and established measurement invariance by age and gender. Subscale and total scores correlate well with existing mental health measures and generally diverge from personality measures. Reliability was good or excellent across all subscales. Conclusions The Unmind Index is a robust measure of MHWB that can help to identify target areas for intervention in nonclinical users of a mental health app. We argue that there is value in measuring mental ill health and mental well-being together, rather than treating them as separate constructs.