Abstract

BackgroundUnmind 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.ObjectiveDue 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.MethodsIn 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.ResultsStudies 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.ConclusionsThe 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.

Highlights

  • BackgroundPoor mental health affects hundreds of millions of people worldwide, impacting individual quality of life and creating a significant economic burden for employers [1,2,3]

  • Single-factor exploratory factor analysis (EFA) procedure outlined in the previous section, the item pool was reduced from 118 items to 57 items across the 7 scales

  • This work demonstrated the Unmind Index is a robust measure of mental health and well-being (MHWB) that is underpinned by a general factor and 7 underlying constructs

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Summary

Introduction

BackgroundPoor mental health affects hundreds of millions of people worldwide, impacting individual quality of life and creating a significant economic burden for employers [1,2,3]. Digital, mental health platform providing employees with tools to help them track, maintain, and improve their mental health and well-being (MHWB) and allowing employers to gain insight into the overall well-being of their employees through anonymized, aggregated data. Consistent with the contemporary understanding of mental health as a complete state of physical, mental, and social well-being [10], the Unmind approach encourages users to take a holistic approach to understanding and managing their MHWB This holistic approach may be relevant for promoting regular, proactive use of the platform in working adults. Digital, mental health platform with tools to help users track, maintain, and improve their mental health and well-being (MHWB). We argue that there is value in measuring mental ill health and mental well-being together, rather than treating them as separate constructs

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