AI Article Synopsis

  • Unmind is a digital platform designed to support workplace mental health by providing users with tools to track and improve their mental well-being, while offering employers insights into their workforce's overall mental health.
  • The goal was to create a new well-being index that addresses the limitations of current measures, focusing on both mental health symptoms and positive aspects of well-being.
  • The research involved multiple studies that developed a 26-item index with seven subscales (Calmness, Connection, Coping, Happiness, Health, Fulfilment, and Sleep), demonstrating good reliability, validity, and the ability to measure mental health across different demographics.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8804960PMC
http://dx.doi.org/10.2196/34103DOI Listing

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