Mohammad Mahmud, Narayan Kuleindiren, Steph Suddell, Raphael Paul Rifkin-Zybutz, Parivrudh Sharma, Temidayo Osunronbi, Olivia Pounds, Hamzah Selim, Anushka Patchava, Aaron Lin, Ali Alim-Marvasti
{"title":"Mindstep Mood and Cause Examination (MMCE): The Preferred Tool for Remote Digital Depression Screening","authors":"Mohammad Mahmud, Narayan Kuleindiren, Steph Suddell, Raphael Paul Rifkin-Zybutz, Parivrudh Sharma, Temidayo Osunronbi, Olivia Pounds, Hamzah Selim, Anushka Patchava, Aaron Lin, Ali Alim-Marvasti","doi":"10.1101/2024.08.07.24311602","DOIUrl":null,"url":null,"abstract":"Background: Digital health technologies are increasingly being used to monitor, assess, and treat depressive symptoms in the community. However, many such technologies rely on screening tools which were originally designed for use in primary care clinics, such as the Patient Health Questionnaire (PHQ-9). These scales are symptom-focused and do not capture the wider experiences of the patient. We developed a new screen for assessing depressive symptoms in a digital setting. Named the Mindstep Mood and Cause Examination (MMCE), it was designed to replicate the predictive capabilities of the PHQ-9, while improving user experience and capturing broader determinants of mental health. Method: This was a cross-sectional study, conducted fully remotely on Prolific. Participants (n=367) completed both the PHQ-9 and the MMCE, in a randomised order. Responses on the MMCE were examined for a range of psychometric properties, including: internal consistency, item selectivity, and convergence with PHQ-9 scores. User experience was assessed with a theory-led acceptability scale and compared across both mental health measures. Thematic analysis was used to analyse participants' free text responses, describing their experience of completing the scales. Results: The MMCE displayed good internal consistency and strong convergence with the PHQ-9 (r = 0.70), accounting for 49% of the variance in PHQ-9 scores. The MMCE also demonstrated robust predictive capability for the PHQ-9 using a moderate depression symptom cut-off of 10, with an Area Under Curve (AUC) of 0.84. In direct comparisons between the scales, 259 of 367 users (70.1%) preferred the MMCE and the MMCE outperformed the PHQ-9 in 8 out of 12 user experience categories. Conclusions: The MMCE has demonstrated validity in predicting PHQ-9 scores and offers an improved user experience, while additionally encouraging the user to examine the underlying causes of their depressive symptoms. However, additional research is necessary to evaluate the MMCE in terms of repeated assessments for effective depression monitoring.","PeriodicalId":501388,"journal":{"name":"medRxiv - Psychiatry and Clinical Psychology","volume":"77 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Psychiatry and Clinical Psychology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.08.07.24311602","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Digital health technologies are increasingly being used to monitor, assess, and treat depressive symptoms in the community. However, many such technologies rely on screening tools which were originally designed for use in primary care clinics, such as the Patient Health Questionnaire (PHQ-9). These scales are symptom-focused and do not capture the wider experiences of the patient. We developed a new screen for assessing depressive symptoms in a digital setting. Named the Mindstep Mood and Cause Examination (MMCE), it was designed to replicate the predictive capabilities of the PHQ-9, while improving user experience and capturing broader determinants of mental health. Method: This was a cross-sectional study, conducted fully remotely on Prolific. Participants (n=367) completed both the PHQ-9 and the MMCE, in a randomised order. Responses on the MMCE were examined for a range of psychometric properties, including: internal consistency, item selectivity, and convergence with PHQ-9 scores. User experience was assessed with a theory-led acceptability scale and compared across both mental health measures. Thematic analysis was used to analyse participants' free text responses, describing their experience of completing the scales. Results: The MMCE displayed good internal consistency and strong convergence with the PHQ-9 (r = 0.70), accounting for 49% of the variance in PHQ-9 scores. The MMCE also demonstrated robust predictive capability for the PHQ-9 using a moderate depression symptom cut-off of 10, with an Area Under Curve (AUC) of 0.84. In direct comparisons between the scales, 259 of 367 users (70.1%) preferred the MMCE and the MMCE outperformed the PHQ-9 in 8 out of 12 user experience categories. Conclusions: The MMCE has demonstrated validity in predicting PHQ-9 scores and offers an improved user experience, while additionally encouraging the user to examine the underlying causes of their depressive symptoms. However, additional research is necessary to evaluate the MMCE in terms of repeated assessments for effective depression monitoring.