Mindstep Mood and Cause Examination (MMCE): The Preferred Tool for Remote Digital Depression Screening
Mahmud, M.; Kuleindiren, N.; Suddell, S.; Rifkin-Zybutz, R. P.; Sharma, P.; Osunronbi, T.; Pounds, O.; Selim, H.; Patchava, A.; Lin, A.; Alim-Marvasti, A.
Show abstract
BackgroundDigital 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. MethodThis 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. ResultsThe 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. ConclusionsThe 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Changes in hospital staff mental health during the Covid-19 pandemic: longitudinal results from the international COPE-CORONA study 96%
- Psychosocial factors associated with mental health and quality of life during the COVID-19 pandemic among low-income urban dwellers in Peninsular Malaysia 95%
- A One-Arm Pilot Trial of a Telehealth CBT-Based Group Intervention Targeting Transdiagnostic Risk for Emotional Distress 95%
Similar papers in this journal
- The mental health of NHS staff during the COVID-19 pandemic: a two-wave cohort study 95%
- ‘I had no life. I was only existing’ . Factors shaping the mental health and wellbeing of people experiencing long Covid: a qualitative study 95%
- The prevalence, incidence, prognosis and risk factors for depression and anxiety in a UK cohort during the COVID-19 pandemic 94%
Similar papers in this journal
- Development and use analysis of ‘gestioemocional.cat’, a web app for promoting emotional self-care and access to professional mental health resources during the covid-19 pandemic 95%
- Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered Clinical Decision Support System: A Longitudinal Feasibility Study 95%
- Development of the NeuroFlow Severity Score and Comparison With Validated Measures for Depression and Anxiety 94%
Similar papers in this journal
Similar papers in this journal
- Social networking service, patient-generated health data, and population health informatics: patterns and implications for using digital technologies to support mental health 95%
- Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric In-Patients 94%
- Assessing ChatGPT’s Mastery of Bloom’s Taxonomy using psychosomatic medicine exam questions 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.