Mindstep Mood and Cause Examination (MMCE): The Preferred Tool for Remote Digital Depression Screening

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
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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.
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Mindstep 情绪和病因检查 (MMCE):远程数字抑郁症筛查的首选工具
背景:数字健康技术越来越多地被用于监测、评估和治疗社区中的抑郁症状。然而,许多此类技术依赖于最初设计用于初级保健诊所的筛查工具,如患者健康问卷(PHQ-9)。这些量表以症状为中心,无法捕捉患者更广泛的经历。我们开发了一种新的屏幕,用于在数字环境中评估抑郁症状。它被命名为 "Mindstep 情绪和病因检查(MMCE)",旨在复制 PHQ-9 的预测能力,同时改善用户体验并捕捉更广泛的心理健康决定因素。研究方法这是一项在 Prolific 上完全远程进行的横断面研究。参与者(367 人)按照随机顺序完成 PHQ-9 和 MMCE。研究人员对 MMCE 的回答进行了一系列心理测量特性的检查,包括:内部一致性、项目选择性以及与 PHQ-9 分数的趋同性。用户体验采用理论主导的可接受性量表进行评估,并在两种心理健康测量中进行比较。主题分析用于分析参与者的自由文本回答,描述他们完成量表的体验。结果MMCE 与 PHQ-9 具有良好的内部一致性和较强的趋同性(r = 0.70),占 PHQ-9 分值差异的 49%。在中度抑郁症状临界值为 10 时,MMCE 对 PHQ-9 的预测能力也很强,曲线下面积(AUC)为 0.84。在量表之间的直接比较中,367 位用户中有 259 位(70.1%)更喜欢 MMCE,在 12 个用户体验类别中,MMCE 有 8 个类别的表现优于 PHQ-9。结论:MMCE在预测PHQ-9分数方面已被证明是有效的,并提供了更好的用户体验,同时还能鼓励用户检查其抑郁症状的根本原因。不过,还需要进行更多的研究,以评估 MMCE 在重复评估方面是否能有效监测抑郁症。
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