Depression Screening Using Daily Mental-Health Ratings from a Smartphone Application for Breast Cancer Patients

Abstract

BackgroundMobile mental-health trackers are mobile phone apps that gather self-reported mental-health ratings from users. They have received great attention from clinicians as tools to screen for depression in individual patients. While several apps that ask simple questions using face emoticons have been developed, there has been no study examining the validity of their screening performance.ObjectiveIn this study, we (1) evaluate the potential of a mobile mental-health tracker that uses three daily mental-health ratings (sleep satisfaction, mood, and anxiety) as indicators for depression, (2) discuss three approaches to data processing (ratio, average, and frequency) for generating indicator variables, and (3) examine the impact of adherence on reporting using a mobile mental-health tracker and accuracy in depression screening.MethodsWe analyzed 5792 sets of daily mental-health ratings collected from 78 breast cancer patients over a 48-week period. Using the Patient Health Questionnaire-9 (PHQ-9) as the measure of true depression status, we conducted a random-effect logistic panel regression and receiver operating characteristic (ROC) analysis to evaluate the screening performance of the mobile mental-health tracker. In addition, we classified patients into two subgroups based on their adherence level (higher adherence and lower adherence) using a k-means clustering algorithm and compared the screening accuracy between the two groups.ResultsWith the ratio approach, the area under the ROC curve (AUC) is 0.8012, indicating that the performance of depression screening using daily mental-health ratings gathered via mobile mental-health trackers is comparable to the results of PHQ-9 tests. Also, the AUC is significantly higher (P=.002) for the higher adherence group (AUC=0.8524) than for the lower adherence group (AUC=0.7234). This result shows that adherence to self-reporting is associated with a higher accuracy of depression screening.ConclusionsOur results support the potential of a mobile mental-health tracker as a tool for screening for depression in practice. Also, this study provides clinicians with a guideline for generating indicator variables from daily mental-health ratings. Furthermore, our results provide empirical evidence for the critical role of adherence to self-reporting, which represents crucial information for both doctors and patients.

Mobile Phone Apps for Quality of Life and Well-Being Assessment in Breast and Prostate Cancer Patients: Systematic Review

“…Based on titles and abstracts, 18 records were selected for full text screening; 13 out of these 18 [ 29 - 41 ] being discarded for various reasons (see Multimedia Appendix 1 ). A total of 5 publications [ 42 - 46 ] were finally included. An interrater agreement of kappa=.561 was found, reaching a moderate agreement according to Landis and Koch [ 47 ].…”

“…The 5 studies included a total of 644 patients, mean age 52.16 years (sample sizes and mean ages listed in Table 1 ). Of these, 3 studies were conducted in Korea [ 42 , 44 , 46 ], 1 in the United States [ 43 ], and 1 in Sweden [ 45 ]. All main authors affiliations were university departments [ 42 - 46 ].…”

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study

“…Systematic reviews have summarized the therapeutic effects of technology mediated mental health information systems [15]. For example, mental health apps on mobile phones could improve the accessibility to treatment and facilitate proactive seeking for professional help [13,16]. …”

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study (Preprint)

“…Systematic reviews have summarized therapeutic effects of technology mediated mental health information systems [16]. For example, reviews of the efficacy of mental health apps on mobile devices reported that mobile health apps had the potential to improve accessibility to seek treatment [17,19]. In comparison, our work represents the first effort towards designing and developing mobile behavioral sensing technology with the objective of enhancing the efficiency, accessibility, and scalability of mental health services at university counseling centers.…”