RetroSearch Browse

Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://pubmed.ncbi.nlm.nih.gov/27492880/ below:

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

Depression Screening Using Daily Mental-Health Ratings from a Smartphone Application for Breast Cancer Patients Sanghee Lim, Yul Ha Min, Yong-Wook Shin, Byungtae Lee, Guiyun Sohn, Kyung Hae Jung, Jae-Ho Lee, Byung Ho Son, Sei Hyun Ahn, Soo-Yong Shin, Jong Won Lee

Affiliations

Affiliation

¹ College of Business, KAIST, Seoul, Republic Of Korea.

PMID: 27492880
PMCID: PMC4990716
DOI: 10.2196/jmir.5598

Item in Clipboard

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

Junetae Kim et al. J Med Internet Res. 2016.

Authors Junetae Kim¹, Sanghee Lim, Yul Ha Min, Yong-Wook Shin, Byungtae Lee, Guiyun Sohn, Kyung Hae Jung, Jae-Ho Lee, Byung Ho Son, Sei Hyun Ahn, Soo-Yong Shin, Jong Won Lee Affiliation

¹ College of Business, KAIST, Seoul, Republic Of Korea.

PMID: 27492880
PMCID: PMC4990716
DOI: 10.2196/jmir.5598

Item in Clipboard

Abstract

Background: Mobile 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.

Objective: In 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.

Methods: We 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.

Results: With 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.

Conclusions: Our 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.

Keywords: breast cancer (neoplasms); depression; mental health; smartphone applications.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: None declared.

Figures
Figure 1

Three mental logs in the…

Figure 1

Three mental logs in the Pit-a-Pat app: (A) Sleep satisfaction, (B) Mood, and…

Figure 1

Three mental logs in the Pit-a-Pat app: (A) Sleep satisfaction, (B) Mood, and (C) Anxiety.

Figure 2

Illustration of data conversion from…

Figure 2

Illustration of data conversion from daily mental-health logs into biweekly indicators with frequency…

Figure 2

Illustration of data conversion from daily mental-health logs into biweekly indicators with frequency and ratio approaches: (A) Daily scores of sleep quality during 2 weeks, (B) assigned scores of 1 to the days when the reported score is higher than the cut-off value, (C) calculated scores in a biweekly format.

Figure 3

Results of ROC analysis: (A)…

Figure 3

Results of ROC analysis: (A) ROC curves calculated from three models (full samples),…

Figure 3

Results of ROC analysis: (A) ROC curves calculated from three models (full samples), (B) ROC curves calculated from three models (subsample excluding the daily logs reported on the day the PHQ-9 is administered).

Figure 4

Graphs for ROC comparisons of…

Figure 4

Graphs for ROC comparisons of subsamples by adherence level: (A) ROCs by adherence…

Figure 4

Graphs for ROC comparisons of subsamples by adherence level: (A) ROCs by adherence levels with the ratio approach, (B) ROCs by adherence levels with the average approach, (C) ROCs by adherence levels with the frequency model.