Informatics for Depression Detection and Management (Research Paper Sample)
Paper instructions:
1. Select 1 article that used the informatics methodology you would like to utilize (or you are interested in) for your research (e.g., predictive modeling using EHRs, NLP using clinical notes, sensor data analysis).
2. Explain the methodology including a brief description of the method, analyzing steps, strengths, and weaknesses (2-3 pages)
3. Describe how this methodology can be used for your research (1-2 pages)
4. Maximum 5 pages excluding title page and reference, double-spaced, APA 7th
Informatics for Depression Detection and Management
Student’s Name
Institutional Affiliation
Course
Date
Informatics for Depression Detection and Management
Depression is a prevalent mental disorder affecting millions of people around the globe. Compared to other health problems that affect the physical aspect of an individual’s life, reporting depression requires that an individual provides verbal information or written explanations. Depressed anxious individuals may not find enough time to visit hospitals or make a physical contact with a physician for timely intervention (Meyerhoff et al., 2018). Despite the availability of evidence-based, many patients go untreated due to barriers to access and poor management of signs and symptoms. Digital sensors such as smart watches and mobile-based applications have emerged as effective strategies for detecting signs and symptoms of depression for early intervention. This paper discusses the method of sensor data analysis as used in the research study by Moukaddam et al. (2019). The second and concluding section discusses how I intend to use the method for my project for in monitoring heart rate variability and sleep quality using sensor data from smart watch and ring devices.
Sensor Data Analysis
Moukaddam et al. (2019) used the Smart-phone and OnLine usage-based eValuation for Depression (SOLVD) application to explore how the use of digital technology in tracking and collecting sensor data on mood changes and correlates with detection and management of depression. The SOLVD app is downloaded and installed on android mobile devices of targeted adults and older adults (preferable age of 18-75 years). According to Moukaddam et al. (2019), the SOLVD app uses both user-facing mood reminder module and a background mobile logger responsible for recording sensor
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