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우울 증상과 수준에 따른 패시브 센싱의 패턴 조사

Investigation of a Passive Sensing Pattern by the Depression Symptom and Level

정경미|정승민
한국건강심리학회 (Korean Health Psychological Association)|2021.07|한국심리학회지: 건강 (Korean Journal of Health Psychology)|제26권 제4호|pp. 807-833|DOI 10.17315/kjhp.2021.26.4.009

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참고문헌

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우울depression증상symptomlevel수준패시브 센싱passive sensing
주제분류심리과학

초록

패시브 센싱은 스마트 기기를 통해 행동, 생리, 사회, 환경적 지표를 객관적이고 자동적으로 수집하는 방법으로, 최근 정신장애 평가 도구로서의 가능성이 탐색되고 있다. 그 중 우울장애에 대한 연구가 가장 활발한데, 우울장애는 증상과 수준에 따라 발현 양상이 상이할 수 있음에도 불구하고, 센서데이터를 이용한 연구에서는 이런 측면에 대한 고려가 제한적이다. 본 연구에서는 우울 증상별 관련이 높은 센서데이터를 규명하고, 우울 수준에 따라 센서데이터의 차이가 있는지를 확인하고자 하였다. 이를 위해 총 64명의 대학생 및 대학원생을 우울 수준에 따라 세 가지 집단(정상군, 경도 우울군, 고도 우울군)으로 분류하고, 총 30일간 우울장애의 9개 증상에 대한 자기보고 자료를 1일 4회, 반복적으로 수집하였다. 동시에, 스마트폰 및 스마트워치를 사용하여 총 8개의 센서데이터를 항시 수집하였고, 여기서 총 14개의 특성정보를 1일 4개의 값으로 추출하였다. 연구 결과, 각 증상별로 서로 다른 특성정보가 유의미한 관계를 가지는 것으로 나타났는데, DSM-5에 따른 9개의 증상 중, 2개 증상(흥미 저하, 피로감)은 모든 특성정보와 관련 있고, 5개 증상(우울한 기분, 수면 문제, 집중력 저하, 정신운동 문제, 자살사고)은 일부 특성정보와만 관련 있었지만, 나머지 2개 증상(식욕 문제, 무가치감)은 어떠한 특성정보와도 관련 없는 것으로 나타났다. 또한, 우울 수준에 따라 모든 특성정보 값에 유의미한 차이가 있는 것으로 나타났는데, 집단 차의 세부양상은 특성정보마다 달랐다. 활동성 및 이동성 지표는 정상군에 더 높게 나타났고, 휴대폰 잠금해제 기록과 평균 수면 시간과의 차이는 우울군에서 더 높게 나타났으며, 통화기록과 방문 장소의 개수는 경도 우울군에서 가장 높게 나타났다. 본 결과를 바탕으로, 연구의 임상적 함의와 한계점에 대해 논의하였다.

Abstract

Passive sensing, which collects behavioral, physiological, social, and environmental indices with smart devices in an objective and automatic manner, is recently being explored as a tool for evaluating mental disorders. Among them, studies on depressive disorder are the most commonly performed. Although the expression patterns of depressive disorder may differ according to the symptoms and levels, studies that take this point into account while examining the relationship with sensor data are very limited. The purpose of this study was to identify sensor data that is highly related to depression symptoms, and to determine whether there is a difference in sensor data according to the level of depression. A total of 64 college students and graduate students were classified into three groups (normal, mildly depressed, and severely depressed) according to the level of depression. For a total of 30 days, self-reported data on 9 symptoms of depressive disorder were repeatedly collected 4 times a day. At the same time, a total of 8 sensor data were collected throughout the day using a smartphone and a smartwatch, and a total of 14 features were extracted as 4 values ​​per day. According to the results of this study, different features were found to have a significant relationship with each symptom. Some symptoms were found to be related with all features, some symptoms were related with only partial features, and some symptoms were not related with any features. In addition, it was found that there were significant differences in all features ​​according to the level of depression, and detailed patterns were different for each feature. Implications and limitations of the current study and directions for future research were further discussed.

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