Using Mobile Daily Mood and Anxiety Self-ratings to Predict Depression Symptom Improvement

Soumyashree Sahoo,Chinmaey Shende, Md Zakir Hossain, Parit Patel, Xinyu Wang,Md Ishtyaq Mahmud,Jinbo Bi,Jayesh Kamath,Alexander Russell,Dongjin Song,Bing Wang

2024 IEEE/ACM CONFERENCE ON CONNECTED HEALTH APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES, CHASE 2024（2024）

Univ Connecticut

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Abstract

Effective treatment of depression is important for the well-being of individuals and the overall health of the society. The current treatment approach calls for monitoring and assessing depression symptoms using self-administered or clinician-administered questionnaires, which are burdensome, costly, and may suffer from recall and desirability bias. In this study, we explore using daily 5-point Likert-scale mood and anxiety survey in place of burdensome clinical depression questionnaires for monitoring depression treatment. Specifically, we collect daily mood and anxiety surveys using a smartphone app, and use them to predict depression symptom improvement in a clinical depressed population. Using a dataset from 67 participants, we show that both mood and anxiety features obtained from the daily survey have significant correlation with clinical questionnaire scores. We then develop a family of machine learning models that use mood and anxiety features (separately or in combination) to predict symptom improvement status on a weekly basis. The best prediction F 1 score achieved by these models is 0.65. While this accuracy is lower than what is achieved by clinical questionnaires (best F 1 score being 0.71), daily survey is much less burdensome, and hence we believe that it provides a promising direction in monitoring depression symptom improvement. We further show that the prediction accuracy is not sensitive to missing data, allowing not very regular responses in practice. Last, we show that adding more historical data beyond the current week does not provide much benefits in improving prediction accuracy, and daily mood/anxiety self-ratings can predict improvement status accurately one or two weeks in the future.

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Key words

Depressive Symptoms,Improvement In Depressive Symptoms,Daily Mood,Machine Learning,Prediction Accuracy,Recall Bias,Machine Learning Models,F1 Score,Diagnosis Of Depression,Treatment Of Depression,Improve Prediction Accuracy,Day Of The Survey,Clinical Questionnaire,Assessment Of Depressive Symptoms,Discourage,Learning Algorithms,Support Vector Machine,Random Forest,Weeks Of Data,Mood Ratings,XGBoost,Radial Basis Function Kernel,Daily Data,Precision And Recall,Prediction Scenarios,Patient Health Questionnaire-9 Scores,Sequence Prediction,Sensor Data

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【要点】：本研究探索使用每日情绪和焦虑自我评分代替传统的抑郁问卷，以监测抑郁症状的改善，并开发机器学习模型预测症状改善情况，取得了较好的预测效果。

【方法】：通过智能手机应用收集67名参与者的每日5点李克特量表情绪和焦虑调查数据，利用这些数据开发了一系列机器学习模型，以预测抑郁症状的改善情况。

【实验】：使用67名参与者的数据集，实验结果表明，每日调查所得的情绪和焦虑特征与临床问卷得分有显著相关性，最佳预测模型的F1得分为0.65，虽然低于临床问卷的0.71，但每日调查的负担较小，且预测准确度对缺失数据不敏感，增加了实用性。此外，增加历史数据对预测准确度的提升有限，每日情绪/焦虑自我评分能够准确预测一至两周内的症状改善情况。