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SOS-DR: a social warning system for detecting users at high risk of depression

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Abstract

Mental disorders have become a major disease observed in people with a contemporary lifestyle. Similar to many physical diseases, prevention and earlier detection are critical for mental health. This paper proposes a mobile device-based system, referred to as the SOcial warning System for Depression Risk (SOS-DR), to automatically estimate the risk of a user experiencing depression, identify users with a high risk, and provide them with help by recommending useful information (such as the Center for Epidemiological Studies Depression Scale (CES-D) questionnaire) and warning their close friends. For these purposes, SOS-DR provides a friendly interface for users to post on Facebook and derives a risk score of depression for each user by monitoring the frequencies with which symptoms of depression are mentioned in his/her online posts. Once a user’s risk score is detected to be high, SOS-DR recommends useful information and warns the user’s close friends to take additional and timely care of him/her. In the inference of risk scores, SOS-DR adopts a weighted Bayesian method and introduces a time decay factor in the calculations of symptom weights to highlight the impor- tance of recent symptoms. Therefore, the SOS-DR can monitor users’ mental states in daily life and send timely alerts. Evaluations using real online posts have showed that the effectiveness of time decay-weighted Bayesian methods is highly consistent with that of the CES-D. System usability studies and qualitative comparisons with other similar systems have also demonstrated the ease of use and the advantages of SOS-DR.

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Notes

  1. 1 http://www.who.int/.

  2. 2 http://psychcentral.com/.

  3. 3 http://www.mayoclinic.org/.

  4. Note that as the concept of depression ontology is followed, the implementation of the SOS-DR is not limited to the use of the depression ontology proposed in [4, 13]. Instead, as depressive symptoms will be even well studied in the future, the SOS-DR can be further enhanced with a better depression ontology that provides more precise and detailed depressive symptoms and observation probabilities.

  5. Later in the experiments, we will show the performances of the SOS-DR that applies the four different weighting functions to demonstrate the advantages of considering the symptom frequency and time decay factors in the weighting functions. Nevertheless, exploiting the best form of the weighting function is beyond the scope of this paper and will be left for the future work.

  6. Diary.com, http://diary.com/.

  7. 7 http://mydepressiondiary.tumblr.com/http://adiaryofadepressedstudent.blogspot.tw/.

  8. 8 http://www.measuringusability.com/sus.php.

    1. Step 1.

      Subtract one from the user responses for each odd-numbered item.

    2. Step 2.

      Subtract five from the user responses for each even-numbered item.

    3. Step 3.

      Add the converted responses for each user and multiply that total by 2.5.

  10. 10 http://counsellingresource.com/quizzes/depression-testing/goldberg-depression/.

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Funding

This work is supported in part by the University System of Taipei Joint Research Program through grant USTP-NTPU-TMU-106-02 and by the Ministry of Science and Technology, R.O.C. through grants MOST 104-2221-E-305-010-, MOST 105-2221-E-305-010-, MOST 105-2221-E-305-012-, and MOST 106-2221-E-305-014-.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan

    Chih-Hua Tai, Ying-En Fang & Yue-Shan Chang

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  1. Chih-Hua Tai
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Correspondence to Chih-Hua Tai.

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Tai, CH., Fang, YE. & Chang, YS. SOS-DR: a social warning system for detecting users at high risk of depression. Pers Ubiquit Comput 26, 837–848 (2022). https://doi.org/10.1007/s00779-017-1092-3

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  • DOI: https://doi.org/10.1007/s00779-017-1092-3

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