Mental Health Mobile Application with Diagnosis, Sentiment Analysis and Chatbot

Authors

  • Tanvi Gadgil  Computer Department, MMCOE, Pune, Maharashtra, India
  • Shailaja Jadhav  Assistant Professor, Computer Department, MMCOE, Pune, Maharashtra, India
  • Anjali Kumari  Computer Department, MMCOE, Pune, Maharashtra, India
  • Mrunali Dasari   Computer Department, MMCOE, Pune, Maharashtra, India
  • Chanchal Bhangdia  Computer Department, MMCOE, Pune, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT228327

Keywords:

Sentiment Analysis, Mental Health Tracker, Chatbot, Apps, Smart-Phone, Depression, common mental disorders, approaches to mental wellbeing.

Abstract

Mobile phones are probably one of the fastest growing and most rapidly adopted technologies in the world. The various apps and their health features are still relatively new, but their popularity is growing rapidly. The purpose of this study is to explore multiple elements of mental health applications. This study examines many aspects of mental health-related applications available on the Google Play Store between 2016 and 2020. We used a list of keywords such as mental health, mental illness, mental illness, mental illness remedies, and mental illness remedies to search for apps in the Google Play store. Various applications and programming tools were used to scrape the data. According to our findings, psychiatric apps primarily address the following symptoms: depression, anxiety, general mental health, stress, post-traumatic stress disorders, bipolar disorders, panic disorders, Sleep disorders, schizophrenia, compulsive disorders, substance abuse (drugs and alcohol), addiction (techniques, etc.). The app, on the other hand, offers different approaches to improving mental health. Relaxation, stress management, symptom tracking, soothing audio, journaling, connecting with mental health resources, interpersonal support, meditation, mood tracking, etc. are one of the approaches. These simple and engaging mental health apps have addressed specific mental health issues. The most common strategy for dealing with these issues is relaxation. It was not possible to predict the reliability of these applications based on their ratings and the number of users rated.

References

  1. Institute of Health Metrics and Evaluation. Global Health Data Exchange (GHDx).(Accessed 1 May 2021).
  2. Artur Rocha, Mario Ricardo Henriques, Jo ̃ao Correia Lopes, Rui Camacho, Michel Klein, Gabriele Modena, Pepijn Van de Ven, Elaine McGovern, Eric Tousset, Thibaut Gauthier, and Lisanne Warmerdam. Ict4depression: Service oriented architecture applied to the treatment of depression. In 2012 25th IEEE International Symposiumon Computer-Based Medical Systems (CBMS), 2012
  3. Ariel S. Teles, Francisco J. Silva, Artur Rocha, Jo ̃ao Correia Lopes, Donal O’Sullivan, Pepijn Van de Ven, and Markus Endler. Towards situation-aware mobile applications in mental health. In 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), pages 349–354, 2016.
  4. Martha Neary, Stephen M. Schueller, State of the Field of Mental Health Apps, Cognitive and Behavioral Practice, Volume 25, Issue 4, 2018, ISSN 1077-7229
  5. Monney G, Penzenstadler L, Dupraz O, Etter JF, Khazaal Y. mHealth App for Cannabis Users: Satisfaction and Perceived Usefulness. Front Psychiatry. 2015 Aug 27;6:120. doi: 10.3389/fpsyt.2015.00120. PMID: 26379561; PMCID: PMC4550753.
  6. Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S, Sarris J. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World Psychiatry. 2017 Oct;16(3):287-298. doi: 10.1002/wps.20472. PMID: 28941113; PMCID: PMC5608852.
  7. Weisel KK, Fuhrmann LM, Berking M, Baumeister H, Cuijpers P, Ebert DD. Standalone smartphone apps for mental health-a systematic review and meta-analysis. NPJ Digit Med. 2019 Dec 2;2:118. doi: 10.1038/s41746-019-0188-8. PMID: 31815193; PMCID: PMC6889400.
  8. Grading quality of evidence and strength of recommendations BMJ 2004; 328 :1490 doi:10.1136/bmj.328.7454.1490
  9. Costello, KL, Floegel, D. “Predictive ads are not doctors”: Mental health tracking and technology companies. Proc Assoc Inf Sci Technol. 2020; 57:e250. https://doi.org/10.1002/pra2.250
  10. Vázquez FL, Torres  Á,  Díaz  O, Páramo M,  Otero P, Blanco  V.  Cognitive behavioral  intervention  via a smartphone app for non-professional caregivers with depressive symptoms: study protocol for a randomized controlled trial. Trials 2018;19(1). 
  11. Lui JHL, Marcus DK, Barry CT. Evidence-based apps? A review of mental health mobile applications in a psychotherapy context. Profess Psychol Res Pract 2017;48(3):199–210.
  12. A. Jiao, “An Intelligent Chatbot System Based on Entity Extraction Using RASA NLU and Neural Network,” J. Phys. Conf. Ser., vol. 1487, no. 1, 2020, doi: 10.1088/1742-6596/1487/1/012014
  13. Carver CS. You want to measure coping but your protocol's too long: consider the brief COPE. Int J Behav Med. 1997;4(1):92-100. doi: 10.1207/s15327558ijbm0401_6. PMID: 16250744.
  14. Vázquez FL, Torres  Á,  Díaz O, Páramo M,  Otero P, Blanco  V.  Cognitive behavioral intervention via a
  15. smartphone app for non-professional caregivers with depressive symptoms: study protocol for a randomized controlled trial. Trials 2018;19(1).
  16. ”Insights from user reviews to improve mental health apps” Felwah Alqahtani, Rita OrjiFirst Published January 10, 2020 Research Article https://doi.org/10.1177/1460458219896492
  17. Islam, Md. Aminul & Choudhury, Naziat. (2020). Original Research Article: Mobile Apps for Mental Health: a content analysis. Indian Journal of Mental Health. 7. 222. 10.30877/IJMH.7.3.2020.222-229.
  18. B. Nandi, M. Ghanti and S. Paul, "Text based sentiment analysis," 2017 International Conference on Inventive Computing and Informatics (ICICI), 2017, pp. 9-13, doi: 10.1109/ICICI.2017.8365326.
  19. B. Saju, S. Jose and A. Antony, "Comprehensive Study on Sentiment Analysis: Types, Approaches, Recent Applications, Tools and APIs," 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), 2020, pp. 186-193, doi: 10.1109/ACCTHPA49271.2020.9213209.
  20. R. Hu, L. Rui, P. Zeng, L. Chen and X. Fan, "Text Sentiment Analysis: A Review," 2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018, pp. 2283-2288, doi: 10.1109/CompComm.2018.8780909.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-06-30

Issue

Volume 8, Issue 3, May-June-2022

Section

Research Articles

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How to Cite

[1]
Tanvi Gadgil, Shailaja Jadhav, Anjali Kumari, Mrunali Dasari , Chanchal Bhangdia, " Mental Health Mobile Application with Diagnosis, Sentiment Analysis and Chatbot, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 3, pp.94-100, May-June-2022. Available at doi : https://doi.org/10.32628/CSEIT228327