Study of the Computer-Based Interventions for Adolescents in the Prevention and Treatment of Depression and Anxiety

Main Article Content

Devata Anekar, Yogesh Deshpande,Ranjeetsingh Suryawanshi,Sunil L. Bangare


There is a growing interest in using digital technology to assist and enhance the psychological health of adolescents, and there is a growing sign that these techniques are beneficial. Co-design is a process that involves the lively participation of investors and clinicians from the standard methods that are used in the creation of interventions.

Objective: This review is to regulate the applicability of computer-based interventions for adolescents with psychological disorders like anxiety and or depression. A literature study and analysis of current intervention technology practices for adolescents were conducted..

Methods: Databases including Medline, PsychInfo, and Web of Science were examined, along with recommendations, reviews, and reference lists. Subsequently, key elements of co-design relevant to practice were identified and extracted. Additionally, case studies and methodologies reported by active investigators in the field were incorporated. A preliminary review encompassing 11 papers and a detailed review comprising 22 papers within the domain were completed.

Results: Topics that are discovered through review are the values of co-design ways of including and attracting a variety of researchers with the difficulties associated with co-design.

Conclusion: Our findings highlight the tremendous benefits of employing computer-based therapies for sadness and anxiety. There is a shortage of evidence to guide the co-design of computer-based gadgets that can help adolescents recover their psychological health. The findings of this study show the need for more research initiatives to improve functioning and clinical symptoms.

Article Details

Author Biography

Devata Anekar, Yogesh Deshpande,Ranjeetsingh Suryawanshi,Sunil L. Bangare

[1]Devata Anekar

2Yogesh Deshpande

3Ranjeetsingh Suryawanshi

4Sunil L. Bangare


[1] Department of Computer Engineering, Vishwakarma University, Pune, India


2Department of Computer Engineering, Vishwakarma University and Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, India                                ,

3Department of Computer Engineering Vishwakarma Institute of Technology, Pune, India

4Associate Professor, Department of Information Technology, Sinhgad Academy of Engineering, Savitribai Phule Pune University, Pune, India,



R. A. Calvo, D. N. Milne, M. S. Hussain, and H. Christensen, “Natural language processing in mental health applications using non-clinical texts,” 2017. doi: 10.1017/S1351324916000383.

C. Laske et al., “Innovative diagnostic tools for early detection of Alzheimer’s disease,” Alzheimer’s Dement., vol. 11, no. 5, pp. 561–578, 2015, doi: 10.1016/j.jalz.2014.06.004.

R. Gupta et al., “Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions,” in Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge - AVEC ’14, 2014, pp. 33–40. doi: 10.1145/2661806.2661810.

M. Poonkodi, A. Srinivasan, B. Tumma, and S. Ramaswamy, “A Comprehensive Healthcare System to Detect Depression,” Indian J. Sci. Technol., vol. 9, no. 47, 2016, doi: 10.17485/ijst/2015/v8i1/108470.

J. Joshi et al., “Multimodal assistive technologies for depression diagnosis and monitoring,” J. Multimodal User Interfaces, vol. 7, no. 3, pp. 217–228, 2013, doi: 10.1007/s12193-013-0123-2.

S. Luz, S. de la Fuente, and P. Albert, “A Method for Analysis of Patient Speech in Dialogue for Dementia Detection,” Resour. Process. Linguist. paralinguistic extra-linguistic Data, 2018, [Online]. Available:

H. Lee, Y. Ko, H. Jeong, C. Han, Y. Kim, and S. Joe, “P.2.a.016 Distinguishing quantitative EEG findings between adjustment disorder and major depressive disorder,” Eur. Neuropsychopharmacol., vol. 22, p. S233, 2012, doi: 10.1016/s0924-977x(12)70345-0.

S. Abdullah and T. Choudhury, “Sensing Technologies for Monitoring Serious Mental Illnesses,” IEEE Multimed., vol. 25, no. 1, pp. 61–75, 2018, doi: 10.1109/MMUL.2018.011921236.

D. Zhou et al., “Tackling mental health by integrating unobtrusive multimodal sensing,” in Proceedings of the National Conference on Artificial Intelligence, 2015, vol. 2, pp. 1401–1408. [Online]. Available:

X. Zhou, K. Jin, Y. Shang, and G. Guo, “Visually Interpretable Representation Learning for Depression Recognition from Facial Images,” IEEE Trans. Affect. Comput., 2018, doi: 10.1109/TAFFC.2018.2828819.

G. Valenza, A. Lanatà, R. Paradiso, and E. P. Scilingo, “Advanced technology meets mental health: How smartphones, textile electronics, and signal processing can serve mental health monitoring, diagnosis, and treatment,” IEEE Pulse, vol. 5, no. 3, pp. 56–59, 2014, doi: 10.1109/MPUL.2014.2309582.

D. Yang, J. W. Hur, Y. Bin Kwak, and S. W. Choi, “A systematic review and meta-analysis of applicability of web-based interventions for individuals with depression and quality of life impairment,” Psychiatry Investig., vol. 15, no. 8, pp. 759–766, 2018, doi: 10.30773/pi.2018.03.15.

P. Gooding and T. Kariotis, “A Scoping Review of Algorithmic and Data-Driven Technology in Online Mental Healthcare : What is Underway and What Place for Ethics and Law ? Table of Contents,” JMIR, 2020.

J. Borghouts et al., “Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review,” J. Med. Internet Res., vol. 23, no. 3, 2021, doi: 10.2196/24387.

S. D’Alfonso et al., “Artificial intelligence-assisted online social therapy for youth mental health,” Front. Psychol., vol. 8, no. JUN, 2017, doi: 10.3389/fpsyg.2017.00796.

A. Søgaard Neilsen and R. L. Wilson, “Combining e-mental health intervention development with human computer interaction (HCI) design to enhance technology-facilitated recovery for people with depression and/or anxiety conditions: An integrative literature review,” Int. J. Ment. Health Nurs., vol. 28, no. 1, pp. 22–39, 2019, doi: 10.1111/inm.12527.

C. Su, Z. Xu, J. Pathak, and F. Wang, “Deep learning in mental health outcome research: a scoping review,” Translational Psychiatry, vol. 10, no. 1. Springer US, 2020. doi: 10.1038/s41398-020-0780-3.

Y. Tyshchenko, “Depression and anxiety detection from blog posts data,” Nat. Precis. Sci., Inst. Comput. Sci., Univ. Tartu, Tartu …, 2018, [Online]. Available:

A. Fatima, Y. Li, T. T. Hills, and M. Stella, “Dasentimental: Detecting depression, anxiety, and stress in texts via emotional recall, cognitive networks, and machine learning,” Big Data Cogn. Comput., vol. 5, no. 4, Dec. 2021, doi: 10.3390/bdcc5040077.

E. G. Lattie, E. C. Adkins, N. Winquist, C. Stiles-Shields, Q. E. Wafford, and A. K. Graham, “Digital mental health interventions for depression, anxiety and enhancement of psychological well-being among college students: Systematic review,” J. Med. Internet Res., vol. 21, no. 7, 2019, doi: 10.2196/12869.

M. Köhnen, L. Kriston, M. Härter, J. Dirmaier, and S. Liebherz, “Rationale and design of a systematic review: Effectiveness and acceptance of technology-based psychological interventions in different clinical phases of depression management,” BMJ Open, vol. 9, no. 3, pp. 1–9, 2019, doi: 10.1136/bmjopen-2018-028042.

J. S. Feather, M. Howson, L. Ritchie, P. D. Carter, D. T. Parry, and J. Koziol-McLain, “Evaluation methods for assessing users’ psychological experiences of web-based psychosocial interventions: A systematic review,” J. Med. Internet Res., vol. 18, no. 6, pp. 1–13, 2016, doi: 10.2196/jmir.5455.

F. Alqahtani and R. Orji, “Insights from user reviews to improve mental health apps,” Health Informatics J., vol. 26, no. 3, pp. 2042–2066, 2020, doi: 10.1177/1460458219896492.

I. A. Nanomi Arachchige, P. Sandanapitchai, and R. Weerasinghe, “Investigating machine learning & natural language processing techniques applied for predicting depression disorder from online support forums: A systematic literature review,” Information (Switzerland), vol. 12, no. 11. MDPI, Nov. 01, 2021. doi: 10.3390/info12110444.

A. Le Glaz et al., “Machine learning and natural language processing in mental health: Systematic review,” Journal of Medical Internet Research, vol. 23, no. 5. JMIR Publications, p. 15708, 2021. doi: 10.2196/15708.

A. B. R. Shatte, D. M. Hutchinson, and S. J. Teague, “Machine learning in mental health: A scoping review of methods and applications,” Psychological Medicine, vol. 49, no. 09. Cambridge University Press, pp. 1426–1448, Jul. 12, 2019. doi: 10.1017/S0033291719000151.

S. Chancellor, E. P. S. Baumer, and M. De Choudhury, “Who is the ‘human’ in human-centered machine learning: The case of predicting mental health from social media,” Proc. ACM Human-Computer Interact., vol. 3, no. CSCW, 2019, doi: 10.1145/3359249.

M. R. Hoque, M. S. Rahman, N. J. Nipa, and M. R. Hasan, “Mobile health interventions in developing countries: A systematic review,” Health Informatics J., vol. 26, no. 4, pp. 2792–2810, 2020, doi: 10.1177/1460458220937102.

A. Rajput, “Natural language processing, sentiment analysis, and clinical analytics,” in Innovation in Health Informatics: A Smart Healthcare Primer, 2019, pp. 79–97. doi: 10.1016/B978-0-12-819043-2.00003-4.

K. Zeberga, M. Attique, B. Shah, F. Ali, Y. Z. Jembre, and T. S. Chung, “A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model,” Comput. Intell. Neurosci., vol. 2022, pp. 1–18, Mar. 2022, doi: 10.1155/2022/7893775.

F. Burger, M. A. Neerincx, and W. P. Brinkman, “Technological State of the Art of Electronic Mental Health Interventions for Major Depressive Disorder: Systematic Literature Review,” J. Med. Internet Res., vol. 22, no. 1, p. e12599, 2020, doi: 10.2196/12599.

R. Grist, A. Croker, M. Denne, and P. Stallard, “Technology Delivered Interventions for Depression and Anxiety in Children and Adolescents: A Systematic Review and Meta-analysis,” Clin. Child Fam. Psychol. Rev., vol. 22, no. 2, pp. 147–171, 2019, doi: 10.1007/s10567-018-0271-8.

A. Fonseca and J. Osma, “Using information and communication technologies (Ict) for mental health prevention and treatment,” Int. J. Environ. Res. Public Health, vol. 18, no. 2, pp. 1–6, 2021, doi: 10.3390/ijerph18020461.

D. M. Caldwell et al., “School-based interventions to prevent anxiety and depression in children and young people: a systematic review and network meta-analysis,” The Lancet Psychiatry, vol. 6, no. 12, pp. 1011–1020, 2019, doi: 10.1016/S2215-0366(19)30403-1.

S. Stjerneklar, E. Hougaard, A. D. Nielsen, M. M. Gaardsvig, and M. Thastum, “Internet-based cognitive behavioral therapy for adolescents with anxiety disorders: A feasibility study,” Internet Interv., vol. 11, no. December 2017, pp. 30–40, 2018, doi: 10.1016/j.invent.2018.01.001.

S. T. A. Shatte, D. Hutchinson, “Machine learning in mental health: A systematic scoping review of methods and applications Adrian B. R. Shatte*”.

S. Saddichha, M. Al-Desouki, A. Lamia, I. A. Linden, and M. Krausz, “Online interventions for depression and anxiety – A systematic review,” Health Psychology and Behavioral Medicine, vol. 2, no. 1. Taylor & Francis, pp. 841–881, 2014. doi: 10.1080/21642850.2014.945934.

E. J. Costello, “Early Detection and Prevention of Mental Health Problems: Developmental Epidemiology and Systems of Support,” J. Clin. Child Adolesc. Psychol., vol. 45, no. 6, pp. 710–717, 2016, doi: 10.1080/15374416.2016.1236728.

B. Aryana, L. Brewster, and J. A. Nocera, “Design for mobile mental health: an exploratory review,” Health Technol. (Berl)., 2018, doi: 10.1007/s12553-018-0271-1.

V. P. Cornet and R. J. Holden, “Systematic review of smartphone-based passive sensing for health and wellbeing,” J. Biomed. Inform., vol. 77, no. December 2017, pp. 120–132, 2018, doi: 10.1016/j.jbi.2017.12.008.

T. W. Boonstra, J. Nicholas, Q. J. Wong, F. Shaw, S. Townsend, and H. Christensen, “Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions,” J. Med. Internet Res., vol. 20, no. 7, p. e10131, 2018, doi: 10.2196/10131.