METHODS article
Front. Psychol.
Sec. Addictive Behaviors
This article is part of the Research TopicNeuropsychological Mechanisms Underlying Risk, Resilience, and Intervention Response in Youth Substance UseView all 5 articles
Protocol for AI-Based Prediction of Problematic Digital Technology Use Among Indian Youth: A Centre for Advanced Research on Addictive Behaviours Initiative
Provisionally accepted
YATAN PAL SINGH BALHARA1*
Rajeev Ranjan2
Siddharth Sarkar1
Simran Kaur1
Ragul Ganesh3
Shivanand Kattimani3
Vandena Saxena4
Subhash Das5
Vishal Dhiman6
Rachna Bhargava1Tanmay Joshi7
Anindo Majumdar7
Swarndeep Singh8Bichitra Nanda Patra1- 1All India Institute of Medical Sciences, New Delhi, India
- 2All India Institute of Medical Sciences Patna, Patna, India
- 3Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
- 4University of Delhi, New Delhi, India
- 5North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences, Shillong, India
- 6All India Institute of Medical Sciences Rishikesh, Rishikesh, India
- 7All India Institute of Medical Sciences Bhopal, Bhopal, India
- 8Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
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Background: Artificial intelligence (AI) and machine learning have an important role in mental health research by helping to predict and prevent digital addiction and problematic digital technology use. These include behaviours linked with internet, smartphones, gaming, social media, gambling, over-the-top (OTT) platforms watching, pornography watching, shopping/ buying, and excessive screen time. Objective: This multi-centre study aims to develop and validate an AI-based predictive model to identify Indian youth at risk of problematic use of digital technology and associated psychological outcomes such as stress, anxiety, depression, and addiction. The study corresponds to one of the objectives of the Centre for Advanced Research on Addictive Behaviours (CAR-AB) initiative. Methods: Students aged ≥12 years from schools and colleges across six Indian sites (New Delhi, Bhopal, Patna, Puducherry, Rishikesh, and Shillong) will be recruited. Data will be collected on demographic, psychological, behavioural, cognitive, socio-environmental, and digital phenotype correlates using validated instruments. Machine-learning models, including ensemble and deep-learning methods, will be trained, validated, and interpreted using explainable AI techniques. Expected Outcomes: The study will develop a validated and interpretable predictive model for early detection of
Keywords: Addiction, AI-based predictive model, Anxiety, Depression, Digital Addiction, machine learning, Problematic technology use, Youth mental health
Received: 25 Nov 2025; Accepted: 03 Feb 2026.
Copyright: © 2026 BALHARA, Ranjan, Sarkar, Kaur, Ganesh, Kattimani, Saxena, Das, Dhiman, Bhargava, Joshi, Majumdar, Singh and Patra. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: YATAN PAL SINGH BALHARA
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