Evidence map›Paper›PMID 41924059›Full record

ArticleFrontiers in psychology2026

Protocol for AI-based prediction of problematic digital technology use among Indian youth: a Centre for Advanced Research on Addictive Behaviours initiative.

Yatan Pal Singh Balhara, Rajeev Ranjan, Siddharth Sarkar, Simran Kaur, Ragul Ganesh, Shivanand Kattimani, Vandena Saxena, Subhash Das, Vishal Dhiman, Rachna Bhargava and 4 more

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Yatan Pal Singh BalharaBehavioral Addictions Clinic (BAC) and Centre for Advanced Research on Addictive Behaviours (CAR-AB), National Drug Dependence Treatment Centre (NDDTC), All India Institute of Medical Sciences (AIIMS), New Delhi, India.
Rajeev RanjanAll India Institute of Medical Sciences, Patna, India.
Siddharth SarkarAIIMS, New Delhi, India.
Simran KaurDepartment of Physiology, All India Institute of Medical Sciences (AIIMS), New Delhi, India.
Ragul GaneshJawaharlal Institute of Postgraduate Medical Education & Research (JIPMER), Puducherry, India.
Shivanand KattimaniJawaharlal Institute of Postgraduate Medical Education & Research (JIPMER), Puducherry, India.
Vandena SaxenaCIE, Department of Education, University of Delhi, New Delhi, India.
Subhash DasDepartment of Psychiatry, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Shillong, India.
Vishal DhimanAll India Institute of Medical Sciences, Rishikesh, India.
Rachna BhargavaAIIMS, New Delhi, India.
Tanmay JoshiAll India Institute of Medical Sciences, Bhopal, India.
Anindo MajumdarAll India Institute of Medical Sciences, Bhopal, India.
Swarndeep SinghVardhman Mahavir Medical College (VMMC), Safdarjung Hospital, New Delhi, India.
Bichitra Nanda PatraDepartment of Psychiatry, AIIMS, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 behaviors 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-center 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, behavioral, 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 vulnerability to problematic use of digital technology. Findings are expected to inform targeted interventions, guide digital-wellness policies, and support the integration of predictive tools within educational, work, and community settings.

Indexed as

addictionAI-based predictive modelanxietydepressiondigital addictionmachine learningproblematic technology useyouth mental health

Identifiers

PMID41924059
PMCPMC13036647

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.