A Multicomponent Intervention to Reduce Screen Time Among Children Aged 2-5 Years in Chandigarh, North India: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Excessive digital screen exposure (≥1 hour per day) is associated with limited growth and development in children. OBJECTIVE: This study aims to develop and assess a multicomponent intervention program's effectiveness in reducing excessive screen time among children aged 2-5 years. METHODS: A theory-based multicomponent intervention known as Program to Lower Unwanted Media Screens (PLUMS) at the household level has been developed. It is based on the social cognitive theory for children and self-determination theory for caregivers. After pretesting, a randomized control trial will be conducted to assess this intervention's effectiveness among healthy children aged 2-5 (±3 months) years and their primary caregivers who have at least one digital media gadget at home in zone three of Chandigarh (population of 2,730,035). A sample size of 428 children is estimated per arm. PLUMS includes disseminating specific information, education, communication in the form of videos and posters to the primary caregivers, and conducting motivational interviewing as and when needed. Children will be provided suggestions for playful activities as alternatives to digital media gadgets. The primary outcome is the mean change in the duration of screen time, and secondary outcomes are sleep duration and patterns, emotional-behavioral problems, and level of physical activity of the children. Per-protocol and intention-to-treat analyses will be conducted using SPSS for Macintosh, Version 25.0. RESULTS: The intervention package will be disseminated once a week for 8 weeks to the participants via the caregivers' preferred means of communication. The endline assessment will be done immediately postintervention and after the 6 months of follow-up. The Institute's ethics committee, Postgraduate Institute of Medical Education and Research, Chandigarh, India, has approved this study (INT/IEC/2019/000711). The Indian Council of Medical Research, New Delhi (3/1/3/Next-100/JRF-2015/HRD), and PGIMER, Chandigarh (71/2-Edu-16/92, Dated 08/01/2018) funded this study. CONCLUSIONS: PLUMS might be effective in reducing excessive screen time among children aged 2-5 years in a North Indian Union Territory. TRIAL REGISTRATION: Clinical Trial Registry India CTRI/2017/09/009761; https://tinyurl.com/53q6dpjs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24106.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".