ArticleFrontiers in pediatrics2022
Communicating Risk for Obesity in Early Life: Engaging Parents Using Human-Centered Design Methodologies.
Article in Frontiers in pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed, 3 citations in OpenAlex.
- End User Needs and Perspectives for a Digital Opioid Safety Tool in Adolescents and Young Adults With Inflammatory Bowel Disease: A Qualitative Human-Centered Design Study.JMIR formative research · 2026Article
- "Let me take the lead": Qualitative Findings From the Parent-Preferred Topics for Childhood Obesity Study.AJPM focus · 2025Article
- Factors Influencing Health-Related Practices Among Hispanic Parents: A Formative Study to Inform Childhood Obesity Prevention.Children (Basel, Switzerland) · 2025Article
- An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation.Proceedings of machine learning research · 2024Article
- Human-centered designed communication tools for obesity prevention in early life.Preventive medicine reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Objective: Pediatricians are well positioned to discuss early life obesity risk, but optimal methods of communication should account for parent preferences. To help inform communication strategies focused on early life obesity prevention, we employed human-centered design methodologies to identify parental perceptions, concerns, beliefs, and communication preferences about early life obesity risk. Methods: We conducted a series of virtual human-centered design research sessions with 31 parents of infants <24 months old. Parents were recruited with a human intelligence task posted on Amazon's Mechanical Turk, via social media postings on Facebook and Reddit, and from local community organizations. Human-centered design techniques included individual short-answer activities derived from personas and empathy maps as well as group discussion. Results: Parents welcomed a conversation about infant weight and obesity risk, but concerns about health were expressed in relation to the future. Tone, context, and collaboration emerged as important for obesity prevention discussions. Framing the conversation around healthy changes for the entire family to prevent adverse impacts of excess weight may be more effective than focusing on weight loss. Conclusions: Our human-centered design approach provides a model for developing and refining messages and materials aimed at increasing parent/provider communication about early life obesity prevention. Motivating families to engage in obesity prevention may require pediatricians and other health professionals to frame the conversation within the context of other developmental milestones, involve the entire family, and provide practical strategies for behavioral change.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.