ArticleJMIR aging2020
Digital Media as a Proponent for Healthy Aging in the Older Chinese American Population: Longitudinal Analysis.
Article in JMIR aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- The quality and reliability of short videos about chronic obstructive pulmonary disease on TikTok and Bilibili: a cross-sectional study.Journal of thoracic disease · 2026Article
- Influence of social media on the oral health-related quality of life and cognitive function in elderly individuals: A cross-sectional study.Journal of education and health promotion · 2026Article
- Mental health of older Asian Americans: Current issues, updates, and future directions.World journal of psychiatry · 2025Review
- Barriers to Online Dementia Information and Mitigation.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2022Article
- Use of technology and social media in dementia care: Current and future directions.World journal of psychiatry · 2021Review
- Emerging wearable technology applications in gastroenterology: A review of the literature.World journal of gastroenterology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEnsuring health literacy among underserved populations is essential amid an aging population. Accessible and appropriate (both culturally and linguistically) information is important when considering digital media education for older Chinese Americans.
objectiveThis study aims to investigate how social media fare over time in disseminating health information and how we may most effectively educate this population.
methodsFor this study, 5 geriatric-themed educational videos about Parkinson disease, fall prevention, gastrointestinal health, oral health, and pulmonary disease were uploaded to YouTube. Data were collected over a 40-month period. Descriptive statistics and chi-square analysis were used to compare results from the first and second 20-month periods.
resultsIn 40 months, the 5 videos in aggregate accrued 1171.1 hours of watch time, 7299 views, and an average view duration of 9.6 minutes. Comparing the first and second 20-month periods, there was a significant increase in mobile device usage, from 79.4% (3541/4458) to 83.3% (2367/2841). There was no significant difference in the usage of various external traffic sources and methods of sharing, with WhatsApp accounting for the majority of sharing in both 20-month periods.
conclusionsOur study provides insight into where to focus future strategies to optimize digital media content, and how to best recruit, direct, and disseminate health education to an older adult Chinese American population. Combining the success of YouTube, social media, and messaging platforms such as WhatsApp can help to transcend cultural and linguistic barriers to promote healthy aging.
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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 OpenQuestion graph.