Evidence map›Paper›PMID 35353056›Full record

Observational studyJournal of medical Internet research2022

Content Analysis of Nicotine Poisoning (Nic Sick) Videos on TikTok: Retrospective Observational Infodemiology Study.

Vidya Purushothaman, Tiana McMann, Matthew Nali, Zhuoran Li, Raphael Cuomo, Tim K Mackey

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.6field-weighted citation impact, top 7% of its field
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

12 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. A Narrative Review of Nicotine and Alcohol Addiction Recovery Content on TikTok.Journal of the Korean Academy of Child and Adolescent Psychiatry · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Promotion of E-Cigarettes on TikTok and Regulatory Considerations.International journal of environmental research and public health · 2023
    Article
  11. Article
  12. Article
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

6 authors at 1 institution in 1 country.

Vidya PurushothamanGlobal Health Policy and Data Institute, San Diego, CA, United States.ORCID 0000-0002-6270-448X
Tiana McMannGlobal Health Policy and Data Institute, San Diego, CA, United States.ORCID 0000-0003-1964-5549
Matthew NaliGlobal Health Policy and Data Institute, San Diego, CA, United States.ORCID 0000-0002-0565-8202
Zhuoran LiGlobal Health Policy and Data Institute, San Diego, CA, United States.ORCID 0000-0001-6192-4301
Raphael CuomoGlobal Health Policy and Data Institute, San Diego, CA, United States.ORCID 0000-0002-8179-0619
Tim K MackeyGlobal Health Policy and Data Institute, San Diego, CA, United States.ORCID 0000-0002-2191-7833
University of California San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTikTok is a microvideo social media platform currently experiencing rapid growth and with 60% of its monthly users between the ages of 16 and 24 years. Increased exposure to e-cigarette content on social media may influence patterns of use, including the risk of overconsumption and possible nicotine poisoning, when users engage in trending challenges online. However, there is limited research assessing the characteristics of nicotine poisoning-related content posted on social media.

objectiveWe aimed to assess the characteristics of content on TikTok that is associated with a popular nicotine poisoning-related hashtag.

methodsWe collected TikTok posts associated with the hashtag #nicsick, using a Python programming package (Selenium) and used an inductive coding approach to analyze video content and characteristics of interest. Videos were manually annotated to generate a codebook of the nicotine sickness-related themes. Statistical analysis was used to compare user engagement characteristics and video length in content with and without active nicotine sickness TikTok topics.

resultsA total of 132 TikTok videos associated with the hashtag #nicsick were manually coded, with 52.3% (69/132) identified as discussing firsthand and secondhand reports of suspected nicotine poisoning symptoms and experiences. More than one-third of nicotine poisoning-related content (26/69, 37.68%) portrayed active vaping by users, which included content with vaping behavior such as vaping tricks and overconsumption, and 43% (30/69) of recorded users self-reported experiencing nicotine sickness, poisoning, or adverse events such as vomiting following nicotine consumption. The average follower count of users posting content related to nicotine sickness was significantly higher than that for users posting content unrelated to nicotine sickness (W=2350.5, P=.03).

conclusionsTikTok users openly discuss experiences, both firsthand and secondhand, with nicotine adverse events via the #nicsick hashtag including reports of overconsumption resulting in sickness. These study results suggest that there is a need to assess the utility of digital surveillance on emerging social media platforms for vaping adverse events, particularly on sites popular among youth and young adults. As vaping product use-patterns continue to evolve, digital adverse event detection likely represents an important tool to supplement traditional methods of public health surveillance (such as poison control center prevalence numbers).

Indexed as

Electronic Nicotine Delivery SystemsSocial MediaAdolescentAdultHumansInfodemiologyNicotineRetrospective StudiesYoung AdultNicotineadverse effectscontent analysise-cigaretteinfodemiologynicotinenic sickpublic healthsmokingsocial mediaTikToktobaccovaping

Identifiers

PMID35353056
PMCPMC9008518
OpenAlexW4220992872

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.