ArticleJournal of healthcare engineering2021
Health Informatics on Adolescents Smoking Based on the Miryoku Engineering Analysis Framework.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 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
3 citing papers in PubMed.
- Promoting active aging through assistive product design innovation: a preference-based integrated design framework.Frontiers in public health · 2023Article
- Retracted: Health Informatics on Adolescents Smoking Based on the Miryoku Engineering Analysis Framework.Journal of healthcare engineering · 2023Article
- Reliability and validity of Foot Posture Index (FPI-6) for evaluating foot posture in participants with low back pain.Scientific reports · 2022Article
Corrections and comments
- Retraction · 2023-08-02Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Smoking among young people has aroused widespread concern in society. The number of young people who smoke has increased year by year, which has largely affected their health. Therefore, how to improve the prevention and control rate of tobacco harm, thereby reducing the smoking rate of young people has become an important issue. Based on the glamour engineering research method that can specifically solve the problem of attractiveness between users and products, the user preference of young people's smoking behavior is studied through the evaluation structure method and quantitative type I analysis, and the potential attractive factors are analyzed. Among them, there are 7 important attractive characteristics that affect the first attempt to smoke. These seven factors are divided into two aspects, namely, personal preference factors and environmental factors. The statistical data obtained through data analysis will be tested in the KMO field. The KMO values are 0.827 and 0.687, respectively, and the statistical results have obvious scores, so this study is effective. By quantifying one category, the partial correlation coefficients of different categories can be found, that is, specific solutions. This method provides a reference for ways and means to curb smoking behavior and also provides a case study for studying user perception. According to the research results, we seek ways to influence the smoking preferences of young people and then adopt some artificial methods to intervene in the generation of these factors, so as to achieve the purpose of reducing smoking among young people.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.