Background: Health behavior(HB) is an action taken by a person who pursues good health and prevents illness. Health behavior, thus, reflects a person's health beliefs and attracts, particularly, on published papers in academics. However, who is the most influential author(MIA) with highly-cited papers on HB remains unknown.
Objective: The purpose of this study is to apply the authorship-weighted scheme (AWS) developed by authors to select the MIA on HB using the visual displays on Google Maps.
Methods: We obtained 1,116 abstracts published between 2012 and 2016 from Medline based on the keywords of (health[Title]) and (behavior[Title] or behavior [Title]) on September 22, 2018. The author names, countries/areas, and Pubmed paper IDs were recorded. The AWS was applied to (1)select the most productive authors(MPA) using social network analysis(SNA); (2) discover the MIA using h-indexes and author impact factors(AIF) dispersed on Google Maps, and (3)display the countries/areas distributed for the x-index in geography. Pajek software was performed to determine the partition categories of clusters.
Results: We found that the MPA and MIA are Matthew K Nock(US) and Erika A Waters(US) for the MPA and MIA, respectively. All visual representations that are the form of a dashboard can be easily displayed on Google Maps. The most influential countries are the US(=19.03) and Australia(=6.46) with the highest x-indexes. Readers are suggested to manipulate them on their own on Google Maps.
Conclusion: Many individual researchers’ achievements (IRA) were determined using h-index, AIF, x-index, or other bibliometric indices without quantifying author contributions. We demonstrated visualized representations on Google Maps using the AWS developed by authors to measure authors’ influences in a specific discipline. The research approach using the AWS to quantify the authors’ contributions can be applied to measure IRA in the future.
Background: Many studies have indicated a relationship between smoking cessation and a history of depression. However, few studies have examined the association between smoking cessation and current depression and even fewer evidence come from mainland China. The aim of this study is to determine the prevalence of smoking quitters, the correlates of successful smoking cessation, and its relationship with depressive symptoms in Northwest China.
Methods: A total of 7,644 subjects who met the study’s entry criteria were randomly selected from the urban areas of three provinces in Northwest China and interviewed using standardized assessment tools, including basic characteristics of households and detailed information on family members. All respondents provided informed consent.
Results: people with depression symptom have a more than 1.5-fold risk of abstinence from smoking than those without depression (OR=1.54; 95% CI, 1.2 to 1.9) and the likelihood ratio test for two models reach statistical significance (x2=13.2, p<0.001). Smoking quitters have a more than 1.5-fold risk of having depressive symptoms than current smokers (OR=1.54; 95% CI, 1.2 to 1.9) and the likelihood ratio test for two models is also statistically significant (x2=6449.85, p<0.001).
Conclusions: The prevalence of smoking quitters in urban areas of Northwest China is very low. After controlling certain confounders, smoking cessation is associated with current depressive symptoms. More rigorous surveys are needed to elucidate the barriers to smoking cessation in China. Government bodies in China should implement appropriate strategies and execute effective measures to mitigate its harmful consequences.
Background: Dengue fever (DF) is an important health problem in Asia. We examined it using its clinical symptoms to predict DF.
Methods: We extracted statistically significant features from 17 DF-related clinical symptoms in 177 pediatric patients (69 diagnosed with DF) using (1) the unweighted summation score and (2) the non-parametric HT person fit statistic, which jointly combine (3) the weighted score (yielded by logistic regression) to predict DF risk.
Results: Six symptoms (Family History, Fever ≥ 39°C, Skin Rash, Petechiae, Abdominal Pain, and Weakness) significantly predicted DF. When a cutoff point of −1.03 (p = 0.26) suggested combining the weighted score and the HT coefficient, the sensitivity was 0.91 and the specificity was 0.76. The area under the ROC curve was 0.88, which was a better predictor: specificity was 5.56% higher than for the traditional logistic regression.
Conclusions: Six simple symptoms analyzed using logistic regression were useful and valid for early detection of DF risk in children. A better predictive specificity increased after combining the non-parametric HT coefficient to the weighted regression score. A self-assessment using patient smartphones is available to discriminate DF and may eliminate the need for a costly and time-consuming dengue laboratory test.
Background: Health behavior is an action taken by a person to maintain, attain, or regain good health and to prevent illness. As such, health behavior reflects a person's health beliefs and attracts many published papers in academics. However, who is the most influential author(MIA) remains unknown.
Objective: The purpose of this study is to apply the algorithm of between centrality(BC) in social network analysis(SNA) to select the MIA on the topic of health behavior using the visual displays on Google Maps.
Methods: We obtained 3,593 abstracts from Medline based on the keywords of (health[Title]) and (behavior[Title] or behavior [Title]) on June 30, 2018. The author names, countries/areas, and author-defined keywords were recorded. The BCs were applied to (1)select the MIA using SNA; (2)display the countries/areas distributed for the 1st author in geography, (3) discover the author clusters dispersed on Google Maps, and (4)investigate the keywords dispersed for the cluster related to the MIA on a dashboard. Pajek software was performed to yield the BC for each entity(or say node).
Results: We found that the MIA is Spring, Bonnie(US). All visual representations that are the form of a dashboard can be easily displayed on Google Maps. The most influential country and the keywords are the US and health behavior. Readers are suggested to manipulate them on their own on Google Maps.
Conclusion: Social network analysis provides wide and deep insight into the relationships with the pattern of international author collaborations. If incorporated with Google Maps, the dashboard can release much more information regarding our interesting topics for us in academics. The research approach using the BC to identify the same author names can be applied to other bibliometric analyses in the future.