scholarly journals Health Goal Attainment of Patients With Chronic Diseases in Web-Based Patient Communities: Content and Survival Analysis

10.2196/19895 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e19895
Author(s):  
Jiahe Song ◽  
Pei Xu ◽  
David B Paradice
Keyword(s):  
Social Media ◽  
Survival Analysis ◽  
Chronic Diseases ◽  
Goal Attainment ◽  
Hazard Ratio ◽  
Data Set ◽  
Web Based ◽  
Self Reflection ◽  
Health Goals ◽  
Health Goal

Background Activities directed at attaining health goals are a major part of the daily lives of those fighting chronic diseases. A proliferating population of patients with chronic diseases are participating in web-based patient communities, wherein they can exchange health information and pursue health goals with others virtually. Objective In this study, we aimed to understand the effect of participation in social media–enabled web-based patient communities on health goal attainment. In particular, we studied the antecedents of health goal attainment in terms of social support and self-reflection in web-based patient communities. Methods This data set consists of web-based health management activities of 392 patients across 13 health support groups, that is, groups with medical issues such as high blood pressure, diabetes, and breast cancer; the data of the activities were collected from a leading web-based patient community. Content analysis was used to code the social interactions among the patients on the web-based platform. Cox regression for survival analysis was used to model the hazard ratio of health goal attainment. Results Our analysis indicated that emotional support from web-based patient communities can increase patients’ probability of achieving their goals (hazard ratio 1.957, 95% CI 1.416-2.706; P<.001) while informational support does not appear to be effective (P=.06). In addition, health-related self-reflection increases the patients’ likelihood of goal attainment through web-based patient communities (hazard ratio 1.937, 95% CI 1.318-2.848; P<.001), but leisure-oriented self-reflection reduces this likelihood (hazard ratio 0.588, 95% CI 0.442-0.784; P<.001). Conclusions Social media–enabled web-based platforms assist health goal management via both social interaction and personal discipline. This study extends the understanding of web-based patient communities by investigating the effects of both social and cognitive factors on goal attainment. In particular, our study advocates that health goals relating to chronic conditions can be better managed when patients use the facilities of web-based health communities strategically.

scholarly journals Health Goal Attainment of Patients With Chronic Diseases in Web-Based Patient Communities: Content and Survival Analysis (Preprint)

2020 ◽  
Author(s):  
Jiahe Song ◽  
Pei Xu ◽  
David B Paradice
Keyword(s):  
Social Media ◽  
Survival Analysis ◽  
Chronic Diseases ◽  
Goal Attainment ◽  
Hazard Ratio ◽  
Data Set ◽  
Web Based ◽  
Self Reflection ◽  
Health Goals ◽  
Health Goal

BACKGROUND Activities directed at attaining health goals are a major part of the daily lives of those fighting chronic diseases. A proliferating population of patients with chronic diseases are participating in web-based patient communities, wherein they can exchange health information and pursue health goals with others virtually. OBJECTIVE In this study, we aimed to understand the effect of participation in social media–enabled web-based patient communities on health goal attainment. In particular, we studied the antecedents of health goal attainment in terms of social support and self-reflection in web-based patient communities. METHODS This data set consists of web-based health management activities of 392 patients across 13 health support groups, that is, groups with medical issues such as high blood pressure, diabetes, and breast cancer; the data of the activities were collected from a leading web-based patient community. Content analysis was used to code the social interactions among the patients on the web-based platform. Cox regression for survival analysis was used to model the hazard ratio of health goal attainment. RESULTS Our analysis indicated that emotional support from web-based patient communities can increase patients’ probability of achieving their goals (hazard ratio 1.957, 95% CI 1.416-2.706; <i>P</i>&lt;.001) while informational support does not appear to be effective (<i>P</i>=.06). In addition, health-related self-reflection increases the patients’ likelihood of goal attainment through web-based patient communities (hazard ratio 1.937, 95% CI 1.318-2.848; <i>P</i>&lt;.001), but leisure-oriented self-reflection reduces this likelihood (hazard ratio 0.588, 95% CI 0.442-0.784; <i>P</i>&lt;.001). CONCLUSIONS Social media–enabled web-based platforms assist health goal management via both social interaction and personal discipline. This study extends the understanding of web-based patient communities by investigating the effects of both social and cognitive factors on goal attainment. In particular, our study advocates that health goals relating to chronic conditions can be better managed when patients use the facilities of web-based health communities strategically.

Health goals among American adults: Prevalence, characteristics, and barriers

2016 ◽  
Vol 23 (10) ◽  
pp. 1350-1355
Author(s):  
Marina Milyavskaya ◽  
Daniel Nadolny
Keyword(s):  
Social Support ◽  
Goal Setting ◽  
Goal Attainment ◽  
Implementation Intentions ◽  
Goal Pursuit ◽  
Health Goals ◽  
Health Goal ◽  
Actual Use ◽  
Reported Health

Although numerous factors have been demonstrated in laboratory settings to lead to more successful health goal attainment, their actual use in daily goal pursuit is unknown. This study examines spontaneously reported health goals and their characteristics in a sample of 557 American adults. Participants responded to questions about health and health goals, with items assessing motivation, social support, and implementation intentions. In all, 66 percent of respondents had a health goal, 26 percent of participants had implementation intentions, and 47 percent received support from close others. Results suggest that interventions should focus on encouraging goal setting, teaching implementation intentions, and educating close others in providing support.

Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives (Preprint)

2018 ◽  
Author(s):  
Anika Oellrich ◽  
George Gkotsis ◽  
Richard James Butler Dobson ◽  
Tim JP Hubbard ◽  
Rina Dutta
Keyword(s):  
Social Media ◽  
Family Relationships ◽  
Text Processing ◽  
Automated Analysis ◽  
Health Concern ◽  
Dementia Diagnosis ◽  
Data Set ◽  
Social Media Data ◽  
Real Time Processing ◽  
Media Data

BACKGROUND Dementia is a growing public health concern with approximately 50 million people affected worldwide in 2017 and this number is expected to reach more than 131 million by 2050. The toll on caregivers and relatives cannot be underestimated as dementia changes family relationships, leaves people socially isolated, and affects the finances of all those involved. OBJECTIVE The aim of this study was to explore using automated analysis (i) the age and gender of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) relevant subreddits authors are posting to, (iv) the types of messages posted and (v) the content of these posts. METHODS We analysed Reddit posts concerning dementia diagnoses. We used a previously developed text analysis pipeline to determine attributes of the posts as well as their authors to characterise online communications about dementia diagnoses. The posts were also examined by manual curation for the diagnosis provided and the person affected. Furthermore, we investigated the communities these people engage in and assessed the contents of the posts with an automated topic gathering technique. RESULTS Our results indicate that the majority of posters in our data set are women, and it is mostly close relatives such as parents and grandparents that are mentioned. Both the communities frequented and topics gathered reflect not only the sufferer's diagnosis but also potential outcomes, e.g. hardships experienced by the caregiver. The trends observed from this dataset are consistent with findings based on qualitative review, validating the robustness of social media automated text processing. CONCLUSIONS This work demonstrates the value of social media data sources as a resource for in-depth studies of those affected by a dementia diagnosis and the potential to develop novel support systems based on their real time processing in line with the increasing digitalisation of medical care.

Estimating the number of cases during COVID-19 pandemics using a web-based tool: first week results of “COVID-19 TRENDS” social media project in the Araba-Alava province in Basque country, Spain. (Preprint)

2020 ◽  
Author(s):  
Ignacio Garitano ◽  
Manuel Linares ◽  
Laura Santos ◽  
Ruth Gil ◽  
Elena Lapuente ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Response Rate ◽  
Basque Country ◽  
Case Definition ◽  
Public Health Action ◽  
Web Based ◽  
Health Action ◽  
Estimate Number ◽  
Media Project

UNSTRUCTURED On 28th February a case of COVID-19 was declared in Araba-Álava province, Spain. In Spain, a confinement and movement restrictions were established by Spanish Government at 14th March 2020. We implemented a web-based tool to estimate number of cases during the pandemic. We present the results in Áraba-Álava province. We reached a response rate of 10,3% out a 331.549 population. We found that 22,4 % fulfilled the case definition. This tool rendered useful to inform public health action.

scholarly journals Quantifying Media Effects, Its Content, and Role in Promoting Community Awareness of Chikungunya Epidemic in Bangladesh

Epidemiologia ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 84-94
Author(s):  
Mst. Marium Begum ◽  
Osman Ulvi ◽  
Ajlina Karamehic-Muratovic ◽  
Mallory R. Walsh ◽  
Hasan Tarek ◽  
...  
Keyword(s):  
Social Media ◽  
Web Based ◽  
Study Objective ◽  
The Public ◽  
Mainstream Media ◽  
Related Information ◽  
Vector Borne ◽  
Media Outlet ◽  
And Control

Background: Chikungunya is a vector-borne disease, mostly present in tropical and subtropical regions. The virus is spread by Ae. aegypti and Ae. albopictus mosquitos and symptoms include high fever to severe joint pain. Dhaka, Bangladesh, suffered an outbreak of chikungunya in 2017 lasting from April to September. With the goal of reducing cases, social media was at the forefront during this outbreak and educated the public about symptoms, prevention, and control of the virus. Popular web-based sources such as the top dailies in Bangladesh, local news outlets, and Facebook spread awareness of the outbreak. Objective: This study sought to investigate the role of social and mainstream media during the chikungunya epidemic. The study objective was to determine if social media can improve awareness of and practice associated with reducing cases of chikungunya. Methods: We collected chikungunya-related information circulated from the top nine television channels in Dhaka, Bangladesh, airing from 1st April–20th August 2017. All the news published in the top six dailies in Bangladesh were also compiled. The 50 most viewed chikungunya-related Bengali videos were manually coded and analyzed. Other social media outlets, such as Facebook, were also analyzed to determine the number of chikungunya-related posts and responses to these posts. Results: Our study showed that media outlets were associated with reducing cases of chikungunya, indicating that media has the potential to impact future outbreaks of these alpha viruses. Each media outlet (e.g., web, television) had an impact on the human response to an individual’s healthcare during this outbreak. Conclusions: To prevent future outbreaks of chikungunya, media outlets and social media can be used to educate the public regarding prevention strategies such as encouraging safe travel, removing stagnant water sources, and assisting with tracking cases globally to determine where future outbreaks may occur.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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