Tweets Related to Motivation and Physical Activity for Obesity-Related Behavior Change: Descriptive Analysis

Whose tweets about obesity and weight loss gain the most attention: celebrities, political, or medical authorities?

BACKGROUND

X (formerly Twitter) is a unique social medium where many famous people and health institutions post and interact with casual users. We aimed to explore reactions to tweets about obesity and weight loss from accounts representing celebrities, politicians, sportsmen, and health authorities.

METHODS

We collected tweets from n = 2444 X profiles representing seven categories: celebrities, politicians, sportsmen, medical specialists, medical journals, medical universities, and health institutions. We retrieved tweets from the accounts and selected tweets about, e.g., obesity, overweight, body mass index, and weight loss. We conducted sentiment analysis, descriptive statistics, and multivariable quantile regression modeling. In quantile regression models, each tau represents a decile from 0.1 to 0.9 of the dependent variable (number of likes or retweets). Therefore, a tau value of 0.5 represents the 5th decile, the 50th percentile, and the median of the dependent variable.

RESULTS

The final dataset consisted of n = 8989 tweets. Achieving a large number of likes (taus 0.7, 0.8, and 0.9) was positively associated with posts written by celebrities, politicians, medical journals, and universities, while it was negatively associated with tweets authored by health institutions or medical specialists. In the case of a significant number of retweets, a positive association was observed for all account types, except for health institutions, for which the relationship was negative. These relationships were independent of verification status, the number of followers, tweet length, and sentiment.

CONCLUSIONS

Tweets concerning obesity and weight loss originating from accounts representing health institutions garnered fewer likes and retweets compared to other types of accounts, including non-medical ones. A limitation of the study is the relatively small number of tweets emanating from non-medical accounts. A X informational campaign about obesity should engage non-medical accounts with many followers to reach as many users as possible.

Investigating Older Adults' Use of a Socially Assistive Robot via Time Series Clustering and User Profiling: Descriptive Analysis Study

BACKGROUND

The aging population and the shortage of geriatric care workers are major global concerns. Socially assistive robots (SARs) have the potential to address these issues, but developing SARs for various types of users is still in its infancy.

OBJECTIVE

This study aims to examine the characteristics and use patterns of SARs.

METHODS

This study analyzed log data from 64 older adults who used a SAR called Hyodol for 60 days to understand use patterns and their relationship with user characteristics. Data on user interactions, robot-assisted content use, demographics, physical and mental health, and lifestyle were collected. Time series clustering was used to group users based on use patterns, followed by profiling analysis to relate these patterns to user characteristics.

RESULTS

Overall, 4 time series clusters were created based on use patterns: helpers, friends, short-term users, and long-term users. Time series and profiling analyses revealed distinct patterns for each group. We found that older adults use SARs differently based on factors beyond demographics and health. This study demonstrates a data-driven approach to understanding user needs, and the findings can help tailor SAR interventions for specific user groups.

CONCLUSIONS

This study extends our understanding of the factors associated with the long-term use of SARs for geriatric care and makes methodological contributions.

Promoting a healthy lifestyle: exploring the role of social media and fitness applications in the context of social media addiction risk

The popularity of social networks turns them into a legal method for promoting a healthy lifestyle, which benefits not only people but also different countries' governments. This research paper aimed to examine the Keep fitness app integrated into WeChat, Weibo and QQ as regards long-term improvements in health-related behaviors (physical activity, nutrition, health responsibility, spiritual growth, interpersonal relationships and stress management) and assess the associated risk of increased social media addiction. Students from Lishui University in China (N = 300) participated in this study, and they were formed into control and experimental groups. The Healthy Lifestyle Behavior Scale and Social Media Disorder Scale were used as psychometric instruments. The Keep app was found to improve respondents' scores on the parameters of physical activity, nutrition and health responsibility (P = 0.00). However, the level of dependence on social media did not change in either the control or the experimental group during the year of research (P ≥ 0.05). It is concluded that fitness apps can be an effective tool to promote healthy lifestyles among young people in China and other countries. The feasibility of government investment in fitness apps to promote healthy lifestyles is substantiated.

Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis

BACKGROUND

During the COVID-19 pandemic, Twitter (recently rebranded as "X") was the most widely used social media platform with over 2 million cancer-related tweets. The increasing use of social media among patients and family members, providers, and organizations has allowed for novel methods of studying cancer communication.

OBJECTIVE

This study aimed to examine pediatric cancer-related tweets to capture the experiences of patients and survivors of cancer, their caregivers, medical providers, and other stakeholders. We assessed the public sentiment and content of tweets related to pediatric cancer over a time period representative of the COVID-19 pandemic.

METHODS

All English-language tweets related to pediatric cancer posted from December 11, 2019, to May 7, 2022, globally, were obtained using the Twitter application programming interface. Sentiment analyses were computed based on Bing, AFINN, and NRC lexicons. We conducted a supplemental nonlexicon-based sentiment analysis with ChatGPT (version 3.0) to validate our findings with a random subset of 150 tweets. We conducted a qualitative content analysis to manually code the content of a random subset of 800 tweets.

RESULTS

A total of 161,135 unique tweets related to pediatric cancer were identified. Sentiment analyses showed that there were more positive words than negative words. Via the Bing lexicon, the most common positive words were support, love, amazing, heaven, and happy, and the most common negative words were grief, risk, hard, abuse, and miss. Via the NRC lexicon, most tweets were categorized under sentiment types of positive, trust, and joy. Overall positive sentiment was consistent across lexicons and confirmed with supplemental ChatGPT (version 3.0) analysis. Percent agreement between raters for qualitative coding was 91%, and the top 10 codes were awareness, personal experiences, research, caregiver experiences, patient experiences, policy and the law, treatment, end of life, pharmaceuticals and drugs, and survivorship. Qualitative content analysis showed that Twitter users commonly used the social media platform to promote public awareness of pediatric cancer and to share personal experiences with pediatric cancer from the perspective of patients or survivors and their caregivers. Twitter was frequently used for health knowledge dissemination of research findings and federal policies that support treatment and affordable medical care.

CONCLUSIONS

Twitter may serve as an effective means for researchers to examine pediatric cancer communication and public sentiment around the globe. Despite the public mental health crisis during the COVID-19 pandemic, overall sentiments of pediatric cancer-related tweets were positive. Content of pediatric cancer tweets focused on health and treatment information, social support, and raising awareness of pediatric cancer.

A Novel Approach to Characterize State-level Food Environment and Predict Obesity Rate Using Social Media Data: Correlational Study

BACKGROUND

Community obesity outcomes can reflect the food environment to which the community belongs. Recent studies have suggested that the local food environment can be measured by the degree of food accessibility, and survey data are normally used to calculate food accessibility. However, compared with survey data, social media data are organic, continuously updated, and cheaper to collect.

OBJECTIVE

The objective of our study was to use publicly available social media data to learn the relationship between food environment and obesity rates at the state level.

METHODS

To characterize the caloric information of the local food environment, we used food categories from Yelp and collected caloric information from MyFitnessPal for each category based on their popular dishes. We then calculated the average calories for each category and created a weighted score for each state. We also calculated 2 other dimensions from the concept of access, acceptability and affordability, to build obesity prediction models.

RESULTS

The local food environment characterized using only publicly available social media data had a statistically significant correlation with the state obesity rate. We achieved a Pearson correlation of 0.796 between the predicted obesity rate and the reported obesity rate from the Behavioral Risk Factor Surveillance System across US states and the District of Columbia. The model with 3 generated feature sets achieved the best performance.

CONCLUSIONS

Our study proposed a method for characterizing state-level food environments only using continuously updated social media data. State-level food environments were accurately described using social media data, and the model also showed a disparity in the available food between states with different obesity rates. The proposed method should elastically apply to local food environments of different sizes and predict obesity rates effectively.