Characterizing Sleep Issues Using Twitter.

BACKGROUND: Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon. OBJECTIVE: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues. METHODS: Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, "can't sleep", Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues. RESULTS: User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues. CONCLUSIONS: We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered.

The Relationship Between Digital Activity and Bedtime, Sleep Duration, and Sleep Quality in Chinese Working Youth.

Purpose: The study aims to examine whether and how digital activities influence sleep issues among working Chinese youth. Methods: This study used data from the China Family Panel Studies (CFPS) a Chinese population-based survey, and employed the multilevel ordinal logistic regression model (MOLRM) to test the associations between digital engagement (whether to engage in digital activity, frequency, and duration) and sleep issues (bedtime, sleep duration, and quality) among Chinese working youth. Additionally, the restricted cubic spline model (RCSM) was adopted to fit the MOLRM to evaluate the nonlinear relationship between digital activity duration and sleep quality, and thus determine the optimal range of digital activity duration. Results: The analysis included 7849 working young adults. The digital usage rate was 84.11%. Digital use was not significantly associated with average, workday, or free-day sleep duration, after controlling for all potential confounders. However, most digital activity indicators could significantly predict bedtime and sleep quality. Furthermore, the RCSM indicated a non-linear relationship pattern between digital activity duration and sleep quality, with a weekly peak point of 25 h. Age significantly moderated the relationship between digital activity, sleep duration and bedtime. Younger youth who used digital media more frequently and for a longer time tended to sleep later and had shorter sleep duration than older youth. Conclusion: Digital usage significantly predicted later bedtime among Chinese working youth; however, it was not linked with sleep duration on workdays or free days. In parallel, a nonlinear correlation between digital activity duration and sleep quality indicated that appropriate digital activity duration (less than 25 h weekly) may contribute to good sleep quality.

Strength training and light physical activity reduces the apnea-hypopnea index in institutionalized older adults.

OBJECTIVES: To determine the effect of 7 weeks of resistance training and walking on the apnea-hypopnea index (AHI) in institutionalized older adults compared with a usual care control group. DESIGN: Secondary analysis of data from a randomized controlled trial. SETTING: Ten nursing and 3 assisted living facilities in Arkansas. PARTICIPANTS: Institutionalized older adults. INTERVENTIONS: Exercise group (EG) performed supervised resistance training to arm and hip extensors on 3 days a week with additional 2 days a week of light walking. Usual care group (UC) participated in the usual activities provided within their living facility. MEASUREMENTS: Two nights of polysomnography before and following 7-week intervention. RESULTS: Adjusted means in the EG group showed a decrease in AHI from 20.2 (SD ±1.3) at baseline to 16.7 (SD ±0.9) at 7 weeks. Absolute strength gains were not associated with improved AHI. CONCLUSION: Supervised resistance training and light walking reduced the severity of obstructive sleep apnea in institutionalized older adults.