Danielle A. Wallace

Background: People share health-related experiences and treatments, such as for insomnia, in digital communities. Natural language processing tools can be leveraged to understand the terms used in digital spaces to discuss insomnia and insomnia treatments. Objective: The aim of this study is to summarize and chart trends of insomnia treatment terms on a digital insomnia message board. Methods: We performed a natural language processing analysis of the r/insomnia subreddit. Using Pushshift, we obtained all r/insomnia subreddit comments from 2008 to 2022. A bag of words model was used to identify the top 1000 most frequently used terms, which were manually reduced to 35 terms related to treatment and medication use. Regular expression analysis was used to identify and count comments containing specific words, followed by sentiment analysis to estimate the tonality (positive or negative) of comments. Data from 2013 to 2022 were visually examined for trends. Results: There were 340,130 comments on r/insomnia from 2008, the beginning of the subreddit, to 2022. Of the 35 top treatment and medication terms that were identified, melatonin, cognitive behavioral therapy for insomnia (CBT-I), and Ambien were the most frequently used (n=15,005, n=13,461, and n=11,256 comments, respectively). When the frequency of individual terms was compared over time, terms related to CBT-I increased over time (doubling from approximately 2% in 2013-2014 to a peak of over 5% of comments in 2018); in contrast, terms related to nonprescription over-the-counter (OTC) sleep aids (such as Benadryl or melatonin) decreased over time. CBT-I–related terms also had the highest positive sentiment and showed a spike in frequency in 2017. Terms with the most positive sentiment included “hygiene” (median sentiment 0.47, IQR 0.31-0.88), “valerian” (median sentiment 0.47, IQR 0-0.85), and “CBT” (median sentiment 0.42, IQR 0.14-0.82). Conclusions: The Reddit r/insomnia discussion board provides an alternative way to capture trends in both prescription and nonprescription sleep aids among people experiencing sleeplessness and using social media. This analysis suggests that language related to CBT-I (with a spike in 2017, perhaps following the 2016 recommendations by the American College of Physicians for CBT-I as a treatment for insomnia), benzodiazepines, trazodone, and antidepressant medication use has increased from 2013 to 2022. The findings also suggest that the use of OTC or other alternative therapies, such as melatonin and cannabis, among r/insomnia Reddit contributors is common and has also exhibited fluctuations over time. Future studies could consider incorporating alternative data sources in addition to prescription medication to track trends in prescription and nonprescription sleep aid use. Additionally, future prospective studies of insomnia should consider collecting data on the use of OTC or other alternative therapies, such as cannabis. More broadly, digital communities such as r/insomnia may be useful in understanding how social and societal factors influence sleep health.

Light is the primary entraining cue for the circadian system and has other, non-circadian, effects on health. Sex differences in light exposure patterns could drive sex differences in health outcomes. Real-world light exposure (measured with wrist-worn ActiGraph GT3X+) was investigated in the 2011–2014 U.S.-based National Health and Nutrition Examination Survey (NHANES) with metrics reflecting duration in and timing of exposure at different light levels. Sex differences were tested using two-sample t-tests and linear regression models. Occupational and physical activity-related factors were also explored as contributing factors using linear regression models adjusted for age, season, and race/ethnicity. 11,314 NHANES participants (age range: 3–80+, 52.2% females) were included in the analysis. The results show males spending approximately 52% more time in bright light than females, with this sex difference beginning in childhood. While further research is needed, these findings may be due to sex differences in indoor vs. outdoor activities.

Bright light therapy (BLT) may treat depression symptoms,¹ but how light exerts mood-boosting effects is still under investigation. Here, we evaluate sleep regularity in the association between bright light and depression symptoms.

Sleep is essential to maintaining health and wellbeing of individuals, influencing a variety of outcomes from mental health to cardiometabolic disease. This study aims to assess the relationships between various sleep phenotypes and blood metabolites. Utilizing data from the Hispanic Community Health Study/Study of Latinos, we performed association analyses between 40 sleep phenotypes, grouped in several domains (i.e., sleep disordered breathing (SDB), sleep duration, timing, insomnia symptoms, and heart rate during sleep), and 768 metabolites measured via untargeted metabolomics profiling. Network analysis was employed to visualize and interpret the associations between sleep phenotypes and metabolites. The patterns of statistically significant associations between sleep phenotypes and metabolites differed by superpathways, and highlighted subpathways of interest for future studies. For example, some xenobiotic metabolites were associated with sleep duration and heart rate phenotypes (e.g. 1H-indole-7-acetic acid, 4-allylphenol sulfate), while ketone bodies and fatty acid metabolism metabolites were associated with sleep timing measures (e.g. 3-hydroxybutyrate (BHBA), 3-hydroxyhexanoylcarnitine (1)). Heart rate phenotypes had the overall largest number of detected metabolite associations. Many of these associations were shared with both SDB and with sleep timing phenotypes, while SDB phenotypes shared relatively few metabolite associations with sleep duration measures. A number of metabolites were associated with multiple sleep phenotypes, from a few domains. The amino acids vanillylmandelate (VMA) and 1-carboxyethylisoleucine were associated with the greatest number of sleep phenotypes, from all domains other than insomnia. This atlas of sleep-metabolite associations will facilitate hypothesis generation and further study of the metabolic underpinnings of sleep health.

Light is a salient environmental exposure, serving as the primary entraining cue for the circadian system and having other, non-circadian, effects on health. Gender differences in light exposure patterns could contribute to gender differences in health outcomes and would have important implications for sleep and circadian research. Gender differences in real-world light exposure (measured over a week with wrist-worn ActiGraph GT3X+ devices) were investigated in cross- sectional data from the 2011-2014 National Health and Nutrition Examination Survey (NHANES). Measures of time above light threshold (TALT), individual photoperiod (IP), first and last timing of light (FTL and LTL, respectively), and mean light timing revised (MLiTR) at different light intensity thresholds were derived. Gender differences in light exposure were tested using two-sample t-tests, Watson’s two-sample test of homogeneity, and linear regression models. Exploratory analyses to investigate work and physical activity-related factors in relation to bright light exposure were also conducted. A total of 11,318 NHANES participants (age range: 3-80+, 52.2% women) with 6 days of valid actigraphy and light data were included in the analysis. The findings suggest that for every 60 minutes of bright light (≥1,000 lux) that men receive, women receive 39.6 minutes. Men spend approximately 52% more time in bright light than women and this gender difference begins in childhood. The IP of bright light exposure is also longer for men, with earlier first and later last timing of bright light exposure compared to women. These gender differences were robust across ages and between race and ethnicity groups. While further research is needed, these gender differences in light exposure may be due to gender differences in indoor vs. outdoor activities. Future studies of gender differences in response to light exposure should consider light exposure history in study design and analysis. The results of this study may inform future health disparities research and support the importance of the study of light as an important environmental exposure and component of the human exposome.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Consumer-grade wearables, such as Fitbits, are a promising, cost-effective methodology for objectively assessing sleep and physical activity in youth with pain. Objective: The current study investigated the acceptability and feasibility of implementing Fitbits for youth with acute and chronic pain in and out of hospital settings while maintaining data security and patient confidentiality. Methods: We investigated participant experience of Fitbit usage over three to four weeks for a sample of youth with acute pain undergoing surgical procedures (N=34, mean age=14.46, SD=3.70, 47.06% female) and a sample of youth with chronic pain enrolled in an intensive interdisciplinary pain treatment program (N=28, mean age=15.00, SD=2.33, 17.86% female). We assessed the acceptability of Fitbit usage through survey items probing comfortability (0=extremely uncomfortable to 10=extremely comfortable), perceived burdensomeness (0=not burdensome at all to 10=extremely burdensome), and open-ended issues or concerns. Feasibility was assessed by tracking the daily compliant wear of the Fitbit device, which was operationalized as more than 600 minutes of daily wear time. We created an automated data pipeline to ensure data security, patient confidentiality, and quality. Results: Acceptability findings revealed high levels of reported comfort (acute pain: M=8.56, SD=1.43; chronic pain: M=8.27, SD=1.69) and low levels of perceived burdensomeness (acute: M=0.68, SD=1.17; chronic: M=1.15, SD=1.38) related to Fitbit wearing in both samples. Transient concerns of mild wrist irritation and sleep discomfort were occasionally reported across both samples (15.79% of participants). Feasibility findings indicated high feasibility (acute: median compliance rate of 86.67%; chronic: median compliance rate of 96.65%) for the study duration in both samples. Conclusions: These findings support using Fitbits as an acceptable and feasible method of objective data collection regarding sleep and physical activity for youth with pain. Findings also highlight the logistics of implementing consumer-grade wearable devices throughout all stages of the clinical research process.

BACKGROUND People share health-related experiences and treatments, such as for insomnia, in digital communities. Natural language processing tools can be leveraged to understand the terms used in digital spaces to discuss insomnia and insomnia treatments. OBJECTIVE The aim of this study is to summarize and chart trends of insomnia treatment terms on a digital insomnia message board. METHODS We performed a natural language processing analysis of the r/insomnia subreddit. Using Pushshift, we obtained all r/insomnia subreddit comments from 2008 to 2022. A bag of words model was used to identify the top 1000 most frequently used terms, which were manually reduced to 35 terms related to treatment and medication use. Regular expression analysis was used to identify and count comments containing specific words, followed by sentiment analysis to estimate the tonality (positive or negative) of comments. Data from 2013 to 2022 were visually examined for trends. RESULTS There were 340,130 comments on r/insomnia from 2008, the beginning of the subreddit, to 2022. Of the 35 top treatment and medication terms that were identified, melatonin, cognitive behavioral therapy for insomnia (CBT-I), and Ambien were the most frequently used (n=15,005, n=13,461, and n=11,256 comments, respectively). When the frequency of individual terms was compared over time, terms related to CBT-I increased over time (doubling from approximately 2% in 2013-2014 to a peak of over 5% of comments in 2018); in contrast, terms related to nonprescription over-the-counter (OTC) sleep aids (such as Benadryl or melatonin) decreased over time. CBT-I–related terms also had the highest positive sentiment and showed a spike in frequency in 2017. Terms with the most positive sentiment included “hygiene” (median sentiment 0.47, IQR 0.31-0.88), “valerian” (median sentiment 0.47, IQR 0-0.85), and “CBT” (median sentiment 0.42, IQR 0.14-0.82). CONCLUSIONS The Reddit r/insomnia discussion board provides an alternative way to capture trends in both prescription and nonprescription sleep aids among people experiencing sleeplessness and using social media. This analysis suggests that language related to CBT-I (with a spike in 2017, perhaps following the 2016 recommendations by the American College of Physicians for CBT-I as a treatment for insomnia), benzodiazepines, trazodone, and antidepressant medication use has increased from 2013 to 2022. The findings also suggest that the use of OTC or other alternative therapies, such as melatonin and cannabis, among r/insomnia Reddit contributors is common and has also exhibited fluctuations over time. Future studies could consider incorporating alternative data sources in addition to prescription medication to track trends in prescription and nonprescription sleep aid use. Additionally, future prospective studies of insomnia should consider collecting data on the use of OTC or other alternative therapies, such as cannabis. More broadly, digital communities such as r/insomnia may be useful in understanding how social and societal factors influence sleep health.