English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Although sleep is critical to maintaining health and quality of life, inadequate sleep duration and/or quality is common. It can be difficult to distinguish sleep problems that may be addressed through adjustments to lifestyle versus issues that may represent a more serious condition requiring medical intervention. SmartSleep Analyzer is a cloud-based questionnaire and scoring algorithm designed to categorize respondents according to likely sleep problems as follows: obstructive sleep apnea (OSA), snoring, trouble falling asleep or staying asleep, delayed sleep phase disorder (DSPD), shift work disorder (SWD), chronic sleep restriction (CSR), or no sleep problem. Primary, secondary, and tertiary categorizations are provided, where applicable. The objective of this study was to validate the questionnaire scoring algorithm categorization/s against a sleep physician assessment. Methods From 2,316 available records, 90 complete questionnaires were randomly selected for this analysis. The questionnaire scoring algorithm categorization was compared against the consensus assessment of three independent sleep physicians who each reviewed the answers to all questions before arriving at a diagnosis. Results The questionnaire respondents (70% female) were aged 42.2±14.5 years, had a mean BMI of 32.0±7.7 kg/m2, and self-reported sleep duration of 6.5±1.4 hours/night. The primary, secondary, or tertiary categorization of the questionnaire scoring algorithm matched the primary consensus categorization of the physicians 90.6% of the time (95% confidence interval (CI): 82.6 to 95.7). When OSA and snoring were grouped, agreement increased to 98.9% (95% CI: 94.0 to 100). In all analyses undertaken, the accuracy of questionnaire scoring algorithm against the physicians exceeded the accuracy of the physicians when compared to each other. Conclusion These results demonstrate that our questionnaire and scoring algorithm performs well in identifying sleep problems that may impact adult respondents, using physician-review as the comparison standard. Support Philips

1191 Development of a Clinically-Validated Questionnaire and Scoring Algorithm Designed to Identify Common Sleep Problems Among Adults