Priyanka Kapoor1, Aman Chowdhry2, Poonam Sengar3, Abhishek Mehta4. 1. Orthodontics & Dentofacial Orthopaedics, Faculty of Dentistry, Jamia Millia Islamia, New Delhi, 110025, India. 2. Oral Pathology & Microbiology, Faculty of Dentistry, Jamia Millia Islamia, New Delhi, 110025, India. 3. Faculty of Dentistry, Jamia Millia Islamia, New Delhi, 110025, India. 4. Public Health Dentistry, Faculty of Dentistry, Jamia Millia Islamia, New Delhi, 110025, India.
Abstract
INTRODUCTION: Obstructive Sleep Apnea (OSA), the most prevalent form of sleep-related breathing disorder has practical and financial limitations in diagnosis by polysomnography, hence OSA risk-assessment can identify OSA-related symptoms early. OBJECTIVES: To develop a mobile application for OSA-risk assessment and tests its validity, feasibility, and application in a hospital-based pilot sample. STUDY DESIGN AND METHODS: The study comprised of two parts. PART I: Development of a mobile application "OSA-Risk Assessment Tool" using automated questionnaires. PART II: A pilot study to screen OSA-risk in 200 patients (100 adults,100 children) from the orthodontic OPD of a Govt. Dental Hospital, using the mobile application. Internal validation by manual and mobile-based methods was done on 30 random patients. Non-parametric tests assessed the statistical differences between OSA-risk and nonOSA-risk variables. RESULTS: The prevalence of OSA-risk was 21.4% in adults and 8% in children. In adults, OSA-risk showed significantly greater neck circumference (p = 0.0001), waist circumference(p = 0.001), body mass index(p = 0.008), daytime sleepiness, headache, and mouth breathing(p = 0.0001). In children, OSA-risk is associated with a dry mouth on awakening, daytime sleepiness, and mouth breathing, and nocturnal enuresis. The low OSA-risk patients were suggested standardized preventive management counseling and orthodontic interventions while medium to high-risk underwent sleep-specialist referrals. CONCLUSIONS: This study supports the feasibility and usability of the mobile application "OSA-risk assessment tool" in a hospital setup. This cost-effective tool can be advocated for self-evaluation, early detection, and awareness in pandemic times. The future upgraded versions may include preventive modules and real-time coordination with the nearest sleep clinics and specialists.
INTRODUCTION: Obstructive Sleep Apnea (OSA), the most prevalent form of sleep-related breathing disorder has practical and financial limitations in diagnosis by polysomnography, hence OSA risk-assessment can identify OSA-related symptoms early. OBJECTIVES: To develop a mobile application for OSA-risk assessment and tests its validity, feasibility, and application in a hospital-based pilot sample. STUDY DESIGN AND METHODS: The study comprised of two parts. PART I: Development of a mobile application "OSA-Risk Assessment Tool" using automated questionnaires. PART II: A pilot study to screen OSA-risk in 200 patients (100 adults,100 children) from the orthodontic OPD of a Govt. Dental Hospital, using the mobile application. Internal validation by manual and mobile-based methods was done on 30 random patients. Non-parametric tests assessed the statistical differences between OSA-risk and nonOSA-risk variables. RESULTS: The prevalence of OSA-risk was 21.4% in adults and 8% in children. In adults, OSA-risk showed significantly greater neck circumference (p = 0.0001), waist circumference(p = 0.001), body mass index(p = 0.008), daytime sleepiness, headache, and mouth breathing(p = 0.0001). In children, OSA-risk is associated with a dry mouth on awakening, daytime sleepiness, and mouth breathing, and nocturnal enuresis. The low OSA-risk patients were suggested standardized preventive management counseling and orthodontic interventions while medium to high-risk underwent sleep-specialist referrals. CONCLUSIONS: This study supports the feasibility and usability of the mobile application "OSA-risk assessment tool" in a hospital setup. This cost-effective tool can be advocated for self-evaluation, early detection, and awareness in pandemic times. The future upgraded versions may include preventive modules and real-time coordination with the nearest sleep clinics and specialists.
Authors: Joachim Behar; Aoife Roebuck; Mohammed Shahid; Jonathan Daly; Andre Hallack; Niclas Palmius; John Stradling; Gari D Clifford Journal: IEEE J Biomed Health Inform Date: 2015-01 Impact factor: 5.772
Authors: Adam V Benjafield; Najib T Ayas; Peter R Eastwood; Raphael Heinzer; Mary S M Ip; Mary J Morrell; Carlos M Nunez; Sanjay R Patel; Thomas Penzel; Jean-Louis Pépin; Paul E Peppard; Sanjeev Sinha; Sergio Tufik; Kate Valentine; Atul Malhotra Journal: Lancet Respir Med Date: 2019-07-09 Impact factor: 30.700
Authors: Louise M O'Brien; Carolyn B Mervis; Cheryl R Holbrook; Jennifer L Bruner; Nigel H Smith; Nechia McNally; M Catherine McClimment; David Gozal Journal: J Sleep Res Date: 2004-06 Impact factor: 3.981