Kate Sutherland1,2, Fernanda R Almeida3, Taiyun Kim4, Elizabeth C Brown5,6, Fiona Knapman5,7, Joachim Ngiam2, Jean Yang4, Lynne E Bilston5,7, Peter A Cistulli1,2. 1. Sleep Research Group, Charles Perkins Centre and Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia. 2. Department of Respiratory and Sleep Medicine, Royal North Shore Hospital, St Leonards, New South Wales, Australia. 3. Department of Oral Health Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, British Columbia, Canada. 4. Charles Perkins Centre and School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia. 5. Neuroscience Research Australia, Randwick, New South Wales, Australia. 6. Prince of Wales Hospital, Sydney, New South Wales, Australia. 7. University of New South Wales, Sydney, New South Wales, Australia.
Abstract
STUDY OBJECTIVES: Oral appliance (OA) therapy usage can be objectively measured through temperature-sensing data chips embedded in the appliance. Initial reports of group data for short-term treatment usage suggest good nightly hours of usage. However, individual variability in treatment usage patterns has not been assessed. We aimed to identify OA treatment usage subtypes in the first 60 days and the earliest predictors of these usage patterns. METHODS: OSA patients were recruited for a study of OA therapy with an embedded compliance chip (DentiTrac, Braebon, Canada). Fifty-eight participants with 60 days of downloadable treatment usage data (5-minute readings) were analyzed. A hierarchical cluster analysis was used to group participants with similar usage patterns. A random forest classification model was used to identify the minimum number of days to predict usage subtype. RESULTS: Three user groups were identified and named: "Consistent Users" (48.3%), "Inconsistent Users," (32.8%) and "Non-Users" (19.0%). The first 20 days provided optimal data to predict the treatment usage group a patient would belong to at 60 days (90% accuracy). The strongest predictors of user group were downloaded usage data, average wear time, and number of days missed. CONCLUSIONS: Granular analysis of OA usage data suggests the existence of treatment user subtypes (Consistent, Inconsistent, and Non-Users). Our data suggest that 60-day usage patterns can be identified in the first 20 days of treatment using downloaded treatment usage data. Understanding initial treatment usage patterns provide an opportunity for early intervention to improve long-term usage and outcomes. CITATION: Sutherland K, Almeida FR, Kim T, et al. Treatment usage patterns of oral appliances for obstructive sleep apnea over the first 60 days: a cluster analysis. J Clin Sleep Med. 2021;17(9):1785-1792.
STUDY OBJECTIVES: Oral appliance (OA) therapy usage can be objectively measured through temperature-sensing data chips embedded in the appliance. Initial reports of group data for short-term treatment usage suggest good nightly hours of usage. However, individual variability in treatment usage patterns has not been assessed. We aimed to identify OA treatment usage subtypes in the first 60 days and the earliest predictors of these usage patterns. METHODS: OSA patients were recruited for a study of OA therapy with an embedded compliance chip (DentiTrac, Braebon, Canada). Fifty-eight participants with 60 days of downloadable treatment usage data (5-minute readings) were analyzed. A hierarchical cluster analysis was used to group participants with similar usage patterns. A random forest classification model was used to identify the minimum number of days to predict usage subtype. RESULTS: Three user groups were identified and named: "Consistent Users" (48.3%), "Inconsistent Users," (32.8%) and "Non-Users" (19.0%). The first 20 days provided optimal data to predict the treatment usage group a patient would belong to at 60 days (90% accuracy). The strongest predictors of user group were downloaded usage data, average wear time, and number of days missed. CONCLUSIONS: Granular analysis of OA usage data suggests the existence of treatment user subtypes (Consistent, Inconsistent, and Non-Users). Our data suggest that 60-day usage patterns can be identified in the first 20 days of treatment using downloaded treatment usage data. Understanding initial treatment usage patterns provide an opportunity for early intervention to improve long-term usage and outcomes. CITATION: Sutherland K, Almeida FR, Kim T, et al. Treatment usage patterns of oral appliances for obstructive sleep apnea over the first 60 days: a cluster analysis. J Clin Sleep Med. 2021;17(9):1785-1792.
Authors: Craig L Phillips; Ronald R Grunstein; M Ali Darendeliler; Anastasia S Mihailidou; Vasantha K Srinivasan; Brendon J Yee; Guy B Marks; Peter A Cistulli Journal: Am J Respir Crit Care Med Date: 2013-04-15 Impact factor: 21.405
Authors: Terri E Weaver; Greg Maislin; David F Dinges; Thomas Bloxham; Charles F P George; Harly Greenberg; Gihan Kader; Mark Mahowald; Joel Younger; Allan I Pack Journal: Sleep Date: 2007-06 Impact factor: 5.849
Authors: Emer Van Ryswyk; Craig S Anderson; Nicholas A Antic; Ferran Barbe; Lia Bittencourt; Ruth Freed; Emma Heeley; Zhihong Liu; Kelly A Loffler; Geraldo Lorenzi-Filho; Yuanming Luo; Maria J Masdeu Margalef; R Doug McEvoy; Olga Mediano; Sutapa Mukherjee; Qiong Ou; Richard Woodman; Xilong Zhang; Ching Li Chai-Coetzer Journal: Sleep Date: 2019-10-09 Impact factor: 5.849
Authors: Rohit Budhiraja; Sairam Parthasarathy; Christopher L Drake; Thomas Roth; Imran Sharief; Pooja Budhiraja; Victoria Saunders; David W Hudgel Journal: Sleep Date: 2007-03 Impact factor: 5.849
Authors: Kate Sutherland; Olivier M Vanderveken; Hiroko Tsuda; Marie Marklund; Frederic Gagnadoux; Clete A Kushida; Peter A Cistulli Journal: J Clin Sleep Med Date: 2014-02-15 Impact factor: 4.062
Authors: Marijke Dieltjens; Olivier M Vanderveken; Bharati Shivalkar; Gilles Van Haesendonck; Chloé Kastoer; Hein Heidbuchel; Marc J Braem; Caroline M Van De Heyning Journal: J Clin Sleep Med Date: 2022-03-01 Impact factor: 4.062
Authors: Hasthi U Dissanayake; Juliana T Colpani; Kate Sutherland; Weiqiang Loke; Anna Mohammadieh; Yi-Hui Ou; Philip de Chazal; Peter A Cistulli; Chi-Hang Lee Journal: Clin Cardiol Date: 2021-11-17 Impact factor: 2.882