A Lifestyle Program for Severe Comorbid Obstructive Sleep Apnea With Severe Obesity
Phase: NA
Conditions: Obstructive Sleep Apnea, Obesity
Sponsor: Mayo Clinic

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 1 of 13 
 
 
General Study Information 
 
Principal Investigator:       Roberto P Benzo, MD 
 
 
 
 
 
 
 
 
 
Study Title:    A Lifestyle Program for Severe Comorbid Obstructive Sleep Apnea with Severe Obesity  
 
 
Research Question and Aims 
 
Hypothesis:  Patients with Obstructive Sleep Apnea (OSA) and obesity will have positive behavior changes as 
the result of participating in a remote monitoring and health coaching program.  The positive behavior changes 
include improved control of their OSA via adherence to prescribed medical devices, reductions in obesity and 
improved quality of life.  
 
Aims, purpose, or objectives:  Mayo Clinic in collaboration with Minnesota Health Solutions (MHS) will 
develop a data driven system for persons with severe obesity sleep apnea that utilizes remote monitoring with 
health coaching to create behavior changes aimed at improving health and quality of life.   In previous NIH 
funding, Mayo and MHS have previously developed a home-based monitoring system with health coaching for 
COPD patients (IRB’s  14-009016, 17-009449, 18-002453).  MHS will modify the system for patients with 
severe obesity sleep apnea.  Mayo Clinic will conduct the at-home feasibility testing and qualitative interviews. 
 
Background:  Obstructive sleep apnea (OSA) is a disorder characterized by episodic cessation of breathing 
during sleep with intermittent hypoxemia and sleep fragmentation. Its effects go far beyond sleep and it is 
associated with decreased neurocognitive function, reduced quality of life and increases in hypertension, insulin 
resistance and cardiovascular disease.3,7,23 The prevalence of clinically significant OSA has been estimated at 
7% for adult males and 5% for adult females.1–3 Researchers have found though that OSA likely has a higher 
undiagnosed prevalence, up to 21% in the general population3, and that prevalence increases with age.7,24 The 
strongest risk factor for developing OSA is obesity and it is estimated that more than half of the prevalence of 
OSA is attributable to excess body weight.7,25  
 
First line therapy for OSA has been positive airway pressure (PAP); however, despite many technological 
advances in PAP therapy, adherence to use, a behavior, remains a problem. Using best current practices, 
acceptable PAP adherence at 31-90 days is seen in only 75% of patients21 and only 65% will be adherent at one-
year22. Although PAP is considered the best single therapy for OSA, this is only applied during sleep and only 
addresses airway patency with no direct impact on obesity or the many comorbidities that contribute to and 
perpetuate OSA. Most of the chronic conditions associated with sleep apnea are related to behavior. Hence, 
there is a need for interventions that promote compliance with PAP and promote behavior change to improve 
obesity and other comorbidities. We propose to develop a remote system that utilizes effective digital 
intervention techniques in combination with data monitoring and health coaching to create behavior change. 
Health coaching informed by biomonitoring can create the conditions for behavioral change by providing 
motivation and feedback, which support the development of self-efficacy to create change. 
 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 2 of 13 
 
Health coaching has been shown to improve outcomes in the most prevalent chronic conditions including 
COPD, diabetes and heart failure. Health coaching via telehealth provides a unique engagement that supports 
behavior change through the theory of planned behavior. Through coaching, patients have the opportunity to 
develop the motivation to change. Remote monitoring provides patients and the health coach direct feedback 
that can bring awareness of measures, such as activity level, weight, sleep quality and PAP adherence. Properly 
presented and used these data become a unique tool to inform the dialogue for behavior change. Patients are 
able to develop the self-efficacy to support behavior change. Best practices in digital intervention are well-
suited to health coaching and support the development of a system that is scalable, accessible, and billable. 
 
Multiple studies have shown that the annual health care costs of OSA patients are double the costs for age and 
sex matched controls.26–30 These additional costs are due to hospitalizations, physician fees, specialist fees, and 
prescriptions.7 Additionally, researchers have shown that effectively treated patients show a gradual decrease in 
healthcare costs while matched controls continue to show an in increase in healthcare costs.31,32  
Obesity is also associated with increased health expenditures compared to people with a normal body weight 
and it is estimated that 9% of annual health expenditures are due to obesity.33 Most of this extra cost is due to 
health care conditions associated with obesity, including diabetes and cardiovascular disease.17–20 However, it is 
difficult to separate the contributions of OSA and obesity from the costs of treating common comorbidites.5 
 
There are multiple bi-directional relationships between a wide range of comorbidities and OSA7,34 and there is 
substantial sharing of genetic polymorphisms between OSA and obesity. OSA influences hormonal regulation 
of appetite and metabolism via intermediaries such as leptin and grehlin, and treatment with PAP tends to 
influence these factors in favor of weight loss.35 However, without changes in activity and diet, most studies 
demonstrate ongoing weight gain in newly treated obese OSA patients. Both obesity and OSA have independent 
contributions to cardiovascular risk. While PAP therapy remains an essential part of OSA treatment,25,26,36,37 its 
use does not directly impact obesity or all of the comorbidities associated with severe OSA syndrome. Most of 
the chronic conditions associated with OSA are related to behavior. Additionally, PAP has significant problems 
with adherence, a behavior. 
 
There is a need for interventions that lead to behavior change, resulting in weight loss and reduced risk of 
cardiovascular events, and these should be recommended to all patients with OSA.25 A consistent finding in 
clinical studies is that weight reductions result in improvement in OSA symptoms. In a group of patients with 
obesity, diabetes and OSA, an intensive lifestyle intervention led to more weight loss and more than three times 
the rate of remission from OSA than the controls receiving standard diabetes support and education.38 
 
Health coaching has demonstrated effectiveness for behavior change and chronic disease management. While 
still limited, there is growing evidence that health coaching can be used for weight management, physical 
activity promotion, and medication adherence.39–41 A recently completed meta-analysis that included 15 
randomized trials found that health coaching could easily be delivered and provided motivation strategies for 
behavior change (physical activity, dietary behavior, health responsibility and stress management) among 
patients with cardiovascular risk factors.42 A previous study by PI Benzo that utilized health coaching for 
patients with severe COPD after hospitalization showed decreased readmission and sustainable improvements 
in quality of life.43 In another study by Benzo of severe obesity in patients with chronic lung disease, an eight 
week intervention with weekly health coaching calls resulted in weight loss and improvements in body 
composition, walking tests, mindful eating behavior and quality of life.44 Additionally, this study incorporated 
the Weight Watchers app for recording food and an activity monitor to capture daily steps.  
 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 3 of 13 
 
The use of remote monitoring devices offers an additional opportunity to enhance health coaching. A pilot study 
that combined wearable devices with health coaching to promote healthy lifestyle changes in overweight 
patients resulted in clinically significant weight loss over 16 weeks.45 Participants had good adherence to 
wearing their devices and lost more weight as time went along. The researchers hypothesize that effectiveness 
was increased by participants completing questions in advance of health coaching sessions, which allowed the 
focus in sessions to be on relationship building and problem solving. In another recently completed study that 
used health coaching and remote monitoring for weight loss in obese patients, participants who received health 
coaching had increased compliance with device adherence and higher rates of weight loss than participants who 
did not receive the health coaching.46 Health coaching combined with remote monitoring offers the possibility 
of a relatively inexpensive intervention that is scalable, billable and able to be delivered remotely. Patients who 
have difficulty accessing a facility, for any reason, may be more likely and able to participate.  
 
Engaging digital intervention techniques are well-suited to health coaching as many of the effective strategies 
are common to both. While there is no single strategy that is effective for engagement for everyone, there are 
proven strategies to increase the likelihood of engagement and success. Specific strategies that the system will 
incorporate to increase engagement that work well with health coaching are goal setting, self-monitoring, 
interactivity, accountability and personalization. Goal Setting, which will be facilitated through health coaching, 
is most effective when it is used together with self-monitoring and accountability which increases engagement 
through enhanced motivation.47 Self-monitoring involves participants monitoring their own behavior in regard 
to a goal.48 In the proposed system, self-monitoring will be supported by providing data and feedback to the 
participants. Accountability will be facilitated by providing participant data to the health coaches who can help 
clarify the participant’s goals, monitor progress, and reflect on their progress.49 The system will be interactive 
through the use of the health coaches, which has been found to be more engaging and more persuasive for 
digital health interventions.50 Finally, the system will support personalization and will be adaptive to 
individual’s needs with the goal of behavior change.51 Other strategies may be incorporated as they are deemed 
effective in development and testing (e.g., reminders). The proposed system will be built for usability and with 
high quality visual design. While usability itself does not increase engagement, a system that is not usable will 
be a barrier to the program and technology use.47 A high quality visual design supports usability and it can 
directly impact engagement.47 We propose a user-centered design process to ensure the usability and appeal of 
the visual design of the system.  
 
Preliminary evidence shows that an effective digital health intervention that utilizes remote monitoring and 
health coaching to address obesity may be as effective as an in-person program, in addition to having benefits in 
accessibility and scalability.52 Recent preliminary research also shows that this type of intervention is feasible, 
acceptable, and effective for older adults living in rural areas to address obesity.53 This is particularly important 
as there has been limited study of older adults participation in digital health interventions.53 
 
Scientific Premise : Severe OSA associated with obesity and other comorbidities is highly influenced by 
behavior. Early evidence suggests that health coaching is effective at supporting patients with behavior changes 
and chronic disease management. Health coaching associated with remote monitoring has been tested and offers 
promise to address behavior, including PAP use, physical activity, and mindful eating. Based on our previous 
NIH funded work in other chronic conditions, we hypothesize that a remote monitoring system with health 
coaching for severe OSA with obesity and comorbidity is feasible and may improve quality of life. 
Additionally, it may create conditions for behavior change resulting in increased adherence to PAP use as well 
as addressing obesity and other comorbid conditions. If effective these findings are extremely important to 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 4 of 13 
 
addressing a critical health problem through a solution that is relatively inexpensive, scalable, billable, and 
accessible. 
 
We propose to develop a home-based system for obese patients with OSA. The proposed system will provide 
patients with remote guidance, personalized reports, and monitoring integrated with health coaching. Health 
coaches will also receive individualized patient information, formatted in accessible and usable way. 
Monitoring will include key indicators: PAP use, sleep quality, physical activity, weight and symptom days 
(self-reported well-being, fatigue, sleepiness, and daily activity). The system is compliant to bill for remote 
patient monitoring through existing Medicare and private insurance billing codes.  
 
 
 
Study Design and Methods 
 
Methods:  Describe in lay terms, completely detailing the research activities that will be conducted by Mayo 
Clinic staff under this protocol. 
The proposed system (see Figure 1) will use technology to facilitate remote health coaching for people with 
severe comorbid OSA with the goal of encouraging behavior change. The system will be built upon the existing 
home-based pulmonary rehabilitation (PR) system that was jointly developed by Mayo and MHS. The PR 
system was well-received and has proven effective, reliable, and easy to use. The overall architecture will be 
reused for the OSA coaching system, but a new set of sensors, cloud services, and data reporting capabilities 
will need to be integrated and developed. 
 
The system will be designed so that a newly-diagnosed patient can immediately benefit from health coaching, 
including equipment setup, goal setting, and trend monitoring in all pertinent aspects of care: PAP management, 
sleep scheduling, sleep duration, diet, exercise, medications and more. When they register for the program, 
patients will be provided with a set of pre-configured sensors and a dedicated Android tablet. The tablet will 
exclusively run a custom-built Android application that graphs patient progress, asks periodic self-report 
questions, sends secure text messages between the health coach and patient, displays timely reminders, and 
plays helpful videos to assist with sensor use. The tablet and sensors upload the patient’s activities, 
physiological data, and answers to the daily self-report questions to a secure website. This website also allows 
health coaches to view patient data via overview, weekly, daily, and trend reports. Health coaches call patients 
regularly to celebrate progress, assist with any issues, and set new goals. 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 5 of 13 
 
 
Figure 1. A diagram of the proposed health coaching system. 
 
 
In the past, MHS and Mayo jointly developed an in-home PR system that was well-received and is being widely 
deployed. Patients receive an Android tablet running an app that plays exercise videos, graphs patient progress, 
asks periodic self-report questions, sends text messages between the health coach and patient, displays timely 
reminders, and shows instructional videos to assist with sensor use. The app runs in “kiosk” mode, ensuring that 
the patient cannot close the app, accidentally misconfigure the tablet, or use the tablet for other purposes (e.g., 
web browsing, watching movies). Each tablet has a unique API key that allows it to sync the patient’s activities, 
physiological data, and daily self-report answers to a secure website. Health coaches can login to this website to 
review the data for each of their patients.   
 
MHS engineers will provision a web server and database for the OSA coaching system. The web server will be 
an AWS EC2 t2.micro instance running Ubuntu Linux 18.04 with an .NET Core 3.1 application (or newer). The 
database will be an AWS RDS db.t2.micro instance running MariaDB 10.3.23 (or newer).   
Software development best practices will be used throughout development. All source code will be versioned 
using Git and backed up GitHub.com. 
 
Patients will be provided with a system (Android tablet linked to a Withings Body Smart scale, Withings Sleep 
Tracking Mat, and a Withings Move Activity and Sleep Watch, see Appendix 1 for Withings device 
information) to take home and instructions to use it for two weeks along with their newly prescribed PAP 
machine. Patients will be asked to weigh themselves at least once a week, to use the PAP machine and sleep 
mat daily, and to monitor their steps.  Patients will also be asked to complete self-report questions at least 6 of 
the seven days and to complete a health coaching call twice per week. The activity monitor watch is to be worn 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 6 of 13 
 
at all times. The participant tablet home screen display will have a simple To-Do list. To initiate an activity, the 
user selects the corresponding activity from the “To-Do” column. When the participant finishes  
the selected activity, it appears in the “Done” column. The application provides instructions before and between 
each activity. Participants will also be able to see limited reports of their activity as will the health coach. In 
phase II, this reporting capability will be flushed out. The health coaches are an established part of Dr. Benzo’s 
laboratory and have been trained to support participants in the behavior change process.55 
 
The 5 self-reported questions are:  
 
Following the 2 week testing period , participants will be interviewed by the Research Coordinator to provide qualitative 
feedback on the acceptability and usability of the system, including what they liked and did not like about the app 
interface, the concept of home-based system, and the health coaching. Participants will be asked to describe any struggles 
with using the system or past efforts in behavior change. Participants will also complete a brief socio-demographic survey. 
Additionally, the system securely collects and stores the following patient data: timestamped interactions with the 
Android app, answers to the daily questionnaire, physiological and activity data collected by the scale, sleep mat and 
activity monitor. All data will be reviewed and visualized to understand patient usage, identify potential barriers, and to 
plan further developments to the system. 
  
The interviews will be recorded for subsequent transcription of the interactions.  The transcribed interviews will be 
examined for common themes.    Each interview will vary between 20 and 30 minutes and will be performed by a member 
of the Mindful Breathing Laboratory who is trained in Motivational Interviewing and Qualitative Interviewing.   
Additional members from the Lab will assist in the transcription of the interviews as well as theme identification. Data 
collection and analysis will be overseen by Dr. Benzo. (A guided interview script will be used.)   
 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 7 of 13 
 
 
 
Subject Information 
 
Target accrual: 15 
 
Adult patients who are newly diagnosed with severe OSA and severe obesity (BMI>35) will be recruited from 
the Mayo Clinic Center for Sleep Medicine. A Research Study Coordinator will contact qualified patients prior 
to their follow-up appointment in the Sleep Center.  This appointment typically takes place 2 or 3 months after 
the initial consultation with a sleep provider.  This window will give the patient time to adjust to using their new 
sleep apnea machine. The study team will use Epic’s Slicer Dicer feature to search the appointment return type 
and add the required BMI and age requirement.  A member of the study team will contact via telephone eligible 
patients using an approved phone script.  If interested, the patient will have the option of receiving the consent 
form electronically through Ptrax or a paper copy through the US Mail.  A member of the study team is 
available if the patient would like to meet in person during their follow up visit with their provider.   Patients 
may also be referred at the time of their return visit.    
 
Inclusion Criteria:  
• 18 years of age or older 
• Diagnosis of OSA 
• BMI > or = to 35 
 
Exclusion Criteria:   
• Patients with an inability to provide good data or follow commands (patients who are disoriented, have a 
severe neurologic or psychiatric condition).   
 
 
 
 
 
Review of medical records, images, specimens  
 
The study involves data that exist at the time of IRB submission and data that will be generated after IRB 
submission. Include this activity in the Methods section.   
 
To determine eligibility and obtain contact information, a study team member may review the participant’s 
medical record. 
 
 
Qualitative Data Analysis 
 
 
Power Statement:  This is a feasibility study, no sample size is required. 
 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 8 of 13 
 
Data Analysis Plan:    
  A qualitative analysis , in the way of likert-scale questions, will be completed when the participant has 
completed the intervention.  The qualitative approach will provide a framework for which detailed information 
about the participants experience is collected and eventually analyzed.    
 
. 
 
Bibliography & References Cited 
 
1.  
Bixler EO, Vgontzas AN, Lin H-M, et al. Prevalence of Sleep-disordered Breathing in Women. Am J 
Respir Crit Care Med. 2001;163(3):608-613. doi:10.1164/ajrccm.163.3.9911064 
2.  
Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered 
breathing among middle-aged adults. N Engl J Med. 1993;328(17):1230-1235. 
doi:10.1056/NEJM199304293281704 
3.  
Young T, Peppard PE, Gottlieb DJ. Epidemiology of obstructive sleep apnea: a population health 
perspective. Am J Respir Crit Care Med. 2002;165(9):1217-1239. doi:10.1164/rccm.2109080 
4.  
Kushida CA, Efron B, Guilleminault C. A predictive morphometric model for the obstructive sleep 
apnea syndrome. Ann Intern Med. 1997;127(8 Pt 1):581-587. doi:10.7326/0003-4819-127-8_part_1-
199710150-00001 
5.  
Ronksley PE, Hemmelgarn BR, Tsai WH. Comorbidity and healthcare costs in patients with obstructive 
sleep apnoea. Breathe. 2011;8(2):95-104. doi:10.1183/20734735.011311 
6.  
Logan AG, Perlikowski SM, Mente A, et al. High prevalence of unrecognized sleep apnoea in drug-
resistant hypertension. J Hypertens. 2001;19(12):2271-2277. doi:10.1097/00004872-200112000-00022 
7.  
Lee W, Nagubadi S, Kryger MH, Mokhlesi B. Epidemiology of obstructive sleep apnea: a population-
based perspective. Expert Review of Respiratory Medicine. 2008;2(3):349-364. doi:10.1586/17476348.2.3.349 
8.  
West SD, Nicoll DJ, Stradling JR. Prevalence of obstructive sleep apnoea in men with type 2 diabetes. 
Thorax. 2006;61(11):945-950. doi:10.1136/thx.2005.057745 
9.  
Mooe T, Rabben T, Wiklund U, Franklin KA, Eriksson P. Sleep-disordered breathing in men with 
coronary artery disease. Chest. 1996;109(3):659-663. doi:10.1378/chest.109.3.659 
10.  
Y P, H K, J H, S L, A J, M B. An independent association between obstructive sleep apnoea and 
coronary artery disease. Eur Respir J. 1999;14(1):179-184. doi:10.1034/j.1399-3003.1999.14a30.x 
11.  
Javaheri S. Sleep disorders in systolic heart failure: a prospective study of 100 male patients. The final 
report. Int J Cardiol. 2006;106(1):21-28. doi:10.1016/j.ijcard.2004.12.068 
12.  
Oldenburg O, Lamp B, Faber L, Teschler H, Horstkotte D, Töpfer V. Sleep-disordered breathing in 
patients with symptomatic heart failure: a contemporary study of prevalence in and characteristics of 700 
patients. Eur J Heart Fail. 2007;9(3):251-257. doi:10.1016/j.ejheart.2006.08.003 
13.  
Sin DD, Fitzgerald F, Parker JD, Newton G, Floras JS, Bradley TD. Risk Factors for Central and 
Obstructive Sleep Apnea in  450 Men And Women with Congestive Heart Failure. Am J Respir Crit Care Med. 
1999;160(4):1101-1106. doi:10.1164/ajrccm.160.4.9903020 
14.  
Parra O, Arboix A, Bechich S, et al. Time course of sleep-related breathing disorders in first-ever stroke 
or transient ischemic attack. Am J Respir Crit Care Med. 2000;161(2 Pt 1):375-380. 
doi:10.1164/ajrccm.161.2.9903139 
15.  
Bassetti CL, Milanova M, Gugger M. Sleep-disordered breathing and acute ischemic stroke: diagnosis, 
risk factors, treatment, evolution, and long-term clinical outcome. Stroke. 2006;37(4):967-972. 
doi:10.1161/01.STR.0000208215.49243.c3 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 9 of 13 
 
16.  
Hui DSC, Choy DKL, Wong LKS, et al. Prevalence of Sleep-Disordered Breathing and Continuous 
Positive Airway Pressure Compliance: Results in Chinese Patients With First-Ever Ischemic Stroke. Chest. 
2002;122(3):852-860. doi:10.1378/chest.122.3.852 
17.  
Marin JM, Carrizo SJ, Vicente E, Agusti AGN. Long-term cardiovascular outcomes in men with 
obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an 
observational study. Lancet. 2005;365(9464):1046-1053. doi:10.1016/S0140-6736(05)71141-7 
18.  
Nieto FJ, Young TB, Lind BK, et al. Association of sleep-disordered breathing, sleep apnea, and 
hypertension in a large community-based study. Sleep Heart Health Study. JAMA. 2000;283(14):1829-1836. 
doi:10.1001/jama.283.14.1829 
19.  
Reichmuth KJ, Austin D, Skatrud JB, Young T. Association of Sleep Apnea and Type II Diabetes. Am J 
Respir Crit Care Med. 2005;172(12):1590-1595. doi:10.1164/rccm.200504-637OC 
20.  
Yaggi HK, Concato J, Kernan WN, Lichtman JH, Brass LM, Mohsenin V. Obstructive Sleep Apnea as a 
Risk Factor for Stroke and Death. New England Journal of Medicine. 2005;353(19):2034-2041. 
doi:10.1056/NEJMoa043104 
21.  
Cistulli PA, Armitstead J, Pepin J-L, et al. Short-term CPAP adherence in obstructive sleep apnea: a big 
data analysis using real world data. Sleep Med. 2019;59:114-116. doi:10.1016/j.sleep.2019.01.004 
22.  
Morrone E, Giordano A, Carli S, et al. Something is changing in adherence to CPAP therapy: real world 
data after 1 year of treatment in patients with obstructive sleep apnoea. Eur Respir J. 2020;55(3). 
doi:10.1183/13993003.01419-2019 
23.  
Young T, Skatrud J, Peppard PE. Risk factors for obstructive sleep apnea in adults. JAMA. 
2004;291(16):2013-2016. doi:10.1001/jama.291.16.2013 
24.  
Fietze I, Laharnar N, Obst A, et al. Prevalence and association analysis of obstructive sleep apnea with 
gender and age differences – Results of SHIP-Trend. Journal of Sleep Research. 2019;28(5):e12770. 
doi:https://doi.org/10.1111/jsr.12770 
25.  
Pleava R, Mihaicuta S, Serban CL, et al. Long-Term Effects of Continuous Positive Airway Pressure 
(CPAP) Therapy on Obesity and Cardiovascular Comorbidities in Patients with Obstructive Sleep Apnea and 
Resistant Hypertension—An Observational Study. Journal of Clinical Medicine. 2020;9(9):2802. 
doi:10.3390/jcm9092802 
26.  
Kim J, Tran K, Seal K, et al. Interventions for the Treatment of Obstructive Sleep Apnea in Adults a 
Health Technology Assessment. CADTH Optimal Use Report; 2017. 
27.  
Smith R, Ronald J, Delaive K, Walld R, Manfreda J, Kryger MH. What are obstructive sleep apnea 
patients being treated for prior to this diagnosis? Chest. 2002;121(1):164-172. doi:10.1378/chest.121.1.164 
28.  
Tarasiuk A, Greenberg-Dotan S, Brin YS, Simon T, Tal A, Reuveni H. Determinants affecting health-
care utilization in obstructive sleep apnea syndrome patients. Chest. 2005;128(3):1310-1314. 
doi:10.1378/chest.128.3.1310 
29.  
Tarasiuk A, Greenberg-Dotan S, Simon-Tuval T, Oksenberg A, Reuveni H. The effect of obstructive 
sleep apnea on morbidity and health care utilization of middle-aged and older adults. J Am Geriatr Soc. 
2008;56(2):247-254. doi:10.1111/j.1532-5415.2007.01544.x 
30.  
Greenberg-Dotan S, Reuveni H, Simon-Tuval T, Oksenberg A, Tarasiuk A. Gender differences in 
morbidity and health care utilization among adult obstructive sleep apnea patients. Sleep. 2007;30(9):1173-
1180. doi:10.1093/sleep/30.9.1173 
31.  
Albarrak M, Banno K, Sabbagh AAl, et al. Utilization of Healthcare Resources in Obstructive Sleep 
Apnea Syndrome: a 5-Year Follow-Up Study in Men Using CPAP. Sleep. 2005;28(10):1306-1311. 
doi:10.1093/sleep/28.10.1306 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 10 of 13 
 
32.  
Banno K, Manfreda J, Walld R, Delaive K, Kryger MH. Healthcare utilization in women with 
obstructive sleep apnea syndrome 2 years after diagnosis and treatment. Sleep. 2006;29(10):1307-1311. 
doi:10.1093/sleep/29.10.1307 
33.  
Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: 
payer-and service-specific estimates. Health Aff (Millwood). 2009;28(5):w822-831. 
doi:10.1377/hlthaff.28.5.w822 
34.  
Kuvat N, Tanriverdi H, Armutcu F. The relationship between obstructive sleep apnea syndrome and 
obesity: A new perspective on the pathogenesis in terms of organ crosstalk. The Clinical Respiratory Journal. 
2020;14(7):595-604. doi:https://doi.org/10.1111/crj.13175 
35.  
Romero-Corral A, Caples SM, Lopez-Jimenez F, Somers VK. Interactions between obesity and 
obstructive sleep apnea: implications for treatment. Chest. 2010;137(3):711-719. doi:10.1378/chest.09-0360 
36.  
Wozniak DR, Lasserson TJ, Smith I. Educational, supportive and behavioural interventions to improve 
usage of continuous positive airway pressure machines in adults with obstructive sleep apnoea. Cochrane 
Database Syst Rev. 2014;(1):CD007736. doi:10.1002/14651858.CD007736.pub2 
37.  
Rotenberg BW, Murariu D, Pang KP. Trends in CPAP adherence over twenty years of data collection: a 
flattened curve. J Otolaryngol Head Neck Surg. 2016;45(1):43. doi:10.1186/s40463-016-0156-0 
38.  
Foster GD, Borradaile KE, Sanders MH, et al. A Randomized Study on the Effect of Weight Loss on 
Obstructive Sleep Apnea Among Obese Patients With Type 2 Diabetes: The Sleep AHEAD Study. Archives of 
Internal Medicine. 2009;169(17):1619-1626. doi:10.1001/archinternmed.2009.266 
39.  
Butterworth S, Linden A, McClay W, Leo MC. Effect of motivational interviewing-based health 
coaching on employees’ physical and mental health status. J Occup Health Psychol. 2006;11(4):358-365. 
doi:10.1037/1076-8998.11.4.358 
40.  
Olsen JM, Nesbitt BJ. Health coaching to improve healthy lifestyle behaviors: an integrative review. Am 
J Health Promot. 2010;25(1):e1-e12. doi:10.4278/ajhp.090313-LIT-101 
41.  
Mao A, Chen C, Magana C, Caballero B, Olayiwola J. A Mobile Phone-Based Health Coaching 
Intervention for Weight Loss and Blood Pressure Reduction in a National Payer Population: A Retrospective 
Study. - Abstract - Europe PMC. JMIR Mhealth and Uhealth. 2017;5(6):e80. doi:10.2196/mhealth.7591 
42.  
An S, Song R. Effects of health coaching on behavioral modification among adults with cardiovascular 
risk factors: Systematic review and meta-analysis. Patient Education and Counseling. 2020;103(10):2029-2038. 
doi:10.1016/j.pec.2020.04.029 
43.  
Benzo RP, Kirsch JL, Hathaway JC, McEvoy CE, Vickers KS. Health Coaching in Severe COPD After 
a Hospitalization: A Qualitative Analysis of a Large Randomized Study. Respir Care. 2017;62(11):1403-1411. 
doi:10.4187/respcare.05574 
44.  
Dupuy-McCauley KL, Novotny PJ, Benzo RP. Treating Severe Obesity to Reduce Dyspnea in Patients 
With Chronic Lung Disease: A Pilot Mixed Methods Study. CHEST. 2020;158(3):1128-1131. 
doi:10.1016/j.chest.2020.02.032 
45.  
Hurst I, Bixenstine PJ, Casillas C, et al. A telehealth intervention to promote weight loss and physical 
activity in overweight primary care patients. Healthcare. 2021;9(1):100509. doi:10.1016/j.hjdsi.2020.100509 
46.  
Alencar M, Johnson K, Gray V, Mullur R, Gutierrez E, Dionico P. Telehealth-Based Health Coaching 
Increases m-Health Device Adherence and Rate of Weight Loss in Obese Participants. Telemed J E Health. 
2020;26(3):365-368. doi:10.1089/tmj.2019.0017 
47.  
Strauss G. Engagement techniques for digital health interventions. Limbix. Published January 19, 2021. 
Accessed February 8, 2021. https://blog.limbix.com/engagement-techniques-for-digital-health-interventions-
b14e4128993b 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 11 of 13 
 
48.  
Crane D, Garnett C, Michie S, West R, Brown J. A smartphone app to reduce excessive alcohol 
consumption: Identifying the effectiveness of intervention components in a factorial randomised control trial. 
Scientific Reports. 2018;8(1). doi:10.1038/s41598-018-22420-8 
49.  
Pratap A, Grant D, Vegesna A, et al. Evaluating the Utility of Smartphone-Based Sensor Assessments in 
Persons With Multiple Sclerosis in the Real-World Using an App (elevateMS): Observational, Prospective Pilot 
Digital Health Study. JMIR mHealth and uHealth. 2020;8(10):e22108. doi:10.2196/22108 
50.  
Oh J, Sundar SS. How Does Interactivity Persuade? An Experimental Test of Interactivity on Cognitive 
Absorption, Elaboration, and Attitudes. Journal of Communication. 2015;65(2):213-236. 
doi:10.1111/jcom.12147 
51.  
Lustria MLA, Noar SM, Cortese J, Stee SKV, Glueckauf RL, Lee J. A Meta-Analysis of Web-Delivered 
Tailored Health Behavior Change Interventions. Journal of Health Communication. 2013;18(9):1039-1069. 
doi:10.1080/10810730.2013.768727 
52.  
Silberman JM, Kaur M, Sletteland J, Venkatesan A. Outcomes in a digital weight management 
intervention with one-on-one health coaching. PLOS ONE. 2020;15(4):e0232221. 
doi:10.1371/journal.pone.0232221 
53.  
Batsis JA, Petersen CL, Clark MM, et al. Feasibility and acceptability of a technology-based, rural 
weight management intervention in older adults with obesity. BMC Geriatrics. 2021;21(1):44. 
doi:10.1186/s12877-020-01978-x 
54.  
Benzo RP, Kramer KM, Hoult JP, Anderson PM, Begue IM, Seifert SJ. Development and Feasibility of 
a Home Pulmonary Rehabilitation Program With Health Coaching. Respiratory Care. 2018;63(2):131-140. 
doi:10.4187/respcare.05690 
55.  
Benzo R, Vickers K, Ernst D, Tucker S, McEvoy C, Lorig K. Development and feasibility of a self-
management intervention for chronic obstructive pulmonary disease delivered with motivational interviewing 
strategies. Journal of cardiopulmonary rehabilitation and prevention. 2013;33(2):113-122. 
doi:10.1097/HCR.0b013e318284ec67 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 12 of 13 
 
Appendix 1: 
 
Withings Sleep Mat 
 
 
 
Withings Smart Scale 
 
 

Minimal Risk Protocol Template [IRB 10429.010] 
 
Effective: 9/20/2017 
 
 
 
 
 
 
 
 
Page 13 of 13 
 
Withings Move Activity and Sleep Watch