Adherence to Medical Treatments for Telemedicine Patients
Phase: NA
Conditions: Stroke, Medication Adherence
Sponsor: University of Virginia

IRB-HSR #  21012: Adherence to Medical Treatments for Telemedicine Patients 
Version Date: 04/09/19 
Page Number: 1 of 10 
 
 
 
 
 
 
Adherence to Medical Treatments for Telemedicine Patients (Protocol) 
 
NCT04180475 
 
Version Date: 04/09/2019 
 

IRB-HSR #  21012: Adherence to Medical Treatments for Telemedicine Patients 
Version Date: 04/09/19 
Page Number: 2 of 10 
 
PROTOCOL 
Background 
1. Provide the scientific background, rationale and relevance of this project.   
Proper adherence to prescribed medications is a fundamental requirement for effective 
health outcomes. The possible consequences of poor medication adherence include 
reduced effectiveness of treatments, deterioration of health conditions, longer recovery 
time, increased cost, irrecoverable damages to health, hospitalization, and even death. 
Despite the severe consequences, the medication adherence rate among patients is 
significantly low1, 2, 3. According to the World Health Organization (WHO), “Adherence to 
long- term therapy for chronic illnesses in developed countries averages 50%. In developing 
countries, the rates are even lower”1. Poor medication adherence is a public health 
problem, and WHO identifies it as a worldwide problem of striking magnitude. It causes 
about 33-69% of all the medication associated hospital admissions in the U.S.4.  
One of the main reasons for poor medication adherence is forgetfulness. People often 
forget to take medication at the appropriate time, and even sometimes take wrong 
dosages. Though forgetfulness is more prevalent in people with reduced cognitive ability 
such as the elderly, it is also very common to healthy and young people due to factors like 
daily routine, habits, and changes in medication regimen. Several studies show that 
medication adherence is improved significantly with the use of automated reminder 
systems5, 6, 7.  
A number of smart phone applications for medication reminder and tracking are available in 
app stores8,9. Researchers have also designed, developed and evaluated reminder systems 
using smart phones10, 11. However, smart phone apps are not convenient and effective 
enough for medication reminder and tracking. Though smart phones can be used by one 
hand when it is laid on or hung against some surfaces, most of the times users need to hold 
the phone by one hand and use it by another. Occupation of the hands for using the 
phones, and the attention required to use the apps justify that smartphone-based systems 
are significantly intrusive, and are not convenient, particularly for long-term and complex 
medication regimens. Also, a user is very likely to miss a reminder at home when the phone 
is far enough from his/her location at the time the reminder is given. For instance, a user 
may miss a reminder when he/she is busy in the kitchen, but the phone is in the bedroom 
far away from the user. Moreover, remainders may be missed while listening to songs, TVs 
or videos even if the phone is located near the user. In many situations like in meetings and 
classrooms, smart phones typically need to be kept silent, and users often forget to return 
the devices back to the non-silent mode when silence is not required any more. It is very 
likely that a user misses some reminders in such scenarios.  
This study will test the feasibility of MedRem, a novel medication reminder and tracking 
system on wearable wrist devices, specifically with the Apple Watch 4. The study team will 
load the MedRem app onto two Apple Watch 4s for use during the study. As the device is 

IRB-HSR #  21012: Adherence to Medical Treatments for Telemedicine Patients 
Version Date: 04/09/19 
Page Number: 3 of 10 
placed on the wrist, it is free from the above-mentioned limitations of smart phones. 
However, one of the major challenges in developing interactive systems for the wrist 
devices is their form factor. The touch screens available on these devices are tiny and much 
smaller compared to smart phones and tablet computers. MedRem enables user 
interactions by incorporating speech recognition and text-to-speech features along with 
clever interface design. The tiny display of the device is used for minimal inputs and 
outputs, while a user can retrieve and provide more information from/to the system 
through voice commands. Personalized models are built and updated over time to reduce 
errors in recognizing users’ voice commands, and thus better user experience is provided.  
Smart wearable devices like smart watches and wrist bands are usually enriched with many 
features like touch screens, microphones, sensors, Bluetooth and Wi-Fi. These devices are 
being used widely in healthcare applications including activity tracking, wellbeing 
monitoring, and reminders. Harmony is a hand wash monitoring and reminder system that 
uses inertial sensors of the smart watches in detecting hand wash activities of the wearer, 
and Bluetooth beacon-based localization technique to trigger reminders when required12. A 
diary-like system for diabetes patients is presented in another study that uses both 
smartphones and smartwatches to log information from and provide reminders to diabetes 
patients13. SPARK is a framework that combines smartphones and smartwatches together in 
monitoring symptoms of patients with Parkinson’s Disease. It also supports physicians in 
providing tele-interventions to the patients14. Fabian et al. propose to show pictures of the 
drugs on the display of the wrist device to reduce confusion of the patients when multiple 
drugs need to be taken15. Some smartphone apps also synchronize reminders with smart 
watches16. However, these systems use only the small display of the wrist device, and 
therefore cannot provide detailed information related to a reminder using the wrist device 
only. Also, wrist devices used in the existing systems do not support rescheduling the 
reminders. Most of these reminders and tracking systems are aimed for specific group of 
patients or users. In contrast, MedRem is a general-purpose medication reminder and 
tracking system that can be customized according to the patient’s needs. It combines 
speech recognition and text- to-speech technologies with intelligent interface design in 
providing reminders and tracking intakes.  
One of the more complex issues in healthcare surrounds patient non-compliance with medical 
advice and treatment recommendations. Coupled with challenges in access to care faced by 
patients in rural or other underserved areas, the development of innovative solutions to 
chronic disease management is imperative. The UVA Center for Telehealth has long provided 
services to patients regardless of geographic barriers, and, in particular, for those facing serious 
health disparities. As is the case with traditional in-person services, clinicians are challenged to 
ensure (or monitor) that daily adherence to treatment is completed. The field of remote patient 
monitoring is growing, and new sensing and actuation technologies have the potential to 
monitor for adherence and to support interventions that improve adherence. The UVA School 
of Engineering and Applied Sciences and the UVA Center for Telehealth will collaborate on this 
study.   

IRB-HSR #  21012: Adherence to Medical Treatments for Telemedicine Patients 
Version Date: 04/09/19 
Page Number: 4 of 10 
The proliferation of inexpensive sensing and actuation technology has the potential to bring 
improved health care to underserved areas. Telemedicine has made great strides in this regard. 
However, one of the most challenging problems is supporting patients in adhering to the (daily) 
treatments. Excellent remote health care will not work if the patients do not act on the advice 
and treatments. New, inexpensive, and easy to use technology (such as proposed here) has the 
potential to track adherence and, importantly, provide just-in-time, adaptive and learned 
interventions to increase adherence and thereby improve the health in rural communities. In 
this work we will focus on stroke patients, but the results of this work can be generalized to 
other medical conditions which will increase the future funding possibilities. Such solutions will 
not only improve health, but also reduce hospital readmissions. Underlying technical solutions 
will also have impact in producing robust and usable wearables and be a novel for voice 
processing in certain important contexts.  
In this study, we will deploy the MedRem system to obtain a small amount of clinical and social 
acceptance data for a particular type of stroke patient. We will have two phases of deployment. 
In the first phase, we will deploy the system to control/test subjects. In the second phase, we 
will deploy the system in homes (each for 4 weeks) in order to collect some preliminary pre-
pilot data. These systems will be deployed to stroke patients. Since this is a small study we will 
focus on stroke victims who are able to participate with a voice command home health system 
(i.e. no prohibitive cognitive impairments or language deficits), who have requirements for 
home- based therapy regimens (i.e. physical/occupational therapy), and who require 
medications for secondary stroke prevention (e.g. antithrombotic therapy to prevent ischemic 
stroke, stroke risk factor management with blood pressure, diabetes, cholesterol medications). 
We will identify patients by working with Dr. Andrew Southerland, a neurologist in the UVA 
Health System, who has extensive experience collaborating on mobile telehealth projects in 
stroke patients.  
We will focus on the clinical value of improved rehabilitation by voice-based reminder 
interventions. The smart watch will identify exercise and provide the frequency and type of 
intervention for both medications and exercise. For exercise, we will interact with the patients 
(via audio on the watch) at specific times of the day to determine if they have completed that 
exercise. These messages will be prerecorded and loaded onto the MedRem app. We will also 
measure heart rate and heart rhythm using the smart watch’s heart monitoring capabilities and 
inertial measurement units using the smart watch’s accelerometer, gyroscope, and 
magnetometer.  
Then, in future proposals other issues will be investigated including ability with respect to 
activity of daily living skills, attitudes, depression, anxiety, long-term use, and effect of 
reminders related to side effects. We would also extend the work to address caregiver issues of 
their own health and stress and anxiety. It is also possible to extend the use of the smart 
watches to assess the quality of the exercise regimen.  
Objectives/Hypothesis  
The purpose of this study (Phase 1) is to:  

IRB-HSR #  21012: Adherence to Medical Treatments for Telemedicine Patients 
Version Date: 04/09/19 
Page Number: 5 of 10 
 determine the feasibility of the Apple Watch medication/exercise reminder system by 
deploying the system to two control subjects.  
 
The purpose of this study (Phase 2) is to: 
 assess the subjects’ adherence to medications, exercise and checking heart rate (and 
quality of the exercise), with the use of MedRem 
 assess the acceptance and usability of the MedRem system.  
 
The hypothesis is that patients will use the system and that there are improvements in 
rehabilitation time and capability compared to averages for this population. 
 
Study Design: Biomedical 
1.  Will controls be used? 
Yes. 
►IF YES, explain the kind of controls to be used. 
We will test the system with two control patients.  
 
2. What is the study design? Case series 
 
3. Does the study involve a placebo? No. 
Human Participants 
Ages:  ≥18  years 
Sex: 
All 
Race: All 
 
Subjects- see below 
 
1.  Provide target # of subjects (at all sites) needed to complete protocol. 
Phase 1: 2 
Phase 2: 8 
 
2.  Describe expected rate of screen failure/ dropouts/withdrawals from all sites.   
Phase 1: 0% 
Phase 2: 20% 
 
3.  How many subjects will be enrolled at all sites?    
Phase 1: 2 
Phase 2: 10 
 
4.  How many subjects will sign a consent form under this UVa protocol?     
12.  Phase 1 subjects will sign a Dry Run consent. 
Inclusion/Exclusion Criteria 
1. List the criteria for inclusion  

IRB-HSR #  21012: Adherence to Medical Treatments for Telemedicine Patients 
Version Date: 04/09/19 
Page Number: 6 of 10 
Phase 1 (Controls) 
 18 – 75 years old 
 Good general health (self-reported), with no history of CVA 
 English-speaking 
 Willingness and ability to comply with scheduled visits and study procedures 
 
Phase 2 (Stroke Patients) 
 18 – 75 years old 
 English-speaking 
 Willingness and ability to comply with scheduled visits and study procedures 
 Have requirements for home- based therapy regimens (i.e. physical/occupational 
therapy) 
 Require medications for secondary stroke prevention (e.g. antithrombotic therapy to 
prevent ischemic stroke, stroke risk factor management with blood pressure, diabetes, 
cholesterol medications). 
 Have a Modified Rankin Scale for Neurologic Disability (MDS) of 2 – 4 
 
2.  List the criteria for exclusion 
 
Phase 1 (Controls) 
 Pregnant women, fetuses, neonates, children, prisoners, cognitively impaired, non-
English speaking subjects 
 Prohibitive cognitive impairments or language deficits 
 Does not have wireless internet connection at home 
 
Phase 2 (Stroke Patients) 
 Pregnant women, fetuses, neonates, children, prisoners, cognitively impaired, non-
English speaking subjects 
 Have a Modified Rankin Scale for Neurologic Disability (MDS) of 0-1 or 5 
 Prohibitive cognitive impairments or language deficits 
 Significant weakness, dystonia, or spasticity that will prevent proper use and response 
to the Apple Watch device 
 Live in nursing home or rehabilitation facility  
 Does not have wireless internet connection at home 
 
3.  List any restrictions on use of other drugs or treatments. 
n/a 
Statistical Considerations 
1. 
Is stratification/randomization involved? 
No. 
 
2.  What are the statistical considerations for the protocol?  
Phase 1: 

IRB-HSR #  21012: Adherence to Medical Treatments for Telemedicine Patients 
Version Date: 04/09/19 
Page Number: 7 of 10 
The objective of Phase 1 is to determine the feasibility of the Apple Watch medication/exercise 
reminder system by deploying the system to two control subjects.  
The primary endpoint of this phase to assess the usability of the system and to work out any 
technical problems. The usability will be assessed by the ‘Test Patient MedRem Usability 
Survey.’ (see attached) 
 
Phase 2:  
The objective of Phase 2 is to determine the effectiveness of the Apple Watch 
medication/exercise reminder system by deploying the system to stroke patients. The goal is to 
assess the clinical value of improved rehabilitation by voice-based reminder interventions.  
 
The primary endpoint of this phase is to assess the patients’ adherence to medications and 
exercise. Adherence will be measured by patient responses to questions prompted by the 
MedRem System.  
 
The secondary endpoint of this phase is to assess the acceptance and usability of the system. 
Acceptance and usability will be measured by the Intervention Patient MedRem Usability 
Survey (see attached). Steps taken and distance covered data will also be collected from the 
Apple Watch. 
 
3.  Provide a justification for the sample size used in this protocol.   
2 control subjects will suffice in determining the feasibility of the device software.  
 
8 study subjects will suffice in determining the effectiveness of the device software. We will aim 
to recruit 10 subjects, accounting for a dropout rate of 20%. 
 
4.  What is your plan for primary variable analysis? 
The primary variable will be analyzed by counting whether the patient responded to the 
MedRem system prompts and whether the patient confirms that the medication was taken or 
therapy exercise was completed.  
 
5.  What is your plan for secondary variable analysis?  
The secondary variable will be measured based on the survey results. We will also analyze the 
steps taken, and distance covered data collected from the Apple Watch. 
 
6. Have you been working with a statistician in designing this protocol? No.  
 
7.  Will data from multiple sites be combined during analysis?  No. 
Study Procedures-Biomedical Research 
1.  What will be done in this protocol?    
Phase 1:  

IRB-HSR #  21012: Adherence to Medical Treatments for Telemedicine Patients 
Version Date: 04/09/19 
Page Number: 8 of 10 
The MedRem system will be deployed to two control subjects who will interact with the 
system by responding to medication and exercise reminder prompts. Subjects will be 
provided with oral and written information regarding the watch and MedRem system.   
Test subjects will not take any sham medications or exercise regimens, but will be 
provided a sham reminder schedule. They will simply respond verbally to prompts and 
press a button on the watch when finished speaking as if they are taking the medication 
or doing the exercise. After one week of interacting with the system, the study team will 
follow up with the subject and determine the functionality of the system by asking 
survey questions. If changes need to be made, the subject will interact with the system 
for one more week to test out the new changes. At the end of each week of the testing 
period, the subject will respond to a survey over the phone (see attached ‘Test Patient 
MedRem Usability Survey’).  
 
Phase 2:  
After a patient is identified, recruited, and consented, the study team will introduce the 
MedRem system to the stroke patient. The patient will be thoroughly trained before 
taking the device home, and will be given written information and instructions for the 
AppleWatch and the MedRem system. A member of the study will have to come to the 
patient’s home to connect the apple watch to the patient’s home wifi. The subject will 
be asked to wear the watch while awake during their daily activities. Subjects will be 
given contact information for a member of the study team to be available by telephone 
if they have questions once they begin to use the watch. The subject’s physician and 
physical therapist, who are also members of the study team, will collect all medications 
and treatment regimens for the subject and give that information to the programming 
team. The programming team will upload the medication and treatment regimens to 
the MedRem system. The licensed providers, on the study team, will review the  list of 
medication and physical therapy exercises before the information is sent to the subject’s 
MedRem system on their Apple Watch.     
The subject’s medication will be set by time of day they have specified in discussion with 
their health care provider. As above, the provider will review the medication regimen 
before it is sent to the subject. The MedRem app will ask each subject at the specified 
time if they have taken their medication and the specific name of the medication will 
appear on the MedRem app. For exercise regimens, the MedRem app will ask at specific 
times of the day if the subject has performed their exercise. The MedRem system will 
also ask the subject to check their heart rhythm using the Applewatch twice a day at 
specified times. Patients will respond verbally to the Medrem app and will press a 
button to indicate a finished response.  The heart rhythm will not be stored in the 
MedRem database but the time stamp from each ECG taken will be recorded. All ECGs 
will be deleted once a timestamp is documented.   
 
Each week, a member of the study team will follow-up with the patient to see how well 
the system is working and if any reminders need to be adjusted for time of day. The app 
is controlled remotely, so the computer science study team member who is the 
subject’s health care provider can make any adjustments without having to physically 

IRB-HSR #  21012: Adherence to Medical Treatments for Telemedicine Patients 
Version Date: 04/09/19 
Page Number: 9 of 10 
see the patient.  The study team will also confer with the patient’s physician if any 
adjustments to their medication or exercise regimen need to be made. All adjustments 
will be assigned and reviewed by their subject’s licensed provider using the same review 
process as stated above. The study team will administer the ‘Intervention Patient 
MedRem Usability Survey’ over the phone at the end of each week of the study period. 
After one month, the study team will call the patient and administer a survey over the 
phone (see attached). At the end of the study period, the patient will return the device 
at their next follow-up appointment in the Neurology clinic. 
 
At the end of study participation, a licensed health care provider will assist the subject 
to transition to a conventional method for keeping track of medications and exercise 
routines.  
 
2. If this protocol involves study treatment, explain how a subject will be transitioned from 
study treatment when they have completed their participation in the study.  n/a 
Bibliography 
1. E. Sabate ́, Adherence to long-term therapies: evidence for action. World Health 
Organization, 2003.  
2. A. Bozek and J. Jarzab, “Adherence to asthma therapy in elderly patients,” Journal of 
Asthma, vol. 47, no. 2, pp. 162–165, 2010.  
3. P. Latry, M. Pinet, A. Labat, J.-P. Magand, C. Peter, P. Robinson, K. Martin-Latry, and M. 
Molimard, “Adherence to anti-inflammatory treatment for asthma in clinical practice in 
france,” Clinical therapeu- tics, vol. 30, pp. 1058–1068, 2008.  
4.  L. Osterberg and T. Blaschke, “Adherence to medication,” New Eng- land Journal of 
Medicine, vol. 353, no. 5, pp. 487–497, 2005.  
5. M. Vervloet, A. J. Linn, J. C. van Weert, D. H. De Bakker, M. L. Bouvy, and L. Van Dijk, 
“The effectiveness of interventions using electronic reminders to improve adherence to 
chronic medication: a systematic review of the literature,” Journal of the American 
Medical Informatics Association, vol. 19, no. 5, pp. 696–704, 2012.  
6. R. E. Sarabi, F. Sadoughi, R. J. Orak, K. Bahaadinbeigy, et al., “The effectiveness of mobile 
phone text messaging in improving medication adherence for patients with chronic 
diseases: A systematic review,” Iranian Red Crescent Medical Journal, no. In Press, 2016.  
7. S. L. Spoelstra, C. W. Given, A. Sikorskii, C. K. Coursaris, A. Ma- jumder, T. DeKoekkoek, 
M. Schueller, and B. A. Given, “Feasibility of a text messaging intervention to promote 
self-management for patients prescribed oral anticancer agents.” in Oncology nursing 
forum, vol. 42, no. 6, 2015, pp. 647–657.  
8. “Android app store,” https://play.google.com/store/apps?hl=en.  
9.  “Apple app store,” https://itunes.apple.com/us/genre/ios/id36?mt=8.  
10. M.-Y. Wang, P. Tsai, J. W.-S. Liu, and J. K. Zao, “Wedjat: a mobile phone based medicine 
in-take reminder and monitor,” in Bioinfor- matics and BioEngineering, 2009. BIBE’09. 
Ninth IEEE International Conference on. IEEE, 2009, pp. 423–430.  
11.  J. M. Silva, A. Mouttham, and A. El Saddik, “Ubimeds: a mobile application to improve 
accessibility and support medication adher- ence,” in Proceedings of the 1st ACM 

IRB-HSR #  21012: Adherence to Medical Treatments for Telemedicine Patients 
Version Date: 04/09/19 
Page Number: 10 of 10 
SIGMM international workshop on Media studies and implementations that help 
improving access to disabled users. ACM, 2009, pp. 71–78.  
12. M. A. S. Mondol and J. A. Stankovic, “Harmony: A hand wash monitoring and reminder 
system using smart watches,” EAI Endorsed Transactions on Ambient Systems, vol. 15, 
no. 6, 2015.  
13. E.Arsand,M.Muzny,M.Bradway,J.Muzik,andG.Hartvigsen,“Per- formance of the first 
combined smartwatch and smartphone diabetes diary application study,” Journal of 
diabetes science and technology, vol. 9, no. 3, pp. 556–563, 2015.  
14. V. Sharma, K. Mankodiya, F. De La Torre, A. Zhang, N. Ryan, T. G. Ton, R. Gandhi, and S. 
Jain, “Spark: personalized parkinson disease in- terventions through synergy between a 
smartphone and a smartwatch,” in Design, User Experience, and Usability. User 
Experience Design for Everyday Life Applications and Services. Springer, 2014, pp. 103–
114.  
15. F. Sailer, M. Pobiruchin, M. Wiesner, and G. Meixner, “An approach to improve 
medication adherence by smart watches,” Studies in health technology and informatics, 
vol. 210, p. 956, 2015.  
16. “Medisafe meds & pill reminder,” https://play.google.com/store/apps/ 
details?id=com.medisafe.android.client&hl=en.