A Proof-of-concept, Open-label, Feasibility Study to Evaluate Mobile Applications and Biosensing (mHealth) Devices to Monitor Physical Activity and Respiratory Function in Smokers With and Without Respiratory Symptoms/COPD
Conditions: Signs and Symptoms, Respiratory, Mobile Applications, Activity Trackers, Respiratory Function Tests
Sponsor: Kazakhstan Academy of Preventive Medicine

1 
 
INVESTIGATOR INITIATED STUDIES 
STUDY PROTOCOL  
1. GENERAL INFORMATION  
Date  
3 June 2019 
IIS Number 
PMI.IIS.2016.1 
NCT 
04081961 
Study Type 
Open-label, Three-arm Study  
Study title 
A Proof-of-concept, Open-label, Feasibility Study to Evaluate Mobile Applications 
and Biosensing (mHealth) Devices to Monitor Physical Activity and Respiratory 
Function in Smokers with and without Respiratory Symptoms/COPD 
Sponsor/Investigator details 
Full name: Almaz Sharman 
Address: 66 Klochkov Street, Almaty, Republic of Kazakhstan 050008 
Email: asharman@zdrav.kz 
Phone number: +7-777-111-4202 
Study facility 
Name: Academy of Preventive Medicine 
Address: 66 Klochkov Street, Almaty, Republic of Kazakhstan 050008 
Website: www.academypm.org 
 
 
 
 
 
 
 
 

2 
 
Project schedule/Gantt chart 
 
 
 
Regulatory framework 
The study will be conducted in accordance with the approved study protocol and standard operating procedures 
that meet the guidelines provided by the International Council for Harmonization E6 for Good Clinical Practice 
(GCP) in clinical studies (ICH, 2016). 
The investigator will ensure that this study is conducted in accordance with the ethical principles founded in the 
most recent revision of the Declaration of Helsinki and in accordance with Order #142 of Kazakhstan’s Ministry 
of Health, dated 2 April, 2018 ‘On Approval of the Regulations for Conducting Biomedical Experiments, 
Preclinical (Non-Clinical) and Clinical Studies, As Well As the Requirements for Preclinical and Clinical Sites’. 
According to a letter from the National Center for investigation of pharmaceuticals, medical devices and medical 
equipment of the Ministry of Health No14-16-351/2955-03 dated 6 February 2019, the Center informs that 
“..observational studies in which medical interventions conducted within the framework a routine medical 
practice in accordance with approved protocols of diagnosis and treatment; in accordance with Regulations for 
conducting medical-biological experiments, pre-clinical (non-clinical) and clinical studies, and also requirements 
for pre-clinical and clinical facilities, approved by the Order of the Ministry of Health No 142 dated 2 April 2018 

3 
 
(hereinafter – order No 142), are considered to be non-interventional. Based on description that you presented 
in your letter No 1023-03 dated 15.01.2019, the study that you plan to conduct entitled “A Proof-of concept, 
Open-label, Feasibility Study to Evaluate Mobile Applications and Biosensing (mHealth) Devices to Monitor 
Physical Activity and Respiratory Function in Smokers with and without Respiratory Symptoms/COPD” is non-
interventional. Non-interventional studies are regulated by paragraph 8, chapter 3 of the Order No 142. 
According to P2, item 14 pp6 of Regulations for classification of safety of medical devices and medical 
equipment approved by the Order No 764 of the Ministry of Health dated 24 November 2009, medical devices 
which are planned to be used in the abovementioned study – 1) Anamed OEM device for monitoring of physical 
activity and vital signs, and 2) Smart mobile spirometer AirNext are classified as Class 2a medical devices. 
Assessment of materials used in non-interventional studies that use class 2a medical devices is not necessary at 
the National Center for investigation of pharmaceuticals, medical devices and medical equipment of the 
Ministry of Health. 
2. SCIENTIFIC INFORMATION 
Executive Summary 
Title of Study: 
A Proof-of-concept, Open-label, Feasibility Study to Evaluate Mobile Applications and Biosensing (mHealth) 
Devices to Monitor Physical Activity and Respiratory Function in Smokers with and without Respiratory 
Symptoms/COPD. 
 
Investigational mHealth Devices: 
 
Anamed OEM (Original Equipment Manufacturer) device - physical activity and vital signs 
monitoring; 
 
Air Next mobile spirometry device; 
 
Investigational Software Applications: 
mHealth software or websites:   
 Symptomaster.com,  
 Zdrav.kz, and  
 Medintel.kz. 
 
Study Objectives: 
To examine the feasibility and acceptability of using mHealth devices in detecting vitality parameters in 
current smokers with and without respiratory symptoms/COPD (e.g., heart rate, blood oxygenation, 
steps/motion) for a future big-scale study. 

4 
 
To assess the utility (accuracy and precision) of using mHealth devices in detecting vitality parameters in 
current smokers with and without respiratory symptoms/COPD (e.g., heart rate, blood oxygenation, 
steps/motion) 
 
Outcome measures: 
Outcome measures are defined as rates of recruitment, retention, and adherence as well as safety of the 
intervention. Study participant-perceived acceptability is also included in the feasibility assessment. 
Recruitment. Recruitment is defined as the number of potential participants screened for study eligibility 
versus the number of persons who enrolled in the study. 
Retention. Retention is defined as the proportion of participants enrolled who completed the intervention 
and all study measures. 
Protocol adherence. Adherence to the study protocol is determined as the proportion of participants enrolled 
from whom all mHealth parameters registered every day. 
 
Study Design: 
This is proof-of-concept, open-label, three-arm, observational, single-center study. Cohorts of nine 
participants each will be enrolled to use mHealth devices while undergoing the current standard of care 
based on their smoking disease states or lack of disease states.  
Arm 1: Nine “non-COPD” healthy, smokers; 
Arm 2: Nine “grey zone” smokers (i.e., FEV1/FVC ≥ 0.70 after bronchodilator, and CAT ≥10 and 
6MWT<450m); 
Arm 3: Nine smokers diagnosed with Stage I–III COPD. 
 
Safety: 
Safety and tolerability will be evaluated through adverse event (AE) assessment, lung function tests, vital 
signs, supportive care medications, and changes in laboratory parameters (chemistry and hematology). 
 
 
Number of Planned Participants: 
Twenty-seven smokers using mHealth devices. 
 
Main Criteria for Inclusion: 
Participants must meet all of the following criteria to be eligible to enroll in the study: 
1. 40–59 years of age. 
2. Current smoker: 
 Asymptomatic current smokers: no symptoms or radiological findings (CAT, 6-minute walk test 
[6MWT]) and preserved pulmonary function based on spirometry (forced expired volume in 1 
second/forced vital capacity [FEV1/FVC] of at least 0.70 after bronchodilator and FVC is 80% or more 

5 
 
of the expected value) and respiratory symptoms (COPD assessment test [CAT]<10); and functionally 
capable (6MWT≥450m); 
 “Grey zone” current smokers: initially preserved pulmonary function based on spirometry, but with 
clinical symptoms based on CAT (>10) and 6MWT (<450). 
 Current smokers with a confirmed diagnosis of COPD (GOLD stage I–III). 
3.  Able to use and willing to be trained to use mHealth devices. 
4. Willing to provide written informed consent to participate in the study. 
 
Study Duration: 
This will be a 90-day study conducted in two stages 
 
Stage 1. Initial period of using the mHealth devices  (Days 1–21).: To evaluate feasibility of the 
intervention the accuracy and precision of collecting vitality parameters (e.g., heart rate, blood 
oxygenation, steps/motion) on mHealth devices. 
 
Step 2 Main period of using the mHealth devices  (Days 22–90): To evaluate the feasibility of 
utilizing mHealth devices in participants reminded daily versus not reminded to use them. 
 
Use of Investigational Devices: 
Step 1 (Days 1–21): Participants will wear AnaMed OEM devices for 22+ hours per day, with a 2-hour break 
to charge the devices. They will also measure spirometric parameters using the Air Next spirometry device 
on Days 0, 7, and 14, and 21. 
 
Step 2 (Days 22–90): Participants will wear AnaMed OEM devices 22+ hours per day, with a 2-hour break to 
charge the devices. They will also measure spirometric parameters using the Air Next spirometry device 
against standards on Days 28, 35, 56 and 90 of the study. 
 
Access to device-derived data will occur via cloud to collect, store, and summarize data.  
There will be a scalable infrastructure for integrating and processing sensor data, which involves the 
following: 
 
integration of data from the Garmin and AnaMed OEM device sensors; 
 
processing, storage, and auditing of data;  
 
dashboards for compliance and operational oversight; 
Application Programming Interface (API) to transfer data to external analytics. 
 
Statistical Methods: 
 
Sample Size: 
The primary endpoints of the study are rates of recruitment rate, adherence and retention. In order to 
assess the feasibility of intervention, we plan to recruit 27 participants. We conservatively estimate that 
30% of the people invited to participate will be recruited to the study with a 95% confidence interval within 
± 9%. We also assume the dropout rate will be 15%. The accuracy of the estimated retention rate will be at 
least ± 13%. Further, we believe that 90% of participants will be adhere to use of mHealth devices. In this 
case, the accuracy of the estimate will be at least 11%. All calculations are based on two-way 95% 
confidence intervals. 

6 
 
 
Statistical Analysis Plan 
Presented in details in relevant section 
 
Study Context: 
The proposed study is the first step in a series of studies aiming to investigate the utility (i.e., sensitivity, 
accuracy, and reproducibility) and feasibility/effectiveness of using mHealth devices in improving the 
treatment, assessment, compliance, and outcomes in smokers with and without respiratory 
symptoms/COPD. 
The results from this proof-of-concept, open-label device feasibility study will be used to finalize the 
protocol for a randomized, open-label, placebo-controlled, single-center, two-arm, 12-month study 
designed to assess clinical feasibility of this intervention. The Study 2 will provide data on the clinical 
feasibility of using mHealth devices to monitor the health of current smokers with respiratory symptoms, 
including “grey zone” smokers and COPD stage I–III patients. 
The long-term aim, after demonstrating proof-of-concept in this study, is to incorporate mHealth devices 
into an ongoing 5-year longitudinal cohort observational study in ~1,200 smokers to monitor selected 
vitality parameters and other co-morbidities. 
 
Background & Rationale 
Background  
Chronic obstructive pulmonary disease (COPD), the fourth leading cause of death in Kazakhstan, is an 
important global public health problem. COPD accounted for 3.2 million deaths worldwide in 2015 (GBD 
2015 Risk Factors Collaborators, 2015) and is the fourth leading cause of death globally (Lozano et al., 
2010). In Kazakhstan, 1.4 million people are believed to have COPD based on estimations from neighboring 
countries (Adeloye et al., 2015). COPD negatively affects the quality of life and is a major health care burden 
(Global Initiative for Chronic Obstructive Lung Disease [GOLD], 2017). COPD is the third leading cause of 
hospital readmission within 30 days (Shah et al., 2016). 
COPD is a heterogeneous condition, with a variety of disease-related phenotypes (Friedlander et al., 2007; 
Silverman, 2007). The chronic airflow limitation that characterizes COPD is caused by obstructive 
bronchiolitis and parenchymal destruction (emphysema). Cigarette smoke is the most common risk factor 
for COPD. Although COPD is traditionally defined by airflow obstruction in a spirometry test, smoking-
associated effects on the lungs related to COPD also include emphysema, gas trapping, and chronic 
bronchitis (National Center for Chronic Disease Prevention and Health Promotion, 2014). Systemic effects 
(e.g., on heart and muscles) and associated comorbidities (e.g., heart failure, metabolic disorders, sleep 
apnea syndrome, and depression) may complicate the course of disease posing challenges in the 
management of COPD (GOLD, 2017; Qaseem et al., 2011). Smokers with symptoms suggestive of COPD who 
do not qualify for diagnosis of COPD based on spirometry are referred to as “grey zone” COPD patients. 
They have preserved pulmonary function (forced expired volume in 1 second/forced vital capacity 
[FEV1/FVC] of at least 0.70 after bronchodilator and FVC  ≥80% of the expected value) and respiratory 

7 
 
symptoms (COPD assessment test [CAT] ≥10). Emphysema without airway obstruction is common in 
smokers (Woodruff et al., 2016; Regan et al., 2015).  
Continuous monitoring is important for the management of COPD. Telemonitoring using mobile health 
(mHealth) devices has the potential to promote self-management, improve control, increase quality of life, 
and prevent hospital admissions (Stroetmann et al., 2015, Rubio et al., 2017; Liu et al., 2016; Himes and 
Weitzman, 2016; Ho et al., 2016). Technological advances in mHealth home telemonitoring (eHealth) 
programs and systems can affect patient care in COPD (Pedone et al., 2013; Hernandez et al., 2015; 
Blumenthal et al., 2014; Nowiński et al., 2015; Himes and Weitzman, 2016). mHealth devices are an 
emerging opportunity in clinical studies and their utility (i.e., sensitivity, accuracy, and reproducibility) has 
been assessed for telemonitoring in COPD (Pedone et al., 2013). 
Telemonitoring is a promising alternative or adjunct to provision of traditional health care services in COPD 
(Nimmon et al., 2013). Although some studies have shown that telemonitoring may improve some clinical 
outcomes and reduce health care costs (Lundell et al., 2015, Cruz et al., 2014), the effects of telehealth 
interventions on emergency department attendance, hospital admissions, duration of admissions, health-
related quality of life, costs, and mortality remain less certain (Nimmon et al., 2013; Pinnock et al., 2013; 
Bolton et al., 2011; Cartwright et al., 2013; Kenealy et al., 2015; Sanchez-Morillo et al., 2016).  
In a recent study of telemedicine in the home setting using multiple activity sensor monitoring equipment in 
COPD patients, the augmentation of traditional telemedicine methods with motion sensing, spirometry, and 
symptom diaries appeared feasible (Tillis et al., 2017). In a literature review (141 randomized trials; 
n=37,695) of studies of eHealth practices such as telemetry, telephone calls, or home visits by nurse 
specialists, most studies were relatively short term (<6 months) and did not yield strong evidence for 
telemedicine in the management of chronic diseases (Wootton, 2012). However, the comparisons of 
outcomes in studies using telehealth applications are difficult due to advances in monitoring and 
communications technology and heterogeneity in the type of monitoring, disease entity and severity, and 
variations in the process of care brought about by the telemedicine intervention (Himes and Weitzman, 
2016).  
Implementing telemedicine and mHealth has allowed clinicians to intervene earlier and prevent 
complications but there remain the challenges of alarm frequency and response, which need to be 
implemented into the workflow (Mohktar et al., 2015). Data flow and workflow processes need to be 
designed with precision at the outset if telemedicine is to be applied in clinical practice.  
Although peak flow monitoring has been used for the home detection of asthma exacerbations, and studies 
in the past have monitored vital signs and symptoms in patients with COPD (Antoniades et al., 2012), few 
studies have attempted to deploy spirometry in the home monitoring of COPD (Pradella et al., 2015). With 
technical advances, spirometry is increasingly being used to track the progress of COPD over time and to 
identify acute exacerbations (Ferguson et al., 2000; Lee et al., 2006; Schermer et al., 2003; Wilt et al., 2005; 
Chawla et al., 2014).  
While the number of COPD mHealth devices is rapidly increasing, most have not been validated as clinically 
effective tools for the management of disease. In addition to empowering patients and facilitating disease 
self-management, the mHealth concept offers promise to aid COPD researchers in personalized treatments 
based on patient-specific profiles that integrate symptom occurrence and medication usage with 
environmental and genomic data. An integrated and targeted practice-managed approach that uses 
mHealth technologies in primary care settings will be most effective for the early identification, monitoring, 

8 
 
and management of chronic diseases, particularly COPD and cardio-metabolic syndrome (i.e., combined 
diabetes mellitus, systemic arterial hypertension, central obesity, and hyper-lipidemia). 
Health information technologies are revolutionizing health care by assisting patients in self-monitoring and 
decision-making, driving a shift toward a care model increasingly centered on personal use of digital and 
web-based tools (Finkelstein et al., 2012; Mandl and Kohane, 2012; Eggleston and Weitzman, 2014; 
Sharman et al., 2017). Because there is a dearth of evidence that direct-to-consumer mHealth tools are 
effective or that they provide accurate disease recommendations, they are not yet widely used in clinical 
practice. Nonetheless, the preponderance of mHealth is gradually increasing in health care, industry, and as 
the subject of research (Vegesna et al., 2017). 
Rationale 
The current study is designed to investigate feasibility of using mHealth devices to improve the treatment, 
assessment, compliance, and outcomes in smokers with and without respiratory symptoms/COPD. The 
study aims to reveal and address the anticipated barriers to the acceptance and implementation of mHealth 
devices in this patient population and clinical setting. As is well documented, the more attention patients 
receive from medical personnel, the better their clinical outcomes. Here we are attempting to use device-
driven monitoring applications, interactive reminders, and teaching modules to deliver a constant positive 
feedback loop to patients to improve their health decisions. 
Over the long-term, we hope to employ mHealth devices to monitor selected vitality parameters and other 
co-morbidities in a planned 5-year cohort observational study in ~1,200 smokers. Specifically, after we 
determine that AnaMed OEM device and Air Next mobile spirometer demonstrate accurate results and the 
most feasible way of wearing them, and depending on the outcome of the study, they will be introduced to 
record data from a randomized subsample of participants in an observational cohort study including 
smokers of combustible cigarettes and users of IQOS with heatsticks). Potentially, this methodology can be 
utilized to record FVC, FEV1 and their ratios as well as blood oxygenation, heart rate, respiratory rate and 
other important parameters from COPD patients. Should there be consistent differences between the 
results that come from mHealth devices vs standard diagnostic equipment, an adjustment coefficient can be 
applied to each measure. 
The following items will be investigated in the current study to aid the identification of devices that are 
accurate, sensitive, and reproducible in their ability to collect and process information necessary to assist 
the treatment of smokers with or without respiratory symptoms/COPD: 
 
prevalence of technical data collection issues; 
 
similarity of device-collected data compared with those from established, clinically proven 
methods; 
 
ability to integrate and compare data across management platforms; 
 
operational ease of devices and their functions for patients and staff; 
 
convenience of patient and staff training programs or ongoing device support; 
 
device effectiveness in detecting compliance or ease of use in entering patient compliance; 
 
ability to monitor the patients at different times of the day and in different conditions (e.g., indoors 
vs. outdoors; at rest vs. during exercise); 
 
being suitable for both chronic monitoring and real-time “on demand” monitoring 
 
 

9 
 
Scientific hypothesis, objective(s) & intended methodologies 
To assess the feasibility and usefulness of mHealth devices in current smokers with and without respiratory 
symptoms/COPD. 
To assess the utility (i.e., validity and reproducibility) of mHealth devices in detecting vitality parameters in 
current smokers with and without respiratory symptoms/COPD (e.g., heart rate; blood oxygenation; 
steps/motion; FEV1, FVC, and their ratio). 
 
Study Design and Study Plan 
This is proof-of-concept, open-label, three-arm, observational, single-center study. Cohorts of twenty-seven 
participants each will be enrolled to use mHealth devices while undergoing the current standard of care 
based on smoking disease state or lack of disease state.  
mHealth Devices Used in Arms 1–3: 
 
Arm 1: Nine “non-COPD”, otherwise healthy, smokers; 
 
Arm 2: Nine “grey zone” smokers (i.e., FEV1/FVC ≥0.70 after bronchodilator treatment, CAT ≥10; 
6MWT<450m); 
 
Arm 3: Nine smokers diagnosed with Stage 1 - 3 COPD. 
Safety and tolerability will be evaluated through adverse events (AEs), lung function tests, vital signs, and 
supportive care medications. 
Detailed study procedures can be found in Table 1. 
Description of Study Days  
Table 1. Schedule of Assessments and Study Activities 
Device Assessment Period 
Clinical Feasibility Study 
Period 
Screening 
Baseline 
Visit 
Interim 
Visit 
Final 
Visit 
Interim Visits 
Final 
Visit 
Visit 
1 
2 
3 
4 
5 
6 
7 
8 
Days 
1 
7 
14 
21 
28 
35 
56 
90 
Informed consent process 
X 
Study eligibility and smoking status 
X 
Review 
medical 
history 
(including 
physical 
examination 
and 
BMI 
measurement) 
X 
X 
X 
X 
X 
X 
X 
X 
COPD assessment test 
X 
Spirometry* 
X 
X 
X 
X 
X 
X 
X 
X 
6-minute walk test  
X 
X 
X 
X 
X 
X 
X 
X 

10 
 
Provide the study requirements handout 
(explain study/visit requirements 
X 
Dichotomous questionnaire for visit 
readiness 
X 
Provide mHealth devices** 
X 
Assessment of AnaMed OEM device+ 
Continuous 
monitoring 
Assessment 
of 
Air 
Next 
mobile 
spirometer against standard++ 
Continuous monitoring 
 
 
 
* 
Spirometry will be performed to diagnose and monitor COPD. 
**  Provision of AnaMed OEM device, Air Next mobile spirometer and instruction/review of how to use these 
tools (print and verbal instructions). 
# 
Morning video, text/voice reminders to wear AnaMed OEM device, perform spirometry using AirNext 
device.  
## Evening communication/feedback through video communication participants will share their experience 
and feedback on mHealth device usage. Feedback will be received by a clinical investigator.. 
a 
In each study group, there will be three participants who will be reminded every morning by text 
message or phone call and contacted every evening by phone or chat services (e.g., Skype, WhatsApp, 
Viber, SMS) to share their experiences and feedback on mHealth device usage; three participants who 
will receive morning reminders only, but no evening communication/feedback; and three participants 
will receive no morning reminders nor evening communication/feedback.   
+ 
Participants’ peripheral capillary oxygen saturation (SpO2) will be measured at each Study Center visit 
using industry standard pulse oximetry devices.  
++  Participants will host a mobile spirometer (Air Next) at home for once daily measurements. 
Measurements will be validated at Study Center visits using an industry standard device before and after 
the use of a bronchodilator.  
Screening Period, Visit 1 (Days −10 to −1) 
Procedures to be performed within 10 days prior to study start: 
 
informed consent process; 
 
study eligibility and smoking status; 
 
pregnancy test; 
 
recording of tobacco use history;  
 
review of medical history (including physical examination and BMI measurement); 
 
CAT; 
 
spirometry; 
 
6-minute walk test; 
 
provision of the study requirements handout (explain study/visit requirements); 
 
provision of mHealth devices. 
Device Assessment Period, Baseline, Visit 2 (Day 7) 
Procedures to be performed on Day 7 (Table 1): 

11 
 
 
6-minute walk test with  heart and breathing rates before and after the test 
 
Assessment of AnaMed OEM device SpO2 against standard pulse oximetry, heart rate and breath rate 
measurements.  
 
Comparison of AnaMed OEM device Step/Motion parameters with actual step counts and data from 
Garmin device. The latter will be worn by the participants while taking 6-minute-walk-test and 
measures on motion will be taken for the purpose of comparisons. 
 
Assessment of Air Next mobile spirometer against standard using BTL-08 SPIRO device for spirometry 
measurements. 
 
Administration of questionnaire of user experience. 
 
Administration of performance questionnaire on technical aspects. 
Device Assessment Period, Interim Visit 3 (Day 14) 
Procedures to be performed on Day 14 (Table 1): 
 
6-minute walk test with  heart and breathing rates before and after the test 
 
Assessment of AnaMed OEM device SpO2 against standard pulse oximetry, heart rate and breath rate 
measurements.  
 
Comparison of AnaMed OEM device Step/Motion parameters with actual step counts and data from 
Garmin device. 
 
Assessment of Air Next mobile spirometer against standard using BTL-08 SPIRO device for spirometry 
measurements. 
 
Administration of questionnaire of user experience. 
 
Administration of performance questionnaire on technical aspects. 
Device Assessment Period, Final Visit 4 (Day 21) 
Procedures to be performed on Day 21 (Table 1): 
 
6-minute walk test with  heart and breathing rates before and after the test 
 
Assessment of AnaMed OEM device SpO2 against standard pulse oximetry, heart rate and breath rate 
measurements.  
 
Comparison of AnaMed OEM device Step/Motion parameters with actual step counts and data from 
Garmin device. 
 
Assessment of Air Next mobile spirometer against standard using BTL-08 SPIRO device for spirometry 
measurements. 
 
Administration of questionnaire of user experience. 
 
Administration of performance questionnaire on technical aspects. 
Observational Period with mHealth Device, Visit 5 (Day 28) 
Procedures to be performed on Day 28  (Table 1): 
 
6-minute walk test with  heart and breathing rates before and after the test 
 
Continued Assessment of AnaMed OEM device SpO2 against standard pulse oximetry, heart rate and 
breath rate measurements.  
 
Comparison of AnaMed OEM device Step/Motion parameters with actual step counts and data from 
Garmin device. 

12 
 
 
Continued Assessment of Air Next mobile spirometer against standard using BTL-08 SPIRO device for 
spirometry measurements. 
 
Administration of questionnaire of user experience. 
 
Administration of performance questionnaire on technical aspects. 
Observational Period with mHealth Device, Visit 6 (Day 35) 
Procedures to be performed on Day 35 (Table 1): 
 
6-minute walk test with  heart and breathing rates before and after the test 
 
Continued Assessment of AnaMed OEM device SpO2 against standard pulse oximetry, heart rate and 
breath rate measurements. 
 
Comparison of AnaMed OEM device Step/Motion parameters with actual step counts and data from 
Garmin device. 
 
Continued Assessment of Air Next mobile spirometer against standard using BTL-08 SPIRO device for 
spirometry measurements. 
 
Administration of questionnaire of user experience. 
 
Administration of performance questionnaire on technical aspects. 
Observational Period with mHealth Device, Visit 7 (Day 56) 
Procedures to be performed on Day 56 (Table 1): 
 
6-minute walk test with  heart and breathing rates before and after the test 
 
Continued Assessment of AnaMed OEM device SpO2 against standard pulse oximetry, heart rate and 
breath rate measurements. 
 
Comparison of AnaMed OEM device Step/Motion parameters with actual step counts and data from 
Garmin device. 
 
Continued Assessment of Air Next mobile spirometer against standard using BTL-08 SPIRO device for 
spirometry measurements. 
 
Administration of questionnaire of user experience. 
 
Administration of performance questionnaire on technical aspects. 
Observational Period with mHealth Device, Final Visit 8 (Day 90) 
Procedures to be performed on Day 90 (Table 1): 
 
6-minute walk test with  heart and breathing rates before and after the test 
 
Continued Assessment of AnaMed OEM device SpO2 against standard pulse oximetry, heart rate and 
breath rate measurements.  
 
Comparison of AnaMed OEM device Step/Motion parameters with actual step counts and data from 
Garmin device. 
 
Continued Assessment of Air Next mobile spirometer against standard using BTL-08 SPIRO device for 
spirometry measurements. 
 
Administration of questionnaire of user experience. 
 
Administration of performance questionnaire on technical aspects. 

13 
 
Selection of Study Population 
Inclusion Criteria 
Participants will meet all of the following criteria to be eligible to enroll in the study: 
 
40–59 years of age. 
 
Current smokers who are currently smoking conventional cigarettes with a minimum of 10 pack-year 
smoking history1: 
 
Asymptomatic current smokers: no symptoms (CAT<10, 6MWT≥450m) and preserved 
pulmonary function based on spirometry (FEV1/FVC of at least 0.70 after bronchodilation 
treatment and FVC ≥80% of the expected value) and respiratory symptoms (CAT ≥10); OR 
 
“Grey zone” current smokers: initially preserved pulmonary function based on spirometry, but 
with clinical symptoms based on CAT (>10) and 6MWT (<450); OR 
 
Current smokers with a confirmed diagnosis of COPD (GOLD stage I–III). 
 
Able to use and willing to be trained to use mHealth devices. 
 
Provide written informed consent to participate in the study. 
Smokers will undergo the current standard of care based on their smoking disease states or lack of disease 
state. 
Exclusion Criteria 
Participants meeting any of the following exclusion criteria are not eligible to enroll in the study: 
 
Smokers with COPD exacerbation (defined as a change in symptoms requiring increased doses of 
current medicines or the prescription of new medicines, e.g., corticosteroids or antibiotics) that has 
not resolved at least 28 days prior to screening. Smokers with COPD exacerbations occurring after 
screening but before the first study visit will also be excluded. 
 
Smokers with pneumonia or other respiratory tract infections that have not resolved at least 14 days 
prior to screening. In addition, any participant that experiences pneumonia occurring after screening 
but before the first study visit will also be excluded. 
 
Smokers with other active respiratory disorders: tuberculosis, lung cancer, significant 
bronchiectasis, sarcoidosis, bronchial asthma, lung fibrosis, pulmonary hypertension, interstitial 
lung diseases, or other active pulmonary diseases.  
 
Any co-morbid medical condition that in the opinion of the investigator would make participation in 
the study unsafe or unfeasible, including conditions that prohibit completion of exercise testing, 
such as orthopedic, neurological, cardiovascular, or other conditions that significantly impair normal 
biomechanical movement patterns and limit the ability to walk/cycle, as judged by the investigator. 
 
Use of supplemental oxygen therapy. 
 
Inability to abstain from smoking during the period in which the participant is admitted to the 
Kazakhstan Academy of Preventive Medicine (KAPM) COPD Center. 
 
A history of allergy or hypersensitivity to metal, particularly stainless steel. 
 
Any vital sign indicator, for example, hypertension or tachycardia at rest that, at the discretion of 
the investigator, would make participation in the study unsafe or unfeasible. 

14 
 
 
Women who test positive for pregnancy during screening, lactating women, or women planning on 
becoming pregnant during the study. 
 
Participants using assistive devices like walking aids, as these are likely to interfere with physical 
activity. 
 
Other patients who are considered ineligible for the study by the investigator. 
 
Discontinuation Criteria 
Early Discontinuation of the Study 
The study may be discontinued at the sole discretion of KAPM, for any reason, including medical or ethical 
reasons affecting the continued performance of the study, or difficulties in the recruitment of participants. 
Early Discontinuation of Individual Participants 
Participants may be discontinued from study at the discretion of the investigator for any of the following 
reasons: 
 
unacceptable AE(s) or failure to tolerate wearing the device; 
 
participant decides to discontinue wearing device; 
 
any medically appropriate reason or significant protocol violation, in the opinion of the investigator. 
Participants may decide to discontinue wearing device for any reason. The investigator must determine the 
primary reason for a participant ’s discontinuation of wearing device and record this information on the 
Case Report Form (CRF). Participants who are prematurely withdrawn from wearing device are not eligible 
to re-initiate wearing device on this protocol at a later date. 
 
 
Study Procedures and Methods 
 
Methodology 
The mHealth devices will be compared for defined outcomes against industry standards. Additionally, a 
questionnaire will be administered to assess the participants’ perceptions of the mHealth technologies 
used. 
Initial evaluations will include the assessment of biosensing devices based on selected vitality parameters, 
such as heart rate, blood oxygenation, steps/motion in healthy current smokers and current smokers 
diagnosed with COPD with and without respiratory obstruction. The main goal of the study is to assess the 
validity, reliability, and feasibility of the following mHealth devices: 
 AnaMed OEM device - physical activity and vital signs monitoring device;  
                                                          
1 Pack-years will be calculated by taking the average number of cigarettes smoked per day divided by 20 
and multiplied by the number of years smoked.  

15 
 
 Air Next mobile spirometry device; https://www.nuvoair.com/spirometry.html 
 
Study Devices and Assessments 
mHealth devices will be assessed for validity using the outcome measures and the established clinically 
proven methods outlined in Table 2 
Table 2. Assessments of mHealth Devices Versus Standard of Care 
Objectives 
Technology 
Evaluated 
Industry 
Standards  
Outcome Measures/ Data Collection Instrument 
Spirometry 
Air Next mobile 
spirometer 
software-derived 
endpoints (FEV1, 
FVC, PEF)  
BTL-08 SPIRO 
before 
(without) and 
post-
bronchodilator 
use 
• 
FEV1, FVC, PEF (Air Next) 
• 
FEV1, FVC, PEF (BTL-08 SPIRO) 
Physical 
activity/ 
Exercise 
capacity 
AnaMed OEM 
device software-
derived 
endpoints (step 
counts, distance) 
6MWT 
• Step counts (total number of steps taken during a 
6MWT): software-derived from Garmin Vívofit 3 
and AnaMed OEM device; manually counted 
• Distance walked (total meters achieved during a 
6MWT): software-derived from Garmin Vívofit 3 
and AnaMed OEM device; manually counted 
Blood 
oxygenation 
saturation 
AnaMed OEM 
device 
software-
derived 
endpoint SpO2 
Vive DMD 1003 
pulse oxymeter 
SpO2 (AnaMed OEM device; Vive DMD 1003 
pulse-oximeter) 
 
Heart rate 
AnaMed OEM 
device 
software-
derived 
endpoint heart 
rate 
Manually 
counting heart 
rate during rest 
after and 6-
minute-walk 
test 
• Heart rate (AnaMed OEM device) 
• Heart rate (manually counted/determined during 
a period of rest and 6-minute-walk test) 
•  
Skin 
temperature 
AnaMed OEM 
device 
software-
derived 
endpoint skin 
temperature 
Measured 
temperature in 
armpit with a 
standard 
thermometer  
• Skin temperature (AnaMed OEM device) 
• Temperature measured in armpit using a standard 
thermometer 
 
User 
experience 
and 
 
 
• 
Questionnaire (see  
• 
Table 3) 

16 
 
compliance 
Wearing episodes as assessed by software-
derived parameters  
Prolonged 
data 
recording 
Mobile 
biosensing 
device-derived 
endpoints over 2 
weeks in 
ambulatory 
setting 
 
 
• Distance (AnaMed OEM device): recorded 22+ 
hours per day  
• Energy expenditure (AnaMed OEM device): 
recorded 22+ hours per day 
• Heart rate, heart rate variability (AnaMed OEM 
device): recorded 22+ hours per day 
 
Spirometry (Air Next Mobile Spirometer): 1 
measurement per day (evening) 
Device 
performance 
 
 
• 
Distance (AnaMed OEM device): recorded 
22+ hours per day  
• 
Energy expenditure (AnaMed OEM device): 
recorded 22+ hours per day 
• 
Heart rate, heart rate variability (AnaMed 
OEM device): recorded 22+ hours per day 
 
• 
Spirometry (Air Next Mobile Spirometer): 1 
measurement per day (evening) 
Collection of 
AEs and SAEs 
 
 
• From study start until end of follow-up period 
(Day 90) 
 
 
• 
 
 
 
 
*The main potential technical issues that will be assessed include inaccurate readings/device 
malfunctions, inconsistent data transfer, inefficient battery usage, USB connections, step-dependent 
process, registration system difficulties, lack of streamlined support, cellular communication 
coverage, triggered false alerts to home health teams, and issues with availability of technical 
support. 
Physical Examinations  
Physical examinations will be conducted during each visit based on the Stanford Medicine 25 
comprehensive clinical assessment to identify clinical signs of abnormalities. This will be in addition to 
standard anthropometric measurements and vital sign assessments. 
Vital Signs 
The following vital signs will be assessed during the participant’s visits: pulse rate and blood oxygenation.  
Pulmonary Function Test – Spirometry 
The Air Next mobile spirometer will be used by patients to assess respiratory function. Patients will hold 
their hands on tubular grips or use wrist clamps. Subsequent respiratory efforts will allow the determination 
of inspiratory capacity and FEV1. 

17 
 
Once the diagnosis of COPD has been established, GOLD nomenclature will be used to grade the severity 
according to the degree to which the measured FEV1 is lower than the patient’s predicted value 
(http://www.goldcopd.org):  
 
GOLD stage 1 (mild disease): FEV1 ≥80% of the predicted value;  
 
GOLD stage 2 (moderate disease): FEV1 ≥50% and <80% of the predicted value;  
 
GOLD stage 3 (severe disease): FEV1 ≥30% and <50% of the predicted value;  
 
GOLD stage 4 (very severe disease): FEV1 <30% of the predicted value. 
At the KAPM COPD Center, standard spirometry data will be collected using the BTL-08 SPIRO 
(www.btlnet.com/products-spirometry-spirometry-btl-08-spiro-pro) spirometry system.  Each spirometer 
used in this study will be tested and continuously standardized with a 3 L syringe. Each clinical coordinator 
will be certified after spirometry training and quality assessments will be performed throughout the study.  
Participants will be categorized for analysis using the GOLD staging system according to their spirometry, 
which will be performed before and after two inhalations of salbutamol (0.1 μg per inhalation). Among the 
criteria needed to make a diagnosis of COPD are deficits in the rate at which one can forcefully exhale. Most 
experts consider a low ratio (<0.70) of the FEV1 to the FVC after bronchodilator use to be a key diagnostic 
criterion. 
Bronchodilator responsiveness will be considered positive if the participant has a ≥12% change in FEV1 or 
FVC above pre-bronchodilator measurements.  
6-Minute Walk Test 
The 6MWT is a simple and effective test. A 100-ft hallway is needed and no exercise equipment or advanced 
training for technicians is required. This test measures the distance that a patient can quickly walk on a flat, 
hard surface in a period of 6 minutes.  
COPD Assessment Test 
The CAT is used as an add-on test with existing assessments in COPD (e.g., with FEV1). It is a simple and 
reliable measure of health status in COPD. The CAT assists patients and their physicians in quantifying the 
impact of COPD on the patient’s health. The CAT is a validated, short (8-item) questionnaire to be 
completed by patients.. 
User Experience Questionnaire 
Participants will be administered a questionnaire to assess their mHealth device user experience. One 
questionnaire will be administered per each device. The questions will address comfort levels and ease of 
daily vital measurements. The results will be largely qualitative because of the small sample sizes but will 
help to reveal some early patterns for understanding patient attitudes toward these devices. 
Interviews will be audio-recorded, fully transcribed, and analyzed thematically to explore the patient’s 
experience of wearing the AnaMed OEM device device and using the Air Next mobile spirometer. 
The interviews will be conducted by clinical investigators not involved with the quantitative monitoring or 
analysis to reduce the possibility of bias. 
Participant Registrations and Training 
The KAPM research team will register patients for each mHealth device. Installation and user guides for 
each technology will include labelled photographs and written instructions to be used by all teams and 

18 
 
patients during setup. All equipment will be tested before deployment. Training will be provided on setup 
and installation as well as individual checklists, decision trees, and troubleshooting information. The break 
for charging will be at a standard time (20:00) across arms. In addition to direct phone communication, 
Whastapp, SMS and other types of messaging systems will be used for sharing daily experiences each 
evening to assist with assessing the level of comfort and address issues with wearing AnaMed OEM device 
device and using AirNext mobile spirometer 
 
Table 3 User mHealth Device Satisfaction Questionnaire 
 
Question 
Strongly 
disagree 
Disagree 
Neutral 
Agree 
Strongly agree 
I feel comfortable using the equipment provided to 
complete my daily assessments.  
 
 
 
 
 
It was easy to learn how to use the equipment 
provided to complete my daily assessments.  
 
 
 
 
 
Overall, I am satisfied with how easy it is to 
complete my daily assessments.  
 
 
 
 
 
Instructions (such as online help, on-screen 
messages, and other documentation) provided 
with the equipment are clear.  
 
 
 
 
 
When problems arise, research and/or home 
health personnel are available to assist with 
troubleshooting, replacement of equipment. 
 
 
 
 
 
When problems arise that require the assistance of 
KAPM technical support, someone is available to 
assist and is helpful in troubleshooting equipment.  
 
 
 
 
 
DATA METHODS 
Registration 
The following items will be completed at registration: 
 
Study participants will register and identified as being part of the study based on their assigned code. 
 
After entering the activation code, wearable device study participants will complete the registration 
process and create a wearable account. 
 
Participants will sync their wearable devices (Garmin Vívofit 3 activity tracker and AnaMed OEM 
device multisensor) and Air Next mobile spirometry device by signing in to their account. 
 
Once registered, the wearable device account information will be authorized.  
 
Once associated, the system will inquire internal systems at regular intervals for newly synced data at 
the participant level. For example, for AnaMed OEM device device it will be important to determine 
the interval of data collection: every second, every 10 seconds, etc. Such parameter will be set at the 
time of device registration. The sampling rate will be documented during the data collection. 

19 
 
 
Data will be stored in a local cloud system. The entire process of data ingestion and storage will be 
audited according to ALCOA standards (Food and Drug Administration, 2016). 
 
Whenever a participant syncs new activity data to their device cloud, those data will be ingested, 
processed, and archived and then aggregated and summarized in JSON data format by summarization 
services. 
Data Collection 
The data from the Garmin Vívofit 3 activity tracker, AnaMed OEM device multisensor and Air Next 
spirometer will be collected, stored, and summarized to allow for visualization dashboards to track 
participant compliance and extract sensor data when the study is complete. There will be a scalable 
infrastructure for integration and processing of sensor data that involves the following:  
 
integration of data from the Garmin, AnaMed OEM device, and Air Next spirometer sensors; 
 
processing, storage and auditing of data;  
 
dashboards for compliance and operational oversight;  
 
APIs to transfer data to external analytics. 
Mobile Health Apps 
Participants will be provided with a smartphone (iPhone) to perform and visualize measurements and to 
assess their health status using the symptomaster.com application and zdrav.kz database as tools to 
determine potential AEs. The participants are expected to keep smartphones, which will serve as 
compensation for participation in the study. The mHealth devices will be collected after the study completion. 
This will be reflected in the Informed Consent Form. 
Figure 3. Interrelationship of our proprietary SmartHealth technologies in the early detection, diagnosis, 
and management of common diseases. 

20 
 
 
 
 
We developed an expert SmartHealth technology called symptomaster.com, which helps patients to 
establish the probable causes of the symptoms of diseases without assistance from a healthcare 
professional. Using a computer, a smartphone, or a tablet, a patient inputs his/her symptoms into the 
system when then produces the most likely preliminary diagnosis. After receiving the diagnosis, a patient 
can refer to zdrav.kz, an online library that contains information about 1000 most common diseases, and 
their causes, symptoms, and ways to prevent and treat them. Our technologies allow a patient to make an 
informed decision on whether they should seek immediate medical assistance by calling an ambulance or 
consult a doctor on their next routine visit (Sharman et al., 2017). 
In partnership with developers from Poland and the United States we designed a new product called 
medintel.kz (known in Europe as dxmate.com), a technology that uses machine learning to diagnose 
diseases. The technology is publicly available to all medical professionals. It is designed in such a way so that 
the more information about symptoms and other manifestations of diseases and clinical conditions is 
entered into the system the more accurate are the algorithms that lead to more precise disease diagnosis 
(Sharman et al., 2017).  
MedIntel.kz is updated in real time, and new information comes not only from scientific data but also from 
statistical information that doctors and clinics input into its machine learning algorithms. In the future, 
doctors can input genetic information from their patients and their relatives into the system to produce 
more accurate diagnoses and identify risk factors and conditions that predispose a patient to certain 
illnesses. 

21 
 
Symptomaster can be used to capture symptoms that may be related to AEs and severe AEs (SAEs). Further 
assessment can be done by a physician assistant during home visit or remotely by using MedIntel 
technology to make a decision about the need for a participant to visit KAPM’s COPD Center for further 
assessment.  
SAFETY ASSESSMENTS 
The proposed study is an observational cohort study which uses study procedures with well-established risk 
profiles. According to classification based on risk determination (Institute of Translational Health Sciences, 
2010), the study fits to the second category, for which independent study monitor is recommended. CRO 
(Synergy) will perform this function.     
Safety Monitoring 
The principal investigator (PI) will monitor the study, including review of study conduct, enrollment, and AEs 
with prompt reporting of AEs and other study-related information to the local and national ethics 
committees, sponsor, and other agencies as appropriate. Any deviations and changes in risks will be 
reported to the ethics committees, along with an annual status report. In addition, a detailed plan for 
oversight of the procedures by study staff will be provided.  
Adverse Events 
Adverse Event Report (AER) forms will be used to collect initial and follow-up information for non-serious 
and serious AEs, from Baseline through to Day 90. The following information will be collected for all AEs:  
• 
date of onset; 
• 
description of the AE; 
• 
severity; 
• 
relation to investigational devices; 
• 
action taken with the study intervention; 
• 
outcome; 
• 
date of resolution. 
If treatment is required for the AE, this will be recorded including the type of treatment, duration and any 
other relevant details. AE information will be captured on source documents and the investigator will sign 
off on each individual AE.  
All AEs occurring during the study are to be followed up in accordance with good medical practice until they 
are resolved, stabilized, or judged no longer clinically significant or, if a chronic condition, until fully 
characterized. Any AEs that are considered treatment-related (i.e., possibly related or related) will be 
followed until resolution or stabilization. 
AE Severity 
AE severity (intensity) will be classified as mild, moderate, or severe according to the following definitions:  
• Mild: causing no limitation in normal activities. 

22 
 
• Moderate: causing some limitation in normal activities. 
• Severe: causing significant limitation in or the inability to perform normal activities. 
The PI will assess and assign the severity of all reported AE’s.  
Relationship  
The PI is responsible for determining the relationship between an AE and the study products.  
The relationship will be classified as one of the following:  
• Probable: good reasons and sufficient documentation to assume a causal relationship. 
• Possible: a causal relationship is conceivable and cannot be dismissed. 
• Unlikely: the event is most likely related to an etiology other than the trial product.  
• Definitely not related: the event is not related to the study product. 
 
Expected AEs are those events which are reported to the study site by the sponsor or those that can 
be found in the peer-reviewed literature. By definition, unexpected AEs are those events which are not 
reported to the study site by the sponsor and not found in the literature, or are not consistent with the 
specificity or severity described by the sponsor or in the literature. An unanticipated problem is an 
unforeseen event that occurs during the course of a research trial that potentially increases the risk to 
participants or others; adversely affects the rights, safety, or welfare of participants; or affects the integrity 
of the study. All unanticipated problems involving risks to human subjects or others will be reported 
promptly to the ethics committees and sponsor (KAPM).  
Serious Adverse Events 
A serious adverse event (SAE) is defined as any untoward medical occurrence that at any dose: results in 
death, is life-threatening, requires inpatient hospitalization or prolongation of existing hospitalization (social 
admissions excluded), results in a persistent or significant disability/incapacity, is a congenital 
anomaly/birth defect or is an important medical event.  
Any SAE occurring in a participant after providing informed consent and until 30 days after completing the 
study will be recorded and reported.  
All SAEs, whether related or unrelated to investigational devices, whether expected or unexpected, will be 
reported to the sponsor within 24 hours of site knowledge of the event.  
All SAEs will also be reported to the ethics committees.  
If an ongoing SAE changes in its intensity or relationship to study product or additional information 
becomes available, a follow-up SAE report will be sent to the sponsor and Ethics Committee.  
It is KAPM’s decision whether or not an SAE should be reported to Kazakhstan’s health authorities. 

23 
 
ETHICAL CONSIDERATIONS 
Ethical Conduct of the Study 
The investigator will ensure that this study is conducted in accordance with the ethical principles founded in 
the most recent revision of the Declaration of Helsinki and in accordance with Order #142 of Kazakhstan’s 
Ministry of Health, dated April 2, 2018. 
The study will be conducted in accordance with the approved study protocol and standard operating 
procedures that meet the guidelines provided by the International Council for Harmonisation E6 for Good 
Clinical Practice (GCP) in clinical studies (ICH, 2016). 
Participation Information and Consent 
All participants in the proposed study shall be identified and contacted for informed consent pending 
approval of the National Ethics Committee of the Ministry of Health and a local ethics committee. The 
investigator will fully inform participants of all pertinent aspects of the trial, including the written 
information approved by the ethics committee. 
Prior to the first study-specific procedure, the written ICF will be signed and personally dated by the 
participant and by the physician who conducted the informed consent discussion. One copy of the written 
information and signed consent form will be given to each participant and one copy will be retained in the 
investigator's study records. 
Subject Confidentiality and Disclosure 
Health records shall be handled according to Kazakhstan’s Law on Protected Personal Information. All 
personal information will be removed, and demographic information will be anonymized, consistent with 
Kazakhstan legal requirements, GCP, and ethics committee policy.  
Data on participants collected on CRFs during the trial will be documented in an anonymous fashion and the 
participant will only be identified by the participant number, and by his/her initials if also required. If, as an 
exception, it is necessary for safety or regulatory reasons to identify the participant, all parties are bound to 
keep this information confidential. 
The investigator will guarantee that all persons involved will respect the confidentiality of any information 
concerning the trial participants. All parties involved in the study will maintain strict confidentiality to assure 
that neither the person nor the family privacy of a participant participating in the trial is violated. Likewise, 
the appropriate measures shall be taken to prevent access of non-authorized persons to the trial data. 
Disclosure of Information 
All information provided to the investigator, or their designee, will be kept strictly confidential. No 
disclosure shall be made except in accordance with a right of publication granted to the investigator. 
QUALITY CONTROL AND ASSURANCE 
The sponsor (KAPM) will implement the quality assurance and quality control system in accordance with the 
standard operating procedures specified by the sponsor to ensure that the implementation of the study and 
the generation, recording, and reporting of data are in compliance with the following: 

24 
 
1) The clinical study protocol. 
2) Standards stipulated in Article 14, Paragraph 3 and Article 80-2 of the Act on Securing Quality, 
Efficacy and Safety of Pharmaceuticals, Medical Devices, Regenerative and Cellular Therapy 
Products, Gene Therapy Products, and Cosmetics. 
3) GCP ordinance. 
In addition, the sponsor will perform quality control at each stage of data handling to ensure the reliability 
and proper processing of all study-related data. The methods for quality control will be prepared in advance 
in accordance with the standard operating procedure specified by the sponsor, and the implementation will 
be recorded. 
The sponsor’s responsible auditor will perform GCP auditing as part of quality assurance operations to 
determine whether the study is conducted in compliance with GCP, the clinical study protocol, and the 
written procedures independently and separately from the regular monitoring and study quality control 
operations. 
 
 
Statistical Analysis Plan 
For this proof-of-concept phase, access to device-derived data will be enabled via a cloud-to-cloud solution. 
Graphical and/or statistical comparisons will be made between the mobile biosensing devices-derived data 
and the data derived from the clinical standards, and between the three different study groups. 
The goal of statistical analysis is to determine accuracy and precision of mhealth biosensing devices in 
assessing such parameters as pulse oxymetry, heart rate, spirometric values where standard diagnostic 
procedures will be used as gold standards.   
The final statistical analysis plan will be written as a separate document and completed after finalizing the 
protocol before first subject in. Descriptive statistics will be used to summarize required qualitative and 
quantitative study elements (e.g., proportion, mean, standard error, median and inter-quartile range, 95% 
confidence interval). 
Exploratory graphical analysis will be done prior to numerical analysis. Histograms and two-dimensional 
scatterplots of raw data will provide information on the univariate and bivariate distributions of the 
variables, focusing on distribution of variables and relation between the variables (whether there is a linear 
or nonlinear relationship) and so on. Additionally, preliminary graphs will be used to screen raw data by 
highlighting obvious data errors. 
Tabulations will be produced for appropriate disposition, demographics, baseline, safety, and clinical 
parameters. 
Statistical comparisons will be made between the mobile biosensing devices-derived data and the data 
derived from the standard diagnostic equipment and methods: 
· 
pulse oximetry 
· 
heart rate 
· 
breath rate 

25 
 
· 
number of steps 
· 
FEV1 
· 
FVC 
· 
FEV1/FVC 
 
Agreement  analysis will be performed for both binary and quantitative measures. For binary variables, 
percent of  agreement (overall, positive and negative agreement) as well as Kappa coefficient, p-value and 
95% confidence interval will be calculated. 
For  two  quantitative measures of a parameter, we will use the Bland-Altman method (Bland-Altman plot 
and limits of agreement). The Bland-Altman plot analysis will allow to evaluate a bias between the mean 
differences, and to estimate an agreement interval, within which 95% of the differences between two 
quantitative methods of measurement are included. We will define a priori the limits of maximum 
acceptable differences (limits of agreement expected) for each quantitative measurements (pulse oximetry, 
heart rate, breath rate, number of steps, FEV1, FVC). In addition, correlation analysis 
will be run:  Pearson’s coefficient and 95% confidence interval will be calculated.    
The agreement analysis will be done for base-line, 7-day, 14-day, 21-day, 28-day, 56-day, and 90-day visits 
separately and for the data pooled from all measurements. A within-subject study design will be accounted 
to assess accuracy and precision for a single mobile device.  
All statistical analysis will be done for all participants and by study group. Additionally, we will compare 
trends of binary and quantitative outcomes from three study groups wearing mobile devices. 
Analyses will be performed using SPSS and R (version 3).  
Changes in the Planned Analysis 
All deviations from the original statistical analysis plan will be documented and provided in the final clinical 
study report. 
Data Management Plan 
All study data will be stored at the IT Unit of the Academy of Preventive Medicine of Kazakhstan, Almaty, 
Kazakhstan. Verification of eligibility will be completed via a web questionnaire after participants sign the 
research consent form, and participants tracked for completion of all study data. If a participant is excluded 
or discontinues during or after the study procedures, the specific exclusion or discontinuation reason will be 
recorded in the database. 
All electronic files will be encoded using 128-bit advanced encryption standard and password protected on 
a computer with both hardware and software firewalls. The locator form and any documents with 
identifying information will be kept in a separate folder and kept locked in filing cabinets. 
Data analysis will be performed only in aggregate. All data will be used for research purposes only and no 
participant will be identified when the data are analyzed, presented, or published. No individually 
identifiable information will be published. 

26 
 
Publication & Presentation Strategy 
The study targets clinicians, biomedical researchers, statisticians, health care policy makers, and interested 
public. Clinicians and biomedical researchers are considered to be professional users, data contributors, and 
key opinion leaders. Other professional users include statisticians and health care policy makers.  
The use of the study results by the local and international scientific communities will depend on 
publications in peer-reviewed English and Russian language professional/academic society journals. The 
project is planned to result in numerous publications in peer-reviewed scientific journals as well as 
presentations at international conferences and symposiums. For now, we plan to publish paper(s) in 
international peer-reviewed journals presenting intermediate and final results of the proof-of-
concept/Assessment study and the randomized control study. 
The Academy of Preventive Medicine will confirm the values and outcomes of its programs through 
outcomes analysis and comprehensive reporting that will be meaningful and, most importantly, 
transparent. 
 
REFERENCES 
1. Adeloye D, Chua S, Lee C, et al. Global Health Epidemiology Reference Group (GHERG). Global 
and regional estimates of COPD prevalence: Systematic review and meta-analysis. J Glob Health. 
2015;5(2):020415. Review.  
2. Antoniades NC, et al. Pilot study of remote telemonitoring in COPD. Telemed J E Health. 
2012;18:634–640. 
3. Bitsaki M, Koutras C, Koutras G, et al. ChronicOnline: Implementing a mHealth solution for 
monitoring and early alerting in chronic obstructive pulmonary disease. Health Informatics 
J. 2016 Apr 21. pii: 1460458216641480 
4. Blumenthal JA, Emery CF, Smith PJ et al. The effects of a telehealth coping skills intervention on 
outcomes in chronic obstructive pulmonary disease: primary results from the INSPIRE-II study. 
Psychosom Med 2014;76:581–592. 
5. Bolton CE, Waters CS, Peirce S, et al. Insufficient evidence of benefit: a systematic review of 
home telemonitoring for COPD. J Eval Clin Pract. 2011; 17: 1216–1222. 
6. Bryant J, McDonald VM, Boyes A, et al. Improving medication adherence in chronic obstructive 
pulmonary disease: a systematic review. Respir Res. 2013;14:109.  
7. Cartwright M, Hirani SP, Rixon L, et al. Effect of telehealth on quality of life and psychological 
outcomes over 12 months (whole systems demonstrator telehealth questionnaire study): nested 
study of patient reported outcomes in a pragmatic, cluster randomized controlled trial. BMJ, 
2013;346:f653–f653. 
8. Chawla H, Bulathsinghala C, Tejada JP et al. Physical activity as a predictor of 30-day hospital 
readmission after a discharge for a clinical exacerbation of chronic obstructive pulmonary 
disease. Ann Am Thorac Soc. 2014;11:1203–1209. 

27 
 
9. Cruz J, Brooks D, Marques A. Home telemonitoring effectiveness in COPD: a systematic review. 
Int J Clin Pract. 2014;68:369–378. 
10. Donaldson GC, Seemungal TAR, Patel IS, et al. Longitudinal changes in the nature, severity and 
frequency of COPD exacerbations. Eur Respir J. 2003;22(6):931–936.   
11. Eggleston EM, Weitzman ER. Innovative uses of electronic health records and social media for 
public health surveillance. Curr Diab Rep. 2014;14:468.  
12. Ferguson GT, Enright PL, Buist AS, et al. Office spirometry for lung health assessment in adults: A 
consensus statement from the National Lung Health Education Program. Chest. 2000;4:1146–
1161. 
13. Finkelstein J, Knight A, Marinopoulos S, et al. Enabling patient-centered care through health 
information technology. Rockville: Agency for Healthcare Research and Quality (US); 2012.  
14. Food and Drug Administration. 2016. Data integrity and compliance with CGMP: Guidance for 
industry. Available from: https://www.fda.gov/downloads/drugs/guidances/ucm495891.pdf. 
(Last accessed 29 January 2018). 
15. Friedlander AL, Lynch D, Dyar LA, et al. Phenotypes of chronic obstructive pulmonary 
disease. COPD. 2007;4(4):355–384.  
16. GBD 2015 Chronic Respiratory Disease Collaborators. Global, regional, and national deaths, 
prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive 
pulmonary disease and asthma, 1990-2015: a systematic analysis for the Global Burden of 
Disease Study 2015. Lancet Respir Med. 2017;5(9):691–706.  
17. GBD 2015 Risk Factors Collaborators. Global, regional, and national comparative risk assessment 
of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-
2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 
2016;388(10053):1659–1724.  
18. Global Adult Tobacco Survey (GATS) Kazakhstan. 2014 Country Report. Available from 
http://www.who.int/tobacco/surveillance/survey/gats/kaz_countryreport_en.pdf?ua=1 (Last 
accessed 29 Jan 2018). 
19. Global Initiative for Chronic Obstructive Lung Disease, 2017. Global Strategy for the Diagnosis, 
Management and Prevention of COPD. 2017 Available from: http://goldcopd.org/gold-2017-global-
strategy-diagnosis-management-prevention-copd/ (Last accessed 29 Jan 2018). 
20. Hernandez C, Garcia-Aymerich J, Grimsmo A, et al. Integrated care services: lessons learned from 
the deployment of the NEXEX project. Int J Integr Care. 2015;15:e006. 
21. Himes BE, Weitzman ER. Innovations in health information technologies 
for chronic pulmonary diseases. Respir Res. 2016;17:38. 
22. Ho TW, Huang CT, Chiu HC, et al. Effectiveness of telemonitoring in patients with chronic 
obstructive pulmonary disease in Taiwan - a randomized controlled trial. Sci Rep. 2016;6:23797. 
23. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for 
Human Use. ICH Harmonised Guideline. Integrated Addendum to ICH E6(R1): Guideline for Good 
Clinical Practice E6(R2). Available from: 
http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E6/E6_R2__S
tep_4_2016_1109.pdf (Last accessed 29 Jan 2018). 

28 
 
24. Institute for Translational Health Sciences Partner Institutions Joint Tool for Data and Safety 
Monitoring Plans. 2010. Available from: https://www.iths.org/wp-content/uploads/Cross-
institutionalDSMPguidelines.doc (Last accessed 29 Jan 2018). 
25. Kenealy TW, Parsons MJG, Rouse APB, et al. Telecare for diabetes, CHF or COPD: effect on quality 
of life, hospital use and costs. A randomised controlled trial and qualitative evaluation. PLoS One 
2015;10:e0116188. 
26. Lee TA, Bartle B, Weiss KB. Spirometry use in clinical practice following diagnosis of COPD. Chest. 
2006;129:1509–1515. 
27. Lindberg A, Bjerg A, Larsson LG, Lundback B. Prevalence and under-diagnosis of COPD by disease 
severity and the attributable fraction of smoking: report from the Obstructive Lung Disease in 
Northern Sweden Studies. Respir Med. 2006;100(2):264–272. 
28. Lipovec CN, Beijers RJ, van den Borst B, et al. The prevalence of metabolic syndrome in chronic 
obstructive pulmonary disease: a systematic review. COPD. 2016;13(3):399–406.  
29. Liu L, Stroulia E, Nikolaidis I, et al. Smart homes and home health monitoring technologies for 
older adults: A systematic review. Int J Med Inform. 2016;91:44–59. 
30. Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death 
for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 
2010. Lancet. 2012;380(9859):2095–2128.  
31. Lundell S, Holmner A, Rehn B, et al. Telehealthcare in COPD: a systematic review and meta-
analysis on physical outcomes and dyspnea. Respir Med. 2015;109:11–26. 
32. Mandl KD, Kohane IS. Escaping the EHR trap–the future of health IT. N Engl J Med. 
2012;366:2240–2.  
33. Mannino DM, Buist AS. Global burden of COPD: risk factors, prevalence, and future 
trends. Lancet. 2007;370(9589):765–773.  
34. Mannino DM, Thorn D, Swensen A, Holguin F. Prevalence and outcomes of diabetes, 
hypertension and cardiovascular disease in COPD. Eur Respir J. 2008;32(4):962–969.  
35. McDowell JE, McClean S, FitzGibbon F, Tate S. A randomised clinical trial of the effectiveness of 
home-based health care with telemonitoring in patients with COPD. J Telemed Telecare. 
2015;21(2):80–87. 
36. Mohktar MS, Redmond SJ, Antoniades NC, et al. Predicting the risk of exacerbation in patients 
with chronic obstructive pulmonary disease using home telehealth measurement data. Artif 
Intell Med. 2015;63:51–59.  
37. National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking 
and Health. The health consequences of smoking: 50 years of progress: a report of the Surgeon 
General. Atlanta, GA: Centers for Disease Control and Prevention; 2014. 
38. Nimmon L, Poureslami I, FitzGerald M. Telehealth Interventions for Management of Chronic 
Obstructive Lung Disease (COPD) and Asthma. Int J Health Inf Syst Inform. 2013;8:37–56. 
39. Nowiński A, Romański E, Bieleń P, et al. Pilot program on distance training in spirometry testing 
— the technology feasibility study. Pneumonol Alergol Pol 2015;83:431–435. 
40. Pauwels RA, Buist AS, Calverley PM, et al. Global strategy for the diagnosis, management, and 
prevention of chronic obstructive pulmonary disease. NHLBI/WHO Global Initiative for Chronic 

29 
 
Obstructive Lung Disease (GOLD) Workshop summary. Am J Respir Crit Care Med. 
2001;163:1256–1276.  
41. Pedone C, Chiurco D, Scarlata S, Incalzi RA. Efficacy of multiparametric telemonitoring on 
respiratory outcomes in elderly people with COPD: a randomized controlled trial. BMC Health 
Serv Res. 2013;13:82. 
42. Pinnock H, Hanley J, McCloughan L, et al. Effectiveness of telemonitoring integrated into existing 
clinical services on hospital admission for exacerbation of chronic obstructive pulmonary disease: 
researcher blind, multicentre, randomised controlled trial. BMJ. 2013;347:f6070. 
43. Pradella C, Belmonte G, Maia MN, et al. Home-based pulmonary rehabilitation for Subjects with 
copd: a randomized study. Respir Care. 2015:526–532.  
44. Qaseem A, Wilt TJ, Weinberger SE,  et al. Diagnosis and management of stable chronic 
obstructive pulmonary disease: a clinical practice guideline update from the American College of 
Physicians, American College of Chest Physicians, American Thoracic Society, and European 
Respiratory Society. Ann Intern Med. 2011;155(3):179–191. 
45. Reaven P. Metabolic syndrome. J Insur Med. 2004;36(2):132–142. Review.  
46. Regan EA, Lynch DA, Curran-Everett D, et al; Genetic Epidemiology of COPD (COPDGene) 
Investigators. JAMA Intern Med. 2015;175(9):1539–1549.  
47. Rodriguez-Roisin R: Toward a consensus definition for COPD exacerbations. Chest. 2000, 117(5 
Suppl 2):398S–401S.  
48. Rubinsztajn, R., Przybyłowski T, Maskey-Warzęchowska M, et al. Metabolic syndrome as a factor 
affecting systemic inflammation in patients with chronic obstructive pulmonary disease. Adv Exp 
Med Biol. 2017;1021:55–62.  
49. Rubio N, Parker RA, Drost EM, et al. Home monitoring of breathing rate in people with chronic 
obstructive pulmonary disease: observational study of feasibility, acceptability, and change after 
exacerbation. Int J Chron Obstruct Pulmon Dis. 2017;12:1221–1231. 
50. Russo A, Autelitano M, Bisanti L. Metabolic syndrome and cancer risk. Eur J Cancer. 
2008;44(2):293–297.  
51. Sanchez-Morillo D, Fernandez-Granero MA, Leon-Jimenez A. Use of predictive algorithms in-
home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review. 
Chron Respir Dis. 2016;13(3):264–283.  
52. Schermer TR, Jacobs JE, Chavannes NH, et al. Validity of spirometric testing in a general practice 
population of patients with chronic obstructive pulmonary disease (COPD). Thorax. 2003;58:861–
866. 
53. Shah T, Press VG, Huisingh-Scheetz M, White SR. COPD readmissions: addressing COPD in the era 
of value-based health care. Chest. 2016;150(4):916–926. 
54. Shany T, Hession M, Pryce D, et al. A small-scale randomised controlled trial of home 
telemonitoring in patients with severe chronic obstructive pulmonary disease. J Telemed 
Telecare. 2017;23:650–656. 
55. Sharman A. A new paradigm of primary health care in Kazakhstan: CAJGH. 2014;3(2). 
56. Sharman A. Modernization and growth in Kazakhstan. CAJGH. 2012;1(1). 

30 
 
57. Sharman A, Karmazin D, Korolkov A, Kormschikov N. Artificial intelligence technologies in 
healthcare, expert systems and machine learning. SmartHealth LLC, 2017 
http://vitalem.kz/va/resources/SmartHealth%20technology%20Description.pdf;jsessionid=218D
E04D8737296CC0B859CE0FBA4C5A 
58. Silverman EK. Exacerbations in chronic obstructive pulmonary disease: do they contribute to 
disease progression? Proc Am Thorac Soc. 2007;4(8):586–590.  
59. Stroetmann K, Kubitschke L, Robinson S, et al. How can telehealth help in the provision of 
integrated care? Health systems and policy analysis, policy brief, 13. World Health Organization, 
2010. 
60. Supiyev A, Nurgozhin N, Zhumadilov Z, et al. Levels and distribution of self-rated health in the 
Kazakh population: results from the Kazakhstan household health survey 2012. BMC Public 
Health. 2014;14:768.  
61. Tillis W, Bond WF, Svendsen J, Guither S. Implementation of activity sensor equipment in the 
homes of chronic obstructive pulmonary disease patients. Telemed J E Health. 2017;23(11):920–
929.  
62. Vegesna A, Tran M, Angelaccio M, Arcona S. Remote patient monitoring via non-invasive digital 
technologies: a systematic review. Telemed J E Health. 2017;23(1): 3–17. 
63. Vestbo J, Hurd SS, Agusti AG, et al. Global strategy for the diagnosis, management, and 
prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit 
Care Med. 2013;187:347–365.  
64. Wilt TJ, Niewoehner D, Kimet C, et al. Use of spirometry for case finding, diagnosis, and 
management of chronic obstructive pulmonary disease (COPD). Evid Rep Technol Assess (Summ). 
2005;(121):1–17. 
65. Woodruff P, Graham Barr R, Bleecker E, et al., for the SPIROMICS Research Group. Clinical 
Significance of Symptoms in Smokers with Preserved Pulmonary Function. N Engl J Med. 
2016;374:1811–1821. 
66. Wootton R. Twenty years of telemedicine in chronic disease management–an evidence 
synthesis. J Telemed Telecare. 2012;18:211–220. 
67. World Health Organization. Global health risks: mortality and burden of disease attributable to 
selected major risks. Geneva: WHO; 2009. 

31 
 
Appendices 
 
ACADEMY OF PREVENTIVE MEDICINE OF KAZAKHSTAN 
The Kazakhstan Academy of Preventive Medicine (KAPM) is nongovernmental organization founded in 
1995 by a group of the nation’s leading health care managers, business leaders, and physician-
researchers (academypm.org). Based on its extensive track record of projects and experience in 
Kazakhstan and Central Asia region, the Academy can offer the best possible combination of technical 
expertise and local know-how. The Academy’s main strengths and specific advantages in this context are 
as follows: 
• 
Proven record of implementing international projects, and excellent knowledge of the national 
and international regulations and procedures. 
• 
Extensive track record of implementing projects in the areas of clinical research, demographic 
and health surveys, medical technologies, health policy and health financing, policy formulation. 
• 
In-house staff with expertise in clinical research, health surveys, biomedical statistics, computer 
engineering, health systems development and management, quality management and control. 
• 
Strong linkages with the Ministry of Health and a range of hospitals and universities, research, 
and public health institutes in Kazakhstan and internationally. 
• 
Access to highly experienced and qualified national, regional and international consultants with 
experience in and around the region of Central Asia and post-Soviet countries. 
• 
In-depth and up-to-date knowledge of important health sector reform processes in the post-
Soviet region gained through experience in long-term projects presented in numerous health 
policy white papers published on the academy’s website academypm.org. 
National Public Health Services: http://www.academypm.org/language/ru/sluzhba-
obshhestvennogo-zdorovya-respubliki-kazaxstan-the-white-paper/  
School Health: physical activity and healthy nutrition: 
http://www.academypm.org/language/ru/programma-zdorovya-shkolnikov-balaman-
rukovodstva-po-ozdorovleniyu-shkolnikov-the-white-paper/  
Personalized and preventive medicine: regulation of biobanks and genetic research: 
http://www.academypm.org/language/ru/personalizirovannaya-i-preventivnaya-medicina-
voprosy-regulirovaniya-biobankov-i-geneticheskix-issledovanij/  
Translational research: http://www.academypm.org/language/ru/perspektivy-
translyacionnoj-mediciny-the-white-paper/ 
More details about the Academy of preventive Medicine is here: 
http://www.academypm.org/language/en/ 
 
 
 

32 
 
 
 
CRO Responsibilities 
TASK 
SPONSOR 
(KAPM) 
CRO 
Model and review informed consent design 
Х 
Х 
Informed consent local version final approval 
 
Х 
Referral site selection 
 
Х 
Develop patient questionnaire for referral site 
 
Х 
Document review (protocol, CRF) and consultancy with KOL’s (for complex documents) 
 
Х 
Develop monitoring manual  
 
Х 
STUDY START-UP ACTIVITIES 
CRA training 
 
Х 
Kick-off meeting 
 
Х 
Referral sites Identification and selection, list of site finalization 
 
Х 
Collection, review and tracking of site regulatory documents 
 
Х 
Prepare and collect the documents for submission to regulatory authority 
 
Х 
ETHICS COMMITTEE / IRB & REGULATORY APPLICATIONS 
Preparation, submission of documents to the local ethics committee 
 
Х 
Assist investigators in submitting the documents and obtaining approval from the local ethics 
committee 
 
Х 
Start and end of study notification to regulatory agencies / ethics comittees 
 
Х 
INVESTIGATOR MEETING 
Investigator meeting planning 
 
Х 
Investigator meeting preparation, training investigators 
 
Х 
SERVICES DURING CLINICAL STUDY CONDUCT 
PM work throughout the study (until first patient in) 
 
Х 
PM work throughout the study (clinical study duration) 
 
Х 
PM work throughout the study (LSO to End of CRO involvement) 
 
Х 
Perform site initiation visits - LOCAL 
 
Х 
Perform interim monitoring visits - REMOTE 
 
Х 
Perform site closeout monitoring visits - LOCAL 
 
Х 

33 
 
TASK 
SPONSOR 
(KAPM) 
CRO 
Maintain TMF IF throughout the study 
 
Х 
Site management 
 
Х 
SAFETY MONITORING 
Reporting SAEs to the local ethics committee, sponsor  
 
X 
 
The 6-Minute Walk Test  
 
 
Reference: ATS Statement: Guidelines for the Six-Minute Walk Test. Available at: 
https://www.thoracic.org/statements/resources/pfet/sixminute.pdf. Accessed on Dec 18, 2017. 

34 
 
The COPD Assessment Test (CAT) 
 
 
 
 
 
 

35 
 
Table of CAT Scores: Impact and Clinical Implications 
CAT 
score  
Impact 
level 
Broad clinical picture of the impact of COPD by 
CAT score 
Possible management considerations 
>30 
Very 
high 
The condition prevents the patient from doing 
what they want to do, and they never have any 
good days. If they can manage to take a bath or 
shower, it takes them a long time. They cannot go 
out of the house for shopping or recreation or do 
their housework. Often, they cannot go far from 
their bed or chair. They feel as if they have become 
an invalid. 
Patient has significant room for 
improvement. In addition to the 
guidance for patients with low and 
medium impact CAT scores consider:  
 
• Referral to specialist care (if you are 
a primary care physician)  
 
Also consider: 
 
• 
Additional 
pharmacological 
treatments  
• Referral for pulmonary rehabilitation  
• 
Ensure 
best 
approaches 
to 
minimizing 
and 
managing 
exacerbations 
>20 
High 
COPD prevents the patient from doing most things 
that they want to do. They are breathless walking 
around the home and when getting washed or 
dressed. They may be breathless when they talk. 
Their cough makes them tired and their chest 
symptoms disturb their sleep on most nights. They 
feel that exercise is not safe for them and 
everything they do seems too much effort. They 
are afraid and panic and do not feel in control of 
their chest problem. 
10-20 
Medium COPD is one of the most important problems that 
the patient has. They have a few good days a week, 
but cough up sputum on most days and have one 
or two exacerbations a year. They are breathless 
on most days and usually wake up with chest 
tightness or wheeze. They get breathless on 
bending over and can only walk up a flight of stairs 
slowly. They either do their housework slowly or 
have to stop for rests. 
Patient has room for improvement – 
optimize management. In addition to 
the guidance provided for patients 
with low impact CAT scores consider:  
 
• Reviewing maintenance therapy – is 
it optimal?  
• Referral for pulmonary rehabilitation  
• 
Ensure 
best 
approaches 
to 
minimizing 
and 
managing 
exacerbations  
• Reviewing aggravating factors – is 
the patient still smoking? 
<10 
Low 
Most days are good, but COPD causes a few 
problems and prevents the patient from doing one 
or two things that they would like to do. They 
usually cough several days a week and get 
breathless when playing sports and games and 
when carrying heavy loads. They have to slow 
down or stop when walking up hills or if they hurry 
when walking on level ground. They get exhausted 
easily. 
• Smoking cessation  
• Annual influenza vaccination • 
Reduce exposure to exacerbation risk 
factors  
• Therapy as warranted by further 
clinical assessment 
5 
 
Upper limit of normal in healthy non-smokers 
 
Reference: Jones PW, Tabberer M, Chen W. Creating scenarios of the impact of COPD and their 
relationship to COPD assessment test (CATTM) scores. BMC Pulmonary Medicine 2011;11:42.