Predictors of Recovery and the App-Facilitated Tele-Rehabilitation (AFTER) Program for COVID Survivors
Phase: NA
Conditions: Covid-19, Deconditioning
Sponsor: University of Colorado, Denver

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Title:  Predictors of Recovery and the App-Facilitated Tele-Rehabilitation 
(AFTER) Program for COVID Survivors 
NCT04663945 
Version Date:  04.23.2022         IRB Approval Date:  05.05.2022 

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Protocol #:  20-2415 
Project Title: Predictors of Recovery and the App-Facilitated Tele-Rehabilitation 
(AFTER) Program for COVID Survivors 
 
 
 
Principal Investigator:  Jennifer Stevens-Lapsley MPT, PhD 
Version Date:  04.23.2021 
 
 
 
 
 
 
  
I. Hypotheses and Specific Aims:   
 
Nine months ago, SARS-CoV-2 was first identified as the cause of the severe disease, COVID-
19. Older adults and adults with comorbidities or disability are at highest risk for morbidity and 
mortality from COVID-19, with adults aged 65 and older accounting for 25% of cases but 80% of 
deaths. However, many healthy middle-aged adults without underlying risk factors also 
experience severe disease, likely driven by a profound and exaggerated inflammatory response. 
Those who develop severe COVID-19 with acute respiratory distress syndrome (ARDS) often 
require prolonged mechanical ventilation with prone positioning to maintain adequate 
oxygenation. In contrast to most other hospitalized patients, patients with COVID-19 have 
limited contact with hospital personnel (including rehabilitation providers) due to infectivity and 
shortages of adequate personal protective equipment. These critically ill patients experience 
more severe disease, are exposed to even more sedation and neuromyotoxic agents, which, in 
turn, are associated with poor long-term outcomes. Even patients with less severe COVID-19 
who do not require lengthy hospitalization often experience prolonged fatigue, myalgias, and 
activity-limiting dyspnea. Although the long-term consequences of COVID-19 are not yet known, 
we hypothesize that the combination of immobility, limited in-hospital interventions, and 
heightened inflammation will have detrimental effects on physical function lasting well beyond 
that seen with other critical illness. The functional consequences of this disease will be further 
exacerbated by more limited opportunities for post-hospitalization rehabilitation with ‘stay-at-
home’ restrictions and the stress and uncertainty of a more extended global pandemic.  
Our overarching hypothesis is that both healthy middle-aged and older adults with 
multimorbidity who experience the ‘accelerated aging’ effects of profound inflammation 
associated with COVID-19 will experience significant ongoing physical and 
neuropsychological impairment (Aim 1 of parent grant, approved through COMIRB 20-
0703 and COMIRB 20-0690). Novel, scalable interventions that can overcome many of the 
barriers imposed by COVID-19 are urgently needed to reverse physical and 
neuropsychological impairments and prevent the long-term functional consequences 
(Aim 2 of parent grant, described in this protocol).  We will investigate this hypothesis 
through the following aim:  
 
Aim: Investigate the feasibility and initial efficacy of a multicomponent tele-rehabilitation 
program during COVID-19 recovery.  
Hypothesis A: Tele-rehabilitation is safe and feasible. We anticipate that no injuries will occur, 
≤20% of telehealth sessions will experience technical difficulties, ≥50% of participants will 
adhere to the exercise program, and ≥50% of participants will use the Platform between face-to-
face online sessions.  
w 
COMIRB Protocol 
COLORADO MULTIPLE INSTITUTIONAL REVIEW BOARD 
CAMPUS BOX F-490    TELEPHONE:  303-724-1055  Fax:  303-724-0990 
 
 

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Hypothesis B: Participation in tele-rehabilitation improves physical function (measured by 30 
Second Chair Stand Test) compared with controls.   
This study will contribute immediately to our knowledge of the course of recovery for survivors of 
COVID-19 and will advance the feasibility of tele-rehabilitation as a more generally useful 
intervention in patients lacking access (distance, availability, mobility) to standard rehabilitative 
services. Overall, development of effective tele-rehabilitation programs as alternatives to 
delivering rehabilitation to populations lacking access could transform the way in which we 
deliver acute rehabilitation and post-hospital care for all patients. 
 
II. Background and Significance:  
 
Significance of the proposed research centers on: 1) the recent COVID-19 pandemic that 
has unknown impacts on long-term health among patients who survive the disease; and 
2) the need to deliver care to patients who are recovering from COVID-19 while 
concurrently in quarantine or with limited in-person care availability. 
COVID-19 is a Pandemic with Unknown Long-Term Impacts on Health 
The novel coronavirus SARS-CoV-2 was first identified as causing severe infection (COVID-19) 
in December 2019.  COVID-19 appears to be associated with greater mortality, thromboembolic 
disease, and acute respiratory distress syndrome (ARDS) compared with usual seasonal 
viruses.1 Older adults and adults with comorbidities or disabilities are among those at highest 
risk for morbidity and mortality,2 although healthy younger adults with a profound, exaggerated 
inflammatory response are also experiencing severe disease. Based in part on the post-
hospitalization CT scan findings of COVID-19,3 experts suspect that a significant portion of 
patients will experience post-COVID decline in lung function and longer-term physical function 
impairments.4,5 Further, some patients with COVID-19 require admission to the intensive care 
unit (ICU) and/or mechanical ventilation, both of which have negative impact on health 
outcomes. Health outcomes following COVID-19 related hospitalization and the long-term 
impact of this infection on return to function is unknown. 
Need to Identify Predictors of Physical and Neuropsychological Recovery After COVID-19 
Beyond lung function impairments, up to half of all survivors experience profound reductions in 
physical, cognitive, and psychologic function post-critical illness.6 This Post-Intensive Care 
Syndrome (PICS) impacts and is associated with marked reductions in health-related quality of 
life, inability to return to work, increased healthcare utilization, and premature death.7 The 
prevalence of PICS is higher with increasing age, and older adults with critical illness 
experience worse long-term outcomes than their younger counterparts.8,9 Early ICU 
rehabilitation and implementation of the standard critical care “ABCDEF bundle” to reduce 
sedation, limit immobility, and promote early ventilator liberation are key to preventing PICS. 
Notably, these interventions are not feasible in the setting of COVID-19.7,8 A shortage of 
personal protective equipment, isolation precautions, and limited personnel make the delivery of 
routine hospital rehabilitation nearly impossible. Additionally, the severity of disease and the 
need for interventions known to increase the risk of PICS, including neuromuscular blockade 
and excessive sedation, place COVID-19 patients at particularly high risk of developing poor 
post-hospitalization outcomes.9 Thus, in addition to the expected high hospital morbidity, post-
hospital recovery will be prolonged, leading to significant reductions in quality of life, mobility, 
and ability to return to work or prior activities.  
Patients hospitalized with less severe COVID-19 are also expected to have major mobility 
limitations following COVID-19. Interventions to promote early mobility in hospitalized patients 
are key for preventing the loss of functional independence experienced by many older adults.10 

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Infection control measures for COVID-19 keep patients restricted to their room with little 
opportunity for walking. For infection control purposes, even in-room chairs have been removed 
from dedicated COVID-19 units, resulting in prolonged bedrest. The commonly experienced 
symptoms of dyspnea, profound fatigue, confusion, dizziness, and myalgias11-13 further limit in-
hospital mobility and are expected to contribute to significant functional impairment following 
hospitalization. 
In addition to physical impairments, the fear and uncertainty associated with COVID-19, 
restriction on hospital visitors, and need for post-hospitalization physical distancing or 
quarantine are expected to contribute to longer-term social isolation, stress, and anxiety.14,15 
Patients are at high risk for delirium because of direct central nervous system invasion of SARS-
CoV-2, high levels of inflammatory markers, prolonged ventilation time, and use of sedation.16 
Additionally, measures to limit hospital-related delirium, including avoidance of urinary catheter 
placement, presence of family members to help orient patients, and bedside sitters for safety 
are severely limited or not possible. These factors will exacerbate the neuropsychological 
effects of critical illness and prolonged hospitalization, placing patients at increased risk for 
adverse neuropsychological outcomes. Indeed, survivors of other novel respiratory viruses 
experienced lower quality of life than survivors with less severe illness11,17,18 or than the general 
population 6 months following hospitalization.12 
Need to Deliver Care to Patients Recovering from COVID-19 with Physical Distancing or in 
Quarantine 
The unprecedented factors associated with care delivery in patients with COVID-19 require 
novel approaches for rehabilitation. Virtual services may be a viable alternative for rehabilitation 
delivery. Tele-rehabilitation in the outpatient setting allows patients to access rehabilitative 
services in their preferred environment, potentially improving adherence and tolerability of long-
term therapy. However, at the time of grant submission, there were no published studies using 
tele-rehabilitation services following a critical care hospital admission and only one registered 
trial (NCT03926533) developing multi-disciplinary telehealth strategies in addition to in-person 
care following a diagnosis of PICS related to septic shock and ARDS. Accessing in-person 
services immediately following COVID-19, however, will not be possible for many discharged 
patients due to personal quarantine requirements or other household contacts with COVID-19. 
Even following the quarantine period, in-person rehabilitation may not be feasible due to 
limitations imposed by physical distancing regulations, closure of outpatient facilities, limited 
personnel, ongoing mobility limitations, need for institutionalized care, or fear of triggering 
memories associated with hospitalization that exacerbate psychological distress. Tele-
rehabilitation has not been as widely implemented as telemedicine because of the need 
for hands-on interventions and concerns regarding safety during activities.15 Therefore, 
developing the means to provide tele-rehabilitation in this population will not only help prepare 
for anticipated future pandemics including a likely second wave of COVID-19, but also facilitate 
future applications to other populations (e.g., rural, mobility-restricted, low resource settings). 
This study proposes to develop these means through innovative applications of existing tele-
rehabilitation technology.  
III. Preliminary Studies/Progress Report:   
 
Preliminary Data 
As of May 11, 2020, when the NIH proposal was submitted, 19,879 persons in Colorado had 
been diagnosed with COVID-19, with the majority (>70%) of those cases occurring in the 
Denver Metropolitan Area. Over 3600 persons had been hospitalized (50% ≥ 60 years) and 987 
had died (91% ≥60 years) as per data from the Colorado Department of Public Health.  As the 

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largest academic and tertiary care hospital in the Denver Metropolitan Area, the University of 
Colorado Hospital (UCH) has cared for a large proportion of these patients.  
Investigative Team Experience 
Our research team includes cross-disciplinary expertise in Infectious Diseases, Geriatrics, 
Critical Care Medicine, Physical Therapy and Rehabilitation, and Statistics, and leverages both 
rich previous collaborative experiences and innovative new collaborations to enable success. 
Co-PI Erlandson is a clinical researcher and physician in infectious diseases with additional 
training in gerontology who has experience in implementing exercise interventions in vulnerable 
older adults with HIV (R01AG066562, K23AG050260), in implementing electronic consultations 
(U01HL142103) and validating electronic disability assessments (R21 AG062380) for older 
adults, and in longitudinal physical function assessments related to statin therapy 
(R01AG054366).  PI Stevens-Lapsley has more than 20 years of clinical research experience in 
working with medically complex patients and interdisciplinary teams in implementing 
rehabilitation programs (NIH R01 NR016209, VA RR&D I21 RX002054, VA RR&D I01 
RX001978, NIH R01 HD065900, NIH R03 AR054538, NIH K23 AG029978, and NIH R01 
AG056585). Dr. Stevens-Lapsley also has prior experience in implementing the tele-
rehabilitation platform in collaboration with Dr. Flynn (NIA R44 AG055341; see Letter of 
Support). Dr. Erlandson and Dr. Stevens-Lapsley have also interacted through the Division of 
Geriatrics IMAGE team.  Dr. Jolley focuses on the role of muscle injury/repair on long-term 
recovery after critical illness, explores biomarkers of muscle injury and regeneration in critically 
ill patients (K23AA026315), and directs the ICU Recovery clinic at UCH.  Additional expertise is 
provided by Dr. Nordon-Craft who has overseen multicomponent rehabilitation programs in 
hospitalized patients receiving intensive care as well as post-hospitalization (home health) in 
collaboration with Dr. Jolley. Dr. MaWhinney (Delson) has collaborated with Dr. Erlandson for 
more than 10 years and has extensive experience in the design and monitoring of clinical trials.   
 
IV. Research Methods 
 
Justification for changes:  
Given the rapidly developing nature of the COVID pandemic and the short timeline for 
submitting the initial NIH grant proposal, we have subsequently made some changes to the 
design of the study that will improve the quality and rigor of the science and allow for more 
robust comparisons. The NIH allows for changes in design that improve the quality and rigor of 
the science. Notably, we have added a control group (n=15) that will receive a tablet with 
standard guidelines for exercise following COVID-19 infection and hospitalization, the Platform 
(for testing only) and a Fitbit activity monitor, but not any formal tele-rehabilitation. To collect 
safety data and encourage retention, a research assistant will contact control participants 
1x/week over the course of the intervention (12 weeks). The intervention group (n=30) will 
receive care as described herein. Therefore, the total projected sample size for this study will be 
45 participants. Including a formal control group in addition to the intervention group will allow 
for more robust evaluation of the value of tele-rehabilitation sessions combined with 
biobehavioral interventions. We will still be able to compare both the intervention and control 
groups described to a no-treatment sample that is followed as part of COMIRB 20-0703. We  
therefore believe the proposed changes are justified to improve the quality of the science and 
the comparisons/conclusions we will be able to make from these data. 
 
Research Design and Methods 
 

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For this rehabilitation pilot, individuals from a separate hospital follow-up study (COMIRB 20-
0703) who are 35 years of age and older and speak English will be randomly approached 
regarding potential participation.  Participants will also be recruited through treatment 
relationships with Dr. Jolley, Dr. Erlandson, and Dr. Pisney (Co-Investigators).  Participants will 
also be recruited via HIPAA A and COMIRB approved flyers in Colorado. Individuals who 
consent to participate will be randomly allocated to the intervention or control group in a 2:1 
ratio; randomization will be stratified based on age (<55 and ≥55), length of mechanical 
ventilation (<5 days, ≥ 5 days), and gender. The App-Facilitated Tele-Rehabilitation (AFTER) 
program involves tele-rehabilitation delivered in individual formats with biobehavioral 
interventions to augment adherence and recovery. Outcome assessors will be blinded to group 
allocation within the AFTER program.  
 
A.  Outcome Measure(s):   
 
Data Collection and Outcomes.  
Outcomes, unless specified otherwise, will be collected at baseline, week 6 (primary endpoint), 
week 12, and week 26. Baseline testing will occur following the pre-session to set up the 
Platform and prior to the Physical Therapy Evaluation (session 1). The primary functional 
outcome for Hypothesis B (30” chair stand test) will be collected through the Platform (more 
details below). Each test session will include the standardized procedures described in the next 
sections. Importantly, all test sessions will occur in the participant’s home and when possible, be 
facilitated via the Platform and verified concurrently by a trained outcomes assessor who 
oversees the testing remotely.  
Feasibility (Hypothesis A) will be measured by program adherence, defined as the number of 
sessions attended divided by the total number of prescribed sessions (n=12). Individuals will be 
considered adherent if they attend at least 75% (9 visits) of the total sessions. Secondary 
adherence will be measured by the number of days the HEP is completed divided by the total 
number of prescribed sessions (n=60 [5x/12 weeks]), with adherence defined by completion of ≥ 
75% (45 sessions) of prescribed sessions. System Usability Scale (SUS) will be collected at 
program end only. The SUS is a 10-item survey that uses a 5-point Likert scale (Strongly 
disagree (1) to Strongly agree (5)). Scores range from 0 to 100 and higher scores indicate better 
usability. It is a valid scale and is frequently used by the Platform to assess its usability. Safety 
will also be measured as a marker of feasibility. Safety events will be categorized and counted; 
safety events will be collected during each session and through the diary feature on the 
Platform. Additionally, falls will be graded using the Falls-Grading Scale.83 The scale ranges 
from 1 to 4; a grade of 1 is defined as a near fall, grade 2 is a fall that does not receive medical 
attention, grade 3 is a fall that requires medical attention but no hospital admission, and grade 4 
is a fall resulting in hospital admission. All the aforementioned outcomes will be collected in the 
intervention group, and only safety will be collected in the control group.  
Initial efficacy for change in physical function will be measured by the 30” Chair Stand Test 
(Hypothesis B primary outcome). The 30” Chair Stand Test requires only a standard height chair 
(approximately 45cm) and can be visualized on video. The test requires the participant to stand 
up and sit down as many times as possible in 30 seconds. To facilitate accuracy of the test over 
video, the participant will be instructed to count out loud during the test each time he/she 
stands. Further, the test will also be facilitated by the Platform. The 30” Chair Stand Test is 
responsive to change. Initial efficacy for behavior change will be assessed by Average Daily 
Step Count, PROMIS Short Form (SF) v1.0 General Self-Efficacy 4a, and PROMIS Short Form 
(SF) v1.0 Self- Efficacy for Managing Chronic Conditions. Average Daily Step Count (per week) 

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will be collected via Fitbit. The PROMIS SF v1.0 General Self-Efficacy 4a was developed from 
the NIH self-efficacy scale, and the short form consists of 4 items rated on a 5-point Likert scale 
[I am not at all confident (1) to I am very confident (5)].84 The PROMIS SF v1.0 Self-Efficacy for 
Managing Chronic Conditions will also be collected since items on this scale are more specific 
to managing symptoms during various tasks. The short form is an 8-item scale rated on a 5-
point Likert scale which is identical to the General Self-Efficacy scale. It has been validated in 
individuals with chronic neurological and general medical conditions.85 For both PROMIS 
measures, a higher score is indicative of higher levels of self-efficacy. Additional outcomes for 
Hypothesis B include 4-stage balance test, Timed Up-and-Go test, MRC dyspnea, and 
activities-specific balance confidence (ABC) scale to identify and monitor fall risk. The PROMIS 
Scale v1.2 Global Health will be used to measure quality of life; a higher score is indicative of 
better quality of life. The 3-Item Loneliness scale will be used to assess loneliness. The Patient 
Health Questionnaire 8 (PHQ8) will be used to measure depression; a higher score indicates 
increased severity of depressive symptoms.  All the aforementioned outcomes will be collected 
in both groups.  
The following is specific to data collected through the Platform: 
In addition to the data regarding comorbidities and hospitalization which is collected through 
medical record review as part of COMIRB 20-0690 and 20-0703, data will be collected through 
the Platform.  The Platform can track usage data, results from tests and measures, and health 
information. Usage data will be used to monitor and describe adherence to prescribed activities 
(e.g., Home exercise program, learning modules). It will also be used to collect the following 
outcomes: 30” Chair Stand Test, Timed Up-and-Go test, 4-stage balance test, system usability 
scale, activities-specific balance confidence (ABC) scale, and the PROMIS Scale v1.2 Global 
Health. While participants will be encouraged to use the Platform for this data collection since it 
is part of our feasibility outcome, alternatives will be provided for the main testing points 
(baseline, week 6, week 12and week 26) to achieve complete data collection. Through the 
health diary feature, participants will be encouraged to log data pertaining to but not limited to 
medical history (medications, comorbidities), smoking status, demographics (age, gender, 
height, weight), and health events (e.g., hospitalization, injury, fall). This data entry is optional 
and completed via self-report. Any data entered into the Platform will be accessible by the 
research team. The participant’s name is NEVER associated with the Platform in any way, and 
once a participant is enrolled, he/she will be assigned a login/password such as 
“Telerehab_P01” to indicate telerehabilitation study and participant 01; the initial account 
password is randomly generated through Blue Marble’s webapp.   
 
B. Description of Population to be Enrolled:   
 
Potential participants will be recruited ideally during the first 2 to 6 weeks following hospital 
discharge to home, though if recruitment targets are slow, enrollment will be allowed up to 12 
weeks post-discharge to home.   
 
Inclusion Criteria: 
1. Confirmed COVID-19, per diagnostic criteria (PCR testing) 
2. Hospitalized for at least 24 hours 
3. 35 years of age or older 
4. Able to provide informed consent 
5. Internet capability to access the platform 
6. Community-dwelling prior to hospitalization 
 

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Exclusion Criteria: 
1. Unstable medical comorbidities that would preclude participation in exercise 
2. Receipt of >1 session of outpatient physical therapy 
3. Known pregnancy, as the impact on physical function, pulmonary function, and other 
outcomes will vary well beyond any expected effects of COVID 19 
4. Not anticipating concurrent additional physical therapy services during study period (12 
weeks) 
 
Participants will be consented either face to face or via remote e-consent.  Participants who 
consent remotely will be provided an e-consent via REDCap.  The e-consent will be available to 
the participant prior to the consent discussion.  A qualified study team member will Zoom 
phone/video call the potential participant.  The potential participant will verify their identity.  The 
study team member will explain the consent process and that the participant can navigate 
forward and backward through the consent form pages and that they may return to the form 
later if they want time to think about their participation.  The study team member will read the 
consent form with the participant and will answer all questions that the participant has.  The 
participant and the study team member will both sign and date the consent form and the 
participant will be able to download or email themselves a copy of the signed form.   
 
C. Study Design and Research Methods   
Participants will be recruited from COMIRB 20-0703 and via HIPAA A throughout Colorado. 
 
Procedures will include 
Virtual assessments include:  
1) 30” Chair Stand Test (Primary Outcome). The 30” Chair Stand Test requires only a standard 
height chair (approximately 45cm) and can be visualized on video. The test requires the 
participant to stand up and sit down as many times as possible in 30 seconds. To facilitate 
accuracy of the test over video, the participant will be instructed to count out loud during the test 
each time he/she stands. Further, the test will also be facilitated by the Platform. The 30” Chair 
Stand Test is responsive to change.  
 
2) Timed Up-and-Go (TUG) Test: The TUG test measures the time it takes for a person to rise 
from a chair, walk 3 meters, turn around, walk back to the chair, and sit down. Patients are 
asked to walk as quickly but as safely as they can. 
 
3) 4-stage balance test: The 4-stage balance test measures static balance in 4 different 
positions (narrow base of support, semi-tandem, tandem, and single-leg). The test is facilitated 
by the Platform; it requires participants to hold each position for up to 10 seconds. If a 
participant is unable to hold a position for 10 seconds, the next hardest position is not performed 
(e.g. if participant unable to hold semi-tandem for 10 seconds, then the test stops, and tandem 
and single-leg stance are not assessed). The 4-stage balance test is indicative of fall risk.  
 
4) MRC Dyspnea. Interviewer-administered or self-report assessment of the perception of 
difficulty breathing during five different tasks. Participants respond either yes or no to each task.  
 
5) Montreal Cognitive Assessment (MoCA)-BLIND. The MoCA-BLIND is an adapted version of 
the original MoCA, a rapid screening instrument for mild cognitive dysfunction. The MoCA-
BLIND assesses different cognitive domains: attention, concentration, memory, language, 
conceptual thinking, calculations, and orientation. The MoCA-BLIND has removed assessments 

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Figure 1. How the Platform works.  
requiring vision, thus can be completed using telehealth or telephone assessments. It will be 
collected only at baseline.  
 
Participants will complete online questionnaires including: 1) Assessment of pre-hospitalization 
and post-hospitalization frailty.  Participants will estimate function using the Clinical Frail Scale, 
an easy to interpret estimate of function than has been validated in several populations. The 
participant will select perceived level of function prior to and following admission ranging from 
very active to bedridden/terminal. Additional questionnaires are described above for Hypotheses 
A and B and will be collected either through the Platform or online via REDCap survey. 
 
Characteristics of patients and of the hospitalization are collected from the medical record in all 
hospitalized COVID-19 patients (COMIRB protocol 20-0690), including duration of 
hospitalization, need for and duration of mechanical ventilation, comorbidities, medications, 
demographics, and zip code among others. Post-hospitalization data is also collected, including 
readmission, need for/duration of supplemental oxygen, receipt of rehabilitation care, imaging, 
and laboratory results. Electronic medical records for participants not hospitalized in the UCH 
system will be requested using a HIPAA A form.    
 
The AFTER Program Procedures (Intervention Group).  
Participants will begin the AFTER program following enrollment into the study and 
randomization to the intervention group. The program was developed around key elements of 
rehabilitation that patients and providers have identified as important aspects: 1) ability to 
communicate with healthcare providers, 2) ability to monitor response to exercise and progress, 
and 3) individualized care that changes based on a person’s response.81 The rehabilitation 
program follows current recommended guidelines and includes therapeutic exercises, functional 
activities, balance training, and aerobic conditioning.26 
The program will consist of 12 individual sessions. 
There will be 3 sessions during the first week, 2 
sessions per week during weeks 2-4, 1 session per 
week during weeks 5-6, and 1 booster session during 
week 9 or 10.  Therapists will be instructed on the 
standardized protocols for individual sessions. During 
weeks 7-12, in addition to the booster session, if 
adherence to the HEP drops below 75% as measured 
through the Platform, the therapist will follow up with 
the participant.  
Platform.  
The Platform is a combination downloadable app 
(patient-side) and web-app (administrative-side) 
software tool that contains digital translations of 
standardized, evidence-based tests, exercises, and 
educational modules for a variety of conditions (Figures 
1&2). The Platform will be used to facilitate 
multicomponent tele-rehabilitation. Participants will use 
the app to test and track their physical function, 
perform avatar-guided exercises, and obtain 
educational content. The ability to track their activity 

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Figure 3. Example of avatar-led exercise 
Figure 4. Rate of Perceived Exertion, Dyspnea 
minutes, repetitions, changes in resistance difficulty, and session participation will provide 
necessary feedback for the biobehavioral strategies. The educational content is based on 
current clinical practice guidelines and recommendations from the National Institute of Health 
(NIH). Core educational modules will be assigned on topics including the following: 
management of anxiety and depression, breathing techniques, and healthy eating.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Therapists can assign personalized avatar-
led home exercise programs (Figure 3) to 
the participant allowing for individual-level 
modifications to the rehabilitation program. 
The participant logs into the app, and data 
is sent instantaneously to the supervising 
researcher to inform care plans. 
The app also has many built-in safety 
features including 0-10 Rate of Perceived 
Exertion (RPE) scales for exertion and 
dyspnea (Figure 4). The Platform is an 
extensive data collection tool and will allow for detailed tracking of recovery from COVID-19. 
The Platform is affordable, accessible, scalable, and sustainable. The Platform has completed 
five validation and efficacy studies and has two ongoing clinical trials funded by NIH 
(R44AG055341, R44HL137502) with another pilot study (R44DC016245) starting soon. The 
investigative team has experience working with 
this tele-rehabilitation technology (NIH 
R44AG055341) (COMIRB 18-2532).  
Multicomponent Tele-Rehabilitation. 
Figure 2. Activities Dashboard. 

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Individual sessions will consist primarily of biobehavioral interventions in addition to assessment 
and progression of the participant’s Home Exercise Program. The first individual session will 
consist of a standard physical therapy evaluation, which requires approximately 60 minutes. 
Goals of the evaluation are to establish the participant’s baseline physical function and identify 
impairments, activity limitations, and participation restrictions to inform the individual’s 
personalized plan of care. The participant will also be oriented to the overall structure of the 
tele-rehabilitation program and the Platform. Remaining individual sessions will primarily focus 
on biobehavioral interventions (30-45 minutes) and assessment and progression of the Home 
Exercise Program (15-20 minutes).   
Biobehavioral Intervention.  
The biobehavioral interventions will be integrated into the tele-rehabilitation program and 
augmented by the Platform. There will be 10-12 individual biobehavioral intervention sessions of 
approximately 30-45 minutes/session. The therapist will provide an overview and will develop an 
action plan with the patient that addresses the seven components of the behavior interventions 
(Table 2). The biobehavioral interventions will focus on increasing physical activity through 
various means. For example, the Fitbit will track daily steps to provide participants feedback 
about daily walking activity; these data will be used to set goals. Additional forms of feedback 
are provided by the Platform and include percentage completion of assigned exercises; these 
data can also be used to set goals. Goals will be set and tracked using the Platform Goal 
feature. Another focus of the biobehavioral intervention is to provide participants with the 
knowledge and tools to progress their exercise plans; emphasis will be placed on encouraging 
participants to dose resistance exercises to their 8-repetition maximum (8-RM). Achievement of 
8-RM dosing means the participant would be unable to complete a 9th repetition during one set. 
This principle has been shown to be effective for improving strength and physical function and 
has been implemented by the research team safely in a medically complex population (Cite- 
Allison’s SNF paper).  
Table 2. Components of the Biobehavioral Intervention 
Intervention 
Progression from Therapist Coaching to Participant Self-Management 
Education 
Therapist delivers education topic* (e.g., Self-Monitoring, 
Problems Solving, Identifying Barrier/Facilitators, Action 
Plans).  
Participant reports most important information 
learned. 
Self-Monitoring 
Therapist guides participant in tracking daily activity 
minutes & session participation since last visit.  
Participant tracks weekly activity minutes and 
session participation. 
Tailored 
Feedback 
Therapist leads collaborative review of activity and 
participation data for action plan goal setting. 
Participant leads review of activity and 
participation data and other physical activity goals. 
Barrier/Facilitator 
Identification 
Therapist guides participant to identify barriers/facilitators 
of goal attainment. 
Participant self-identifies barriers and facilitators 
for goal attainment. 
Promotion of 
Problem Solving 
Collaborative generation of solutions to overcome barriers 
to goal attainment. 
Participant generates solutions to identified 
barriers to goal attainment. 
Action Planning 
Collaborative activity goal generation. Therapist guides, 
using 5-10% increase from daily activity minutes from 
previous week target. 
Participant-led weekly goal generation. Therapist 
ensures independence in action planning. 
Encouragement 
Therapist reviews plan for the next week, while 
encouraging participant on successes attained toward 
improved physical health. 
Participant leads the review of the plan for 
upcoming week. 
* Each week will have a specific “take-home” message linking physical activity and movement behavior to health. Messages will be 
brief and based on research evidence. 
 
 
Home Exercise Program (HEP).  

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The HEP will include therapeutic exercises, functional activities, balance training, aerobic 
conditioning, and breathing practice. Specific exercises will be selected based on results from 
the physical therapy evaluation, and subsequent adjustments will be made based on patient 
preference and priorities which will be enhanced in the biobehavioral interventions. In 
preparation for weeks 7-12, participants will be educated on how and when to progress 
strengthening and aerobic exercises, and they will take a more active role in determining their 
own progression. Participants will collaborate with the therapist to develop a robust program. 
The therapist will remotely monitor as described above, and participants will be responsible for 
progressing their exercises.  
Participants will be screened for safety and high fall risk upon entry into the program, but the 
design of the intervention is to target even the most medically deconditioned participants 
and tailor the program to address any safety concerns (e.g., sitting exercises with 
progression to standing as able). Prior to starting the 6-week program, enrolled participants will 
receive step-by-step instructions to trial the Platform; a research assistant will help the 
participant troubleshoot technical issues. The treating therapist will modify the set-up as needed 
during session 1 and throughout the program. The therapist will monitor and assess the patient, 
progress the rehabilitation program, and track adherence through the Platform and 
videoconferencing. Validated physical function measures and surveys will also be collected 
through the Platform at testing timepoints (baseline, week 6, week 12, and week -26), and may 
be collected up to every 2 weeks during both phases of the program.  
Some telerehabilitation sessions will be audio and/or video recorded to verify interventionist 
fidelity.  Participants will be given the option to allow or to disallow any recordings.   
D.   Description, Risks and Justification of Procedures and Data Collection 
Tools: 
 
Exercise:  
We recognize there may be an increased risk for falls with greater exposure to physical activity 
and reduced functional capacity due to recent hospitalization. However, the increase in balance, 
confidence, and mobility gained through this rehabilitation program may also decrease fall risk 
over the long term.  
Confidentiality and privacy:  
The use of questionnaires and collection of personal medical information poses a risk to 
confidentiality and privacy (see Protection Against Risk below). Additionally, subjects may 
experience distress when answering questions related to their recent hospitalization.   
 
Adequacy of Protection Against Risks 
 
Protection Against Risk:   
The inclusion/exclusion criteria are designed to assure that the sample is as representative as 
possible of the population of patients who are hospitalized for COVID-19 and allows the 
participants most likely to benefit from the rehabilitation intervention while excluding women who 
are known to be pregnant and who would therefore have expected variability beyond that of 
other patients.  
 
Rehabilitation and Exercise:  
Intervention will be carefully monitored and progressed on an individual basis by a licensed 
physical therapist.  Education on muscle soreness, shortness of breath, and fall risk reduction 

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will occur before any intervention is initiated. Education will also include signs of distress that 
should be communicated to the study team (e.g., dizziness, light headedness, symptoms of 
myocardial infarction). Participants who are identified to be at risk for falls will initially perform 
exercises seated or while holding onto a sturdy surface. When training is performed in standing 
or walking (e.g., conditions with a potential risk for falling), safeguards will be taken to ensure a 
sturdy surface is nearby for steadying should a loss of balance occur. For the purpose of safety 
monitoring, we will consider any fall, defined as “inadvertently coming to rest on the ground, 
floor or other lower level, excluding intentional change in position,” (World Health Organization) 
as unrelated, possibly, probably, or definitely related to study procedures.  Baseline fall risk will 
be evaluated using the Platform (ABC scale questionnaire and the 4 Stage Balance Test) to 
inform each individual rehabilitation plan. Exercise will then be tailored to address any safety 
concerns (e.g., sitting exercises with progression to standing as able, additional balance-
focused exercises). Occurrence, circumstances around, and injury associated with any fall will 
be recorded via the Platform’s health diary feature and a red flag notice will display on the 
dashboard if a fall is recorded. The treating physical therapist will also ask about falls and other 
adverse events (AEs) at each session for the intervention group, and a research assistant will 
ask about falls and other AEs during each weekly follow-up for the control group. Due to remote 
nature of the AFTER program, we will institute emergency procedures to respond to any AE that 
occurs during individual sessions. The physical location of participants will be verified prior to 
each individual session in the event that emergency response is necessary (call to 911). Any 
reported falls occurring outside of individual sessions will be discussed at the subsequent 
individual session or within 24 hours if medical care is required. Fall occurrences (injurious and 
non-injurious) and other AEs will be reported directly to the PI. Other exercise side-effects, such 
as muscle pain, fatigue, shortness of breath, and minor strains during therapy will be 
documented and monitored by Dr. Stevens-Lapsley and Dr. Erlandson in consultation with Dr. 
Jolley. Based on the existing literature and our own clinical experience with tele-rehabilitation 
(VA RR&D I01RX002417), we anticipate the frequency of these side-effects to be extremely 
low.95 
 
Confidentiality, privacy, protection of data:   
Risks will be minimized by not including personal identifying information on the forms, when 
possible, and by conducting interviews, functional assessments, and collection of personal 
information in a secure telehealth platform.  All data will be collected using unique patient 
identification codes. All evaluation forms, reports, and other records will be identified by a coded 
number to maintain participant confidentiality. All records will be stored in a locked file cabinet. 
Study data will be collected and managed using REDCap (Research Electronic Data Capture). 
Clinical information will not be released without written permission of the participant, except as 
necessary for monitoring by the IRB or NIH. Finally, subjects will be monitored for evidence of 
distress associated with study completion. Any subjects who express a desire for additional 
support will be referred to mental health services by Dr. Jolley via established clinical referral 
pathways.   
 
    
E.   Potential Scientific Problems:   
Potential Recruitment Difficulties: Due to uncertainty surrounding the COVID-19 pandemic, 
there may be difficulty recruiting 45 participants for the study. For September 22-23, 2020, the 
current census of COVID-19 positive patients in UCHealth hospital was 23 of which 2 were new 
admits in the past 24 hours. In the same time period, there were a total of 27 admitted patients 
(1 new in past 24 hours) who tested positive for COVID-19 in hospitals affiliated with the 
University of Colorado.  
 

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Missing Data: All participants with outcome measurements will be included in analysis. The 
importance of reassessment for data collection will be stressed during enrollment and consent 
and repeated during study participation. Participants will be contacted repeatedly if they miss 
reassessment time points, and all efforts will be made to collect data.  
 
Technical Issues: Technical issues are a concern for telerehabilitation and telehealth programs. 
As such, we have a detailed approach to address any technical issues which may arise.  
1. Participants may call the Research PT (or designee) anytime, during regular business 
hours, with issues or concerns.  
2. If a technical issue should arise that the Research PT cannot address, they will contact 
Blue Marble’s Research Associate, Charlotte Kurchian, to discuss.  
3. If needed, Charlotte will speak directly with the participant to solve the issue. 
4. Although unlikely, Blue Marble’s programmer may need to participate on the call to 
resolve the issue. The programmer has human subjects training.  
5. The Research PT (or designee) will keep track of any and all technical issues as this is a 
feasibility outcome of the study.  
 
F.   Data Analysis Plan:   
 
Sample Size: We estimate the proportion of participants at any session with technical difficulties 
(anticipate ≤20%), use the platform between sessions (anticipate ≥50%), and adhere to the 
intervention (anticipate ≥50%). Assuming these rates, a sample size of 30 and a two-sided 95% 
CI, provides a margin of error of 15% for technical difficulties and 18%, for platform use and 
adherence. We use preliminary 5-time chair rise data to estimate our effect size (SD=8.7) in our 
outcome and assume adjustment variables, including baseline measures, will explain at least 
50% of the total variance (R2). Using linear regression, we anticipate the width of our 95% 
confidence interval for the group coefficient to be +-0.90*1 SD at week 6. We anticipate 
additional precision by using all longitudinal outcome data. We will also provide descriptive 
statistics for the change from baseline over time, for each group. 
 
We hypothesize (B) tele-rehabilitation is associated with improved physical function in 
participants previously admitted to the hospital, as measured by change in the 30” Chair Stand 
Test at 12 weeks. Randomization will be stratified by age (<55, ≥55), sex, and length of 
mechanical ventilation (< 5 days, ≥ 5 days). Hypothesis A: Descriptive statistics will assess 
feasibility (i.e., adherence, SUS score, and safety). Hypothesis B will be analyzed with group 
assignment (AFTER program versus control) as the primary explanatory variable; secondary 
analyses will be made to COMIRB 20-0703 cohort. Analyses will utilize R (https://www.R-
project.org), SAS (Cary, NC), and GraphPad Prism (San Diego, CA). Code will be portable and 
reproducible with documenting libraries and raw data location, as well as code documenting 
cleaning.   
 
G.  Summarize Knowledge to be Gained:   
 
The proposed study will 1) address a great and urgent need to identify predictors of multisystem 
recovery and long-term health in survivors of COVID-19 and 2) evaluate rehabilitative care to 
these individuals even, and especially, when they face post-hospitalization barriers to in-person 
care.   
  
     H.  References: 

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References 
 
1. 
Baud D, Qi X, Nielsen-Saines K, Musso D, Pomar L, Favre G. Real estimates of 
mortality following COVID-19 infection. Lancet Infect Dis. 2020. 
2. 
Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to 
COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive 
care medicine. 2020. 
3. 
Xu YH, Dong JH, An WM, et al. Clinical and computed tomographic imaging features of 
novel coronavirus pneumonia caused by SARS-CoV-2. J Infect. 2020;80(4):394-400. 
4. 
Wang J, Wang BJ, Yang JC, et al. [Advances in the research of mechanism of 
pulmonary fibrosis induced by Corona Virus Disease 2019 and the corresponding 
therapeutic measures]. Zhonghua Shao Shang Za Zhi. 2020;36(0):E006. 
5. 
Han X, Cao Y, Jiang N, et al. Novel Coronavirus Pneumonia (COVID-19) Progression 
Course in 17 Discharged Patients: Comparison of Clinical and Thin-Section CT Features 
During Recovery. Clin Infect Dis. 2020. 
6. 
Herridge MS, Tansey CM, Matte A, et al. Functional disability 5 years after acute 
respiratory distress syndrome. N Engl J Med. 2011;364(14):1293-1304. 
7. 
Elliott D, Davidson JE, Harvey MA, et al. Exploring the scope of post-intensive care 
syndrome therapy and care: engagement of non-critical care providers and survivors in a 
second stakeholders meeting. Crit Care Med. 2014;42(12):2518-2526. 
8. 
Ferrante LE, Pisani MA, Murphy TE, Gahbauer EA, Leo-Summers LS, Gill TM. 
Functional trajectories among older persons before and after critical illness. JAMA Intern 
Med. 2015;175(4):523-529. 
9. 
Ferrante LE, Pisani MA, Murphy TE, Gahbauer EA, Leo-Summers LS, Gill TM. The 
Association of Frailty With Post-ICU Disability, Nursing Home Admission, and Mortality: 
A Longitudinal Study. Chest. 2018;153(6):1378-1386. 
10. 
Hastings SN, Choate AL, Mahanna EP, et al. Early Mobility in the Hospital: Lessons 
Learned from the STRIDE Program. Geriatrics (Basel, Switzerland). 2018;3(4). 
11. 
Tomlins J, Hamilton F, Gunning S, Sheehy C, Moran E, Macgowan A. Clinical features 
of 95 sequential hospitalised patients with novel coronavirus 2019 disease (COVID-19), 
the first UK cohort. J Infect. 2020. 
12. 
Li LQ, Huang T, Wang YQ, et al. COVID-19 patients' clinical characteristics, discharge 
rate, and fatality rate of meta-analysis. Journal of medical virology. 2020. 
13. 
Chen Q, Zheng Z, Zhang C, et al. Clinical characteristics of 145 patients with corona 
virus disease 2019 (COVID-19) in Taizhou, Zhejiang, China. Infection. 2020. 
14. 
Hao F, Tan W, Jiang L, et al. Do psychiatric patients experience more psychiatric 
symptoms during COVID-19 pandemic and lockdown? A case-control study with service 
and research implications for immunopsychiatry. Brain, Behavior, and Immunity. 2020. 
15. 
Laver KE, Adey-Wakeling Z, Crotty M, Lannin NA, George S, Sherrington C. 
Telerehabilitation services for stroke. Cochrane Database Syst Rev. 
2020;1(1):Cd010255. 
16. 
Kotfis K, Williams Roberson S, Wilson JE, Dabrowski W, Pun BT, Ely EW. COVID-19: 
ICU delirium management during SARS-CoV-2 pandemic. Critical Care. 2020;24(1):176. 
17. 
Hui DS, Joynt GM, Wong KT, et al. Impact of severe acute respiratory syndrome (SARS) 
on pulmonary function, functional capacity and quality of life in a cohort of survivors. 
Thorax. 2005;60(5):401-409. 

Protocol Template 
Page 16 
CF-146, Effective 7/10/11 
 
18. 
Batawi S, Tarazan N, Al-Raddadi R, et al. Quality of life reported by survivors after 
hospitalization for Middle East respiratory syndrome (MERS). Health Qual Life 
Outcomes. 2019;17(1):101. 
19. 
McDermott MM, Liu K, Guralnik JM, et al. Home-based walking exercise intervention in 
peripheral artery disease: a randomized clinical trial. Jama. 2013;310(1):57-65. 
20. 
Zanaboni P, Hoaas H, Aarøen Lien L, Hjalmarsen A, Wootton R. Long-term exercise 
maintenance in COPD via telerehabilitation: a two-year pilot study. J Telemed Telecare. 
2017;23(1):74-82. 
21. 
Lorig K, Ritter PL, Laurent DD, et al. Online diabetes self-management program: a 
randomized study. Diabetes Care. 2010;33(6):1275-1281. 
22. 
Hoaas H, Hjalmarsen A, Zanaboni P. Adherence and factors affecting satisfaction in 
long-term telerehabilitation for patients with chronic obstructive pulmonary disease: a 
mixed methods study. BMC Medical Informatics and Decision Making. 2016;16(26). 
23. 
Tabak M, Brusse-Keizer M, van der Valk P, Hermens H, Vollenbroek-Hutten M. A 
telehealth program for self-management of COPD exacerbations and promotion of an 
active lifestyle: a pilot randomized controlled trial. International journal of chronic 
obstructive pulmonary disease. 2014;9:935-944. 
24. 
Burkhart PV, Sabaté E. Adherence to long-term therapies: evidence for action. J Nurs 
Scholarsh. 2003;35(3):207. 
25. 
Griffiths TL, Burr ML, Campbell IA, et al. Results at 1 year of outpatient multidisciplinary 
pulmonary rehabilitation: a randomised controlled trial. Lancet. 2000;355(9201):362-368. 
26. 
Spruit MA, Singh SJ, Garvey C, et al. An official American Thoracic Society/European 
Respiratory Society statement: key concepts and advances in pulmonary rehabilitation. 
Am J Respir Crit Care Med. 2013;188(8):e13-64. 
27. 
Li B, Yang J, Zhao F, et al. Prevalence and impact of cardiovascular metabolic diseases 
on COVID-19 in China. Clin Res Cardiol. 2020;109(5):531-538. 
28. 
Varadhan R, Yao W, Matteini A, et al. Simple biologically informed inflammatory index of 
two serum cytokines predicts 10 year all-cause mortality in older adults. The journals of 
gerontology Series A, Biological sciences and medical sciences. 2014;69(2):165-173. 
29. 
Ferrucci L, Harris TB, Guralnik JM, et al. Serum IL-6 level and the development of 
disability in older persons. Journal of the American Geriatrics Society. 1999;47(6):639-
646. 
30. 
Brinkley TE, Leng X, Miller ME, et al. Chronic inflammation is associated with low 
physical function in older adults across multiple comorbidities. The journals of 
gerontology Series A, Biological sciences and medical sciences. 2009;64(4):455-461. 
31. 
Reuben DB, Cheh AI, Harris TB, et al. Peripheral blood markers of inflammation predict 
mortality and functional decline in high-functioning community-dwelling older persons. 
Journal of the American Geriatrics Society. 2002;50(4):638-644. 
32. 
Yao X, Li H, Leng SX. Inflammation and immune system alterations in frailty. Clin Geriatr 
Med. 2011;27(1):79-87. 
33. 
Schaap LA, Pluijm SM, Deeg DJ, Visser M. Inflammatory markers and loss of muscle 
mass (sarcopenia) and strength. The American journal of medicine. 
2006;119(6):526.e529-517. 
34. 
Janssen SP, Gayan-Ramirez G, Van den Bergh A, et al. Interleukin-6 causes myocardial 
failure and skeletal muscle atrophy in rats. Circulation. 2005;111(8):996-1005. 
35. 
Baltgalvis KA, Berger FG, Peña MM, Davis JM, White JP, Carson JA. Muscle wasting 
and interleukin-6-induced atrogin-I expression in the cachectic Apc ( Min/+ ) mouse. 
Pflugers Archiv : European journal of physiology. 2009;457(5):989-1001. 

Protocol Template 
Page 17 
CF-146, Effective 7/10/11 
 
36. 
Baltgalvis KA, Berger FG, Pena MM, Davis JM, Muga SJ, Carson JA. Interleukin-6 and 
cachexia in ApcMin/+ mice. American journal of physiology Regulatory, integrative and 
comparative physiology. 2008;294(2):R393-401. 
37. 
White JP, Puppa MJ, Sato S, et al. IL-6 regulation on skeletal muscle mitochondrial 
remodeling during cancer cachexia in the ApcMin/+ mouse. Skeletal muscle. 2012;2:14. 
38. 
Kim BJ, Lee SH, Kwak MK, Isales CM, Koh JM, Hamrick MW. Inverse relationship 
between serum hsCRP concentration and hand grip strength in older adults: a 
nationwide population-based study. Aging. 2018;10(8):2051-2061. 
39. 
Marcos-Pérez D, Sánchez-Flores M, Maseda A, et al. Frailty in Older Adults Is 
Associated With Plasma Concentrations of Inflammatory Mediators but Not With 
Lymphocyte Subpopulations. Front Immunol. 2018;9:1056-1056. 
40. 
Schaap LA, Pluijm SM, Deeg DJ, et al. Higher inflammatory marker levels in older 
persons: associations with 5-year change in muscle mass and muscle strength. The 
journals of gerontology Series A, Biological sciences and medical sciences. 
2009;64(11):1183-1189. 
41. 
Soysal P, Stubbs B, Lucato P, et al. Inflammation and frailty in the elderly: A systematic 
review and meta-analysis. Ageing research reviews. 2016;31:1-8. 
42. 
Wåhlin-Larsson B, Wilkinson DJ, Strandberg E, Hosford-Donovan A, Atherton PJ, Kadi 
F. Mechanistic Links Underlying the Impact of C-Reactive Protein on Muscle Mass in 
Elderly. Cellular physiology and biochemistry : international journal of experimental 
cellular physiology, biochemistry, and pharmacology. 2017;44(1):267-278. 
43. 
Wang Y, Welc SS, Wehling-Henricks M, Tidball JG. Myeloid cell-derived tumor necrosis 
factor-alpha promotes sarcopenia and regulates muscle cell fusion with aging muscle 
fibers. Aging cell. 2018;17(6):e12828. 
44. 
Erlandson KM, Allshouse AA, Jankowski CM, et al. Association of functional impairment 
with inflammation and immune activation in HIV type 1-infected adults receiving effective 
antiretroviral therapy. The Journal of infectious diseases. 2013;208(2):249-259. 
45. 
Erlandson KM, Ng DK, Jacobson LP, et al. Inflammation, Immune Activation, 
Immunosenescence, and Hormonal Biomarkers in the Frailty-Related Phenotype of Men 
With or at Risk for HIV Infection. The Journal of infectious diseases. 2017;215(2):228-
237. 
46. 
Lu CC, Chen MY, Chang YL. Potential therapeutic agents against COVID-19: What we 
know so far. Journal of the Chinese Medical Association : JCMA. 2020. 
47. 
Fedson DS, Jacobson JR, Rordam OM, Opal SM. Treating the Host Response to Ebola 
Virus Disease with Generic Statins and Angiotensin Receptor Blockers. mBio. 
2015;6(3):e00716. 
48. 
Fedson DS. Treating influenza with statins and other immunomodulatory agents. 
Antiviral research. 2013;99(3):417-435. 
49. 
Fedson DS. A practical treatment for patients with Ebola virus disease. The Journal of 
infectious diseases. 2015;211(4):661-662. 
50. 
Brassard P, Wu JW, Ernst P, Dell'Aniello S, Smiechowski B, Suissa S. The effect of 
statins on influenza-like illness morbidity and mortality. Pharmacoepidemiology and drug 
safety. 2017;26(1):63-70. 
51. 
Atamna A, Babitch T, Bracha M, et al. Statins and outcomes of hospitalized patients with 
laboratory-confirmed 2017-2018 influenza. European journal of clinical microbiology & 
infectious diseases : official publication of the European Society of Clinical Microbiology. 
2019;38(12):2341-2348. 
52. 
Vandermeer ML, Thomas AR, Kamimoto L, et al. Association between use of statins and 
mortality among patients hospitalized with laboratory-confirmed influenza virus 
infections: a multistate study. The Journal of infectious diseases. 2012;205(1):13-19. 

Protocol Template 
Page 18 
CF-146, Effective 7/10/11 
 
53. 
Frost FJ, Petersen H, Tollestrup K, Skipper B. Influenza and COPD mortality protection 
as pleiotropic, dose-dependent effects of statins. Chest. 2007;131(4):1006-1012. 
54. 
Yuan S. Statins May Decrease the Fatality Rate of Middle East Respiratory Syndrome 
Infection. mBio. 2015;6(4):e01120-01115. 
55. 
Tikoo K, Patel G, Kumar S, et al. Tissue specific up regulation of ACE2 in rabbit model 
of atherosclerosis by atorvastatin: role of epigenetic histone modifications. Biochemical 
pharmacology. 2015;93(3):343-351. 
56. 
Hsieh M-J, Lee W-C, Cho H-Y, et al. Recovery of pulmonary functions, exercise 
capacity, and quality of life after pulmonary rehabilitation in survivors of ARDS due to 
severe influenza A (H1N1) pneumonitis. Influenza and Other Respiratory Viruses. 
2018;12(5):643-648. 
57. 
Godeau E, Debeaumont D, Artaud-Macari E, Lagache L, Bouar GL, Coquart J. 
Sequelae of Acute Respiratory Distress Syndrome: Interest of Rehabilitation. Case 
Reports in Critical Care. 2019;2019:7953141. 
58. 
McDowell K, O'Neill B, Blackwood B, et al. Effectiveness of an exercise programme on 
physical function in patients discharged from hospital following critical illness: a 
randomised controlled trial (the REVIVE trial). Thorax. 2017;72(7):594-595. 
59. 
Denehy L, Skinner EH, Edbrooke L, et al. Exercise rehabilitation for patients with critical 
illness: a randomized controlled trial with 12 months of follow-up. Crit Care. 
2013;17(4):R156. 
60. 
Alison JA, McKeough ZJ, Johnston K, et al. Australian and New Zealand Pulmonary 
Rehabilitation Guidelines. Respirology. 2017;22(4):800-819. 
61. 
Moss M, Nordon-Craft A, Malone D, et al. A Randomized Trial of an Intensive Physical 
Therapy Program for Patients with Acute Respiratory Failure. Am J Respir Crit Care 
Med. 2016;193(10):1101-1110. 
62. 
Morris PE, Berry MJ, Files DC, et al. Standardized Rehabilitation and Hospital Length of 
Stay Among Patients With Acute Respiratory Failure: A Randomized Clinical Trial. 
Jama. 2016;315(24):2694-2702. 
63. 
Pehlivan E, Yazar E, Balci A, Kilic L. Comparison of Compliance Rates and Treatment 
Efficiency in Home-Based with Hospital-Based Pulmonary Rehabilitation in COPD. Turk 
Thorac J. 2019;20(3):192-197. 
64. 
Bourne S, Devos R, North M, et al. Online versus face-to-face pulmonary rehabilitation 
for patients with chronic obstructive pulmonary disease: randomised controlled trial. BMJ 
Open. 2017;7(7). 
65. 
Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth 
chronic disease management on treatment adherence and patient outcomes: a 
systematic review. J Med Internet Res. 2015;17(2):e52. 
66. 
Eakin MN, Rand CS. Improving patient adherence with asthma self-management 
practices: what works? Annals of allergy, asthma & immunology : official publication of 
the American College of Allergy, Asthma, & Immunology. 2012;109(2):90-92. 
67. 
Benzo R, McEvoy C. Effect of Health Coaching Delivered by a Respiratory Therapist or 
Nurse on Self-Management Abilities in Severe COPD: Analysis of a Large Randomized 
Study. Respir Care. 2019;64(9):1065-1072. 
68. 
Zwerink M, Brusse-Keizer M, van der Valk PD, et al. Self management for patients with 
chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 
2014;2014(3):Cd002990. 
69. 
Larson JL, Covey MK, Kapella MC, Alex CG, McAuley E. Self-efficacy enhancing 
intervention increases light physical activity in people with chronic obstructive pulmonary 
disease. Int J Chron Obstruct Pulmon Dis. 2014;9:1081-1090. 

Protocol Template 
Page 19 
CF-146, Effective 7/10/11 
 
70. 
Herridge MS, Cheung AM, Tansey CM, et al. One-Year Outcomes in Survivors of the 
Acute Respiratory Distress Syndrome. New England Journal of Medicine. 
2003;348(8):683-693. 
71. 
Herridge MS, Tansey CM, Matté A, et al. Functional disability 5 years after acute 
respiratory distress syndrome. N Engl J Med. 2011;364(14):1293-1304. 
72. 
De Greef KP, Deforche BI, Ruige JB, et al. The effects of a pedometer-based behavioral 
modification program with telephone support on physical activity and sedentary behavior 
in type 2 diabetes patients. Patient Educ Couns. 2011;84(2):275-279. 
73. 
Geraedts H, Zijlstra A, Bulstra SK, Stevens M, Zijlstra W. Effects of remote feedback in 
home-based physical activity interventions for older adults: a systematic review. Patient 
Educ Couns. 2013;91(1):14-24. 
74. 
Guralnik JM, Ferrucci L, Pieper CF, et al. Lower extremity function and subsequent 
disability: consistency across studies, predictive models, and value of gait speed alone 
compared with the short physical performance battery. The journals of gerontology 
Series A, Biological sciences and medical sciences. 2000;55(4):M221-231. 
75. 
Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-extremity 
function in persons over the age of 70 years as a predictor of subsequent disability. The 
New England journal of medicine. 1995;332(9):556-561. 
76. 
Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery 
assessing lower extremity function: association with self-reported disability and 
prediction of mortality and nursing home admission. Journal of gerontology. 
1994;49(2):M85-94. 
77. 
Simonsick EM, Newman AB, Nevitt MC, et al. Measuring higher level physical function in 
well-functioning older adults: expanding familiar approaches in the Health ABC study. J 
Gerontol A Biol Sci Med Sci. 2001;56(10):M644-649. 
78. 
Fragala MS, Alley DE, Shardell MD, et al. Comparison of Handgrip and Leg Extension 
Strength in Predicting Slow Gait Speed in Older Adults. Journal of the American 
Geriatrics Society. 2016;64(1):144-150. 
79. 
Newman AB, Simonsick EM, Naydeck BL, et al. Association of Long-Distance Corridor 
Walk Performance With Mortality, Cardiovascular Disease, Mobility Limitation, and 
Disability. JAMA. 2006;295(17):2018-2026. 
80. 
Hosey MM, Leoutsakos JS, Li X, et al. Screening for posttraumatic stress disorder in 
ARDS survivors: validation of the Impact of Event Scale-6 (IES-6). Critical care (London, 
England). 2019;23(1):276. 
81. 
Inskip JA, Lauscher HN, Li LC, et al. Patient and health care professional perspectives 
on using telehealth to deliver pulmonary rehabilitation. Chronic Respiratory Disease. 
2018;15(1):71-80. 
82. 
Borg G, Borg E. A new generation of scaling methods: Level-anchored ratio scaling. 
Psychologica. 2001;28:15-45. 
83. 
Davalos-Bichara M, Lin FR, Carey JP, et al. Development and Validation of a Falls-
Grading Scale. Journal of Geriatric Physical Therapy. 2013;36(2). 
84. 
Salsman JM, Schalet BD, Merluzzi TV, et al. Calibration and initial validation of a general 
self-efficacy item bank and short form for the NIH PROMIS(®). Quality of life research : 
an international journal of quality of life aspects of treatment, care and rehabilitation. 
2019;28(9):2513-2523. 
85. 
Gruber-Baldini AL, Velozo C, Romero S, Shulman LM. Validation of the PROMIS(®) 
measures of self-efficacy for managing chronic conditions. Qual Life Res. 
2017;26(7):1915-1924. 
86. 
Breiman L. Random Forests. Machine Learning. 2001;45(1):5-32. 

Protocol Template 
Page 20 
CF-146, Effective 7/10/11 
 
87. 
Lin CY, Bondell H, Zhang HH, Zou H. Variable Selection for Nonparametric Quantile 
Regression via Smoothing Spline AN OVA. Stat. 2013;2(1):255-268. 
88. 
Tibshirani R. Regression Shrinkage and Selection via the Lasso. Journal of the Royal 
Statistical Society Series B (Methodological). 1996;58(1):267-288. 
89. 
Jones SE, Kon SS, Canavan JL, et al. The five-repetition sit-to-stand test as a functional 
outcome measure in COPD. Thorax. 2013;68(11):1015-1020. 
90. 
Meretta BM, Whitney SL, Marchetti GF, Sparto PJ, Muirhead RJ. The five times sit to 
stand test: responsiveness to change and concurrent validity in adults undergoing 
vestibular rehabilitation. J Vestib Res. 2006;16(4-5):233-243. 
91. 
Bryant MS, Bandi VD, Nguyen CK, Lan C, Henson HK, Sharafkhaneh A. Telehealth 
Pulmonary Rehabilitation for Patients With Severe Chronic Obstructive Pulmonary 
Disease. Federal practitioner : for the health care professionals of the VA, DoD, and 
PHS. 2019;36(9):430-435. 
92. 
Guralnik JM, Ferrucci L, Pieper CF, et al. Lower extremity function and subsequent 
disability: consistency across studies, predictive models, and value of gait speed alone 
compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci. 
2000;55(4):M221-231. 
93. 
Fragala MS, Alley DE, Shardell MD, et al. Comparison of Handgrip and Leg Extension 
Strength in Predicting Slow Gait Speed in Older Adults. J Am Geriatr Soc. 
2016;64(1):144-150. 
94. 
Hughes ME, Waite LJ, Hawkley LC, Cacioppo JT. A Short Scale for Measuring 
Loneliness in Large Surveys: Results From Two Population-Based Studies. Res Aging. 
2004;26(6):655-672. 
95. 
Wenger NK, Froelicher ES, Smith LK, et al. Cardiac Rehabilitation as Secondary 
Prevention. Agency for Health Care Policy and Research and National Heart, Lung, and 
Blood Institute. Clin Pract Guidel Quick Ref Guide Clin. 1995;17:1-23. 
96. 
Forster JE, MaWhinney S, Ball EL, Fairclough D. A varying-coefficient method for 
analyzing longitudinal clinical trials data with nonignorable dropout. Contemp Clin Trials. 
2012;33(2):378-385. 
97. 
Moore CM, MaWhinney S, Forster JE, et al. Accounting for dropout reason in 
longitudinal studies with nonignorable dropout. Stat Methods Med Res. 2017;26(4):1854-
1866.