Mobile Health Study and Enhanced Symptom Monitoring to Prevent Severe Illness From COVID-19 in Cancer Patients
Phase: NA
Conditions: Sars-CoV2, Covid-19
Sponsor: University of Oklahoma

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RESEARCH PROTOCOL OUTLINE
Title of Project: Mobile Health Study and Enhanced Symptom Monitoring to Prevent 
Severe Illness from COVID-19 in Cancer Patients
Principal Investigators:
Bethany Hannafon, Ph.D., Department of OB/GYN
Michael Businelle, PhD, Dept. of Family and Preventive Medicine 
Co-Investigators: Summer Frank-Pearce, PhD, College of Public Health
Abstract
The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) outbreak in late 2019 has 
resulted in over 4.28 million confirmed cases of novel coronavirus disease (COVID-19), with 
over 292,000 confirmed deaths worldwide (as of May 13, 2020). Severe disease occurs in 
~10% of cases overall. However, risk for severe disease and mortality is greater in 
immunocompromised cancer patients, requiring scarce ventilation resources and intensive care 
unit space to address rapid clinical deterioration in this population. We propose to use the 
established InsightTM mHealth Platform, a component of the Stephenson Cancer Center (SCC) 
mHealth Shared Resource, to create the “Symptom Tracker” app. The  Symptom Tracker 
(InsightTM) app  will enable real-time monitoring of cancer patient symptoms that are consistent 
with early signs of SARS-CoV-2 infection in this high-risk population by automatically (and 
securely) transferring this information to health care providers. The primary aim of this study is 
to determine the feasibility, ease of use, and perceived utility of this app to monitor symptoms 
and health risk behaviors among cancer patients currently receiving chemotherapy. Health risk 
behaviors, standard COVID-19 screening questions, and a single chemotherapy risk question 
will be assessed daily through the smartphone app. Specific responses will automatically trigger 
an alert to clinic nurses AND provide app-based access to contact the clinic for triage to home 
care or emergency assessment. When/if study participants screen positive for COVID-19, they 
will receive a device to measure their pulse and blood oxygen levels daily. This information and 
COVID-19 symptoms will be monitored daily via the Symptom Tracker (InsightTM) app. This 
study will determine if an app can be used to rapidly report COVID-19 symptoms and quickly 
intervene to reduce morbidity and mortality in immunocompromised cancer patients. Further, 
data from this study will be used to inform the development of future mHealth interventions that 
will be tested in fully powered randomized control trials.
A. Specific Aims
The primary objective of this research is to use innovative mHealth technology to monitor 
symptoms in real-time and facilitate medical triage of high-risk patients. The current study will 
obtain data on the feasibility of an innovative mHealth application to track symptoms and 
automatically intervene upon reported high-risk symptoms among cancer patients who are 
receiving cytotoxic chemotherapy or immunotherapy treatments. This objective will be attained 
by pursuing the following specific aims:
1.
Determine the feasibility, ease of use, and perceived utility of the Symptom Tracker 
(InsightTM) mHealth app to monitor symptoms and health risk behaviors for up to 500 
patients who are receiving chemotherapy to treat cancer. 
a.
On average, participants will complete >50% of all prompted daily phone based 
ecological momentary assessments (EMAs).
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b.
Most participants will report that the app is easy to use, and provides useful 
information (assessed quantitatively and qualitatively during the 24-week phone 
based or RedCap follow-up interview).
2.
Examine participant characteristics and outcomes of those who do and do not test 
positive for COVID-19 during the 24-week monitoring period. 
a.
Among those who undergo enhanced monitoring, we will use descriptive statistics to 
summarize the proportion of patients experiencing the following outcomes: 
manifestations of severe disease, treatment related toxicity, need for hospitalization, 
and SARS-CoV-2 related mortality. 
b.
We will also compare outcomes and tolerability of enhanced self-monitoring for 
severe infection and compare rates of disease related morbidity and mortality to 
baseline rates published in the literature. 
c.
We will use quantitative and qualitative methods to evaluate participant satisfaction 
with the app among those who test positive for COVID-19 and are asked to report 
vital signs each day. 
3.
Collaboratively document and share lessons learned regarding the impact of the COVID-
19 pandemic on cancer care delivery via telemedicine and mHealth technology. We 
commit to a collegial collaboration with other cancer centers funded to study the use of 
telemedicine through the COVID-19 CCSG supplement mechanism. 
This study will also explore the potential reduction of severe COVID-19 mediated disease 
through: 1) reducing time from symptom detection to treatment initiation (i.e., comparing 
treatment initiation times among those who test positive for COVID-19 in this study to published 
literature), and 2) closely monitoring COVID-19 symptoms among those who contract the virus 
(i.e., again, comparing participants in this study to published literature). COVID-19 is a novel 
virus and we do not fully understand its impact on our patient population. This study will provide 
useful, granular information about the daily symptoms of and recovery from COVID-19 in cancer 
patients. Data from this study will be used to pursue NIH R01 funding to formally evaluate the 
effects of using mHealth technology to monitor symptoms and health risk factors and intervene 
in real-time.  
B. Background and Significance
The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) outbreak in late 2019 has 
resulted in approximately 4.28 million confirmed cases of novel coronavirus disease (COVID-
19), with over 292,000 confirmed deaths worldwide (as of May 13, 2020)[1]. The United States 
has reported the greatest number of cases across the world, with a total of 1.4 million new 
cases and over 83,000 attributable deaths as of May 13, 2020. Severe disease accounts for 
approximately 10% of cases and adds tremendous burden on limited medical resources and 
medical personnel. 
Risk factors for severe disease are largely unknown, but clinical experience suggests that 
persons over age 65 with co-morbid conditions, such as immunosuppression from cancer or 
chemotherapy, place cancer patients in the “high-risk” group for severe manifestations of 
COVID-19 mediated disease [2]. Therefore, cancer patients are likely at increased risk for 
COVID-19 infection and may have a poorer prognosis. According to recent data, the risk of 
severe infection in cancer patients approaches 40% with up to a 20% mortality rate [3]. Thus, 
there is increased need for invasive ventilation and intensive care unit admission to circumvent 
rapid clinical deterioration in this population. Novel methods are needed to remotely monitor for 
SARS-CoV-2 infection and rapidly intervene to reduce morbidity and mortality in cancer patients 
exhibiting symptoms of the virus. Given evidence of poor outcomes in cancer patients, early 
intervention may significantly improve outcomes. 
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The potential for smartphone-based treatments has only recently become evident due to the 
substantial increase in smartphone ownership and use. Most Americans (i.e., 81% in 2018) 
have active smartphones;[4] thus smartphone-based intervention apps could play a significant 
role in improving cancer treatment symptom reporting and treatment adherence, and thereby 
improve overall treatment outcomes. Smartphone apps could offer easily accessible, highly 
tailored, and intensive interventions at a fraction of the cost of traditional counseling, thereby, 
overcoming many of the barriers that have hampered use of traditional empirically supported 
treatments.[5, 6]
Mobile technology can assist in overcoming many barriers to mental health care among cancer 
patients by offering immediate access to clinical information and support from any location using 
a low-cost, patient-driven interface. Existing studies have already identified the value and 
effectiveness of personal technology devices in monitoring symptoms and improving adherence 
during and after primary cancer treatments.[7-9] Recent studies also reveal cancer patient 
satisfaction with information delivered through smartphone applications, even when frequent, 
daily assessments were required.[10, 11] Yet to date, few studies have tested mHealth 
technology that identifies and automatically intervenes upon reported symptoms in real-time to 
address the needs of cancer patients. The Advanced Symptom Management System (ASyMS), 
however, is an exception and is a well-studied mHealth program for monitoring systems and 
providing automated feedback among both adult and youth cancer patients in the UK.[7, 12-17] 
ASyMS has demonstrated good feasibility of mHealth to manage physical symptoms and has 
received positive feedback from both patients and providers.[13, 15, 16]  Further research is 
needed to examine mHealth interventions that specifically target symptom monitoring in cancer 
patients. The current study will use ecological momentary assessments (EMAs) to collect 
information about symptoms, exposure to coronavirus, and health risk behaviors among a 
sample of adults undergoing chemotherapy treatments for cancer at the Stephenson Cancer 
Center.    
Our research team and others have demonstrated that smartphones can be used to capture 
moment-to-moment experience, allowing for the measurement of phenomena in real-time within 
natural settings.[18, 19] In addition, many studies have indicated that smartphone prompts can 
be used to increase targeted behaviors (e.g., [20, 21],[22, 23]). Few studies, except our recent 
pilot work [24], have used a participant’s responses to EMAs to automatically prompt tailored 
interventions in real-time. This feasibility study will test a novel smartphone application that will 
assess symptoms and automatically provide information and referrals when needed. 
C. Preliminary Studies/Progress Report  
Our team has used smartphones in 11 previous and ongoing studies to collect data and 
influence health behavior in high-risk samples for up to 6 months, including homeless[25, 26] 
and other socioeconomically disadvantaged adults.[27-31] In Project Prevail, EMA data were 
collected from smokers seeking treatment at a safety-net hospital tobacco cessation clinic 
(N=146).[27, 29, 31] Participants completed 83% of all EMAs. Using just 6 EMA variables (i.e., 
urge, stress, cigarette availability, alcohol use, motivation to quit, proximity to others smoking), 
we created a smoking lapse risk estimator that identified 80% of all smoking lapses within 4 
hours of the lapse (false positive rate = 17%; see a detailed description of the development of 
the risk estimator here [30]). In another study, we developed the Smart-T app that includes on 
demand features (e.g., tips on coping with stress) and incorporates the Prevail lapse risk 
estimator to deliver tailored messages based on a person’s momentary risk for smoking lapse. 
We examined the feasibility of the Smart-T app among smokers recruited from an urban safety-
net hospital tobacco cessation clinic (n=59). In that study, socioeconomically disadvantaged 
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participants completed 87% of all EMAs and received an intensive level of tailored smoking-
cessation treatment messages (102 messages on average) over a 3-week intervention period. 
We found that the on demand app functions were used regularly and the app was well-liked 
(e.g., 97% would like to use the app in the future if they were to relapse, and 85% would refer 
their friends who smoke to use the app).[30] Analyses of EMA data revealed that urges to 
smoke were significantly reduced when tailored urge messages were delivered by the app, as 
compared to instances where other types of messages were delivered (p<.001).[32] We also 
found reductions in stress (p=.021) and cigarette availability (p<.001) following tailored stress 
and cigarette availability messages, compared to other types of messages.[32] A total of 20% of 
participants were biochemically-confirmed abstinent 12 weeks post-quit, [24] which is better 
than many interventions conducted with low SES smokers.[33-38] We recently completed a 3-
armed pilot randomized clinical trial (i.e., Project Smart-T2; OUHSC IRB #7195) that compared 
the Smart-T2 app to standard in-person smoking cessation clinic care and the free NCI 
QuitGuide app. Smokers who attended a clinic based tobacco cessation program were 
randomized to groups and followed for 13 weeks (1 week pre-quit through 12 weeks post-quit). 
All participants were asked to complete EMAs on study-provided smartphones for 5 weeks. 84 
participants were enrolled and 22% of Smart-T, 15% of QuitGuide, and 15% of in-person 
participants achieved biochemically verified 7-day point prevalence abstinence 12 weeks post-
quit. Smart-T3 (R01CA221819; the 3.0 version of Smart-T) was recently funded by the NCI. 
Smart-T Alcohol (i.e., R34AA024584) is an ongoing alcohol use reduction app for homeless 
adults) and Link2Care (i.e., R01MD010733) is an ongoing smartphone-based case 
management intervention for homeless adults. These studies demonstrate the utility, feasibility, 
versatility, and high compliance rates of our innovative smartphone apps. 
As a practicing oncologist within Stephenson Cancer Center, Dr. Bethany Hannafon, Ph.D. is 
knowledgeable about the medical needs of the population that is being recruited for this study, 
and she regularly uses evidenced-based interventions to treat cancer. Her research and clinical 
experience complement Dr. Businelle’s experience with research using mobile technology for 
health improvement. Additional study investigators include Dr. Summer Frank-Pearce 
(biostatistician) and Dr. Bethany Hannafon (basic and translational scientist) have agreed to 
assist with study recruitment and follow-up procedures.
D. Research Design and Methods 
Study Design: A total of 500 treatment-seeking males and females will be asked to complete 
daily monitoring of symptoms and weekly monitoring of health risk factors weekly via 
smartphone app (Aim 1). Those who screen positive for COVID-19 will be triaged to emergency 
or non-emergency medical treatment. COVID-19 positive patients identified for non-emergent 
medical treatment will receive a study-supplied device to measure their oxygen level and heart 
rate and be instructed in the device’s use. They will be prompted to enter vital signs data daily 
(such as body temperature, oxygen level, and heart rate) and questions from the app will 
change to assess the severity of COVID-19 related symptoms and inform changes in level of 
care, to emergency or non-emergency medical management as indicated by self-reporting. The 
proposed study will assess the impact of heightened symptom and vital sign monitoring on the 
early identification of severe manifestations of COVID-19 in this high-risk population (Aim 2). It 
is hypothesized that this will significantly improve COVID-19 related morbidity and mortality 
compared to the national and international rates currently reported in the literature. Figure 1 
displays the participant flow through this 24-week study.
The SCC mHealth Shared Resource has created the versatile and reusable InsightTM mHealth 
Platform. InsightTM is HIPAA compliant and data are encrypted within the app. There will be 4 
sources of data for the current study: 1) in-person data collected in a private room at the SCC 
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(i.e., Baseline visit) in REDcap, 2) smartphone based EMA survey data collected on study-
provided or personal smartphones via the encrypted InsightTM smartphone application 
“Symptom Tracker", 3) qualitative interview data (e.g., opinions about the app, ways the app can 
be improved for future patient monitoring) collected over the phone (or via RedCap if a medical 
condition prevents them from being able to speak for long periods of time) at the conclusion of 
the study, and 4) data extracted from the electronic health record.
Figure 1. Study Participant Flow. 
Measures
 Baseline (in person) Measures. Baseline measures will assess demographic 
characteristics, health risk behaviors, and health related quality of life (See Table 1 and 
Appendix). The Baseline visit will take approximately 30 minutes to complete and data will be 
collected via RedCap (i.e., study screener) and Symptom Tracker app (i.e., baseline 
assessment items will be completed on study provided smartphones or the participant’s 
personal phone). Each baseline questionnaire item will appear on the smartphone screen 
(participants touch the screen to select answers). Staff will be available to help participants who 
may have difficulty during the baseline assessment. 
EMA (phone based) 24 Week Follow-Up Measures. The 24-week follow-up assessment, 
which includes a Treatment Improvement Survey, will be completed over the phone and via 
EMA and will take 15-30 minutes to complete. EMA enables measurement of phenomena in 
real-time, natural settings.[18, 19] EMA items will identify fluctuations in key variables that 
Post-Study 
Assessment
All 
participants
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predict study outcomes with less bias than traditional in-person assessments. EMA data will be 
used to identify and report COVID-19 symptoms to SCC medical staff, assess health risk 
factors, and collect other health related data that may be used to guide future intervention 
development with this population. All participants will download the assigned smartphone app to 
their personal smartphone or receive a smartphone at the baseline visit and will be asked to 
carry it with them at all times for 24 weeks. Participants will be trained to use the smartphone 
app for data collection purposes. Participants will be instructed to click the “call staff” button to 
speak with study research staff in the event that they have difficulty with the phone app. In 
addition, participants will be instructed that they can click the “Call Clinic Staff” button in the app 
to speak with medical professionals about their symptoms. In weeks 1-24, participants will be 
asked to complete one daily survey that will be automatically prompted and administered 
through an encrypted and secure smartphone app. All participants will be asked to self-initiate a 
survey (by clicking a button labeled “Report Symptoms” in the app) in the event that any new 
symptoms arise after the morning survey is completed. Participants will respond to survey 
questions by using the touch screen. 
EMA types. The EMA methodology used in this study will be similar to what has been 
used in our previous studies, and by other researchers.[24, 25, 28, 30, 39-45] Each EMA will 
ask about current mood, medication use, perceived social support, pain, and symptoms 
consistent with COVID-19. This daily survey will take approximately 2 minutes to complete (see 
Table 1 and Appendix). Every Monday, participants will complete additional items focused on 
health risk factors, depression, anxiety, and medication use. The phone will ring and vibrate to 
cue these EMAs for 30 seconds. If the participant has not responded after 5 prompts, the EMA 
will be recorded as missed. Participants may also initiate a survey by clicking a Report 
Symptoms button in the app when COVID-19 symptoms arise later in the day (see Table 1 and 
Appendix). All EMAs will be date and time stamped for future analyses. 
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All participants will be prompted to complete EMAs once per day during the 24-week study time 
period. If a participant contracts COVID-19, they will click the “COVID-19 Positive” button in the 
app. This action will cause to app to ask a different set of questions focused on enhanced 
monitoring COVID-19 symptoms for the next 21 days. At the end of 21 days, the app will return 
to daily prompts of the pre-COVID-19 EMAs. At the conclusion of the 24-week EMA period, 
participants will gain access to a new button in the app that will initiate the 24-week follow-up 
assessment (see Appendix). Participants will receive a daily alert from the app to click the “Final 
Survey” button in the app to complete the end of study assessment. The 24-week phone 
interview (or RedCap interview if a medical condition prevents them from being able to speak for 
long periods of time) will be scheduled at the baseline visit and the date/time of the call will 
appear at the top of the app home screen. A maximum of five phone call/text attempts and one 
mailed letter will occur over two-week period, before data from phone interview (or RedCap 
interview if a medical condition prevents them from being able to speak for long periods of time) 
will be considered unattainable. Those participants who were loaned a mobile phone to use 
during the study will be mailed a stamped envelope to return the phone and charger, or will be 
approached by study staff to reclaim the mobile device during a routine follow-up visit in the 
SCC chemo infusion clinic. Upon receipt of the smartphone and charging equipment, all 
equipment will be sterilized with alcohol-based sanitizer in accordance with current practice 
guidelines put forth by the Centers for Disease Control at the time it is returned by the study 
subject.
EMA completion rates, participant dropout, and feedback on the participant satisfaction survey 
will be used to evaluate feasibility of this type of mobile intervention in this population.
Table 1: Study Measures
FULL BATTERY 
ASSESSMENTS
EMA ASSESSMENTS
MEASURE
ITE
MS 
Scree
n
Bas
e
24-
W
Chart 
Revie
w
Descriptive Variables
Daily Phone Survey (Aim 
1)
Screening Questionnaire
11
X
Sleep
Social Support
Rapid Estimate of Adult Literacy in 
Medicine-Short Form 
9
X
Affect (3 items)
Demographic/Background 
Questionnaire
10
X
Medication
Smoking Questions
6
X
X
Pain
Health Questions
10
X
X
COVID-19 (8 items)
Health Related Quality of Life
4
X
X
Patient Health Questionnaire + 
GAD7
15
X
X
Weekly EMA
Alcohol Questions
1
X
X
Health Behaviors
Treatment Improvement Interview
13
X
Alcohol (2 items)
App Quality Questions
3
X
Depression (2 items)
Chart Review
16
X
Anxiety (2 items)
Emotion
Total number of items
--
20
46
52
15
App evaluation
Time needed to complete 
assessment (minutes)
--
8
10
25
Chemo (7 items)
Note. *Event EMA’s only. Screen = Screening (in person); Base = Baseline (via Insight app); 24-W = 
24-Week Follow-Up (via Insight app and phone call).
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Qualitative interview data. A qualitative interview will be conducted at the end of 
the 24-week study period via a brief phone call (or RedCap if a medical condition 
prevents them from being able to speak for long periods of time). This interview will 
assess opinions about the app, ways the app could be improved for future patient 
monitoring, etc. The follow-up phone call will be recorded for transcription and analyzed 
using NVivo. Recordings will be transcribed by internal staff, Microsoft Transcribe, or by 
approved contractors with whom a Business Associates Agreement has been made. When 
using Microsoft Transcribe, all transcriptions will be reviewed by study staff.
De-identified information will be shared with collaborators and colleagues at other NCI 
Designated Cancer Centers (see Aim 3).
Data from the electronic health record. Relevant data from the EHR will be extracted as 
needed to address any discrepancies or omissions surrounding specified study outcomes that 
have been collected through the smartphone EMA surveys. This supplemental information will 
be collected and stored in REDcap. 
Private identifiable data (e.g., participant names, study assessment dates, and contact 
information) will be required for participant contact and thus will be collected in this study. 
Private identifiable data will be used to contact participants and will be saved in compliance with 
university storage of PHI (e.g., encryption). All printed private identifiable data will be stored in a 
locked filing cabinet in Dr. Hannafon’s research office when not in use and will destroyed 12 
months after data collection is completed. 
Smartphone programming. The mHealth Shared Resource at the OUHSC and Stephenson 
Cancer Center (SCC) has developed the InsightTM mHealth Platform that empowers 
researchers to build, test, and launch technology-based assessment and intervention tools. The 
mHealth resource employs a program manager, four senior programmers, and two research 
technicians, who develop and maintain web and mobile applications and relational databases. 
Applications are developed using state-of-the-art cross-platform design tools. The Resource is 
located within the Oklahoma Tobacco Research Center (OTRC), where Dr. Businelle’s office is 
located. Dr. Businelle is the Scientific Director of the mHealth Shared Resource, which will 
provide the programming services for the proposed project.
Smartphone training. Participants will receive brief systematic instructions (created by the 
researchers) at the baseline visit that demonstrates use of the app features. Instructions will be 
loaded into the app home screen so they may view it at any time. The instructions will describe 
how to use each of the app features (“Call Staff”, “Call Clinic Staff”, “Report Symptoms”, 
“COVID-19 Positive”, and “Update Contact Details” button/options; See Appendix). We have 
achieved high EMA compliance rates (i.e., 82%-87% of all EMA's completed) using similar 
protocols in multiple studies. 
Data loss prevention. In order to overcome potential loss of data if participants lose their phone 
(less than 1% of phones have been lost in most studies), phones will be programmed to connect 
to our secure server each day to upload encrypted data. This will ensure that no collected EMA 
data are lost. This tactic will also allow the researchers to remotely monitor each participant’s 
EMA completion rate and intervene (e.g., call the participant) when this rate is low (calls will be 
noted in the dataset and this variable may be used as a covariate in study analyses). 
Importantly, EMA data are password protected and encrypted on the phone and only encrypted 
data are transmitted to our secure server. Thus, study data are ONLY accessible by the 
research team. If a phone is lost, the app and all study data will be remotely wiped and only one 
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replacement phone will be provided to each participant. 
EMA alert settings. At the baseline visit, a phone set-up wizard is used to set participant sleep 
and wake times for each day of the week (Note: sleep/wake times can be changed for those 
with variable schedules). This practice reduces the likelihood that the phone will ring when 
participants are sleeping. Prompted EMAs will be automatically rescheduled 30 minutes later 
(four times) if a participant does not respond to the prompt.
Compensation. Participants will not be compensated for completing study surveys. Those who 
are loaned study phones and do not complete the 24-week follow-up assessment, will be 
contacted to reschedule and request that loaned phones be returned in person or by mail.
Study application. All patients will receive standard cancer treatment as prescribed by SCC 
health professionals plus baseline, follow-up, and daily EMA assessments relevant to this study. 
Participants who own an Android smartphone (OS version 6.0 or higher) will be encouraged to 
download and use the study app on their own phone. Those who do not own this type of 
smartphone and those who do not have a data plan will be loaned a Samsung smartphone (or 
equivalent) for 24 weeks so that they may complete EMAs. Participants will navigate through 
the EMA program and enter data simply by touching the screen. The study app includes a “Call 
Staff” function/button that automatically calls study staff (e.g., if they have problems completing 
EMAs). Participants will also be asked to self-initiate “Report Symptoms” EMAs when they 
experience new symptoms consistent with COVID-19. Smartphones will automatically collect 
data when on demand features are accessed. Use of personal vs. study provided phones will be 
examined as a covariate in all analyses.
When participants provide a pattern of responses that are suggestive of heightened risk for 
COVID-19 (See algorithm in Appendix), they will receive a suggestion to contact SCC staff, and 
the app will send an encrypted email containing this information to Dr. Hannafon  and other SCC 
staff with a request to contact the participant to assess symptoms (see Appendix) and whether 
urgent medical attention is indicated. Should urgent medical attention be warranted, the patient 
will be instructed to the clinical unit most appropriate to the symptoms they reported through the 
app algorithm or discussed with the clinical nursing or physician staff. Non-emergent patients 
who screen positive for COVID-19 will begin an enhanced symptom and vital sign monitoring 
period that will continue until the participant reports that they are no longer symptomatic and/or 
a medical professional informs them that they no longer need to self-quarantine, which is 
expected to be approximately 21 days. These patients will receive a pulse oximeter (provided by 
the study) and the app questions will change to include daily prompts requesting that the patient 
input morning vital signs (temperature, heart rate, blood oxygen levels (SpO2)) and answer 
questions specific to the severity of symptoms and/or emergence of new symptoms of COVID-
19. If a patient is hospitalized, they can continue to complete the daily assessments if they are 
willing and able to do so. The app will also support check-ins with SCC staff and after-hours 
medical staff as needed after diagnosis of COVID-19 or until the patient has been medically 
released from self-quarantine. Once the participants have completed the enhanced symptom 
and vital sign monitoring portion of the study, the pulse oximeter will be returned to the research 
team at the time of their next scheduled appointment. These devices will then be sterilized with 
both alcohol-based sanitizing products as well as ultraviolet light exposure in accordance with 
practices endorsed by the Centers for Disease Control. 
Participants will continue to use the app to complete a final 24-week follow-up survey and will be 
contacted by phone (or RedCap if a medical condition prevents them from being able to speak 
for long periods of time) to complete a brief qualitative interview 24 weeks after study 
enrollment. Relevant demographic, cancer-specific and COVID-19 related data will be extracted 
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from electronic health records at the conclusion of the study.
E. Chart Review
Study staff will conduct a chart review on OUHSC campus computers using OU Medicine’s 
electronic medical record. Chart review will be conducted after completion of the EMA period. 
Initial evaluation, treatment plan, and follow-up notes will be searched for the following data 
points: 
a) Cancer Stage at Initial Presentation
b) Type of Cancer – text field
c)
History of Previous Cancer Diagnosis 
d) Previous Cancer Treatment
e) History of Previous Cancer Treatment with Surgery
f)
History of Previous Cancer Treatment with Chemotherapy
g) History of Previous Cancer Treatment with Endocrine Therapy
h) History of Previous Cancer Treatment with Radiation
i)
History of Previous Other Types of Cancer Treatment 
j)
Receiving concurrent chemotherapy and radiation
k)
Body Mass Index at start of treatment
l)
Body Mass index at end of treatment
m) Number of no-show and cancelled office visits
n) Referral to Supportive Care
o) Distance in miles from the participant’s home to the SCC? 
p) Does the participant reside in a rural county?
All data collected during the chart review will be assigned to the corresponding participant ID 
number and entered into study electronic databases in accordance with the data and safety-
monitoring plan outlined below. See Appendix for a full list of data that will be collected via chart 
review.
F. Biospecimens
No biospecimens will be collected as part of this study.  
G. Banking/Repository/Database
No biospecimens will be banked for future use. All data will be stored and accessed on OUHSC 
devices/computers.
H. Inclusion / Exclusion Criteria
Individuals will be included in the study if they:
1) Demonstrate > 6th grade English literacy level (i.e., phone based EMAs require >6th grade 
literacy)
2) Present for care at the Stephenson Cancer Center at the University of Oklahoma Health 
Sciences Center
3) Are currently receiving cytotoxic chemotherapy or immunotherapy by intravenous infusion or 
by orally delivered medication 
4) Are greater than or equal to 18 years of age
5) Are willing and able to complete surveys on their personal smartphone or a study provided 
smartphone
6) Are willing and able to provide informed consent
There are no early termination criteria; however, individuals can elect to stop participation in 
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mobile app monitoring at any time. 
I. Gender/Minority/Pediatric Inclusion for Research
This sample will consist of up to 500 male and female cancer patients. Individuals under the age 
of 18 (pediatric population) will be excluded from this study. Minorities will also be included, as 
this study seeks to adequately capture the diversity of patients receiving treatment at the SCC. 
There are no known additional risks for women or minorities. 
J. Recruitment and Enrollment
Participants (N=500) will be recruited through the OU Medical Stephenson Cancer Center 
Infusion Clinic which is located at 800 NE 10th Street, Floor 3, OK 73104. The SCC Infusion 
Clinic provides chemotherapy services for approximately 1200 patients per month. Based upon 
the treatment regimens provided, we anticipate that 250-300 cancer patients will be accrued to 
the trial every quarter. The clinic is staffed by oncologists, pharmacists, and advanced practice 
professionals. Patients will be given a verbal description of the study during a clinic visit, and 
those interested will be screened for study eligibility. Individuals who provide informed consent 
and meet the study inclusion criteria will complete the baseline assessment in the Infusion Clinic 
space. Participants will use study phones or personal smartphones to complete the baseline 
and follow-up assessment and phone-initiated and self-initiated surveys for the entire duration of 
the study. At the time of consent, patients will be provided with a copy of the informed consent 
to keep and will be encouraged to contact the investigators should they have further questions. 
The assessment questions are presented through the InsightTM mHealth platform. No incentive 
or cost reduction for care will be provided to the patient in order to minimize the risk of coercion. 
Several strategies will be utilized to retain study participants. Survey responses are 
automatically uploaded to the secure server every day; thus, study staff can closely monitor 
participant completion of daily surveys, and attempt to make contact with participants who do 
not complete them. Figure 1 displays the participant flow through the study.
K. Risks and Benefits
Participation in this study poses minimal risk to participants. Potential risks include consenting 
process and loss of confidentiality. Each of these risks are discussed below. The consent form 
will clearly describe the potential risks of this study. Thus, participants who feel uncomfortable 
with such assessments or intervention programs are not likely to agree to participate.
Consent process. The patient risk in this clinical study is minimal. As part of the informed 
consent process, patients will be made aware of how their data will be collected. Subjects who 
choose not to take part in the proposed study, will not be penalized or lose benefits to which 
they are entitled. 
Loss of confidentiality. One potential risk is loss of confidentiality. The severity of harm in the 
case of loss of confidentially may range from mild to severe depending upon the individual and 
the specific circumstances. However, the risks of participation in the study are similar to that of 
participation in usual in person or phone-based treatments, as loss of confidentially may be 
experienced in each case. Other possible adverse events might include compromised data 
security and discomfort related to being asked study questions. The research team will monitor 
risk and report adverse events immediately to the IRB.
Intervention. Risk to subjects from the proposed intervention research is minimal. There is 
no known harm from the use of standard thermometers or pulse oximetry units or daily 
monitoring and automated reporting of symptoms to medical professionals. 
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Alternative Treatments. The consent form will explain that, rather than participating in this 
study, individuals may choose to continue treatment in accord with their oncology treatment plan 
at the SCC. 
General Procedures to Reduce Risk. Each participant will be assigned an identification 
number that will be utilized in place of names in all electronic and print data files. The sheet 
containing the links between participant names and identifiers will be kept in locked filing cabinet 
when not in use and will be destroyed 12 months after data collection has been completed. All 
print information, including informed consent and screening questionnaires, will be stored in a 
separate locked filing cabinet in Dr. Hannafon’s research offices. All electronic data (with names 
omitted) will be maintained on the investigators’ computers and all computers, electronic files 
will be encrypted, and password protected. Participant information will not be released to any 
party outside the research team at any time.
In-person data. Participants will complete all assessments on their personal smartphone 
or a study provided smartphone using the Symptom Tracker (InsightTM)  mHealth platform 
app and/or the REDcap program. Participants will be instructed that they may elect to not 
answer questions that make them feel uncomfortable. All questionnaire data is automatically 
saved and password protected. To ensure that research staff are adequately trained in data 
collection, confidentiality, and the protection of human subjects, all project staff will complete 
extensive training focused on each of the following topics: 1) project rationale and objectives; 2) 
the informed consent process; and 3) general data collection procedures (e.g., computer data 
collection, privacy). They will also complete all confidentiality, conflict of interest, and HIPAA 
training programs as required by the University of Oklahoma Health Sciences Center. 
EMA data. Participant responses to study questions will be encrypted and stored on the 
study/personal smartphone. Encrypted data will be automatically uploaded to our secure server 
each day. When/if the app determines that risk for COVID-19 infection is high, or COVID-19 
symptoms are worsening (in those who contract the virus), an encrypted email will be sent from 
our secure server to clinical study staff that includes the participant name, date of birth and 
phone number. The following features are designed to ensure the security of this data: 1) the 
data stored on the smartphone device are in a SQLite database in a sandbox environment, 
where read/write operations are only available through the programming application (i.e., no file 
or output is readable to end users); 2) a 10-12 character password (only known to researchers) 
is required to authenticate the current user before data can be manually accessed; 3) the web 
browser application linking the investigator’s computer to the database is on HTTPS protocol 
(SSL certificate with encryption) which will guarantee the data transfer from web browser to the 
backend database is well protected; and, 4) the backend database is hosted by Microsoft Azure 
and the University of Oklahoma Health Sciences Center. These steps will ensure the security of 
EMA data. Software will be downloaded onto each study phone so that study data can be 
remotely wiped if phones are lost or stolen.
Beyond the above noted issues, the following step will be taken to minimize the likelihood of an 
adverse reaction to the study: The consent form will clearly describe the nature of the 
assessments and intervention. Thus, participants who feel uncomfortable with such 
assessments or intervention programs are not likely to agree to participate.      
Potential Benefits. Potential benefits to participants include the use of study phones during 
the intervention period (at the current time, this benefit is estimated to be unlimited voice and 
text messages and 2 GB high speed data for the duration of their involvement in the study [i.e., 
24 weeks]). Participants may also directly benefit from early detection of and immediate 
intervention upon presentation of COVID-19 symptoms. Finally, participants may also benefit 
from the enhanced symptom monitoring that is proposed.
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Risks in Relation to Benefits. The current study involves very minimal risk to participants, 
and the risks of study participation are similar to that of participation in standard care (e.g., loss 
of confidentiality). All participants will be already seeking standard care before they are provided 
with any information about the research study. In addition, the knowledge gained from this study 
may be utilized to improve our understanding of symptom monitoring, health risk factors, and 
general experiences of individuals who undergo chemotherapy treatments to treat cancer.
We do not anticipate harm to research participants, but the principal investigators will monitor 
unanticipated problems (including serious and non-serious adverse events) and protocol 
deviations. Potential unanticipated problems for this project are all non-medical in nature. 
Unanticipated problems will be submitted within five University business days to the IRB using 
the Unanticipated Problem Report Form. The following information will be included in an 
adverse event report: date of event, attribution to data collection, and outcome of adverse 
events. All adverse events will also be reported in the annual report to the IRB along with a 
Summary of Participant Harms.  
L. Multiple Sites
This is a single-site study.  
M.Statistical Methods  
Aim 1. We will examine the percentage of prompted daily surveys that are completed 
throughout the 24-week intervention period and the percentage of participants who report they 
are satisfied with monitoring and use of mHealth application at the end of the 24-week study 
period. We will compare participant characteristics (e.g., demographics, health behaviors) and 
outcomes between those who are satisfied with the app and monitoring versus those who are 
dissatisfied. We will observe via mHealth app patient health risk factors and readiness to 
change health risk behaviors in SCC cancer patients. We will compare participant 
characteristics using chi-square test or independent t-test, as appropriate. The follow-up phone 
call based qualitative interview (or RedCap interview if a medical condition prevents them from 
being able to speak for long periods of time) will access participant perceptions of each app 
feature and be recorded for transcription and analyzed using NVivo. Recordings will be 
transcribed by internal staff, Microsoft Transcribe, or by approved contractors with whom a 
Business Associates Agreement has been made. When using Microsoft Transcribe, all 
transcriptions will be reviewed by study staff. This information will be used to improve this type 
of symptom monitoring apps in future studies and interventions. 
Aim 2. We will examine participant characteristics among those who are and are not 
transitioned to an Aim 2 monitoring of vital signs and severity of COVID-19 symptoms. We will 
also examine participant characteristics and outcomes for those that require emergent 
intervention versus non-emergent intervention. Among those who undergo enhanced 
monitoring, we will use descriptive statistics to summarize the proportion of patients 
experiencing the following outcomes: manifestations of severe disease, treatment related 
toxicity, need for hospitalization, and COVID-19 related mortality. We will also compare 
outcomes and tolerability of enhanced self-monitoring for severe infection and compare rates of 
disease related morbidity and mortality to baseline rates published in the literature. We will 
evaluate participant satisfaction with the app and identify ways to improve this novel patient 
monitoring technology. We will perform logistic regression to determine the odds of experiencing 
primary outcomes, based upon baseline characteristics (e.g., sex, race/ethnicity, cancer 
stage/type). Kaplan-Meier survival curves and log-rank tests of homogeneity will be used to 
identify the survival functions, describing the time to resolution of symptoms based upon 
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demographic characteristics. Because COVID-19 is a novel virus and we do not fully 
understand its impact on our patient population, this study will provide useful, granular 
information about the daily symptoms of and recovery from COVID-19 in cancer patients.
Aim 3. No analyses proposed, as this is a collaboration aim.  
N. Data and Safety Monitoring Plan 
Data quality and integrity will be held to the highest standard to ensure accurate reporting 
and documentation of the objectives outlined in the proposal. The Institutional Review Board 
and the Office of Clinical Research will monitor data and safety monitoring. The institutional plan 
for data and safety monitoring for all trials funded by the NCI has been adopted by the 
University and will be utilized for conduct of our planned proposal.
Procedures to minimize the risk, including of loss of confidentiality, are described in the 
Protection against Risks section. The study poses minimal risk to participants, therefore the 
principal investigators (Drs. Hannafon  and Businelle) and co-investigators (Drs. Hannafon and 
Frank-Pearce) will undertake continuous monitoring and reporting of events. Although unlikely 
given our experience, any unanticipated problems will be promptly reported to the IRB. The NIH 
will be informed of any actions taken by the IRB as a result of its continuing review. Breach of 
confidentiality is highly unlikely because all data will be encrypted, identified only by numeric 
code, and/or stored in locked file cabinets/online-secure server. A master list of names and 
numbers will be kept in a separate location and is used to facilitate the collection of follow-up 
data. All staff will be fully trained in relevant ethical principles and procedures, particularly 
around confidentiality.
O. Data Sharing
De-identified summary information about the feasibility of using mHealth technology to 
assess risk for COVID-19 and interventions to address risk will be shared with the other 2 
cancer centers that received CCSG funding on a similar topic. We plan to publish the outcomes 
of this study and present findings at local and national conferences.
P. Confidentiality 
Each participant will be assigned an identification number that will be utilized in place of 
names in all electronic and print data files. The sheet containing the links between participant 
names and identifiers will be kept in locked filing cabinet when not in use and will be destroyed 
12 months after data collection has been completed. All print information, including informed 
consent and screening questionnaires, will be stored in a separate locked filing cabinet in the 
investigators’ research offices. All electronic data (with names omitted) will be maintained on the 
investigators’ computers and all computers and electronic files will be encrypted and password 
protected. When/if the app determines that risk for COVID-19 infection is high, or COVID-19 
symptoms are worsening (in those who contract the virus), an encrypted email will be sent from 
our secure server to clinical study staff that includes the participant name, date of birth and 
phone number. Participant information will not be released to any party outside the research 
team at any time.
University IT Security has fully vetted and approved the Insight mHealth Platform that will be 
used to collect data on participant and study provided smartphones. University IT has also 
approved the Insight data storage procedures described above.
Q. Literature Cited
1.
Organization, WH, Coronavirus disease (covid-19) pandemic. 2020: Published online at WHO.int.
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2.
Coronavirus: People at higher risk of severe infection. 2020, Centers for Disease Control 
Published online at CDC.gov.
3.
Liang, W, W Guan, R Chen, W Wang, J Li, K Xu, C Li, Q Ai, W Lu, H Liang, S Li, and J He, 
Cancer patients in sars-cov-2 infection: A nationwide analysis in china. Lancet Oncol, 2020. 
21(3): p. 335-337.
4.
Smith, A. Record shares of americans now own smartphones, have home broadband. 2017  
[cited 2017 January 15]; Available from: http://www.pewresearch.org/fact-
tank/2017/01/12/evolution-of-technology/.
5.
Siahpush, M, HH Yong, R Borland, JL Reid, and D Hammond, Smokers with financial stress are 
more likely to want to quit but less likely to try or succeed: Findings from the international tobacco 
control (itc) four country survey. Addiction (Abingdon, England), 2009. 104(8): p. 1382-90.
6.
Copeland, AL, MS Businelle, DW Stewart, SM Patterson, CJ Rash, and CE Carney, Identifying 
barriers to entering smoking cessation treatment among socioeconomically disadvantaged 
smokers. Journal of Smoking Cessation, 2010. 5: p. 164-171.
7.
Kearney, N, L McCann, J Norrie, L Taylor, P Gray, M McGee-Lennon, M Sage, M Miller, and R 
Maguire, Evaluation of a mobile phone-based, advanced symptom management system (asyms) 
in the management of chemotherapy-related toxicity. Supportive care in cancer : official journal of 
the Multinational Association of Supportive Care in Cancer, 2009. 17(4): p. 437-44.
8.
Wu, YP, LA Linder, P Kanokvimankul, B Fowler, BG Parsons, CF Macpherson, and RH Johnson, 
Use of a smartphone application for prompting oral medication adherence among adolescents 
and young adults with cancer. Oncology nursing forum, 2018. 45(1): p. 69-76.
9.
Pham, Q, JA Cafazzo, and A Feifer, Adoption, acceptability, and effectiveness of a mobile health 
app for personalized prostate cancer survivorship care: Protocol for a realist case study of the 
ned app. JMIR research protocols, 2017. 6(10): p. e197.
10.
Lee, H, KE Uhm, IY Cheong, JS Yoo, SH Chung, YH Park, JY Lee, and JH Hwang, Patient 
satisfaction with mobile health (mhealth) application for exercise intervention in breast cancer 
survivors. Journal of medical systems, 2018. 42(12): p. 254.
11.
Benze, G, F Nauck, B Alt-Epping, G Gianni, T Bauknecht, J Ettl, A Munte, L Kretzschmar, and J 
Gaertner, Proutine: A feasibility study assessing surveillance of electronic patient reported 
outcomes and adherence via smartphone app in advanced cancer. Annals of palliative medicine, 
2017.
12.
Maguire, R, M Miller, M Sage, J Norrie, L McCann, L Taylor, and N Kearney, Results of a uk 
based pilot study of a mobile phone based advanced symptom management system (asyms) in 
the remote monitoring of chemotherapy related toxicity. Clinical Effectiveness in Nursing, 2005. 
9(3): p. 202-210.
13.
Maguire, R, L McCann, M Miller, and N Kearney, Nurse's perceptions and experiences of using of 
a mobile-phone-based advanced symptom management system (asyms) to monitor and manage 
chemotherapy-related toxicity. European journal of oncology nursing : the official journal of 
European Oncology Nursing Society, 2008. 12(4): p. 380-6.
14.
McCall, K, J Keen, K Farrer, R Maguire, L McCann, B Johnston, M McGill, M Sage, and N 
Kearney, Perceptions of the use of a remote monitoring system in patients receiving palliative 
care at home. International journal of palliative nursing, 2008. 14(9): p. 426-31.
15.
McCann, L, R Maguire, M Miller, and N Kearney, Patients' perceptions and experiences of using 
a mobile phone-based advanced symptom management system (asyms) to monitor and manage 
chemotherapy related toxicity. European journal of cancer care, 2009. 18(2): p. 156-64.
16.
Moradian, S, MK Krzyzanowska, R Maguire, PP Morita, V Kukreti, J Avery, G Liu, J Cafazzo, and 
D Howell, Usability evaluation of a mobile phone-based system for remote monitoring and 
management of chemotherapy-related side effects in cancer patients: Mixed-methods study. 
JMIR cancer, 2018. 4(2): p. e10932.
17.
Furlong, E, A Darley, P Fox, A Buick, G Kotronoulas, M Miller, A Flowerday, C Miaskowski, E 
Patiraki, S Katsaragakis, E Ream, J Armes, A Gaiger, G Berg, P McCrone, P Donnan, L McCann, 
and R Maguire, Adaptation and implementation of a mobile phone-based remote symptom 
monitoring system for people with cancer in europe. JMIR cancer, 2019. 5(1): p. e10813.
18.
Shiffman, S, M Hufford, M Hickcox, JA Paty, M Gnys, and JD Kassel, Remember that? A 
comparison of real-time versus retrospective recall of smoking lapses. Journal of Consulting & 
Clinical Psychology, 1997. 65(2): p. 292-300.
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19.
Stone, AA, S Shiffman, JE Schwartz, JE Broderick, and MR Hufford, Patient non-compliance with 
paper diaries. British Medical Journal, 2002. 324(7347): p. 1193-1194.
20.
Thakkar, J, R Kurup, TL Laba, K Santo, A Thiagalingam, A Rodgers, M Woodward, J Redfern, 
and CK Chow, Mobile telephone text messaging for medication adherence in chronic disease: A 
meta-analysis. JAMA internal medicine, 2016. 176(3): p. 340-9.
21.
Haase, J, KB Farris, and MP Dorsch, Mobile applications to improve medication adherence. 
Telemedicine journal and e-health : the official journal of the American Telemedicine Association, 
2017. 23(2): p. 75-79.
22.
Spohr, SA, FS Taxman, and ST Walters, The relationship between electronic goal reminders and 
subsequent drug use and treatment initiation in a criminal justice setting. Addictive behaviors, 
2015. 51: p. 51-6.
23.
Kendzor, DE, Shuval, K., Gabriel, K. P., Businelle, M. S., Ma, P., High, R. R., Cuate, E. L., 
Poonawalla, I. B., Rios, D. M., Demark-Wahnefried, W., Swartz, M. D., & Wetter, D. W., Impact of 
a mobile phone intervention to reduce sedentary behavior in a community sample of adults: A 
quasi-experimental evaluation. Journal of Medical Internet Research, 2016. 18: p. e19.
24.
Businelle, MS, P Ma, DE Kendzor, SG Frank, DJ Vidrine, and DW Wetter, An ecological 
momentary intervention for smoking cessation: Evaluation of feasibility and effectiveness. Journal 
of Medical Internet Research, 2016. 18(12): p. e321.
25.
Businelle, MS, P Ma, DE Kendzor, LR Reitzel, M Chen, CY Lam, I Bernstein, and DW Wetter, 
Predicting quit attempts among homeless smokers seeking cessation treatment: An ecological 
momentary assessment study. Nicotine & Tobacco Research, 2014. 16: p. 1371-1378.
26.
Reitzel, LR, DE Kendzor, N Nguyen, SD Regan, KS Okuyemi, Y Castro, DW Wetter, and MS 
Businelle, Shelter proximity and affect among homeless smokers making a quit attempt. Am J 
Health Behav, 2014. 38(2): p. 161-9.
27.
Bandiera, FC, F Atem, P Ma, MS Businelle, and DE Kendzor, Post-quit stress mediates the 
relation between social support and smoking cessation among socioeconomically disadvantaged 
adults. Drug and alcohol dependence, 2016. 163: p. 71-6.
28.
Kendzor, DE, K Shuval, KP Gabriel, MS Businelle, P Ma, RR High, EL Cuate, IB Poonawalla, DM 
Rios, W Demark-Wahnefried, MD Swartz, and DW Wetter, Impact of a mobile phone intervention 
to reduce sedentary behavior in a community sample of adults: A quasi-experimental evaluation. 
J Med Internet Res, 2016. 18(1): p. e19.
29.
Watkins, KL, SD Regan, N Nguyen, MS Businelle, DE Kendzor, C Lam, D Balis, AG Cuevas, Y 
Cao, and LR Reitzel, Advancing cessation research by integrating ema and geospatial 
methodologies: Associations between tobacco retail outlets and real-time smoking urges during a 
quit attempt. Nicotine Tob Res, 2014. 16 Suppl 2: p. S93-101.
30.
Businelle, MS, P Ma, DE Kendzor, SG Frank, DW Wetter, and DJ Vidrine, Using intensive 
longitudinal data collected via mobile phone to detect imminent lapse in smokers undergoing a 
scheduled quit attempt. Journal of Medical Internet Research, 2016. 18(10): p. e275.
31.
Kendzor, DE, MS Businelle, IB Poonawalla, EL Cuate, A Kesh, DM Rios, P Ma, and DS Balis, 
Financial incentives for abstinence among socioeconomically disadvantaged individuals in 
smoking cessation treatment. American Journal of Public Health and the Nations Health, 2015. 
105: p. 1198-1205.
32.
Hébert, ET, Stevens, E. M., Frank, S. G., Kendzor, D. E., Wetter, D. W., Zvolensky, M. J., 
Buckner, J. D., Businelle, M. S., An ecological momentary intervention for smoking cessation: 
The associations of just-in-time, tailored messages with lapse risk factors. Addictive behaviors, 
2018. 78: p. 30-35.
33.
Okuyemi, KS, James, A. S., Mayo, M. S., Nollen, N., Catley, D., Choi, W. S., et al., 
Pathways to health: A cluster randomized trial of nicotine gum and motivational 
interviewing for smoking cessation in low-income housing. Health Education and 
Behavior, 2007. 34(1): p. 43-54.
34.
Hahn, EJ, MK Rayens, C Chirila, CA Riker, TP Paul, and TA Warnick, Effectiveness of a 
quit and win contest with a low-income population. Preventive Medicine, 2004. 39: p. 543-
550.
35.
Okuyemi, KS, K Goldade, G Whembolua, JL Thomas, S Eischen, B Sewali, H Guo, JE Connett, J 
Grant, and JS Ahluwalia, Motivational interviewing to enhance nicotine patch treatment for 
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smoking cessation among homelessl smokers: A randomized controlled trial. Addiction 
(Abingdon, England), 2013. 108: p. 1136-1144.
36.
Christiansen, BA, KM Reeder, EG TerBeek, MC Fiore, and TB Baker, Motivating low 
socioeconomic status smokers to accept evidence-based smoking cessation treatment: A brief 
intervention for the community agency setting. Nicotine Tob Res, 2015. 17(8): p. 1002-11.
37.
Boland, VC, EA Stockings, RP Mattick, H McRobbie, J Brown, and RJ Courtney, The 
methodological quality and effectiveness of technology-based smoking cessation interventions for 
disadvantaged groups: A systematic review and meta-analysis. Nicotine & Tobacco Research, 
2016.
38.
Bricker, JB, KE Mull, JA Kientz, R Vilardaga, LD Mercer, KJ Akioka, and JL Heffner, Randomized, 
controlled pilot trial of a smartphone app for smoking cessation using acceptance and 
commitment therapy. Drug and alcohol dependence, 2014. 143: p. 87-94.
39.
Shiffman, S, M Hickcox, JA Paty, M Gnys, T Richards, and JD Kassel, Individual differences in 
the context of smoking lapse episodes. Addictive behaviors, 1997. 22(6): p. 797-811.
40.
Wetter, DW, JB McClure, L Cofta-Woerpel, TJ Costello, LR Reitzel, MS Businelle, and P 
Cinciripini, A randomized clinical trial of a palmtop computer-delivered treatment for smoking 
relapse prevention among women. Psychology of Addictive Behaviors, 2011. 25: p. 365-371.
41.
Stone, AA, JE Schwartz, JM Neale, S Shiffman, CA Marco, M Hickcox, J Paty, LS Porter, and LJ 
Cruise, A comparison of coping assessed by ecological momentary assessment and 
retrospective recall. Journal of Personality and Social Psychology, 1998. 74(6): p. 1670-1680.
42.
Shiffman, S, JA Paty, M Gnys, JA Kassel, and M Hickcox, First lapses to smoking: Within-
subjects analysis of real-time reports. Journal of Consulting & Clinical Psychology, 1996. 64(2): p. 
366-379.
43.
Piasecki, TM, ML Cooper, PK Wood, KJ Sher, S Shiffman, and AC Heath, Dispositional drinking 
motives: Associations with appraised alcohol effects and alcohol consumption in an ecological 
momentary assessment investigation. Psychological assessment, 2014. 26(2): p. 363-369.
44.
Simons, JS, RD Dvorak, BD Batien, and TB Wray, Event-level associations between affect, 
alcohol intoxication, and acute dependence symptoms: Effects of urgency, self-control, and 
drinking experience. Addictive behaviors, 2010. 35(12): p. 1045-1053.
45.
Swendsen, JD, H Tennen, MA Carney, G Affleck, A Willard, and A Hromi, Mood and alcohol 
consumption: An experience sampling test of the self-medication hypothesis. Journal of Abnormal 
Psychology, 2000. 109(2): p. 198-204.
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