PrimeMD, a Pain Reporting Smartphone Application for Patients Undergoing Head and Neck Radiation

Jeffrey M. Friedman, MD, PhD

Abstract

Purpose:

Methods:

Results:

Conclusion:

Head and neck cancer patients develop severe, debilitating symptoms during radiation therapy. Ecological momentary assessment (EMA) of patient-reported outcome measures (PROMs), such as pain, may improve provider awareness of morbidity and improve patient engagement and satisfaction in their care. We demonstrated the feasibility of an application (app) for Android and iOS named PrimeMD for four-times-daily monitoring of pain and severe symptoms in head and neck cancer patients.Patients undergoing radiation therapy for head and neck cancer that owned and could use a smartphone were recruited from 1 academic medical center. Patient demographics were measured at enrollment. Patients entered pain ratings, use of pain medications, and the presence of severe symptoms for the duration of their radiation treatments. At the conclusion of radiotherapy patients gave an exit interview.24 patients used the app. Mean age was 59; 46% were female, 92% identified as Caucasian. Forty-two percent had oropharyngeal disease, and all were lymph node positive. Median number of radiation fractions was 33 (range 25-35) with median dose of 6461 cGy; median treatment time 45 days (range 35-50). Median days of at least one use of the app was 12.5 (range 4-45). At the exit interview patients’ median satisfaction was 9/10 and usability was rated at 9.5/10.Patients experiencing pain and severe symptoms from radiotherapy are able to use a smartphone app for real-time monitoring. Adherence may be improved by adjusting alarms to patient preferences and enabling two-way communication with providers.

Introduction

Head and neck cancer is the sixth-most common cancer worldwide; there is a male preponderance and the median age of diagnosis is in the early 1960s.¹ Patients undergoing definitive radiation treatment for locally-advanced head and neck cancer develop severe symptoms during the first 4 weeks of treatment. Radiation mucositis is a debilitating symptom of radiation treatment and affects about 80% of patients; sufferers may become unable to eat within days.^2-4 Approximately half of patients eventually need feeding tube placement to maintain adequate nutrition, and some require hospitalization for supportive care.^2,5

Patients are typically assessed by physicians once every 5 fractions while undergoing daily radiation treatment, sometimes allowing their pain to be untreated between assessments. Mobile smartphone or web-based remote monitoring, whereby patients use an application (app) on their personal devices at predetermined or self-selected intervals to report their symptoms, is a potential solution to improve patient engagement.⁶

The use of such patient-reported outcome monitoring (PROM) systems has been associated with improved symptom control and better engagement with care teams.⁷ One recent randomized control trial that monitored chemotherapy patients’ symptoms in-office as well as via weekly home emails reported improved health-related quality of life as well as fewer emergency department admissions and a longer duration of chemotherapy.⁸

Methods

Study setting, selection criteria, and enrollment

Retrospective recall of symptoms is subject to recall bias and availability.^9,10 Recall of pain in particular is modified by time since the event, the variability of pain over time, and peak intensity of pain.^11,14 Ecological momentary assessment (EMA), or the real-time monitoring of symptoms using paper or electronic diaries, has been recognized as an effective method to increase the accuracy of PRO reporting and has successfully been employed in the monitoring of fibromyalgia pain.^{9, 14-17} In the current study, we developed and studied the feasibility of use for a native Android and iOS (iPhone) smartphone app with 4-times-daily real-time EMA of mucositis symptoms for patients treated with radiation therapy for head and neck cancer. The app was designed with predetermined 4-times-daily intervals versus ad libitum symptom reporting to better approximate real-time EMA to explore its EMA in this older-skewing population. We hypothesized that this app for EMA symptom monitoring in this group of patients suffering from severe symptoms of radiation treatment would be successful in engaging patients in reporting their symptoms to their radiation oncologist.Patients were recruited from one academic radiation oncology clinic from 12/2014-10/2015. Patients were eligible if they were over 18 years old, spoke and read English, were undergoing definitive radiation therapy for head and neck cancer for 5-7 weeks, and if they, or their caretakers, considered to be someone providing the majority of care for activities of daily living, owned an Android or iOS smartphone and demonstrated usage of the notification systems built into their phones. Patients were excluded if they were unable to eat and drink at the time of enrollment, as demonstrated by weight loss or clinical volume depletion. The Vanderbilt Institutional Review Board and Scientific Review Committee approved the study. The trial was registered with the National Cancer Institute (NCI-2014-02426).

Demographic and treatment data collection

App design and flow

Figure 1. Screenshots of PrimeMD user interface.

Physician monitoring of patient reported outcomes

Community engagement and feedback

Data analysis

Results

Participant demographics

Table 1. Demographic Characteristics of Participants.

Disease characteristics and treatment courses

Table 2. Tumor Characteristics of Participants.

EBV indicates Epstein-Barr virus; HPV, human papillomavirus

Data collection and adherence

Exit interview feedback

Discussion

Patients were prospectively identified by reviewing the CT simulation schedule in clinic and approached during their simulation visit. After screening, patients gave informed consent; the enrolling staff installed the app on the patient’s phone and gave instruction on its use. During enrollment, the relative meaning of the pain scale in the application was correlated to real-life symptom severity.Patients’ treatment information was abstracted from their medical records by retrospective chart review. Participants’ demographics were collected by a survey filled out on the day of enrollment. Chart review and survey data were entered into REDCap electronic data capture tools hosted at Vanderbilt University.¹⁸The app, named PrimeMD, was developed as a native application for iOS and Android. The user interface is demonstrated in Figure 1. The app was programmed to alert the patient via an alarm to enter his or her symptoms —including mucositis, odynophagia, and dysphagia - at 4 predetermined intervals, customized to each patient, daily. Users could voluntarily report at non-programmed intervals. The alarm prompted the patient whether they felt “OK” or “could be better,” an interaction designed to take 5 or fewer seconds. Patients reporting that they “could be better” were prompted for their current pain in 3 categories distilled from the traditional 10-point pain rating system for ease of use in the smartphone environment: “well-controlled,” “moderate,” and “severe.” “Well-controlled” was defined as present, but tolerable and not affecting quality of life. “Moderate” was distracting or affected quality of life. “Severe” pain was debilitating and required urgent attention. The patient was asked whether they had taken their pain medication (referred to by specific name for each patient). The patient was then asked whether the symptoms impaired their ability to eat by choosing “no,” “some,” or “I can’t eat.” A final prompt confirmed the accuracy of information before transmission and identified the next alarm interval.Patient responses were uploaded to a secure server accessible only by study staff that collated the above responses over time. Patients reporting “severe” pain or “I can’t eat” for 12 hours, or “moderate” pain for 16 hours, would generate an immediate alert email to the monitoring physician to address severe symptoms.To assess functionality of the app’s design, we collected comments and feedback from a panel of community experts and laypeople convened by the Vanderbilt Community Engagement Studio, a function of the Meharry-Vanderbilt Community Engaged Research Core. Feedback was collected in one 90-minute session facilitated by a Research Core member and the study organizers. The session was summarized by the Research Core and was used to inform development of the app. A questionnaire soliciting comments and suggestions from users was administered at the first visit after the conclusion of radiation therapy.Responses from the app were recorded and stored remotely on the secure physician’s dashboard server in comma-separated value format and subsequently imported into SPSS (version 23) for summaries and analyses. Demographic and treatment data were exported from REDCap into SPSS for those summaries. Frequency distributions were used to summarize nominal and ordinal data (eg, sex, type of treatment). Means and standard deviations summarized normally distributed continuous data (eg, age), otherwise medians and inter-quartile ranges were used (eg, days of app use, days in treatment). We defined feasibility as measured by patient engagement with the app, with the primary outcome measure being days of use; we also examined patient feedback at the exit interview and questionnaire to assess whether patients subjectively considered the app useful and functional.Twenty-three patients and 1 caretaker and patient agreed to take part in the study (24 participants total) . The mean age was 58 years (range 41-82), education 14.2 years (SD=2.9), and 11 (46%) of the patients were female. Most of the patients self-identified as Caucasian (22/24, 91.7%). Approximately half of the sample was married (11/24, 45.8%), more than half employed full- or part-time, and 21% stated that they lived in the country (ie, a rural area). Of those who responded, more than half had incomes of >$60,000 (10/18, 55.6%), approximately half had other medical problems besides the head and neck cancer such as hypertension, diabetes mellitus, or obesity (12/23, 52.2%). See Table 1 for additional details.The primary site of disease was most often the oropharynx (10/24, 41.7%). The most common histology was squamous cell carcinoma (22/24, 91.7%) with HPV expression positive in approximately 24% of patients assessed (5/21). Seventy-five percent of tumors were stage T2 or less (15/20) and all patients with N Stage information (N=20) were lymph node positive. Concurrent chemoradiotherapy was delivered to 88% (21/24) of participants, with a majority receiving carboplatin and/or paclitaxel (see Table 2). The median number of radiation fractions was 33 (range: 25-35) and the median dose to the primary site was 6461cGy (N=20). Median treatment time was 45 days (range 35-50 days).Data were recorded for 18 (75%) of patients; data for 6 patients were lost in a database error. Data were recorded independently of the alarm, so alarm frequency and true compliance were not measurable. Patients used the app at least once a median of 12.5 days (IQR: 6-33, range: 4-45). On days with at least some use, the median number of app uses per day was 4.0 with a range of 1 to 5.The exit interview questionnaire was completed by 18 patients. The median score for general satisfaction was 9/10 (range: 6-10). The respondents were universally very satisfied with the instructions for using PrimeMD, (median: 10/10, range: 8-10) as well as with the usability of the app (median: 9.5/10, range: 4-10). Additionally, patients typically that they would strongly recommend the app to other head and neck cancer patients (median: 10/10, range: 4-10). Perceived benefits of PrimeMD included being closely followed, alarms that could help them take their medications, and that app made them think about how they were feeling. Patients liked how easy, simple and quick PrimeMD was. Detriments noted included bugs in the alarm times, that the alarm sound could be annoying, and the alarm going off at inconvenient times.PROM has successfully been demonstrated in a variety of contexts, including monitoring cancer patients’ pain and chemotherapy.^17,19-24 In the current study, we examined the feasibility of 4-times-daily real-time EMA using a native smartphone app in an older population of head and neck cancer patients. We measured participants’ engagement with the app by analyzing the length of time they used it and the number of pain reports they submitted. To our knowledge, this is the first study to show the feasibility of EMA of PROMs in this of cancer patients undergoing predictable and dramatic symptomatic changes who may have low technological intelligence. Our study was constrained by its small sample size and loss of data from 6 participants. Our sample was also ethnically and racially homogenous. We were also unable to commit changes to the app after deployment to correct bugs or annoying features.

Participants demonstrated enthusiasm regarding PrimeMD and their symptom reporting, reflected in their high and consistent engagement and positive responses on the feedback survey. Our participants were of median age comparable to that of head and neck cancer sufferers nationwide, but 42% of our participants reported household incomes >$60,000/year, higher than the median of $53,657 in the United States.^{1, 25} In addition, our population’s educational attainment broadly reflected trends in the United States; it has been suggested that more-educated populations are more likely to engage with PROMs.^26,27

Although our participants reported high satisfaction with the app and quoted enthusiasm for it, actual usage did not reflect consistently high engagement and adherence with the app. Patients who do not experience severe mucositis symptoms may have found intensive monitoring to be invasive or. Adherence may also have been impacted by bugs in the app. The frequency of alarms and their potential to annoy may also have impacted adherence. Other groups have reported consistently high longitudinal engagement with a platform for PROMs in diverse disciplines, including oncology practices.^21,28 Notably, both these platforms featured a greater level of possible engagement with patients than simple reporting of pain and symptoms and afforded two-way communication with providers.

Our participants’ enthusiasm and reported satisfaction with the app despite inconsistent or poor adherence are reflective of the desire of patients to better engage with their providers.²⁹ This is consistent with previous reports that the use of PROMs in the clinical setting can improve patient engagement and satisfaction independent of health outcomes.^7,30 As other studies of intensive symptom monitoring in the office setting have demonstrated improvements in some health outcomes, patient-provider interaction and buy-in from staff at all levels is important to improve adherence and to maximize the effects of PROMs as well.^8,31

Practical applications

Here we have demonstrated the implementation of four-times-daily ESA for PROMs in a specific population of sufferers of debilitating pain. Our findings suggest that smartphone-based mobile EMA monitoring is feasible for even very-sick populations of older patients, including a smartphone-naïve 82-year old patient, and that those patients welcome such monitoring and engagement with their physicians. For radiation oncologists, the use of such apps could lead to identification of patients with debilitating symptoms and reduce rates of severe pain, poor quality of life, feeding tube placement, and hospitalization. Future iterations of such an app for ESA of PROMs should include increased two-way communication and acknowledgement of engagement with the patient; they should also include greater flexibility in the implementation of daily alarms to avoid patient fatigue with the app in the absence of actionable symptoms.

• Patients undergoing radiation therapy for head and neck cancer tend to be older and male
• Such patients are willing and able to use a smartphone application that intensively monitors their symptoms
• Patients were happy to use our application, and they appreciated the connection with their providers and participating in their own care
• It is particularly important to design intensive real-time monitoring applications to be usable and unobtrusive for patients to use

ABOUT THE AUTHORS

Jeffrey M. Friedman and Anthony Cmelak are with the Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennessee. H. Omer Ikizler is with University of Vermont College of Medicine, Burlington, Vermont. Ryan Ber is with Meharry Medical College, Nashville, Tennessee. Mary S. Dietrich is with the Department of Biostatistics, the Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine, and the Department of Nursing, Vanderbilt University School of Nursing, Nashville, Tennessee. Sheila Ridner is with Vanderbilt University School of Nursing, Nashville, Tennessee.

Address correspondence to: Jeffrey M. Friedman, MD, PhD Hematology Oncology Associates of Central New York 5008 Brittonfield Parkway, Suite 700 East Syracuse, NY 13057 Email: jfriedman@hoacny.com Tel: 315-472-7504

References

References

1. Argiris A, Karamouzis MV, Raben D, Ferris RL. Head and neck cancer. Lancet. 2008;371(9625):1695-1709. doi: 10.1016/S0140-6736(08)60728-X.
2. Rose-Ped AM, Bellm LA, Epstein JB, Trotti A, Gwede C, Fuchs HJ. Complications of radiation therapy for head and neck cancers. The patient's perspective. Cancer Nurs. 2002;25(6):461-467; quiz 468-469.
3. Mortensen HR1, Overgaard J, Specht L, et al. Prevalence and peak incidence of acute and late normal tissue morbidity in the DAHANCA 6&7 randomised trial with accelerated radiotherapy for head and neck cancer. Radiother Oncol. 2012;103(1):69-75. doi: 10.1016/j.radonc.2012.01.002.
4. Trotti A1, Bellm LA, Epstein JB, et al. Mucositis incidence, severity and associated outcomes in patients with head and neck cancer receiving radiotherapy with or without chemotherapy: a systematic literature review. Radiother Oncol. 2003;66(3):253-262.
5. Vlacich G1, Diaz R, Thorpe SW, et al. Intensity-modulated radiation therapy with concurrent carboplatin and paclitaxel for locally advanced head and neck cancer: toxicities and efficacy. Oncologist. 2012;17(5):673-681. doi: 10.1634/theoncologist.2011-0396.
6. Mosa AS, Yoo I, Sheets L. A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak. 2012;12:67. doi: 10.1186/1472-6947-12-67.
7. Kotronoulas G1, Kearney N, Maguire R, et al. What is the value of the routine use of patient-reported outcome measures toward improvement of patient outcomes, processes of care, and health service outcomes in cancer care? A systematic review of controlled trials. J Clin Oncol. 2014;32(14):1480-1501. doi: 10.1200/JCO.2013.53.5948.
8. Basch E, Deal AM, Kris MG, et al. Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial. J Clin Oncol. 2016;34(6):557-565. doi: 10.1200/JCO.2015.63.0830.
9. Smyth JM, Stone AA. Ecological momentary assessment research in behavioral medicine. J Happiness Studies. 2003;4:35. doi:10.1023/A:1023657221954.
10. Spook JE, Paulussen T, Kok G, Van Empelen P. Monitoring dietary intake and physical activity electronically: feasibility, usability, and ecological validity of a mobile-based Ecological Momentary Assessment tool. J Med Internet Res. 2013;15(9):e214. doi: 10.2196/jmir.2617.
11. Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR. Patient compliance with paper and electronic diaries. Control Clin Trials. 2003;24(2):182-199.
12. Stone AA, Broderick JE. Real-time data collection for pain: appraisal and current status. Pain Med. 2007;8 suppl 3:S85-93.
13. Stone AA, Schwartz JE, Broderick JE, Shiffman SS. Variability of momentary pain predicts recall of weekly pain: a consequence of the peak (or salience) memory heuristic. Pers Soc Psychol Bull. 2005;31(10):1340-1346.
14. Broderick JE, Schwartz JE, Vikingstad G, Pribbernow M, Grossman S, Stone AA. The accuracy of pain and fatigue items across different reporting periods. Pain. 2008;139(1):146-57. doi: 10.1016/j.pain.2008.03.024.
15. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1-32.
16. Gwaltney CJ, Shields AL, Shiffman S. Equivalence of electronic and paper-and-pencil administration of patient-reported outcome measures: a meta-analytic review. Value Health. 2008;11(2):322-333. doi: 10.1111/j.1524-4733.2007.00231.x.
17. Garcia-Palacios A, Herrero R, Belmonte MA, et al. Ecological momentary assessment for chronic pain in fibromyalgia using a smartphone: a randomized crossover study. Eur J Pain. 2014;18(6):862-872.
18. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. doi: 10.1016/j.jbi.2008.08.010.
19. Kearney N, Kidd L, Miller M, et al. Utilising handheld computers to monitor and support patients receiving chemotherapy: results of a UK-based feasibility study. Support Care Cancer. 2006;14(7):742-752.
20. Larsen ME, Rowntree J, Young AM, et al. Chemotherapy side-effect management using mobile phones. Conf Proc IEEE Eng Med Biol Soc. 2008;2008:5152-5155. doi: 10.1109/IEMBS.2008.4650374.
21. Ruland CM, Andersen T, Jeneson A, et al. Effects of an internet support system to assist cancer patients in reducing symptom distress: a randomized controlled trial. Cancer Nurs. 2013;36(1):6-17. doi: 10.1097/NCC.0b013e31824d90d4.
22. Stinson JN, Jibb LA, Nguyen C, et al. Development and testing of a multidimensional iPhone pain assessment application for adolescents with cancer. J Med Internet Res. 2013;15(3):e51. doi: 10.2196/jmir.2350.
23. Mooney KH, Beck SL, Friedman RH, Farzanfar R. Telephone-linked care for cancer symptom monitoring: a pilot study. Cancer Pract. 2002;10(3):147-154.
24. Mirkovic J, Kaufman DR, Ruland CM. Supporting cancer patients in illness management: usability evaluation of a mobile app. JMIR Mhealth Uhealth. 2014;2(3):e33. doi: 10.2196/mhealth.3359.
25. DeNavas-Walt C, Proctor BD. US Census Bureau, Current population reports, P60-252, Income and Poverty in the United States: 2014, US Government Printing Office, Washington, DC, 2015.
26. Ryan CL, Bauman K. US Census Bureau, Current population reports, P20-578, Educational attainment in the United States: 2015, US Government Printing Office, Washington, DC, 2015.
27. Judson TJ, Bennett AV, Rogak LJ, et al. Feasibility of long-term patient self-reporting of toxicities from home via the Internet during routine chemotherapy. J Clin Oncol. 2013;31(20):2580-2585. doi: 10.1200/JCO.2012.47.6804.
28. Hjollund NH, Larsen LP, Biering K, Johnsen SP, Riiskjær E, Schougaard LM. Use of patient-reported outcome (PRO) measures at group and patient levels: experiences from the generic integrated PRO system, WestChronic. Interact J Med Res. 2014;3(1):e5. doi: 10.2196/ijmr.2885.
29. “Society for Participatory Medicine National Survey,” 2015. http://participatorymedicine.org/wp-content/uploads/2016/01/SPM-National-Survey-12-2015-Methods-and-Results-1.pdf; or alternatively http://participatorymedicine.org/patients-overwhelmingly-want-partnership-with-their-clinicians/. Accesed 4/17/2017.
30. Chen J, Ou L, Hollis SJ. A systematic review of the impact of routine collection of patient reported outcome measures on patients, providers and health organisations in an oncologic setting. BMC Health Serv Res. 2013;13:211. doi: 10.1186/1472-6963-13-211.
31. Boyce MB, Browne JP. Does providing feedback on patient-reported outcomes to healthcare professionals result in better outcomes for patients? A systematic review Qual Life Res. 2013;22(9):2265-2278. doi: 10.1007/s11136-013-0390-0.