- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06860425
Use of a Comprehensive, Mobile Application to Assist Cancer Patients With Diet, Nutrition and Activity
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
In the last decade, smartphones have become an essential part of society with the mobile application market offering services across all aspects of life, including healthcare. Mobile health care apps have developed a field of their own termed "mobile health" or m-health with apps available to help patients do everything from managing symptoms and tracking medications to improving treatment compliance.
Apps have been developed specifically tailored for oncology patients. One randomized controlled trial provided oncology patients receiving palliative care with a mobile application that used artificial intelligence (AI) to regularly monitor and manage pain between clinic visits. Results showed that the app was an effective tool to manage pain. Patients who used the app reported an overall decrease in pain severity and experienced fewer inpatient hospitalizations due to cancer-related pain compared with patients who did not use the app. M-health applications are well utilized in low-income populations and among both English and Spanish speakers.
Despite the exciting potential m-Health offers for providers and patients, it also presents challenges. One systematic review identified barriers to adoption of m-health apps by health care professionals including difficult user experience (i.e., users found the app difficult to use and navigate), design and technical issues, security concerns, and perceived usefulness. Therefore, when designing an m-Health app, care must be taken to ensure that it is functional, easy to use, and provides patients and providers with valuable information in order to ensure that users will engage with the app. M-health apps should also be designed to supplement information exchanged during clinical encounters, fill in gaps in clinical workflow, and be appropriate for the target patient population.
Low socioeconomic status is associated with increased disease prevalence and low quality of life after diagnosis. Bronx county, New York, which is coterminous with the New York City borough of The Bronx, is an urban, medically underserved and economically depressed area, which is associated with an elevated relative rate of chronic diseases including obesity, diabetes, hypertension and cardiovascular disease. Over 70% of patients served by the Montefiore-Einstein Cancer Center (MECC) live beneath the poverty line suggesting that the patient population served by MECC is at risk for poor nutrition status.
With the goal of improving availability of evidence-based nutritional information to MECC patients, both in the active-treatment and survivorship settings, the study team has developed "RestoreMe," an easy-to-use mobile device application that provides nutrition education, personalized recommendations, recipes, exercises and more. RestoreMe also allows cancer patients and providers an easy-to-use resource with which to communicate directly with one another, which may increase patient engagement and compliance with their overall care.
In this study, the study team will investigate the regularity of app use by patients and which functions of the app are used effectively and most often. Usage trends specific to particular patient subgroups will also be evaluated. The results of this feasibility study will inform a future prospective interventional study using the RestoreMe app.
Study Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Contact
- Name: Rafi Kabarriti, MD
- Phone Number: 914-629-7743
- Email: rkabarri@montefiore.org
Study Contact Backup
- Name: Rikin Gandhi
- Phone Number: 617-416-1216
- Email: rikin.gandhi@einsteinmed.edu
Study Locations
-
-
New York
-
The Bronx, New York, United States, 10461
- Recruiting
- Montefiore Einstein Comprehensive Cancer Center
-
Contact:
- Rafi Kabarriti, MD
- Phone Number: 914-629-7743
- Email: rkabarri@montefiore.org
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Diagnosis of invasive malignancy of the brain, head and neck region, lung, breast (DCIS and invasive disease allowed), prostate, gastrointestinal system or gynecological region
Planned to receive, or have received, radiation therapy treatment with curative intent
- Note: Patients undergoing systemic therapy are eligible
- Note: Any dose/fractionation of curative-intent radiation therapy is eligible
- Patients must have a smartphone or other device with the ability to receive text messages, download and use mobile applications
- Eastern Cooperative Oncology Group (ECOG) performance status 0-2
- Ability to read and write in English
- Provide written informed consent to participate in the study o NOTE: Patients enrolled on another clinical trial are eligible
Exclusion Criteria:
- Metastatic cancer
- Undergoing treatment with palliative intent
- Poorly controlled diabetes (defined as fasting glucose level > 200 mg/dL despite attempts to improve glucose control by fasting duration and adjustment of medications)
- Uncontrolled hypertension
- Any medical condition requiring fluid restriction or nutrient restrictions
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Health Services Research
- Allocation: Non-Randomized
- Interventional Model: Sequential Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Other: Active Treatment
25 patients who prior to initiation of curative treatment.
|
Comprehensive mobile device application
|
|
Other: Follow-up/Survivorship Care
25 patients who are in post-radiation therapy follow-up/survivorship care after completing definitive treatment.
|
Comprehensive mobile device application
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Patient Satisfaction
Time Frame: Upon completion of treatment regimen or up to 12 months (+/1 month) after study entry
|
Patient satisfaction with the RestoreMe app will be measured using the Technology Acceptability Survey (TAS).
The TAS consists of 10 questions including 7 quantitative items which will be used to assess Patient Satisfaction for the purposes of the study.
Each of the 7 quantitative items on the survey represent a unique satisfaction parameter and are scored on a 5-point ordinal scale ranging from 1-5.
Lower scores for an item correlate with increased satisfaction with the specific parameter and higher scores for an item correlate with decreased satisfaction for the specific parameter, yielding an overall possible scoring range of 7-35.
Group scores will be summarized using basic descriptive statistics.
|
Upon completion of treatment regimen or up to 12 months (+/1 month) after study entry
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Frequency of Dailly Engagement - Patients
Time Frame: Up to approximately 12 months (+/1 month) after study entry
|
Frequency of patient engagement will be assessed by the number/percentage of patients who interact with the RestoreMe app on a daily basis (i.e., number/percentage per day) as well as the number/percentage of days with at least one RestoreMe app engagement on a per patient basis (i.e., number/percentage of days with at least one patient engagement).
Results will be summarized and reported by study arm/group using basic descriptive statistics.
|
Up to approximately 12 months (+/1 month) after study entry
|
|
Frequency of Weekly Engagement - Patients
Time Frame: Up to approximately 12 months (+/1 month) after study entry
|
Frequency of patient engagement will be assessed by the number/percentage of patients who interact with the RestoreMe app on a weekly basis (i.e., number/percentage per week) as well as the number/percentage of weeks with at least one RestoreMe app engagement on a per patient basis (i.e., number/percentage of weeks with at least one patient engagement).
Results will be summarized and reported by study arm/group using basic descriptive statistics.
|
Up to approximately 12 months (+/1 month) after study entry
|
|
Frequency of Monthly Engagement - Patients
Time Frame: Up to approximately 12 months (+/1 month) after study entry
|
Frequency of patient engagement will be assessed by the number/percentage of patients who interact with the RestoreMe app on a monthly basis (i.e., number/percentage per month) as well as the number/percentage of months with at least one RestoreMe app engagement on a per patient basis (i.e., number/percentage of months with at least one patient engagement).
Results will be summarized and reported by study arm/group using basic descriptive statistics.
|
Up to approximately 12 months (+/1 month) after study entry
|
|
Frequency of Daily Engagement - Providers
Time Frame: Up to approximately 12 months (+/1 month) after study entry
|
Frequency of provider engagement will be assessed by the number/percentage of providers who interact with the RestoreMe app on a daily basis (i.e., number/percentage per day) as well as the number/percentage of days with at least one RestoreMe app engagement on a per provider basis (i.e., number/percentage of days with at least one provider engagement).
Results will be summarized and reported by study arm/group using basic descriptive statistics.
|
Up to approximately 12 months (+/1 month) after study entry
|
|
Frequency of Weekly Engagement - Providers
Time Frame: Up to approximately 12 months (+/1 month) after study entry
|
Frequency of provider engagement will be assessed by the number/percentage of providers who interact with the RestoreMe app on a weekly basis (i.e., number/percentage per week) as well as the number/percentage of weeks with at least one RestoreMe app engagement on a per provider basis (i.e., number/percentage of weeks with at least one provider engagement).
Results will be summarized and reported by study arm/group using basic descriptive statistics.
|
Up to approximately 12 months (+/1 month) after study entry
|
|
Frequency of Monthly Engagement - Providers
Time Frame: Up to approximately 12 months (+/1 month) after study entry
|
Frequency of provider engagement will be assessed by the number/percentage of providers who interact with the RestoreMe app on a monthly basis (i.e., number/percentage per month) as well as the number/percentage of months with at least one RestoreMe app engagement on a per provider basis (i.e., number/percentage of months with at least one provider engagement).
Results will be summarized and reported by study arm/group using basic descriptive statistics.
|
Up to approximately 12 months (+/1 month) after study entry
|
|
Average Daily Interaction Time - Patients
Time Frame: Up to approximately 12 months (+/1 month) after study entry
|
Average patient interaction time with the RestoreMe app will be measured on a daily basis to evaluate interaction over time.
Group mean daily interaction time will be summarized and reported by study arm/group.
|
Up to approximately 12 months (+/1 month) after study entry
|
|
Average Weekly Interaction Time - Patients
Time Frame: Up to approximately 12 months (+/1 month) after study entry
|
Average patient interaction time with the RestoreMe app will be measured on a weekly basis to evaluate interaction over time.
Group mean weekly interaction time will be summarized and reported by study arm/group.
|
Up to approximately 12 months (+/1 month) after study entry
|
|
Average Monthly Interaction Time - Patients
Time Frame: Up to approximately 12 months (+/1 month) after study entry
|
Average patient interaction time with the RestoreMe app will be measured on a monthly basis to evaluate interaction over time.
Group mean monthly interaction time will be summarized and reported by study group.
|
Up to approximately 12 months (+/1 month) after study entry
|
|
Drop-out over time.
Time Frame: Up to approximately 12 months (+/1 month) after study entry
|
The proportion/percentage of patients who drop out of regular interaction with the RestoreMe app will be summarized and reported.
|
Up to approximately 12 months (+/1 month) after study entry
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Rafi Kabarriti, MD, Montefiore Medical Center
Publications and helpful links
General Publications
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- Protani M, Coory M, Martin JH. Effect of obesity on survival of women with breast cancer: systematic review and meta-analysis. Breast Cancer Res Treat. 2010 Oct;123(3):627-35. doi: 10.1007/s10549-010-0990-0. Epub 2010 Jun 23.
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- Knoops KT, de Groot LC, Kromhout D, Perrin AE, Moreiras-Varela O, Menotti A, van Staveren WA. Mediterranean diet, lifestyle factors, and 10-year mortality in elderly European men and women: the HALE project. JAMA. 2004 Sep 22;292(12):1433-9. doi: 10.1001/jama.292.12.1433.
- Ewertz M, Jensen MB, Gunnarsdottir KA, Hojris I, Jakobsen EH, Nielsen D, Stenbygaard LE, Tange UB, Cold S. Effect of obesity on prognosis after early-stage breast cancer. J Clin Oncol. 2011 Jan 1;29(1):25-31. doi: 10.1200/JCO.2010.29.7614. Epub 2010 Nov 29.
- Kabarriti R, Bontempo A, Romano M, McGovern KP, Asaro A, Viswanathan S, Kalnicki S, Garg MK. The impact of dietary regimen compliance on outcomes for HNSCC patients treated with radiation therapy. Support Care Cancer. 2018 Sep;26(9):3307-3313. doi: 10.1007/s00520-018-4198-x. Epub 2018 Apr 18.
- Chan DSM, Vieira AR, Aune D, Bandera EV, Greenwood DC, McTiernan A, Navarro Rosenblatt D, Thune I, Vieira R, Norat T. Body mass index and survival in women with breast cancer-systematic literature review and meta-analysis of 82 follow-up studies. Ann Oncol. 2014 Oct;25(10):1901-1914. doi: 10.1093/annonc/mdu042. Epub 2014 Apr 27.
- Gagnon MP, Ngangue P, Payne-Gagnon J, Desmartis M. m-Health adoption by healthcare professionals: a systematic review. J Am Med Inform Assoc. 2016 Jan;23(1):212-20. doi: 10.1093/jamia/ocv052. Epub 2015 Jun 15.
- Kamdar, Mihir M., et al.
- Liu, Patrick, et al.
- Mielck A, Vogelmann M, Leidl R. Health-related quality of life and socioeconomic status: inequalities among adults with a chronic disease. Health Qual Life Outcomes. 2014 Apr 25;12:58. doi: 10.1186/1477-7525-12-58.
- NYS DOH. 2013-2014 NYS Expanded Behavioral Risk Factor Surveillance System, data as of 2015. Available: https://www.health.ny.gov/statistics/chac/general/g75.htm. Accessed 23 September 2016.
- Pierce JP, Faerber S, Wright FA, Rock CL, Newman V, Flatt SW, Kealey S, Jones VE, Caan BJ, Gold EB, Haan M, Hollenbach KA, Jones L, Marshall JR, Ritenbaugh C, Stefanick ML, Thomson C, Wasserman L, Natarajan L, Thomas RG, Gilpin EA; Women's Healthy Eating and Living (WHEL) study group. A randomized trial of the effect of a plant-based dietary pattern on additional breast cancer events and survival: the Women's Healthy Eating and Living (WHEL) Study. Control Clin Trials. 2002 Dec;23(6):728-56. doi: 10.1016/s0197-2456(02)00241-6.
- Glade MJ. Food, nutrition, and the prevention of cancer: a global perspective. American Institute for Cancer Research/World Cancer Research Fund, American Institute for Cancer Research, 1997. Nutrition. 1999 Jun;15(6):523-6. doi: 10.1016/s0899-9007(99)00021-0. No abstract available.
- Morote J, Celma A, Planas J, Placer J, de Torres I, Olivan M, Carles J, Reventos J, Doll A. Role of serum cholesterol and statin use in the risk of prostate cancer detection and tumor aggressiveness. Int J Mol Sci. 2014 Aug 6;15(8):13615-23. doi: 10.3390/ijms150813615.
- Kok DE, van Roermund JG, Aben KK, den Heijer M, Swinkels DW, Kampman E, Kiemeney LA. Blood lipid levels and prostate cancer risk; a cohort study. Prostate Cancer Prostatic Dis. 2011 Dec;14(4):340-5. doi: 10.1038/pcan.2011.30. Epub 2011 Jul 5.
- Shafique K, McLoone P, Qureshi K, Leung H, Hart C, Morrison DS. Cholesterol and the risk of grade-specific prostate cancer incidence: evidence from two large prospective cohort studies with up to 37 years' follow up. BMC Cancer. 2012 Jan 19;12:25. doi: 10.1186/1471-2407-12-25.
- Allott EH, Howard LE, Cooperberg MR, Kane CJ, Aronson WJ, Terris MK, Amling CL, Freedland SJ. Postoperative statin use and risk of biochemical recurrence following radical prostatectomy: results from the Shared Equal Access Regional Cancer Hospital (SEARCH) database. BJU Int. 2014 Nov;114(5):661-6. doi: 10.1111/bju.12720. Epub 2014 May 8.
- Jespersen CG, Norgaard M, Friis S, Skriver C, Borre M. Statin use and risk of prostate cancer: a Danish population-based case-control study, 1997-2010. Cancer Epidemiol. 2014 Feb;38(1):42-7. doi: 10.1016/j.canep.2013.10.010. Epub 2013 Nov 22.
- Meyers CD, Kashyap ML. Pharmacologic elevation of high-density lipoproteins: recent insights on mechanism of action and atherosclerosis protection. Curr Opin Cardiol. 2004 Jul;19(4):366-73. doi: 10.1097/01.hco.0000126582.27767.87.
- Xia P, Vadas MA, Rye KA, Barter PJ, Gamble JR. High density lipoproteins (HDL) interrupt the sphingosine kinase signaling pathway. A possible mechanism for protection against atherosclerosis by HDL. J Biol Chem. 1999 Nov 12;274(46):33143-7. doi: 10.1074/jbc.274.46.33143.
- Kotani K, Sekine Y, Ishikawa S, Ikpot IZ, Suzuki K, Remaley AT. High-density lipoprotein and prostate cancer: an overview. J Epidemiol. 2013 Sep 5;23(5):313-9. doi: 10.2188/jea.je20130006. Epub 2013 Aug 27.
- Kurahashi N, Sasazuki S, Iwasaki M, Inoue M, Tsugane S; JPHC Study Group. Green tea consumption and prostate cancer risk in Japanese men: a prospective study. Am J Epidemiol. 2008 Jan 1;167(1):71-7. doi: 10.1093/aje/kwm249. Epub 2007 Sep 29.
- McLarty J, Bigelow RL, Smith M, Elmajian D, Ankem M, Cardelli JA. Tea polyphenols decrease serum levels of prostate-specific antigen, hepatocyte growth factor, and vascular endothelial growth factor in prostate cancer patients and inhibit production of hepatocyte growth factor and vascular endothelial growth factor in vitro. Cancer Prev Res (Phila). 2009 Jul;2(7):673-82. doi: 10.1158/1940-6207.CAPR-08-0167. Epub 2009 Jun 19.
- Askari F, Parizi MK, Jessri M, Rashidkhani B. Fruit and vegetable intake in relation to prostate cancer in Iranian men: a case-control study. Asian Pac J Cancer Prev. 2014;15(13):5223-7. doi: 10.7314/apjcp.2014.15.13.5223.
- Liu B, Mao Q, Cao M, Xie L. Cruciferous vegetables intake and risk of prostate cancer: a meta-analysis. Int J Urol. 2012 Feb;19(2):134-41. doi: 10.1111/j.1442-2042.2011.02906.x. Epub 2011 Nov 28.
- Richman EL, Carroll PR, Chan JM. Vegetable and fruit intake after diagnosis and risk of prostate cancer progression. Int J Cancer. 2012 Jul 1;131(1):201-10. doi: 10.1002/ijc.26348. Epub 2011 Aug 30.
- Hsing AW, Chokkalingam AP, Gao YT, Madigan MP, Deng J, Gridley G, Fraumeni JF Jr. Allium vegetables and risk of prostate cancer: a population-based study. J Natl Cancer Inst. 2002 Nov 6;94(21):1648-51. doi: 10.1093/jnci/94.21.1648.
- Chan R, Lok K, Woo J. Prostate cancer and vegetable consumption. Mol Nutr Food Res. 2009 Feb;53(2):201-16. doi: 10.1002/mnfr.200800113.
- Inoue M, Tajima K, Mizutani M, Iwata H, Iwase T, Miura S, Hirose K, Hamajima N, Tominaga S. Regular consumption of green tea and the risk of breast cancer recurrence: follow-up study from the Hospital-based Epidemiologic Research Program at Aichi Cancer Center (HERPACC), Japan. Cancer Lett. 2001 Jun 26;167(2):175-82. doi: 10.1016/s0304-3835(01)00486-4.
- Ottosson S, Soderstrom K, Kjellen E, Nilsson P, Zackrisson B, Laurell G. Weight and body mass index in relation to irradiated volume and to overall survival in patients with oropharyngeal cancer: a retrospective cohort study. Radiat Oncol. 2014 Jul 22;9:160. doi: 10.1186/1748-717X-9-160.
- American Cancer Society guidelines on nutrition and physical activity for cancer prevention. Available: http://www.cancer.org/acs/groups/cid/documents/webcontent/002577-pdf.pdf. Accessed 23 January 2016.
- Asaro, A. M., Garg, M. K., Haynes, H., & Kabarriti, R. (2018). Customized Text Messaging to Assist Cancer Patients with Diet, Nutrition and Activity: a Pilot Study. International Journal of Radiation Oncology• Biology• Physics, 102(3), e412.
- Rock CL, Doyle C, Demark-Wahnefried W, Meyerhardt J, Courneya KS, Schwartz AL, Bandera EV, Hamilton KK, Grant B, McCullough M, Byers T, Gansler T. Nutrition and physical activity guidelines for cancer survivors. CA Cancer J Clin. 2012 Jul-Aug;62(4):243-74. doi: 10.3322/caac.21142. Epub 2012 Apr 26.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 2022-13979
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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