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This is a single institution feasibility study of the updated RestoreMe app. The investigators plan to recruit 150 participants to this study with participants being recruited either prior to the initiation of their curative treatment or during and after completion of their cancer therapy. This design will allow the investigators to assess the feasibility of using the RestoreMe app in both the active treatment setting and follow up/survivorship setting. Information gathered from this feasibility study will inform future trial design for prospective intervention using the RestoreMe app.
Building on user experience data from the first version of the application, the investigators have enhanced the "RestoreMe" appl to offer an even more refined experience. The updated version incorporates valuable feedback, resulting in improved functionality, more personalized features, and a more intuitive design. It also allows for connectivity to Bluetooth devices and Apple health integration to obtain weight, blood pressure, heart rate, temperature, and step counts. It features customized questionnaires [Patient Reported Outcomes (PROs)] and relevant patient information including Body Mass Index, cancer diagnosis/treatment, and preferences/restrictions to evaluate nutritional status and provide personalized and curated recommendations regarding nutrition (calorie requirements, superfoods, weekly meal plans, shopping lists, supplements), hydration, physical activity and disease education/navigation.
The investigators believe that collection of patient-generated health data, which includes physical activity metrics, physiologic data (e.g., heart rate), and PROs and their integration with symptom reporting and nutritional assessments will significantly enhance the RestoreMe® app enabling it to provide tailored diet, hydration and activity recommendations, robust personalized learning section (disease specific and curated), provide remote symptom and activity monitoring, and promote self-management (through goals and progress). The implementation of RestoreMe® in diverse populations, such as those in the Bronx, has the potential to significantly improve cancer care outcomes. These populations often face additional barriers, including language and cultural differences, that further complicate nutritional management. Multi-language translation, food preferences/restrictions, caregiver engagement and easy-to-use features are built in RestoreMe® to address these barriers.
The investigators expect that most study participants will be enrolled during their visits. At the time of study enrollment, subjects will be provided with access to the RestoreMe app and the devices listed below. Participants will receive a written guide as well as training on device use. Of note, these devices will be provided with no cost to study participants. These devices (with the exception of Garmin Vivofits, which participants can keep and use as long as they wish) will be returned at the time of study completion, either during an in-person clinic visit or using pre-packaged shipping materials.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Intervention | Experimental | RestoreMe mobile phone application for cancer patients undergoing active cancer therapy or in follow-up/survivorship care |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| RestoreMe | Other | RestoreMe mobile device application |
|
| Measure | Description | Time Frame |
|---|---|---|
| User satisfaction of RestoreMe application | To evaluate user satisfaction of RestoreMe app in delivering ongoing nutritional education and personalized dietary recommendations for cancer patients undergoing active cancer therapy or in follow-up/survivorship care as measured by the Technology Acceptability Survey. The overall satisfaction score at 4 weeks will be calculated from adding the scores of 10 individual scorable items (Score range: 10-50) on the Technology Acceptability Survey. The overall score of 3 or below is considered satisfactory. To estimate the mean overall score, we will calculate 95% t-confidence interval. As secondary analyses, we will calculate the confidence intervals for different time points and for individual items. | Every 4 weeks after enrollment until 12 months after study entry |
| Measure | Description | Time Frame |
|---|---|---|
| Feasibility of patient-generated health data collection using Garmin Vivofits- Step Counts | Participants will be asked to wear the Garmin Vivofits continuously during the study period. Data collected using the Garmin Vivofits will include step counts. The investigators will calculate the rate of collecting measurements from more than 50% of the subjects. They define the rate as measurement-specific feasibility rate. To estimate the rate, they will calculate 95% exact binomial confidence interval. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Rafi Kabarriti, MD | Contact | 7184302000 | rkabarri@montefiore.org | |
| Akash Shah | Contact | 7184302000 | ashah1@montefiore.org |
| Name | Affiliation | Role |
|---|---|---|
| Rafi R Kabarriti, MD | Montefiore Medical Center | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32012088 | Background | Zheng C, Chen X, Weng L, Guo L, Xu H, Lin M, Xue Y, Lin X, Yang A, Yu L, Xue Z, Yang J. Benefits of Mobile Apps for Cancer Pain Management: Systematic Review. JMIR Mhealth Uhealth. 2020 Jan 23;8(1):e17055. doi: 10.2196/17055. | |
| 32885681 | Background | Liu P, Astudillo K, Velez D, Kelley L, Cobbs-Lomax D, Spatz ES. Use of Mobile Health Applications in Low-Income Populations: A Prospective Study of Facilitators and Barriers. Circ Cardiovasc Qual Outcomes. 2020 Sep;13(9):e007031. doi: 10.1161/CIRCOUTCOMES.120.007031. Epub 2020 Sep 4. No abstract available. |
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| ID | Term |
|---|---|
| D009369 | Neoplasms |
| D009043 | Motor Activity |
| ID | Term |
|---|---|
| D001519 | Behavior |
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All participants will receive the same intervention
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| First 6 weeks from 7th day after enrollment |
| Feasibility of patient-generated health data collection using Garmin Vivofits- Sedentary and Active Time | Participants will be asked to wear the Garmin Vivofits continuously during the study period. Data collected using the Garmin Vivofits will include measures of sedentary and active time. The investigators will calculate the rate of collecting measurements from more than 50% of the subjects. They define the rate as measurement-specific feasibility rate. To estimate the rate, they will calculate 95% exact binomial confidence interval. | First 6 weeks from 7th day after enrollment |
| Feasibility of patient-generated health data collection using Garmin Vivofits- Heart Rate | Participants will be asked to wear the Garmin Vivofits continuously during the study period. Data collected using the Garmin Vivofits will include heart rate. The investigators will calculate the rate of collecting measurements from more than 50% of the subjects. They define the rate as measurement-specific feasibility rate. To estimate the rate, they will calculate 95% exact binomial confidence interval. | First 6 weeks from 7th day after enrollment |
| Feasibility of patient-generated health data collection using iHealth Fit Nexus HS2S Weighing scale | Participants will be asked to use the iHealth Fit Nexus HS2S Weighing scale once a week during the study period. The investigators will calculate the rate of collecting measurements from more than 50% of the subjects. They define the rate as measurement-specific feasibility rate. To estimate the rate, they will calculate 95% exact binomial confidence interval. | First 6 weeks from 7th day after enrollment |
| Feasibility of patient-generated health data collection using iHealth Track KN550BT blood pressure monitor | Participants will be asked to use iHealth Track KN550BT blood pressure monitor once a week during the study period. The investigators will calculate the rate of collecting measurements from more than 50% of the subjects. They define the rate as measurement-specific feasibility rate. To estimate the rate, they will calculate 95% exact binomial confidence interval. | First 6 weeks from 7th day after enrollment |
| 26078410 | Background | 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. |
| 22957007 | Background | Chang CM, Su YC, Lai NS, Huang KY, Chien SH, Chang YH, Lian WC, Hsu TW, Lee CC. The combined effect of individual and neighborhood socioeconomic status on cancer survival rates. PLoS One. 2012;7(8):e44325. doi: 10.1371/journal.pone.0044325. Epub 2012 Aug 30. |
| 19727846 | Background | Paccagnella A, Morello M, Da Mosto MC, Baruffi C, Marcon ML, Gava A, Baggio V, Lamon S, Babare R, Rosti G, Giometto M, Boscolo-Rizzo P, Kiwanuka E, Tessarin M, Caregaro L, Marchiori C. Early nutritional intervention improves treatment tolerance and outcomes in head and neck cancer patients undergoing concurrent chemoradiotherapy. Support Care Cancer. 2010 Jul;18(7):837-45. doi: 10.1007/s00520-009-0717-0. Epub 2009 Aug 30. |
| 29671062 | Background | 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. |
| 31885583 | Background | Trujillo EB, Claghorn K, Dixon SW, Hill EB, Braun A, Lipinski E, Platek ME, Vergo MT, Spees C. Inadequate Nutrition Coverage in Outpatient Cancer Centers: Results of a National Survey. J Oncol. 2019 Nov 22;2019:7462940. doi: 10.1155/2019/7462940. eCollection 2019. |
| 12122555 | Background | Bauer J, Capra S, Ferguson M. Use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition assessment tool in patients with cancer. Eur J Clin Nutr. 2002 Aug;56(8):779-85. doi: 10.1038/sj.ejcn.1601412. |
| 28134475 | Background | Liptrott S, Bee P, Lovell K. Acceptability of telephone support as perceived by patients with cancer: A systematic review. Eur J Cancer Care (Engl). 2018 Jan;27(1). doi: 10.1111/ecc.12643. Epub 2017 Jan 30. |
| 11527676 | Background | Capra S, Ferguson M, Ried K. Cancer: impact of nutrition intervention outcome--nutrition issues for patients. Nutrition. 2001 Sep;17(9):769-72. doi: 10.1016/s0899-9007(01)00632-3. |
| 26786393 | Background | Ryan AM, Power DG, Daly L, Cushen SJ, Ni Bhuachalla E, Prado CM. Cancer-associated malnutrition, cachexia and sarcopenia: the skeleton in the hospital closet 40 years later. Proc Nutr Soc. 2016 May;75(2):199-211. doi: 10.1017/S002966511500419X. Epub 2016 Jan 20. |
| 28332990 | Background | Ohri N, Kabarriti R, Bodner WR, Mehta KJ, Shankar V, Halmos B, Haigentz M Jr, Rapkin B, Guha C, Kalnicki S, Garg M. Continuous Activity Monitoring During Concurrent Chemoradiotherapy. Int J Radiat Oncol Biol Phys. 2017 Apr 1;97(5):1061-1065. doi: 10.1016/j.ijrobp.2016.12.030. Epub 2016 Dec 25. |
| 32497682 | Background | Paul S, Bodner WR, Garg M, Tang J, Ohri N. Cardiac Irradiation Predicts Activity Decline in Patients Receiving Concurrent Chemoradiation for Locally Advanced Lung Cancer. Int J Radiat Oncol Biol Phys. 2020 Nov 1;108(3):597-601. doi: 10.1016/j.ijrobp.2020.05.042. Epub 2020 Jun 1. |
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