An Assessment of Physical Activity Data Collected via a Smartphone App and a Smart Band in Breast Cancer Survivors: Observational Study

Il Yong Chung, Miyeon Jung, Sae Byul Lee, Jong Won Lee, Yu Rang Park, Daegon Cho, Haekwon Chung, Soyoung Youn, Yul Ha Min, Hye Jin Park, Minsun Lee, Seockhoon Chung, Byung Ho Son, Sei-Hyun Ahn, Il Yong Chung, Miyeon Jung, Sae Byul Lee, Jong Won Lee, Yu Rang Park, Daegon Cho, Haekwon Chung, Soyoung Youn, Yul Ha Min, Hye Jin Park, Minsun Lee, Seockhoon Chung, Byung Ho Son, Sei-Hyun Ahn

Abstract

Background: Although distress screening is crucial for cancer survivors, it is not easy for clinicians to recognize distress. Physical activity (PA) data collected by mobile devices such as smart bands and smartphone apps have the potential to be used to screen distress in breast cancer survivors.

Objective: The aim of this study was to assess data collection rates of smartphone apps and smart bands in terms of PA data, investigate the correlation between PA data from mobile devices and distress-related questionnaires from smartphone apps, and demonstrate factors associated with data collection with smart bands and smartphone apps in breast cancer survivors.

Methods: In this prospective observational study, patients who underwent surgery for breast cancer at Asan Medical Center, Seoul, Republic of Korea, between June 2017 and March 2018 were enrolled and asked to use both a smartphone app and smart band for 6 months. The overall compliance rates of the daily PA data collection via the smartphone walking apps and wearable smart bands were analyzed in a within-subject manner. The longitudinal daily collection rates were calculated to examine the dropout pattern. We also performed multivariate linear regression analysis to examine factors associated with compliance with daily collection. Finally, we tested the correlation between the count of daily average steps and distress level using Pearson correlation analysis.

Results: A total of 160 female patients who underwent breast cancer surgeries were enrolled. The overall compliance rates for using a smartphone app and smart bands were 88.0% (24,224/27,513) and 52.5% (14,431/27,513), respectively. The longitudinal compliance rate for smartphone apps was 77.8% at day 180, while the longitudinal compliance rate for smart bands rapidly decreased over time, reaching 17.5% at day 180. Subjects who were young, with other comorbidities, or receiving antihormonal therapy or targeted therapy showed significantly higher compliance rates to the smartphone app. However, no factor was associated with the compliance rate to the smart band. In terms of the correlation between the count of daily steps and distress level, step counts collected via smart band showed a significant correlation with distress level.

Conclusions: Smartphone apps or smart bands are feasible tools to collect data on the physical activity of breast cancer survivors. PA data from mobile devices are correlated with participants' distress data, which suggests the potential role of mobile devices in the management of distress in breast cancer survivors.

Trial registration: ClinicalTrials.gov NCT03072966; https://ichgcp.net/clinical-trials-registry/NCT03072966.

Keywords: breast neoplasms; mobile apps; mobile phone; patient compliance; quality of life; smartphone; stress, psychological; survivorship; telemedicine; wearable electronic devices.

Conflict of interest statement

Conflicts of Interest: HC is chief executive officer of Swallaby Co, Ltd, Seoul, Republic of Korea.

©Il Yong Chung, Miyeon Jung, Sae Byul Lee, Jong Won Lee, Yu Rang Park, Daegon Cho, Haekwon Chung, Soyoung Youn, Yul Ha Min, Hye Jin Park, Minsun Lee, Seockhoon Chung, Byung Ho Son, Sei-Hyun Ahn. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.09.2019.

Figures

Figure 1
Figure 1
Subject enrollment.
Figure 2
Figure 2
Longitudinal day-level data collection rates: (A) smartphone app; (B) smart band.

References

    1. Mitchell AJ, Ferguson DW, Gill J, Paul J, Symonds P. Depression and anxiety in long-term cancer survivors compared with spouses and healthy controls: a systematic review and meta-analysis. Lancet Oncol. 2013 Jul;14(8):721–732. doi: 10.1016/S1470-2045(13)70244-4.
    1. Zainal NZ, Nik-Jaafar NR, Baharudin A, Sabki ZA, Ng CG. Prevalence of depression in breast cancer survivors: a systematic review of observational studies. Asian Pac J Cancer Prev. 2013;14(4):2649–2656.
    1. Natioanl Comprehensive Cancer Network. 2018. [2019-10-05]. NCCN guidelines for supportive care: distress management .
    1. Andersen BL, DeRubeis RJ, Berman BS, Gruman J, Champion VL, Massie MJ, Holland JC, Partridge AH, Bak K, Somerfield MR, Rowland JH, American Society of Clinical Oncology Screening, assessment, and care of anxiety and depressive symptoms in adults with cancer: an American Society of Clinical Oncology guideline adaptation. J Clin Oncol. 2014 May 20;32(15):1605–1619. doi: 10.1200/JCO.2013.52.4611.
    1. Newell S, Sanson-Fisher RW, Girgis A, Bonaventura A. How well do medical oncologists' perceptions reflect their patients' reported physical and psychosocial problems? Data from a survey of five oncologists. Cancer. 1998 Oct 15;83(8):1640–1651.
    1. Kim J, Lim S, Min YH, Shin Y, Lee B, Sohn G, Jung KH, Lee J, Son BH, Ahn SH, Shin S, Lee JW. Depression screening using daily mental-health ratings from a smartphone application for breast cancer patients. J Med Internet Res. 2016 Aug 04;18(8):e216. doi: 10.2196/jmir.5598.
    1. Basch E, Artz D, Dulko D, Scher K, Sabbatini P, Hensley M, Mitra N, Speakman J, McCabe M, Schrag D. Patient online self-reporting of toxicity symptoms during chemotherapy. J Clin Oncol. 2005 May 20;23(15):3552–3561. doi: 10.1200/JCO.2005.04.275.
    1. Basch E, Iasonos A, Barz A, Culkin A, Kris MG, Artz D, Fearn P, Speakman J, Farquhar R, Scher HI, McCabe M, Schrag D. Long-term toxicity monitoring via electronic patient-reported outcomes in patients receiving chemotherapy. J Clin Oncol. 2007 Dec 01;25(34):5374–5380. doi: 10.1200/JCO.2007.11.2243.
    1. Min YH, Lee JW, Shin Y, Jo M, Sohn G, Lee J, Lee G, Jung KH, Sung J, Ko BS, Yu J, Kim HJ, Son BH, Ahn SH. Daily collection of self-reporting sleep disturbance data via a smartphone app in breast cancer patients receiving chemotherapy: a feasibility study. J Med Internet Res. 2014;16(5):e135. doi: 10.2196/jmir.3421.
    1. Bidargaddi N, Musiat P, Makinen V, Ermes M, Schrader G, Licinio J. Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies. Mol Psychiatry. 2017 Dec;22(2):164–169. doi: 10.1038/mp.2016.224.
    1. Sano A, Yu AZ, McHill AW, Phillips AJK, Taylor S, Jaques N, Klerman EB, Picard RW. Prediction of Happy-Sad mood from daily behaviors and previous sleep history. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:6796–6799. doi: 10.1109/EMBC.2015.7319954.
    1. Wen CKF, Schneider S, Stone AA, Spruijt-Metz D. Compliance with mobile ecological momentary assessment protocols in children and adolescents: a systematic review and meta-analysis. J Med Internet Res. 2017 Apr 26;19(4):e132. doi: 10.2196/jmir.6641.
    1. Turner-McGrievy GM, Beets MW, Moore JB, Kaczynski AT, Barr-Anderson DJ, Tate DF. Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inform Assoc. 2013 May 1;20(3):513–518. doi: 10.1136/amiajnl-2012-001510.
    1. Wang JB, Cadmus-Bertram LA, Natarajan L, White MM, Madanat H, Nichols JF, Ayala GX, Pierce JP. Wearable sensor/device (Fitbit One) and SMS text-messaging prompts to increase physical activity in overweight and obese adults: a randomized controlled trial. Telemed J E Health. 2015 Oct;21(10):782–792. doi: 10.1089/tmj.2014.0176.
    1. Coons SJ, Eremenco S, Lundy JJ, O'Donohoe P, O'Gorman H, Malizia W. Capturing patient-reported outcome (PRO) data electronically: the past, present, and promise of ePRO measurement in clinical trials. Patient. 2015 Aug;8(4):301–309. doi: 10.1007/s40271-014-0090-z.
    1. Gwaltney C, Coons SJ, O’Donohoe P, O’Gorman H, Denomey M, Howry C, Ross J. “Bring Your Own Device” (BYOD) Drug Inf J. 2015 Oct 08;49(6):783–791. doi: 10.1177/2168479015609104.
    1. Benze G, Nauck F, Alt-Epping B, Gianni G, Bauknecht T, Ettl J, Munte A, Kretzschmar L, Gaertner J. PROutine: a feasibility study assessing surveillance of electronic patient reported outcomes and adherence via smartphone app in advanced cancer. Ann Palliat Med. 2017 Oct 26;:1. doi: 10.21037/apm.2017.07.05. doi: 10.21037/apm.2017.07.05.
    1. Burstein HJ, Lacchetti C, Anderson H, Buchholz TA, Davidson NE, Gelmon KE, Giordano SH, Hudis CA, Solky AJ, Stearns V, Winer EP, Griggs JJ. Adjuvant endocrine therapy for women with hormone receptor-positive breast cancer: American Society of Clinical Oncology clinical practice guideline update on ovarian suppression. J Clin Oncol. 2016 Dec 10;34(14):1689–1701. doi: 10.1200/JCO.2015.65.9573.
    1. Giordano SH, Temin S, Davidson NE. Systemic therapy for patients with advanced human epidermal growth factor receptor 2-positive breast cancer: ASCO clinical practice guideline update summary. J Oncol Pract. 2018 Aug;14(8):501–504. doi: 10.1200/JOP.18.00290.
    1. Noone A, Howlader N, Krapcho M, Miller D, Brest A, Yu M. SEER cancer statistics review, 1975-2015. Bethesda: National Cancer Institute; 2018. [2019-08-24].
    1. Glenn T, Monteith S. New measures of mental state and behavior based on data collected from sensors, smartphones, and the Internet. Curr Psychiatry Rep. 2014 Dec;16(12):523. doi: 10.1007/s11920-014-0523-3.
    1. Park EH, Min SY, Kim Z, Yoon CS, Jung K, Nam SJ, Oh SJ, Lee S, Park B, Lim W, Hur MH, Korean Breast Cancer Society Basic facts of breast cancer in Korea in 2014: the 10-Year overall survival progress. J Breast Cancer. 2017 Mar;20(1):1–11. doi: 10.4048/jbc.2017.20.1.1.

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