A Gut Check on the Digital Divide: How Wearable Devices Are Changing Cancer Care

Nitin Ohri, MD, MS

The idea to provide wearable devices to my patients first came to me 6 years ago at a charity spinning event. A head and neck cancer nurse who was training for a marathon showed me her new Fitbit. It occurred to me that the data she was using to guide her training would be valuable for clinicians caring for individuals with cancer. Additionally, the use of wearable devices could also open the door to initiate effective dialogue and enhanced partnerships with our patients. Within a few months, my colleagues and I at Montefiore Einstein Cancer Center in the Bronx, New York, opened our first clinical trial using wearables to monitor patients receiving radiotherapy with concurrent chemotherapy.

More than one-quarter of the Bronx population lives in poverty,¹ approximately 14% of residents have their bachelor’s degree, and anecdotally, few of our patients routinely engage with modern wearable devices. Therefore, Montefiore is well suited to both study the capacity for wearable devices to enhance cancer care and to identify barriers to wearable device use in an underprivileged patient population. The latter has also given us a gut check on the Digital Divide. We are proud to have developed a novel program utilizing wearables to monitor patients during radiotherapy in the Bronx.

Addressing Health Inequities

The Digital Divide is the notion that as digital health becomes a routine component of patient care, we will see an exacerbation of outcome disparities across various subgroups in our society. Early work from our team suggests that low socioeconomic status (SES), rather than belonging to a racial or ethnic minority group, is the key predictor of patients’ inability to benefit from the digital health revolution.

We previously identified low SES as a risk factor for treatment interruptions and hospitalizations during radiotherapy.² As a result of that research, we have navigator programs to assist patients through all steps of care, including enhanced support services such as social work and helping patients overcome obstacles to care such as transportation and housing. Though most of our patients are amenable to using a wearable device provided by our study team, we have observed that SES may affect our ability to collect digital health data.

Study Structure and Early Findings

For our wearable device trials, we selected a waterproof, user-friendly device that did not require charging. We also offered to download individuals’ step data during their routine clinic visits. This workflow allowed us to achieve a 94% data collection rate in our initial pilot study with approximately 40 subjects.3 In that first study (NCT02649569), we found a powerful association between daily step counts and hospitalization risk, with a 38% reduction in acute hospitalization risk for every 1000 steps per day. That finding has been reproduced in our follow-up trials, which now include more than 200 additional participants.

Importantly, we found that wearable devices provide new insights about patients’ functional status. Performance status, which is the most common measure clinicians use to try to quantify general well-being for patients with cancer, is highly subjective and only loosely correlates with clinical outcomes. We have discovered that activity data are superior to performance status as a predictor of hospitalizations, disease recurrence, and death.³

As an example, among patients who utilized wearables device while receiving chemoradiotherapy for advanced non–small cell lung cancer, study participants with low baseline activity levels—defined as having a step count average below the 25th percentile of age-matched healthy controls—were more likely than active patients to be hospitalized during the radiation therapy course (50% vs 9%, respectively; P = .004) and less likely to complete radiotherapy without a 1 week or more delay (67% vs 97%; P = .006).⁴ Further, patients with low baseline activity were 5 times more likely to experience disease recurrence or death. The median progression-free survival was 5.3 months vs 18.3 months (HR for inactivity, 5.10; P < .001).

We recently completed a study utilizing wearable devices to monitor patients receiving systemic therapy for metastatic malignancies, and we are seeing identical effects. It is just a matter of time before wearable device data are routinely incorporated in oncology clinics.

Future Directions

Incomplete wearable device data collection has been more common in our study subjects with low SES. Therefore, we are designing studies to better understand and address this phenomenon, focusing on issues such as internet availability at home and digital health literacy. These insights will be invaluable as we are leading numerous trials, including multi-institutional studies, where patients use wearable devices during cancer therapy. Study objectives include leveraging wearable device data to improve supportive care and prevent hospital admissions, as well as using wearables to understand the toxicity profiles of novel radiotherapy and immunotherapy combinations.

We are also evaluating our ability to collect data using more advanced devices in our patient population. One study will include patients treated at the New York Proton Center, which is the only proton therapy facility in New York and is operated by a consortium of city-based institutions that includes Montefiore, Memorial Sloan Kettering Cancer Center, and Mount Sinai Health System. This novel study will provide insight into how proton radiotherapy preserves patients’ functional status and quality of life compared with standard radiotherapy.

Future studies will expand the scope of how digital health technology can improve cancer patient care. This includes evaluating strategies that may bring more advanced devices to our patient population so we can understand the clinical significance of metrics beyond daily step count, such as heart rate variability.

Why Study Wearable Devices in Oncology?⁵

We have seen that our patients appreciate the close monitoring and personalized care that wearable devices can facilitate. By better understanding barriers to the use of wearables, we can integrate digital health data into our electronic medical records and our routine clinic workflows (FIGURE⁵). We look forward to reporting the outcomes of more of these trials and sharing lessons learned so that more individuals affected by cancer can benefit from the digital health revolution, regardless of SES.

References

1. QuickFacts Bronx County, New York; United States. United States Census Bureau. Accessed November 22, 2021. https:// www.census.gov/quickfacts/fact/table/bronxcountynewyork,US/IPE120220
2. Ohri N, Rapkin BD, Guha C, Kalnicki S, Garg M. Radiation therapy noncompliance and clinical outcomes in an urban academic cancer center. Int J Radiat Oncol Biol Phys. 106;95(2):563-570. doi:10.1016/j.ijrobp.2016.01.043
3. Ohri N, Kabarriti R, Bodner WR, et al. Continuous activity monitoring during concurrent chemoradiotherapy. Int J Radiat Oncol Biol Phys. 2017;97(5):1061-1065. doi:10.1016/j. ijrobp.2016.12.030
4. Ohri N, Halmos B, Bodner WR, et al. Daily step counts: a new prognostic factor in locally advanced non–small cell lung cancer? Int J Radiat Oncol Biol Phys. 2019;105(4):745-751. doi:10.1016/j.ijrobp.2019.07.055
5. Izmailova E, Huang C, Cantor M, Ellis R, Ohri N. Daily step counts to predict hospitalizations during concurrent chemoradiotherapy for solid tumors. J Clin Oncol. 2019;37(suppl 27):293. doi:10.1200/JCO.2019.37.27_suppl.293