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1.
Adv Radiat Oncol ; 9(6): 101491, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38757146

RESUMO

Purpose: During winter 2022, western New York faced 2 major storms with blizzard conditions and record-breaking snowfall. The severe weather resulted in power outages and travel bans. This study investigates the impact of these conditions on patient adherence to radiation therapy. Combining data from a large academic center and its satellite clinic, this single-center study sheds light on the challenges faced by cancer care facilities during severe weather and proposes suggestions to prevent and mitigate harm done by severe weather. Methods and Materials: In this study, data were collected using the MOSAIQ Record and Verify system (v. 2.81) to generate deidentified reports of scheduled and treated patients. The treatment adherence rate was calculated by dividing the number of patients treated by the total number of patients scheduled. Data were specifically collected for patients undergoing treatment on linear accelerators at a primary academic center and a satellite facility. The study focused on working days from November 1, 2022, to March 31, 2023, excluding weekends and holidays (as treatments are not routinely scheduled). Severe weather days were identified using advisories from the National Weather Service and the local institution, including specific periods in November, December, and January. Results: In the study, 15,010 scheduled treatment visits were recorded across the academic center and the satellite clinic. The mean daily treatment adherence rate was 91.7%. Severe weather conditions led to a significant reduction in adherence, with rates dropping to 77.8%. Adherence rates during nonsevere weather days were notably higher at 93.9%. Statistical analysis confirmed the substantial influence of severe weather on adherence (P < .001). Severe weather had a more pronounced impact on the satellite clinic during periods of severe weather, with absolute reduction in adherence rates of 21.9% versus 15% in the primary hospital. Moreover, adherence at the satellite clinic was lower than at the primary hospital site even under standard operating conditions (92.2% vs 94.0%, P < .001). Conclusion: As a part of operational planning, it is important to be aware how severe weather can impact treatment adherence. Study findings underscore the importance of proactive measures to ensure patient access to health care services during adverse weather events and highlight the broader significance of incorporating consideration of social determinants of health into contingency planning for maintaining treatment continuity.

3.
NPJ Digit Med ; 1: 66, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-31304343

RESUMO

Peripheral artery disease (PAD) is a vascular disease that leads to reduced blood flow to the limbs, often causing claudication symptoms that impair patients' ability to walk. The distance walked during a 6-min walk test (6MWT) correlates well with patient claudication symptoms, so we developed the VascTrac iPhone app as a platform for monitoring PAD using a digital 6MWT. In this study, we evaluate the accuracy of the built-in iPhone distance and step-counting algorithms during 6MWTs. One hundred and fourteen (114) participants with PAD performed a supervised 6MWT using the VascTrac app while simultaneously wearing an ActiGraph GT9X Activity Monitor. Steps and distance-walked during the 6MWT were manually measured and used to assess the bias in the iPhone CMPedometer algorithms. The iPhone CMPedometer step algorithm underestimated steps with a bias of -7.2% ± 13.8% (mean ± SD) and had a mean percent difference with the Actigraph (Actigraph-iPhone) of 5.7% ± 20.5%. The iPhone CMPedometer distance algorithm overestimated distance with a bias of 43% ± 42% due to overestimation in stride length. Our correction factor improved distance estimation to 8% ± 32%. The Ankle-Brachial Index (ABI) correlated poorly with steps (R = 0.365) and distance (R = 0.413). Thus, in PAD patients, the iPhone's built-in distance algorithm is unable to accurately measure distance, suggesting that custom algorithms are necessary for using iPhones as a platform for monitoring distance walked in PAD patients. Although the iPhone accurately measured steps, more research is necessary to establish step counting as a clinically meaningful metric for PAD.

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