Mitchell Kamrava

High-dose-rate (HDR) brachytherapy is an effective treatment for prostate and gynecologic cancers, yet current rigid catheter systems constrain trajectories to straight paths, limiting conformity to patient-specific anatomy and compromising dose distribution. To address this, we developed and preliminarily evaluated a semi-automated robotic platform for curvilinear catheter implantation. The system integrates (i) a handheld tendon-driven active needle manipulator with joystick-based control, (ii) an innovative template for initial angulation and (iii) a novel gynecologic applicator incorporating concentric tube robotics for steerable tandem delivery. Patient-specific curvilinear trajectories were preplanned from anonymized MRI/CT datasets, and experimental evaluations were conducted in air and tissue-mimicking phantoms. The handheld manipulator achieved tip displacement errors below 2.89[ ]mm in air and 2.22[ ]mm in phantom, while the combined device–template system reproduced prostate curvilinear trajectories with root mean squared deviations (RMSDs) under 3.67[ ]mm. Integration of the concentric tube robot with the gynecologic applicator achieved uterine trajectories with RMSDs below 1.17[ ]mm. These results demonstrate, for the first time, the feasibility of semi-automated active needle manipulation and concentric tube–assisted applicator design for anatomically conformal HDR brachytherapy. This approach has the potential to improve catheter placement accuracy, enable patient-specific dose escalation and reduce toxicity to organs at risk in both prostate and cervical cancer treatment.

N/a - research letter submission

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The rapid advancement and widespread adoption of wearable devices provides opportunities to collect longitudinal, objective activity and health data and integrate the information directly into a patient’s electronic health record (EHR). Patterns of engagement and factors associated with the use and non-use of wearable devices are currently not well-understood. Objective: We aim to quantify the number of patients still engaged and using wearable devices at one year since each patient’s first day of usage, across a cohort collected over 6 years. We then aim to identify demographic and behavioral factors that statistically significantly predict the likelihood of staying engaged and using wearable devices within the same one-year-since-first-use timespan. Methods: We analyzed connected device data from a large, non-profit academic medical center, which began to incorporate wearable device data into the EHR system in April 2015. We conducted a survival analysis to evaluate time to early disengagement among connected device users and identify factors associated with long-term (one year) engagement in multivariable cox proportional hazard regression models. Results: The analysis included 8,616 patients (median age 45 years, range 18-97, 52.1% male/47.9% female) with available connected device data (e.g., step counts) from the EHR between 2015 and 2022. A total of 5,870 (68.1%) patients were engaged, with active connected devices in the EHR, at one year. Multivariable cox regression models indicated no statistically significant differences between gender groups and race categories. Younger age categories (18-34 years) and lower median daily step counts (<5000) were associated with statistically significant increased hazards for early disengagement at one year. Conclusions: The ongoing development of new sensors and algorithms presents opportunities to expand the capabilities of wearable devices, making them even more integral to healthcare delivery. It is important to quantify and enhance engagement, in order to maximize the benefits of this technology and inform future use of the technologies to improve health outcomes.

BACKGROUND The rapid advancement and widespread adoption of wearable devices provide opportunities to collect longitudinal objective activity and health data and integrate the information directly into a patient’s electronic health record (EHR). Patterns of engagement and factors associated with the use and nonuse of wearable devices are currently not well understood. OBJECTIVE This study aimed to quantify the number of patients still engaged and using wearable devices at 1 year since each patient’s first day of use across a cohort collected over 6 years. We then aimed to identify demographic and behavioral factors that statistically significantly predict the likelihood of staying engaged and using wearable devices within the same 1-year time span since first use. METHODS We analyzed connected device data from a large, nonprofit academic medical center, which began to incorporate wearable device data into the EHR system in April 2015. We conducted a survival analysis to evaluate time to early disengagement among connected device users and identify factors associated with long-term (1 y) engagement in multivariable Cox proportional hazard regression models. RESULTS The analysis included 8616 patients (mean age 45, SD 14.36 y; median 21, IQR 34-55 y; men: n=4489, 52.1%; women: n=4126, 47.9%) with available connected device data (eg, step counts) from the EHR between 2015 and 2022. A total of 5870 (68.13%) patients were engaged with active connected devices in the EHR at 1 year. Multivariable Cox regression models indicated no statistically significant differences between gender groups and race categories. Younger age categories (18-34 y) and lower median daily step counts (<5000) were associated with statistically significant increased hazards for early disengagement at 1 year. CONCLUSIONS The ongoing development of new sensors and algorithms presents opportunities to expand the capabilities of wearable devices, making them even more integral to health care delivery. It is important to quantify and enhance engagement to maximize the benefits of this technology and inform future use of the technology to improve health outcomes. CLINICALTRIAL