Research on the effect of cardiorespiratory (CR) exercise on upper extremity (UE) limb volume is limited in women with breast cancer-related lymphedema (BCRL). The aim of this study was to compare changes in UE volume immediately following a symptom-limited CR treadmill test in women with and without BCRL. As part of a cross-sectional study, 133 women post unilateral BC treatment completed symptom-limited treadmill testing. Bioimpedance spectroscopy (BIS) was used to measure UE resistance before and immediately following treadmill testing. Resistance ratios >1 (unaffected side/affected side) indicate greater volume in the affected limb. T-tests and repeated measures ANOVA were performed to evaluate differences between and within groups. Mean age was 56.2 years (SD 9.4); BMI was 26.13 kg m(-2) (SD 5.04). For women with previously diagnosed BCRL (n = 63), the resistance ratio was 1.116 (SD 0.160) pre-treadmill and 1.108 (SD 0.155) post-treadmill. For women without BCRL (n = 70), the resistance ratio was 0.990 (SD 0.041) pre-treadmill and 1.001 (SD 0.044) post-treadmill. Resistance ratios for women with BCRL were higher than those for women without BCRL at both time points (main effect of group: p < 0.001). No main effects were found for time (p = 0.695). A statistically significant effect was found for the time-by-group interaction (p = 0.002). 78% of the women with BCRL wore a compression garment during testing. Following testing, the women with BCRL demonstrated a non-statistically significant decrease in the resistance ratio, suggesting an immediate decrease in interlimb volume difference. The women without BCRL demonstrated an increase in the resistance ratio.

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.