Chemotherapy-induced peripheral neuropathy (CIPN) may persist after treatment ends and may lead to functional decline and falls. This study compared objective and self-report measures of physical function, gait patterns, and falls between women cancer survivors with and without symptoms of CIPN to identify targets for functional rehabilitation. MethodsA secondary data analysis of 512 women cancer survivors (age, 62 6 6 years; time since diagnosis, 5.8 6 4.1 years) categorized and compared women self-reporting symptoms of CIPN (CIPN+) with asymptomatic women (CIPN2) on the following: maximal leg strength, timed chair stand, physical function battery, gait characteristics (speed; step number, rate, and length; base of support), selfreport physical function and disability, and falls in the past year. ResultsAfter an average of 6 years after treatment, 47% of women still reported symptoms of CIPN. CIPN+ had significantly worse self-report and objectively measured function than did CIPN2, with the exception of maximal leg strength and base of support during a usual walk. Gait was slower among CIPN+, with those women taking significantly more, but slower and shorter, steps than did CIPN2 (all P , .05). CIPN+ reported significantly more disability and 1.8 times the risk of falls compared with CIPN2 (P , .0001). Increasing symptom severity was linearly associated with worsening function, increasing disability, and higher fall risk (all P , .05). ConclusionThis work makes a significant contribution toward understanding the functional impact of CIPN symptoms on cancer survivors. Remarkably, 47% of women in our sample had CIPN symptoms many years after treatment, together with worse function, greater disability, and more falls. CIPN must be assessed earlier in the clinical pathway, and strategies to limit symptom progression and to improve function must be included in clinical and survivorship care plans.J Clin Oncol 35.

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (i.e. before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes.

BackgroundWrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise.ObjectiveThe objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise.MethodsWe assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m2; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias.ResultsFitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of −3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of −4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: −11.4% [SD 35.7]; Garmin: −14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: −1.7% [SD 11.5]; Garmin: −0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: −19.3% [SD 28.9], Garmin: −1.6% [SD 30.6], P<.001) across all activities, and with both correlating poorly with indirect calorimetry measures.ConclusionsTwo common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done.

In our experience, severe pulmonary tuberculosis (PTB) is often complicated by deep venous thrombosis (DVT). Because of the association between inflammation and haemostatic changes that can result in a hypercoagulable state, we have prospectively examined such predisposing factors in representative patients. Sequential analyses in a control group with active PTB showed anaemia, thrombocytosis, elevations in plasma fibrinogen, fibrin(ogen) degradation products (FDP), tissue plasminogen activator (t-PA) and inhibitor (PAI-1) with depressed antithrombin III levels. Age, sex and disease matched individuals with venographically proven DVT had higher FDP (15.8 +/- 14.3 v 3.2 +/- 1.7 micrograms/ml:P < 0.01), t-PA (19.4 +/- 14.9 v 11.3 +/- 0.8 ng/ml:P < 0.01), and functional PAI-1 activity (11.6 +/- 6.3 v 4.2 +/- 4.1:P < 0.01) with lower platelet counts (347 +/- 110 v 563 +/- 230 x 10(9)/1:P < 0.01). Fibrinogen levels in all patients rose during the first 2 weeks of therapy and, together with related disturbances, corrected within 12 weeks. In conclusion, elevated plasma fibrinogen with impaired fibrinolysis coupled with a decrease in antithrombin III and reactive thrombocytosis would appear to favour the development of DVT in PTB.

The addition of glucagon delivery to a closed-loop system with automated exercise detection reduces hypoglycemia in physically active adults with type 1 diabetes.

Loneliness is a common condition in older adults and is associated with increased morbidity and mortality, decreased sleep quality, and increased risk of cognitive decline. Assessing loneliness in older adults is challenging due to the negative desirability biases associated with being lonely. Thus, it is necessary to develop more objective techniques to assess loneliness in older adults. In this paper, we describe a system to measure loneliness by assessing in-home behavior using wireless motion and contact sensors, phone monitors, and computer software as well as algorithms developed to assess key behaviors of interest. We then present results showing the accuracy of the system in detecting loneliness in a longitudinal study of 16 older adults who agreed to have the sensor platform installed in their own homes for up to 8 months. We show that loneliness is significantly associated with both time out-of-home (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$ {\beta } = -0.88$ \end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p<0.01$ \end{document}) and number of computer sessions (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$ {\beta } = 0.78$ \end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p<0.05$ \end{document}). \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$R^{2}$ \end{document} for the model was 0.35. We also show the model’s ability to predict out-of-sample loneliness, demonstrating that the correlation between true loneliness and predicted out-of-sample loneliness is 0.48. When compared with the University of California at Los Angeles loneliness score, the normalized mean absolute error of the predicted loneliness scores was 0.81 and the normalized root mean squared error was 0.91. These results represent first steps toward an unobtrusive, objective method for the prediction of loneliness among older adults, and mark the first time multiple objective behavioral measures that have been related to this key health outcome.

Aims Exercise increases risk of hypoglycemia in type 1 diabetes (T1D). An artificial pancreas (AP) can help mitigate this risk. We tested whether adjusting insulin and glucagon in response to exercise within a dual-hormone AP reduces exercise-related hypoglycemia. Materials and Methods In random order, 21 adults with T1D underwent three 22 h sessions: AP with exercise dosing adjustment (APX), AP with no exercise dosing adjustment (APN), and sensor-augmented pump therapy (SAP). After an overnight stay and 2 hours after breakfast, participants exercised for 45 minutes at 60% of their maximum heart rate with no snack given before exercise. During APX, insulin was decreased and glucagon was increased at exercise onset, while during SAP, subjects could adjust dosing before exercise. The two primary outcomes were percent of time in hypoglycemia (<3.9 mmol/L) and percent of time in euglycemia (3.9–10 mmol/L) from the start of exercise to the end of the study. Results The mean times spent in hypoglycemia (<3.9 mmol/L) after the start of exercise were 0.3% [−0.1, 0.7%] for APX, 3.1% [0.8, 5.3%] for APN, and 0.8% [0.1, 1.4%] for SAP. There was an absolute difference of 2.8% less time in hypoglycemia in APX versus APN (p =0.001) and 0.5% less time in hypoglycemia for APX versus SAP (p = 0.16). Mean time in euglycemia was comparable across conditions. Conclusions Adjusting insulin and glucagon delivery at exercise onset within a dual-hormone AP significantly reduces hypoglycemia compared with no adjustment and performs similarly to SAP when insulin is adjusted before exercise.

In this article, we present several important contributions necessary for enabling an artificial endocrine pancreas (AP) system to better respond to exercise events. First, we show how exercise can be automatically detected using body-worn accelerometer and heart rate sensors. During a 22 hour overnight inpatient study, 13 subjects with type 1 diabetes wearing a Zephyr accelerometer and heart rate monitor underwent 45 minutes of mild aerobic treadmill exercise while controlling their glucose levels using sensor-augmented pump therapy. We used the accelerometer and heart rate as inputs into a validated regression model. Using this model, we were able to detect the exercise event with a sensitivity of 97.2% and a specificity of 99.5%. Second, from this same study, we show how patients’ glucose declined during the exercise event and we present results from in silico modeling that demonstrate how including an exercise model in the glucoregulatory model improves the estimation of the drop in glucose during exercise. Last, we present an exercise dosing adjustment algorithm and describe parameter tuning and performance using an in silico glucoregulatory model during an exercise event.