Remote Health Monitoring for Older Adults and Those with Heart Failure: Adherence and System Usability.

BACKGROUND: Remote health monitoring technology has been suggested as part of an early intervention and prevention care model. Older adults with a chronic health condition have been shown to benefit from remote monitoring but often have challenges with complex technology. The current study reports on the usability of and adherence with an integrated, real-time monitoring system over an extended period of time by older adults with and without a chronic health condition. MATERIALS AND METHODS: Older adults 55 years of age and over with and without heart failure participated in a study in which a telehealth system was used for 6 months each. The system consisted of a wireless wristwatch-based monitoring device that continuously collected temperature and motion data. Other health information was collected daily using a weight scale, blood pressure cuff, and tablet that participants used for health surveys. Data were automatically analyzed and summarized by the system and presented to study nurses. RESULTS: Forty-one older adults participated. Seventy-one percent of surveys, 75% of blood pressure readings, and 81% of daily weight measurements were taken. Participants wore the watch monitor 77% of the overall 24/7 time requested. The weight scale had the highest usability rating in both groups. The groups did not otherwise differ on device usage. CONCLUSIONS: The findings indicate that a health monitoring system designed for older adults can and will be used for an extended period of time and may help older adults with chronic conditions reside longer in their own homes in partnership with the healthcare system.

Differentiating Walking from other Activities of Daily Living in Older Adults Using Wrist-based Accelerometers.

There is a significant body of literature demonstrating that accelerometers placed at various locations on the body can provide the data necessary to recognize walking. Most of the literature, however, either does not consider accelerometers placed at the wrist, or suggests that the wrist is not the ideal location. The wrist, however, is probably the most socially-acceptable location for a monitoring device. This study evaluates the possibility of using wrist accelerometers to recognize walking in the elderly during everyday life to evaluate the amount of time spent walking and, moreover, potentially recognize changes in stability that might lead to falls. Thirty elderly individuals aged 65 years and older were asked to wear a wrist accelerometer for four hours each while simultaneously being video recorded as they went about their normal daily activities. Accelerometer data were then analyzed using both frequency- and time-domain analyses. Particular attention was given to methods capable of being calculated on the wrist device so that future work will not require streaming large amounts of data from the device to the central server. Frequency based analysis to characterize walking in the test set yielded results of 98% area under the receiver operating characteristic curve (AUC). Using a time-series algorithm limited to features calculable on the wrist device, moreover, achieved an AUC of 90%. A small, socially-acceptable, wrist-based device, therefore, can successfully be used to differentiate walking from other activities of daily living in older adults. These findings may enable improved gait monitoring and efforts in falls prevention.

Exercise-induced anaphylaxis as a cause of syncope.

BACKGROUND: Exercise-induced anaphylaxis (EIA) is an under-recognized condition that is a distinct physical allergy. Triggers include varying amounts of exercise, alone or in combination with certain foods or medications (food-dependent EIA, or FDEIA). Therapy is identical to that of any immunoglobulin E-mediated allergic reaction. OBJECTIVES: This case is reported to increase awareness among emergency physicians of EIA and FDEIA. CASE REPORT: A 57-year-old man was found with a diffuse erythematous rash after eating a wheat bagel and walking up five flights of stairs. Emergency medical services found him hypotensive and combative. In the Emergency Department, the patient's blood pressure was 72/27 mm Hg, with an oxygen saturation of 97% on non-rebreather mask. The physical examination was notable for bilateral inspiratory crackles in the lower one-third of the lungs. He received intravenous (i.v.) diphenhydramine 25 mg, i.v. methylprednisolone 125 mg, and 1 L of normal saline, after which his blood pressure improved to 110/54 mm Hg. He was admitted to the hospital where his recovery was uneventful. CONCLUSION: EIA and FDEIA are uncommon forms of physical allergy, but they represent important entities for emergency physicians to consider. Recognition of the association with exercise is key, as recurrences can be prevented by avoiding triggers.

Using principal component analysis to aid bayesian network development for prediction of critical care patient outcomes.

BACKGROUND: Predicting an intensive care unit patient's outcome is highly desirable. An end goal is for computational techniques to provide updated, accurate predictions about changing patient condition using a manageable number of physiologic parameters. METHODS: Principal component analysis was used to select input parameters for critical care patient outcome models. Vital signs and laboratory values from each patient's hospital stay along with outcomes ("Discharged" vs. "Deceased") were collected retrospectively at a Level I Trauma-Military Medical Center in the southwest; intensive care unit patients were included if they had been admitted for burn, infection, or hypovolemia during a 5-year period ending October 2007. Principal component analysis was used to determine which of the 24 parameters would serve as inputs in a bayesian network developed for outcome prediction. RESULTS: Data for 581 patients were collected. Pulse pressure, heart rate, temperature, respiratory rate, sodium, and chloride were found to have statistically significant differences between Discharged and Deceased groups for "Hypovolemia" patients. For "Burn" patients, pulse pressure, hemoglobin, hematocrit, and potassium were found to have statistically significant differences. For a "Combined" group, heart rate, temperature, respiratory rate, sodium, and chloride had statistically significant differences. A bayesian network based on these results, developed for the Combined group, achieved an accuracy of 75% when predicting patient outcome. CONCLUSIONS: Outcome prediction for critical care patients is possible. Future work should explore model development using additional temporal data and should include prospective validation. Such technology could serve as the basis of real-time intelligent monitoring systems for critical patients.

Using Bayesian networks and rule-based trending to predict patient status in the intensive care unit.

Multivariate Bayesian models trained with machine learning, in conjunction with rule-based time-series statistical techniques, are explored for the purpose of improving patient monitoring. Three vital sign data streams and known outcomes for 36 intensive care unit (ICU) patients were captured retrospectively and used to train a set of Bayesian net models and to construct time-series models. Models were validated on a reserved dataset from 16 additional patients. Receiver operating characteristic (ROC) curves were calculated. Area under the curve (AUC) was 91% for predicting improving outcome. The model's AUC for predicting declining outcome increased from 70% to 85% when the model was indexed to personalized baselines for each patient. The rule-based trending and alerting system was accurate 100% of the time in alerting a subsequent decline in condition. These techniques promise to improve the monitoring of ICU patients with high-sensitivity alerts, fewer false alarms, and earlier intervention.

Real-time evaluation of patient monitoring algorithms for critical care at the bedside.

Rapid interpretation of physiological time-series data and accurate assessment of patient state are crucial to patient monitoring in critical care. Algorithms that use artificial intelligence techniques have the potential to help achieve these tasks, but their development requires well-annotated patient data. In this study, we designed a data acquisition system for synchronized collection of physiological time-series data and clinical event annotations at the bedside to support the evaluation of alarm algorithms in real time, and implemented this system in a pediatric intensive care unit (ICU). This system captured vital sign measurements at 1 Hz and 325 clinical alarms generated by the bedside monitor and the 2 instances of false negatives during a monitoring period of 196 hours. The alarm annotations in real time at the bedside indicate that about 89% of these alarms were clinically-relevant true positives; 6% were true positives without clinical relevance; and 5% were false positives. These findings show an improved specificity of the alarm algorithms in the newer generation of bedside monitoring systems and demonstrate that the designed data acquisition system enables real-time evaluation of patient monitoring algorithms for critical care.