The 14-item geriatrics attitudes scale developed in this study shows sound reliability, validity, and sensitivity to change among primary care residents. The performance of other groups of medical trainees and the relationship of attitude changes to specific medical training warrant further investigation.

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This study reports the development and preliminary validation of an instrument to measure geriatrics knowledge of primary care residents. A 23-item test was developed using questions selected from the American Geriatrics Society's Geriatrics Review Syllabus. Ninety-six internal medicine and family practice residents, 14 geriatrics fellows, and 11 geriatrics faculty members participated in the study. Findings support the reliability (Cronbach's ␣ ‫؍‬ 0.66) and validity (content and "known groups") of this short test. Predictive validity and sensitivity of the test to changes in knowledge will have to be further explored as residents progress through their training.

Background Health care, in recent years, has made great leaps in integrating wireless technology into traditional models of care. The availability of ubiquitous devices such as wearable sensors has enabled researchers to collect voluminous datasets and harness them in a wide range of health care topics. One of the goals of using on-body wearable sensors has been to study and analyze human activity and functional patterns, thereby predicting harmful outcomes such as falls. It can also be used to track precise individual movements to form personalized behavioral patterns, to standardize the concept of frailty, well-being/independence, etc. Most wearable devices such as activity trackers and smartwatches are equipped with low-cost embedded sensors that can provide users with health statistics. In addition to wearable devices, Bluetooth low-energy sensors known as BLE beacons have gained traction among researchers in ambient intelligence domain. The low cost and durability of newer versions have made BLE beacons feasible gadgets to yield indoor localization data, an adjunct feature in human activity recognition. In the studies by Moatamed et al and the patent application by Ramezani et al, we introduced a generic framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and extracting indoor localization using BLE beacons, in concert. Objective The study aimed to examine the ability of combination of physical activity and indoor location features, extracted at baseline, on a cohort of 154 rehabilitation-dwelling patients to discriminate between subacute care patients who are re-admitted to the hospital versus the patients who are able to stay in a community setting. Methods We analyzed physical activity sensor features to assess activity time and intensity. We also analyzed activities with regard to indoor localization. Chi-square and Kruskal-Wallis tests were used to compare demographic variables and sensor feature variables in outcome groups. Random forests were used to build predictive models based on the most significant features. Results Standing time percentage ( P <.001, d =1.51), laying down time percentage ( P <.001, d =1.35), resident room energy intensity ( P <.001, d =1.25), resident bed energy intensity ( P <.001, d =1.23), and energy percentage of active state ( P =.001, d =1.24) are the 5 most statistically significant features in distinguishing outcome groups at baseline. The energy intensity of the resident room ( P <.001, d =1.25) was achieved by capturing indoor localization information. Random forests revealed that...

BACKGROUND Although many older adults require skilled nursing facility (SNF) care after acute hospitalization, it is unclear whether Internal Medicine (IM) residents have sufficient knowledge of the care that can be provided at this site. METHODS We developed a 10-item multiple choice pre-test that assessed knowledge of the definition of a SNF, SNF staffing requirements, and SNF services provided on-site. The test was administered to trainees on the first day of a mandatory SNF rotation that occurred during their first, second or third year of training. RESULTS 67 IM residents (41 PGY-1, 11 PGY-2, and 15 PGY-3) were assessed with the test. The mean number of questions answered correctly was 4.9, with a standard deviation of 1.6. Regardless of their level of training, residents had a poor baseline knowledge of SNF care (mean scores 4.2 for PGY-1, 5.3 for PGY-2, and 6.3 for PGY-3 (p<0.0001). Performance on some questions improved with increased level of training but others did not. CONCLUSIONS Medical residents have insufficient knowledge about the type of care that can be provided at a SNF and efforts to improve this knowledge are needed to assure proper triage of patients and safe transitions to the SNF.