A new cryptic species of green pit viper of the genus Trimeresurus Lacépède, 1804 (Serpentes, Viperidae) from northeast India.

A new cryptic species of green pit viper is described from northeast India, based on specimens collected from the state of Mizoram and Meghalaya. The new species is a member of the subgenus Viridovipera and is sister to Trimeresurus medoensis based on molecular data for mitochondrial cytochrome b gene, whereas resembles Trimeresurus gumprechti morphologically. A combination of characters helps delimit the new species from its congeners. Description of the new species highlights the need for dedicated surveys across northeast India to document its reptilian diversity, as this represents the third new species of the genus to be described in the past three years.

Behavioral patterns of older-adults in assisted living.

In this paper, we examine at-home activity rhythms and present a dozen of behavioral patterns obtained from an activity monitoring pilot study of 22 residents in an assisted living setting with four case studies. Established behavioral patterns have been captured using custom software based on a statistical predictive algorithm that models circadian activity rhythms (CARs) and their deviations. The CAR was statistically estimated based on the average amount of time a resident spent in each room within their assisted living apartment, and also on the activity level given by the average number of motion events per room. A validated in-home monitoring system (IMS) recorded the monitored resident's movement data and established the occupancy period and activity level for each room. Using these data, residents' circadian behaviors were extracted, deviations indicating anomalies were detected, and the latter were correlated to activity reports generated by the IMS as well as notes of the facility's professional caregivers on the monitored residents. The system could be used to detect deviations in activity patterns and to warn caregivers of such deviations, which could reflect changes in health status, thus providing caregivers with the opportunity to apply standard of care diagnostics and to intervene in a timely manner.

Impact of passive health status monitoring to care providers and payers in assisted living.

The objective of this study was to assess the impact of passive health status monitoring on the cost of care, as well as the efficiencies of professional caregivers in assisted living. We performed a case-controlled study to assess economic impact of passive health status monitoring technology in an assisted-living facility. Passive monitoring systems were installed in the assisted-living units of 21 residents to track physiological parameters (heart rate and breathing rate), the activities of daily living (ADLs), and key alert conditions. Professional caregivers were provided with access to the wellness status of the monitored residents they serve. The monitored individuals' cost of medical care was compared to that of an age, gender, and health status matched cohort. Similarly, efficiency and workloads of professional caregivers providing care to the monitored individuals were compared to those of caregivers providing care to the control cohort in the control site. Over the 3-month period of the study, a comparison between the monitored and control cohorts showed reductions in billable interventions (47 vs. 73, p = 0.040), hospital days (7 vs. 33, p = 0.004), and estimated cost of care (21,187.02 dollars vs. 67,753.88 dollars with monitoring cost included, p = 0.034). A comparison between efficiency normalized workloads of monitoring and control sites' caregivers revealed significant differences both at the beginning (0.6 vs. 1.38, p = 0.041) and the end (0.84 vs. 1.94, p = 0.002) of the study. The results demonstrate that monitoring technologies have significantly reduced billable interventions, hospital days, and cost of care to payers, and had a positive impact on professional caregivers' efficiency.

Impact of monitoring technology in assisted living: outcome pilot.

This paper describes a study designed to assess the acceptance and some psychosocial impacts of monitoring technology in assisted living. Monitoring systems were installed in 22 assisted living units to track the activities of daily living (ADLs) and key alert conditions of residents (15 of whom were nonmemory care residents). Activity reports and alert notifications were sent to professional caregivers who provided care to residents participating in the study. Diagnostic use of the monitoring data was assessed. Nonmemory care residents were surveyed and assessed using the Satisfaction With Life Scale (SWLS) instrument. Pre- and post-installation SWLS scores were compared. Older adult participants accepted monitoring. The results suggest that monitoring technologies could provide care coordination tools that are accepted by residents and may have a positive impact on their quality of life.

Validation of rule-based inference of selected independent activities of daily living.

This paper explores the validity of a rule-based inference method of selected independent activities of daily living (ADLs). An inexpensive ADL monitoring system was installed in the community for 37 days to monitor a middle-aged, healthy individual living alone. The subject was given a personal digital assistant (PDA), running custom activity diary software, and asked to record activities in real-time. Rule-based activity inference algorithms were refined on data from 17 days, and data from the remaining 20 days were used for validation. The chisquare statistic was computed for 2 x 2 contingency tables comparing activities detected by the algorithms to user-logged activities. The phi (r()) and Cohen's kappa (kappa) coefficients were computed as measures of correlation. After correcting for subject noncompliance in logging activities, the kappa correlation between the meal detection algorithm and the PDA record was 0.84, with 91% sensitivity, and 100% specificity. Similarly, the kappa correlation between the shower detection algorithm and the PDA record is 0.69, with 67% sensitivity and 100% specificity. The detection algorithms and the sensory data did not miss any main meals or showering activities recorded on the PDA. The results suggest that rule-based algorithms can successfully detect meal preparation and showering activities using simple low-cost detectors. The sensors and detection algorithms reported events not recorded by the occupant on the PDA attributed to reporting noncompliance. Overall, the PDA activity journal was a compromise between paper diaries, which are more time consuming to keep, and may result in higher noncompliance errors, and video recording, which is considered intrusive.