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Techniques such as ballistocardiography (BCG) that can provide noninvasive long-term physiological monitoring have gained interest due to a growing recognition of adverse effects from poor sleep and sleep disorders. The noninvasive analysis of physiological signals (NAPS) system is a BCG-based monitoring system developed to measure heart rate, breathing rate, and musculoskeletal movement that shows promise as a general sleep analysis tool. Overnight sleep studies were conducted on 40 healthy subjects during a clinical trial at the University of Virginia. The NAPS system's measures of heart rate and breathing rate were compared to ECG, pulse oximetry, and respiratory inductance plethysmography (RIP). The subjects were split into a training dataset and a validation dataset, maintaining similar demographics in each set. The NAPS system accurately detected heart rate, averaged over the prescribed 30-s epochs, to within less than 2.72 beats per minute of ECG, and accurately detected breathing rate, averaged over the same epochs, to within 2.10 breaths per minute of RIP bands used in polysomnography.

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.

Evans, S. C., Mack, D. C., Mason, K. A. and Thames, H. D. The Proliferative Response of Mouse Jejunal Crypt Cells to Radiation-Induced Cell Depletion is not Mediated Exclusively by Transforming Growth Factor Alpha. Radiat. Res. 155, 864–867 (2001). Several lines of correlative evidence link transforming growth factor alpha (Tgfa, also known as TGF-α) to proliferative activity in jejunal crypt cells. It is therefore tempting to hypothesize that, as a ligand of the epidermal growth factor, it mediates the compensatory proliferative burst in the crypts after radiation-induced cell killing. We have tested this hypothesis by comparing the repopulation response of wild-type and Tgfa-null mice, using the microcolony assay. Mice were exposed whole-body to 137Cs γ rays at a dose of approximately 1.6 Gy/min. Single doses and equal doses separated by 4 and 54 h were given. The rightward shift of the dose–response curves for 54 h was identical for wild-type and Tgfa-null mice, and there was no indication of ...

Quantitative sleep analysis through the use of polysomnography is a well established standard. Finding new ways to approach this, especially over multiple nights, is becoming more important due to a growing recognition of adverse effects from poor sleep and sleep disorders. The Non-Invasive Analysis of Physiological Signals (NAPSTM) system is a ballistocardiography-based monitoring system developed to measure heart rate, breathing rate and musculoskeletal movement that shows promise as a general sleep analysis tool. Overnight sleep studies were conducted on 20 healthy subjects during a validation clinical trial which compared the NAPS system to actigraphy, using polysomnography as the gold standard. The NAPS system [κ = 3D 0.478; 95% CI (0.463, 0.494); p-value < 0.001] outperformed actigraphy [κ = 3D 0.344; 95% CI (0.324, 0.358); p-value < 0.001], largely due to better performance in distinguishing sleep onset times as determined by polysomnography [NAPS mean bias estimate: -2.5 epochs; 95% CI (-16.8, 11.9); p = 3D 0.725 | Actigraphy mean bias estimate: -33.6 epochs; 95% CI (-57.4, -9.7); p = 3D 0.016)].

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.

This paper describes a study designed to assess some psychosocial impacts of monitoring technology on seniors living in Independent Senior Housing. Monitoring systems were installed, in 25 independent living units in an apartment complex, to track the activities of daily living (ADLs) and key alert conditions of residents. Activity reports were sent to informal caregivers. Residents (N=25) were assessed using the Satisfaction With Life Scales (SWLS) instrument, informal caregivers (N=26) were assessed using modified Caregiver Strain Index (CSI) and Caregiver Burden Interview (CBI) instruments, before and after the installation of the monitoring system. Paired t-test for means was applied to the pre- and post- monitoring scores of SWLS, CSI, and CBI. The Wilcoxon matched-pairs signed-ranks nonparametric test was applied to compare the number of informal care hours pre- and post- monitoring. No statistically significant increase was observed on SWLS results. No significant changes in CSI and CBI scores were detected. There was a statistically significant increase in the number of informal care hours provided by the informal caregivers of monitored individuals. The results indicate that monitoring technologies could have enabled informal caregivers to provide more care for their loved ones without increasing their burdens, strain levels or negatively affecting their quality of life.

This paper describes a study designed to assess some psychosocial impacts of monitoring technology in assisted living. Monitoring systems were installed in 15 assisted living units to track the activities of daily living (ADLs) and key alert conditions of residents. Activity reports and alerts were sent to professional caregivers who provided care to residents participating in the study. Residents (N= 15) were assessed using the Satisfaction With Life Scales (SWLS) instrument, professional caregivers (N= 7) were assessed using modified Caregiver Strain index (CSI) and Caregiver Burden Interview (CBI) instruments, before and after the installation of the monitoring system. Pre- and Post-installation scores of psychosocial assessment instruments were compared using t-test for means. A statistically significant increase was observed on SWLS results (p= 0.031). No significant changes in CSI and CBI scores were detected (p= 0.771 and 0.386 respectively). The results indicate that monitoring technologies could provide care coordination tools that may have a positive impact on users' quality of life.

Falls are very prevalent among the elderly. They are the second leading cause of unintentional-injury death for people of all ages and the leading cause of death for elders 79 years and older. Studies have shown that the medical outcome of a fall is largely dependent upon the response and rescue time. Hence, a highly accurate automatic fall detector is an important component of the living setting for older adult to expedite and improve the medical care provided to this population. Though there are several kinds of fall detectors currently available, they suffer from various drawbacks. Some of them are intrusive while others require the user to wear and activate the devices, and hence may fail in the event of user non-compliance. This paper describes the working principle and the design of a floor vibration-based fall detector that is completely passive and unobtrusive to the resident. The detector was designed to overcome some of the common drawbacks of the earlier fall detectors. The performance of the detector is evaluated by conducting controlled laboratory tests using anthropomorphic dummies. The results showed 100% fall detection rate with minimum potential for false alarms

The National Institutes of Health (NIH) Sleep Disorders Research Plan expresses a need for methods that can non-invasively monitor sleep characteristics. Forty subjects were tested using a novel, passive ballistocardiography-based system during an overnight study. We examined our system's ability to measure heart rate as compared to EKG while we also investigated our system's apnea and arousal detection capabilities as compared to conventional polysomnography. We found a strong correlation (r=0.972, p

This paper describes a study designed to assess the impacts of passive health status monitoring technology in home health. Monitoring systems were installed in the homes of 13 home health clients to track physiological parameters (heart rate, breathing rate, and gait), the activities of daily living (ADLs) and key alert conditions of residents, such as falls. Activity reports and alert notifications were sent to professional caregivers in order to refine and target the care administered to clients participating in the study. Informal caregivers of participants were provided with access to the ongoing wellness status of their loved ones. The potential diagnostic utility of the monitoring data, the subjects' quality of life and health related quality of life, as well as the quality of life, strain and burden levels of the informal caregivers were assessed. Pre- and post-installation scores were compared. The results suggest that monitoring technologies could provide care coordination tools that have a positive impact on the perceived quality of life of monitored individuals, as well as a reduction in the strain levels of their informal caregivers, and may have a positive impact on the participants' health related quality of life

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.