This paper describes the approach used to identify elderly people’s needs and attitudes towards applying ambient sensor systems for monitoring daily activities in the home. As elderly are typically unfamiliar with such ambient technology, interactive tools for explicating sensor monitoring –an interactive dollhouse and iPad applications for displaying live monitored sensor activity data– were developed and used for this study. Furthermore, four studies conducted by occupational therapists with more than 60 elderly participants –including questionnaires (n=41), interviews (n=6), user sessions (n=14) and field studies (n=2)– were conducted. The experiences from these studies suggest that this approach helped to democratically engage the elderly as end-user and identify acceptance issues.
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Purpose People with dementia (PwD) often present Behavioral and Psychological Symptoms of Dementia, which include agitation, apathy, and wandering amongst others, also known as challenging behaviors (CBs). These CBs worsen the quality of life (QoL) of the PwD and are a major source/reason of (increased) caregiver burden. The intricate nature of the symptoms implies that there is no “one size fits all solution”, and necessitates tailored approaches for both PwDs and caregivers. To timely prevent these behaviors assistive technology can be utilized to guide caregivers by enabling remote monitoring of contextual, environmental, and behavioral parameters, and subsequently alarming nurses on early-stage behavioral changes prior to the presentation of CBs. Eventually, the system should propose an intervention/action to prevent escalation. In turn, improvement in QoL for both caregivers and PwD living in nursing homes (NHs) is expected. In the current project “MOnitoring Onbegrepen Gedrag bij Dementie met sensortechnologie” (MOOD-Sense), we aim to develop such a monitoring system. The strengths of this new monitoring system lie in its ability to align with the individual needs of the PwD, utilization of a combination of wearables and ambient sensors to obtain contextual data, such as location or sound, and predict or monitor CBs individually rather than in groups, thus facilitating person-centered care, based on ontological reasoning. The project is divided into three parts, Toolbox A, B and C. Toolbox A focuses on obtaining insight in which behaviors are challenging according to nurses and how they are described. Previous studies utilize clinical terminology to describe or classify behavior, we aim to employ concrete descriptions of behavior that are observable and independent of clinical terminology, aligning with nurses who are often the first to notice behavior and can be operationalized such that it can also be aligned with sensor data. As a result, an ontology will be developed based on the data such that sensor data can be integrated into the same conceptual information that standardizes the communication in our monitoring system. Toolbox B focuses on translating data coming from various sensors into the concepts expressed in the ontology, and timely communicate situations of interest to the caregivers. In Toolbox C the focus is exploring interventions/actions employed in practice to prevent CBs. Method In Toolbox A we used a qualitative approach to collect descriptions of CBs. For this purpose, we employed focus groups (FGs) with nursing staff who provide daily care to PwD. In Toolbox B pilot studies were conducted. A set of experiments using sensors in NHs were performed. During each pilot, multiple PwD with CBs in NHs were monitored with both ambient and wearables sensors. The pilots were iteratively approached, which means that insights from previous pilot studies were used to improve consecutive pilot studies. Lastly, the elaboration of Toolbox C is ongoing. Results and Discussion Regarding Toolbox A four FGs were conducted during the period from January 2023 to May 2024. Each FG was comprised of four nurses (n = 16). From the FGs we gained insights into behavioral descriptions and the context of CBs. Although data analysis has to be performed yet, there are indications that changes preceding CBs can be observed, such as frowning or clenching fists for agitation or aggression. Further results will be available soon. Regarding Toolbox B a monitoring system, based on sensors, is developed iteratively (see Figure 1) and piloted in three consecutive NHs from January 2021 to December 2023. Each pilot was comprised of two PwD (n = 6). Analysis of sensor data is ongoing.
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Ambient monitoring systems offer great possibilities for health trend analysis in addition to anomaly detection. Health trend analysis helps care professionals to evaluate someones functional health and direct or evaluate the choice of interventions. This paper presents one case study of a person that was followed with an ambient monitoring system for almost three years and another of a person that was followed for over a year. A simple algorithm is applied to make a location based data representation. This data is visualized for care professionals, and used for inspecting the regularity of the pattern with means of principal component analysis (PCA). This paper provides a set of tools for analyzing longitudinal behavioral data for health assessments. We advocate a standardized data collection procedure, particularly the health metrics that could be used to validate health focused sensor data analyses.
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Wireless sensor networks are becoming popular in the field of ambient assisted living. In this paper we report our study on the relationship between a functional health metric and features derived from the sensor data. Sensor systems are installed in the houses of nine people who are also quarterly visited by an occupational therapist for functional health assessments. Different features are extracted and these are correlated with a metric of functional health (the AMPS). Though the sample is small, the results indicate that some features are better in describing the functional health in the population, but individual differences should also be taken into account when developing a sensor system for functional health assessment.
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OBJECTIVES: to test the effects of an intervention involving sensor monitoring-informed occupational therapy on top of a cognitive behavioural treatment (CBT)-based coaching therapy on daily functioning in older patients after hip fracture.DESIGN, SETTING AND PATIENTS: three-armed randomised stepped wedge trial in six skilled nursing facilities, with assessments at baseline (during admission) and after 1, 4 and 6 months (at home). Eligible participants were hip fracture patients ≥ 65 years old.INTERVENTIONS: patients received care as usual, CBT-based occupational therapy or CBT-based occupational therapy with sensor monitoring. Interventions comprised a weekly session during institutionalisation, followed by four home visits and four telephone consultations over three months.MAIN OUTCOMES AND MEASURES: the primary outcome was patient-reported daily functioning at 6 months, assessed with the Canadian Occupational Performance Measure.RESULTS: a total of 240 patients (mean[SD] age, 83.8[6.9] years were enrolled. At baseline, the mean Canadian Occupational Performance Measure scores (range 1-10) were 2.92 (SE 0.20) and 3.09 (SE 0.21) for the care as usual and CBT-based occupational therapy with sensor monitoring groups, respectively. At six months, these values were 6.42 (SE 0.47) and 7.59 (SE 0.50). The mean patient-reported daily functioning in the CBT-based occupational therapy with sensor monitoring group was larger than that in the care as usual group (difference 1.17 [95% CI (0.47-1.87) P = 0.001]. We found no significant differences in daily functioning between CBT-based occupational therapy and care as usual.CONCLUSIONS AND RELEVANCE: among older patients recovering from hip fracture, a rehabilitation programme of sensor monitoring-informed occupational therapy was more effective in improving patient-reported daily functioning at six months than to care as usual.TRIAL REGISTRATION: Dutch National Trial Register, NTR 5716.
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Background: Intravenous (IV) therapy using short peripheral IV catheters (PIVC) is commonplace with neonatal patients. However, this therapy is associated with high complication rates including the leakage of infused fluids from the vasculature into the surrounding tissues; a condition referred to as, peripheral IV infiltration/extravasation (PIVIE). Objective: The quality improvement project aimed to identify the prevalence of known risk factors for PIVIE in the neonatal intensive care unit (NICU) and explore the feasibility of using novel optical sensor technology to aid in earlier detection of PIVIE events. Methods: The plan, do, study, act (PDSA) model of quality improvement (QI) was used to provide a systematic framework to identify PIVIE risks and evaluate the potential utility of continuous PIVC monitoring using the ivWatch model 400® system. The site was provided with eight monitoring systems and consumables. Hospital staff were supported with theoretical education and bedside training about the system operations and best use practices. Results: In total 113 PIVIE's (graded II-IV) were recorded from 3476 PIVCs, representing an incidence of 3.25%. Lower birth weight and gestational age were statistically significant factors for increased risk of PIVIE (p = 0.004); all other known risk factors did not reach statistical significance. Piloting the ivWatch with 21 PIVCs using high-risk vesicant solutions over a total of 523.9 h (21.83 days) detected 11 PIVIEs (graded I-II). System sensitivity reached 100%; 11 out of 11 PIVIEs were detected by the ivWatch before clinician confirmation. Conclusions: Prevailing risk factors for PIVIE in the unit were comparable to those published. Continuous infusion site monitoring using the ivWatch suggests this technology offers the potential to detect PIVIE events earlier than relying on intermittent observation alone (i.e. the current standard of care). However, large-scale study with neonatal populations is required to ensure the technology is optimally configured to meet their needs.
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PURPOSE: The early detection of a decline in daily functioning of independently living older people can aid health care professionals in providing preventive interventions. To monitor daily activity patterns and, thereby detect a decline in daily functioning, new technologies, such as sensors can be placed in the home environment. The purpose of this qualitative study was to determine the perspectives of older people regarding the use of sensor monitoring in their daily lives.DESIGN AND METHODS: We conducted indepth, semistructured interviews with 11 persons between 68 and 93 years who had a sensor monitoring system installed in their home. The data were analyzed using Interpretative Phenomenological Analysis.RESULTS: The interviewed older persons positively valued sensor monitoring and indicated that the technology served as a strategy to enable independent living. The participants perceived that the system contributed to their sense of safety as an important premise for independent living. Some of the participants stated that it helped them to remain active. The potential privacy violation was not an issue for the participants. The participants considered that health care professionals' continuous access to their sensor data and use of the data for their safety outweighed the privacy concerns.IMPLICATIONS: These results provide new evidence that older persons experience sensor monitoring as an opportunity or strategy that contributes to independent living and that does not disturb their natural way of living. Based on this study, the development of new strategies to provide older people with access to their sensor data must be further explored.
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OBJECTIVES: To study sensor monitoring (use of a sensor network placed in the home environment to observe individuals' daily functioning (activities of daily living and instrumental activities of daily living)) as a method to measure and support daily functioning for older people living independently at home.DESIGN: Systematic review.SETTING: Participants' homes.PARTICIPANTS: Community-dwelling individuals aged 65 and older.MEASUREMENTS: A systematic search in PubMed, Embase, PsycINFO, INSPEC, and The Cochrane Library was performed for articles published between 2000 and October 2012. All study designs, studies that described the use of wireless sensor monitoring to measure or support daily functioning for independently living older people, studies that included community-dwelling individuals aged 65 and older, and studies that focused on daily functioning as a primary outcome measure were included.RESULTS: Seventeen articles met the inclusion criteria. Nine studies used sensor monitoring solely as a method for measuring daily functioning and detecting changes in daily functioning. These studies focused on the technical investigation of the sensor monitoring method used. The other studies investigated clinical applications in daily practice. The sensor data could enable healthcare professionals to detect alert conditions and periods of decline and could enable earlier intervention, although limited evidence of the effect of interventions was found in these studies because of a lack of high methodological quality.CONCLUSION: Studies on the effectiveness of sensor monitoring to support people in daily functioning remain scarce. A road map for further development is proposed.
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Background:Neuropsychiatric symptoms (NPS) are common in affected individuals and can be challenging for (in)formal caregivers. Therefore, they are also referred to as challenging behaviors (CBs). Sensor technology measuring context and behavior can be assistive to effectively manage CBs in an objective fashion. Sensors can help support healthcare professionals, such as nurses, by enabling remote monitoring and alarming on early-stage behavioral changes associated with CBs. This might/ will improve the quality of life (QoL) for both caregivers and clients living in a nursing homes (NH).In the project “MOnitoring Onbegrepen Gedrag bij Dementie met sensortechnologie” (MOOD-Sense), we aim to develop such a monitoring system. Our research focuses on two questions 1) How to develop and implement a monitoring system within the context of nursing homes with parameters on environment, physiology, and behavior, identify and process relevant precursors of challenging behavior with this monitoring system and 2) gain insight in which behaviors are challenging according to nurses and how they are described. This will be represented in an ontology such that sensor data can be translated into the same conceptual information.Methods:The first research question will be examined with a set of experiments in the field (in NH) with an iterative approach. Insights from previous experiments on usability and added value of sensors will be used to improve successive experiments. During each experiment, multiple participants (clients with dementia and CBs) are monitored with both ambient and wearable sensors. For the second research question a qualitative approach is employed, using focus groups (FG) and consensus methods. These FGs will be held amongst nursing staff who are involved in daily care tasks for people with dementia. Subsequently, consensus methods are used to align behavioral descriptors/labels.Results:early findings will be presented at the symposiumDiscussion:Within this project we expect to find precursors of challenging behavior in a personalized fashion based on nurse’s expert knowledge and sensor data. In order to develop a monitoring system that can be embedded within NH’s, real-time alarming, in-situ behavior recognition and trustworthiness are part of our technological requirements. Just-in-time interventions may then be deployed to prevent behavior escalation or the persistence of undesirable situations.
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We aim to set up a continuous low cost monitoring system for electromagnetic fields in the Netherlands, so that a trend in exposure to 5G signals can be observed. A number of options will be explored for this, such as software-defined radio and measurement nodes for specific 5G frequencies. We developed and tested low cost dedicated measurement nodes for four 5G bands: the 800, 1400, 2100 and 3500 MHz bands. Generally, the error is less than 1 dB and close to dynamic range limits (-65 to 5 dBm) the error increases to 3 dB.
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