824 resultados para Daily living


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BACKGROUND The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone). OBJECTIVE We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL. METHODS The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified. RESULTS In this study, 10 healthy participants (6 women, 4 men; mean age 48.8 years; SD 20.0 years; age range 28-79 years) were included. For explorative purposes, one female Alzheimer patient (Montreal Cognitive Assessment score=23, Timed Up and Go=19.8 seconds, Trail Making Test A=84.3 seconds, Trail Making Test B=146 seconds) was measured in parallel with the healthy subjects. In total, 1317 ADL were performed by the participants, 1211 ADL were classified correctly, and 106 ADL were missed. This led to an overall sensitivity of 91.27% and a specificity of 92.52%. Each subject performed an average of 134.8 ADL (SD 75). CONCLUSIONS The non-intrusive wireless sensor system can acquire environmental data essential for the classification of activities of daily living. By analyzing retrieved data, it is possible to distinguish and assign data patterns to subjects' specific activities and to identify eight different activities in daily living. The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time.

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Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.

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Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

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The ability to determine what activity of daily living a person performs is of interest in many application domains. It is possible to determine the physical and cognitive capabilities of the elderly by inferring what activities they perform in their houses. Our primary aim was to establish a proof of concept that a wireless sensor system can monitor and record physical activity and these data can be modeled to predict activities of daily living. The secondary aim was to determine the optimal placement of the sensor boxes for detecting activities in a room. A wireless sensor system was set up in a laboratory kitchen. The ten healthy participants were requested to make tea following a defined sequence of tasks. Data were collected from the eight wireless sensor boxes placed in specific places in the test kitchen and analyzed to detect the sequences of tasks performed by the participants. These sequence of tasks were trained and tested using the Markov Model. Data analysis focused on the reliability of the system and the integrity of the collected data. The sequence of tasks were successfully recognized for all subjects and the averaged data pattern of tasks sequences between the subjects had a high correlation. Analysis of the data collected indicates that sensors placed in different locations are capable of recognizing activities, with the movement detection sensor contributing the most to detection of tasks. The central top of the room with no obstruction of view was considered to be the best location to record data for activity detection. Wireless sensor systems show much promise as easily deployable to monitor and recognize activities of daily living.

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This study describes the discharge destination, basic and instrumental activities of daily living (ADL), community reintegration and generic health status of people after stroke, and explored whether sociodemographic and clinical characteristics were associated with these outcomes. Participants were 51 people, with an initial stroke, admitted to an acute hospital and discharged to the community. Admission and discharge data were obtained by chart review. Follow-up status was determined by telephone interview using the Modified Barthel Index, the Assessment of Living Skills and Resources, the Reintegration to Normal Living Index, and the Short-Form Health Survey (SF-36). At follow up, 57% of participants were independent in basic ADL, 84% had a low risk of experiencing instrumental ADL difficulties, most had few concerns with community reintegration, and SF-36 physical functioning and vitality scores were lower than normative values. At follow up, poorer discharge basic ADL status was associated with poorer instrumental ADL and community reintegration status, and older participants had poorer instrumental ADL, community reintegration and physical functioning. Occupational therapists need to consider these outcomes when planning inpatient and post-discharge intervention for people after stroke.

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Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches.

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Introduction: One of the known risk factors for abuse and neglect of the elderly is the decrease in functional capacity, contributing to self care dependency of instrumental activities of daily living and basic activities of daily living (OMS, 2015). Methods: Cross-sectional study with non probabilistic sample of 333 elderly, performed in a hospital, homes and day centers for the elderly. The data collection protocol included socio-demographic data, Questions to elicit Elder Abuse (Carney, Kahan & Paris, 2003 adap. By Ferreira Alves & Sousa, 2005), scale of instrumental activities of daily living Lawton and Brody and Katz index to assess the level of independence in activities of daily living. Objectives: To evaluate the association between abuse and neglect in the elderly, instrumental activities of daily living and level of independence in activities of daily living. Results: Emotional abuse is significantly correlated with the level of independence in activities of daily living (p = 0.000), older people with less independence tend to have higher levels of emotional abuse. The total abuse is significantly correlated with the levels of independence in activities of daily living (p = 0.002), less independent elderly tend to suffer greater abuse and neglect. There were no statistically significant associations between abuse and neglect and instrumental activities of daily living. Conclusions: The less independent elderly are more vulnerable to situations of abuse and neglect, being more exposed to emotional abuse. These results point to the need for health professionals/ nurses develop prevention interventions, including strategies to support carers and early screening in less independent elderly.

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Introduction: One of the known risk factors for abuse and neglect of the elderly is the decrease in functional capacity, contributing to self care dependency of instrumental activities of daily living and basic activities of daily living (OMS, 2015). Methods: Cross-sectional study with non probabilistic sample of 333 elderly, performed in a hospital, homes and day centers for the elderly. The data collection protocol included socio-demographic data, Questions to elicit Elder Abuse (Carney, Kahan & Paris, 2003 adap. By Ferreira Alves & Sousa, 2005), scale of instrumental activities of daily living Lawton and Brody and Katz index to assess the level of independence in activities of daily living. Objectives: To evaluate the association between abuse and neglect in the elderly, instrumental activities of daily living and level of independence in activities of daily living. Results: Emotional abuse is significantly correlated with the level of independence in activities of daily living (p = 0.000), older people with less independence tend to have higher levels of emotional abuse. The total abuse is significantly correlated with the levels of independence in activities of daily living (p = 0.002), less independent elderly tend to suffer greater abuse and neglect. There were no statistically significant associations between abuse and neglect and instrumental activities of daily living. Conclusions: The less independent elderly are more vulnerable to situations of abuse and neglect, being more exposed to emotional abuse. These results point to the need for health professionals/ nurses develop prevention interventions, including strategies to support carers and early screening in less independent elderly.

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Background: Caring for family members with dementia can be a long-term, burdensome task resulting in physical and emotional distress and impairment. Research has demonstrated significantly lower levels of selfefficacy among family caregivers of people with dementia (CGs) than caregivers of relatives with non-dementia diseases. Intervention studies have also suggested that the mental and physical health of dementia CGs could be improved through the enhancement of their self-efficacy. However, studies are limited in terms of the influences of caregiver self-efficacy on caregiver behaviour, subjective burden and health-related quality of life. Of particular note is that there are no studies on the applicability of caregiver self-efficacy in the social context of China. Objective: The purpose of this thesis was to undertake theoretical exploration using Bandura’s (1997) self-efficacy theory to 1) revise the Revised Caregiving Self-Efficacy Scale (C-RCSES) (Steffen, McKibbin, Zeiss, Gallagher-Thompson, & Bandura, 2002), and 2) explore determinants of caregiver self-efficacy and the role of caregiver self-efficacy and other conceptual constructs (including CGs’ socio-demographic characteristics, CRs’ impairment and CGs’ social support) in explaining and predicting caregiver behaviour, subjective burden and health-related quality of life among CGs in China. Methodology: Two studies were undertaken: a qualitative elicitation study with 10 CGs; and a cross-sectional survey with 196 CGs. In the first study, semi-structured interviews were conducted to explore caregiver behaviours and corresponding challenges for their performance. The findings of the study assisted in the development of the initial items and domains of the Chinese version of the Revised Caregiving Self-Efficacy Scale (C-RCSES). Following changes to items in the scale, the second study, a cross-sectional survey with 196 CGs was conducted to evaluate the psychometric properties of C-RCSES and to test a hypothesised self-efficacy model of family caregiving adapted from Bandura’s theory (1997). Results: 35 items were generated from the qualitative data. The content validity of the C-RCSES was assessed and ensured in Study One before being used for the cross-sectional survey. Eight items were removed and five subscales (caregiver self-efficacy for gathering information about treatment, symptoms and health care; obtaining support; responding to problematic behaviours; management of household, personal and medical care; and controlling upsetting thoughts about caregiving) were identified after principal component factor analysis on the cross-sectional survey data. The reliability of the scale is acceptable: the Cronbach’s alpha coefficients for the whole scale and for each subscale were all over .80; and the fourweek test-retest reliabilities for the whole scale and for each subscale ranged from .64 to .85. The concurrent, convergent and divergent validity were also acceptable. CGs reported moderate levels of caregiver self-efficacy. Furthermore, the level of self-efficacy for management of household, personal and medical care was relatively high in comparison to those of the other four domains of caregiver self-efficacy. Caregiver self-efficacy was also significantly influenced by CGs’ socio-demographic characteristics and the caregiving external factors (CR impairment and social support that CGs obtained). The level of caregiver behaviour that CGs reported was higher than that reported in other Chinese research. CGs’ socio-demographics significantly influenced caregiver behaviour, whereas caregiver self-efficacy did not influence caregiver behaviour. Regarding the two external factors, CGs who cared for highly impaired relatives reported high levels of caregiver behaviour, but social support did not influence caregiver behaviour. Regarding caregiver subjective burden and health-related quality of life, CGs reported moderate levels of subjective burden, and their level of healthrelated quality of life was significantly lower than that of the general population in China. The findings also indicated that CGs’ subjective burden and health-related quality of life were influenced by all major factors in the hypothesised model, including CGs’ socio-demographics, CRs’ impairment, social support that CGs obtained, caregiver self-efficacy and caregiver behaviour. Of these factors, caregiver self-efficacy and social support significantly improved their subjective burden and health-related quality of life; whereas caregiver behaviour and CRs’ impairment were detrimental to CGs, such as increasing subjective burden and worsening health-related quality of life. Conclusion: While requiring further exploration, the qualitative study was the first qualitative research conducted in China to provide an in-depth understanding of CGs’ caregiving experience, including their major caregiver behaviours and the corresponding challenges. Meanwhile, although the C-RCSES needs further psychometric testing, it is a useful tool for assessing caregiver self-efficacy in Chinese populations. Results of the qualitative and quantitative study provide useful information for future studies regarding the explanatory power of caregiver self-efficacy to caregiver behaviour, subjective burden and health-related quality of life. Additionally, integrated with Bandura’s theory, the findings from the quantitative study also suggested a further study exploring the role of outcome expectations in caregiver behaviour, subjective burden and healthrelated quality of life.

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BACKGROUND: This study examined whether objective measures of food, physical activity and built environment exposures, in home and non-home settings, contribute to children's body weight. Further, comparing GPS and GIS measures of environmental exposures along routes to and from school, we tested for evidence of selective daily mobility bias when using GPS data. METHODS: This study is a cross-sectional analysis, using objective assessments of body weight in relation to multiple environmental exposures. Data presented are from a sample of 94 school-aged children, aged 5-11 years. Children's heights and weights were measured by trained researchers, and used to calculate BMI z-scores. Participants wore a GPS device for one full week. Environmental exposures were estimated within home and school neighbourhoods, and along GIS (modelled) and GPS (actual) routes from home to school. We directly compared associations between BMI and GIS-modelled versus GPS-derived environmental exposures. The study was conducted in Mebane and Mount Airy, North Carolina, USA, in 2011. RESULTS: In adjusted regression models, greater school walkability was associated with significantly lower mean BMI. Greater home walkability was associated with increased BMI, as was greater school access to green space. Adjusted associations between BMI and route exposure characteristics were null. The use of GPS-actual route exposures did not appear to confound associations between environmental exposures and BMI in this sample. CONCLUSIONS: This study found few associations between environmental exposures in home, school and commuting domains and body weight in children. However, walkability of the school neighbourhood may be important. Of the other significant associations observed, some were in unexpected directions. Importantly, we found no evidence of selective daily mobility bias in this sample, although our study design is in need of replication in a free-living adult sample.

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It has long been recognised that the majority of care provided in chronic illness comes not from health and social care professionals, but from family and friends. One such illness is chronic obstructive pulmonary disease (COPD), a leading cause of morbidity and mortality in the developed world.To explore the specific care needs of informal caregivers of patients with advanced COPD, interviews were conducted with seven active family caregivers. Interviews were taped, transcribed and content analysed to obtain the caregivers' needs. Results confirm that family caregivers provide direct care with little support and assistance. Participants reported restricted activities of daily living and some emotional distress. There were knowledge deficiencies among caregivers relating to the COPD illness trajectory and little awareness of the potential of palliative care. Family caregivers need social and professional support while caring for a patient at home. This would help to ensure that their physical and emotional health does not suffer. There is a need to devise interventions to ensure family caregivers are supported.

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There is an increase in the number of older adults 85 and over, who are choosing to live alone within the community. Moreover, older adults who live alone are reportedly spending an extensive amount of time alone within the home environment. In an effort to provide additional support and resources to older adults living in the community, a compliment of services are being offered through public and private organizations. These in-home supports focus on the instrumental or functional tasks of daily living, such as personal and rehabilitative care, nourishment, maintenance and upkeep of the home, as well as volunteer social visitation. However leisure resources and programs are not included among these services. Consequently, this creates a gap in leisure provision among this segment of the population. Throughout the life course, an individual's identity, role and purpose are developed and sustained through instrumental work roles in the formal and informal sector, as well as through personally meaningful leisure pastimes and experiences. Although roles shift post retirement, participation in instrumental and expressive activities can provide opportunities through which older adults are able to fulfill their need for agency (individuality and autonomy) and affiliation (social relatedness). Therefore barriers that inhibit instrumental or leisure experiences can negatively impact older adults' quality of life. This study explored the leisure lifestyles of four older adults, all of whom were over 85, lived alone within the community and were oriented to person, time and place. It became apparent that participants ordered their lives around a routine that consisted of instrumental, expressive and socially integrated tasks and activities. Moreover participants purposely chose to remain at home because their home environment facilitated freedom, choice and independence. As a result all four participants viewed their independence within the home as a critical determinant to their overall quality of life. Challenges associated with the home environment, participants' personal capacities and relationships were negotiated on a daily basis. Failure to positively adapt to these challenges inhibited meaningful engagement and personal fulfillment. Traditionally, leisure service delivery has been offered within institutions and through various community based venues. As a result leisure provision has been focused on the needs of the frail elderly who reside in institutions or the well elderly who are able to access leisure amenities within the community. However the growing number of older adults electing to live alone is on the rise. As individuals age the home becomes the preferred context for leisure experiences. If older adults are choosing to live alone, then both their instrumental and leisure needs must be addressed. As a result, it is imperative that leisure professionals extend the scope of service delivery to include home centered older adults.

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The BRAD group is composed of/ Le groupe BRAD est composé de : Sylvie Belleville, Gina Bravo, Louise Demers, Philippe Landreville, Louisette Mercier, Nicole Paquet, Hélène Payette, Constant Rainville, Bernadette Ska and René Verreault.