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            "abstractNote": "BACKGROUND: Falls in long-term care residents with dementia represent a costly but unresolved safety issue. The aim of the present study was to (1) determine the incidence of falls, fall-related injuries and fall circumstances, and (2) identify the relationship between patient characteristics and fall rate in long-term care residents with dementia.\nMETHODS: Twenty long-term care residents with dementia (80 ± 11 years; 60% male) participated. Falls were recorded on a standardized form, concerning fall injuries, time and place of fall and if the fall was witnessed. Patient characteristics (66 variables) were extracted from medical records and classified into the domains: demographics, activities of daily living, mobility, cognition and behavior, vision and hearing, medical conditions and medication use. We used partial least squares (PLS) regression to determine the relationship between patient characteristics and fall rate.\nRESULTS: A total of 115 falls (5.1 ± 6.7 falls/person/year) occurred over 19 months, with 85% of the residents experiencing a fall, 29% of falls had serious consequences and 28% was witnessed. A combination of impaired mobility, indicators of disinhibited behavior, diabetes, and use of analgesics, beta blockers and psycholeptics were associated with higher fall rates. In contrast, immobility, heart failure, and the inability to communicate were associated with lower fall rates.\nCONCLUSIONS: Falls are frequent and mostly unwitnessed events in long-term care residents with dementia, highlighting the need for more effective and individualized fall prevention. Our analytical approach determined the relationship between a high fall rate and cognitive impairment, related to disinhibited behavior, in combination with mobility disability and fall-risk-increasing-drugs (FRIDs).",
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                    "firstName": "Sarah D.",
                    "lastName": "Berry"
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                    "lastName": "Mittleman"
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                    "firstName": "Yuqing",
                    "lastName": "Zhang"
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                {
                    "creatorType": "author",
                    "firstName": "Daniel H.",
                    "lastName": "Solomon"
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                    "creatorType": "author",
                    "firstName": "Lewis A.",
                    "lastName": "Lipsitz"
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                    "creatorType": "author",
                    "firstName": "Elizabeth",
                    "lastName": "Mostofsky"
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                    "firstName": "Dana",
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            "title": "Psychotropic drug initiation or increased dosage and the acute risk of falls: a prospective cohort study of nursing home residents",
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                {
                    "creatorType": "author",
                    "firstName": "Murray A.",
                    "lastName": "Echt"
                },
                {
                    "creatorType": "author",
                    "firstName": "Elizabeth J.",
                    "lastName": "Samelson"
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                    "creatorType": "author",
                    "firstName": "Marian T.",
                    "lastName": "Hannan"
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                    "creatorType": "author",
                    "firstName": "Alyssa B.",
                    "lastName": "Dufour"
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            "abstractNote": "OBJECTIVES: Prevention and public reporting of falls have suffered due to inadequate attention given to the association of falls and cognitive impairment (CI) among nursing home (NH) residents. This study examines the relationship between CI, residence on dementia special care units (SCUs) and other resident characteristics and likelihood of residents experiencing new falls in NHs.\nDESIGN: Retrospective cohort study.\nSETTING AND PARTICIPANTS: A total of 21,587 residents from 381 Minnesota NHs.\nMEASUREMENTS: The NH Minimum Data Set (MDS) for 21,587 residents from 381 Minnesota NHs in the first calendar quarter of 2008 were analyzed. New falls, (fall noted on a current MDS assessment but not on a prior assessment); cognitive status, (as defined by Cognitive Performance Scale); residence on an SCU, and health and functional status covariates were recorded. A random effects logistic regression model was used to examine relationships between new falls and the resident's cognitive status, type of unit, and covariates.\nRESULTS: The likelihood of a new fall had a nonlinear association with CI. Compared with residents with normal or mild CI, the likelihood of a new fall was significantly higher among residents with moderate CI (OR= 1.43). The risk decreased slightly (OR= 1.34) for residents with more advanced CI, whereas the presence of severe CI was not significantly associated with new falls. Overall the likelihood of new falls was significantly higher for residents on SCUs compared with those on conventional units (OR= 1.27).\nCONCLUSIONS: Severity of CI and residence on SCU impact fall incidence and should be accounted for in future fall- prevention interventions and quality-reporting indicators and measures.",
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            "publicationTitle": "Aging Clinical and Experimental Research",
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                    "firstName": "Natalie E.",
                    "lastName": "Leland"
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            "abstractNote": "OBJECTIVES: To examine the relationship between nursing home (NH) organizational characteristics and falls in newly admitted NH residents.\nDESIGN: Observational cross-sectional study from January 1, 2006, to December 31, 2006.\nSETTING: NHs in the United States in 2006.\nPARTICIPANTS: Individuals (n = 230,730) admitted to a NH in 2006 without a prior NH stay and with a follow-up Minimum Data Set (MDS) assessment completed 30 days or more after admission.\nMEASUREMENTS: The relationship between experiencing a fall noted on the MDS assessment and NH characteristics (e.g., staffing, profit and chain status, religious affiliation, hospital-based facility status, number of beds, presence of a special care unit, funding) was examined, adjusting for NH resident characteristics.\nRESULTS: Twenty-one percent of this cohort (n = 47,750) had experienced at least one fall in the NH at the time of the MDS assessment, which was completed for newly admitted NH residents who had at least a 30-day stay. NHs with higher certified nursing assistant (CNA) staffing had lower rates of falls (adjusted odds ratio = 0.97, 95% confidence interval = 0.95-0.99).\nCONCLUSION: For newly admitted NH residents, NHs with higher CNA staffing had a lower fall rate. In an effort to maximize fall prevention efforts, further research is needed to understand the relationship between CNA staffing and falls in this NH population.",
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            "creatorSummary": "Nait Aicha et al.",
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            "version": 209,
            "itemType": "journalArticle",
            "title": "Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Ahmed",
                    "lastName": "Nait Aicha"
                },
                {
                    "creatorType": "author",
                    "firstName": "Gwenn",
                    "lastName": "Englebienne"
                },
                {
                    "creatorType": "author",
                    "firstName": "Kimberley S.",
                    "lastName": "van Schooten"
                },
                {
                    "creatorType": "author",
                    "firstName": "Mirjam",
                    "lastName": "Pijnappels"
                },
                {
                    "creatorType": "author",
                    "firstName": "Ben",
                    "lastName": "Kröse"
                }
            ],
            "abstractNote": "Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data.",
            "publicationTitle": "Sensors (Basel, Switzerland)",
            "publisher": "",
            "place": "",
            "date": "May 22, 2018",
            "volume": "18",
            "issue": "5",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "",
            "series": "",
            "seriesTitle": "",
            "seriesText": "",
            "journalAbbreviation": "Sensors (Basel)",
            "DOI": "10.3390/s18051654",
            "citationKey": "",
            "url": "",
            "accessDate": "",
            "PMID": "",
            "PMCID": "",
            "ISSN": "1424-8220",
            "archive": "",
            "archiveLocation": "",
            "shortTitle": "",
            "language": "eng",
            "libraryCatalog": "PubMed",
            "callNumber": "",
            "rights": "",
            "extra": "PMID: 29786659\nPMCID: PMC5981199",
            "tags": [
                {
                    "tag": "Accelerometry",
                    "type": 1
                },
                {
                    "tag": "Accidental Falls",
                    "type": 1
                },
                {
                    "tag": "Activities of Daily Living",
                    "type": 1
                },
                {
                    "tag": "Aged",
                    "type": 1
                },
                {
                    "tag": "Aged, 80 and over",
                    "type": 1
                },
                {
                    "tag": "Female",
                    "type": 1
                },
                {
                    "tag": "Humans",
                    "type": 1
                },
                {
                    "tag": "Machine Learning",
                    "type": 1
                },
                {
                    "tag": "Male",
                    "type": 1
                },
                {
                    "tag": "Monitoring, Ambulatory",
                    "type": 1
                },
                {
                    "tag": "Neural Networks (Computer)",
                    "type": 1
                },
                {
                    "tag": "accelerometry",
                    "type": 1
                },
                {
                    "tag": "accidental falls",
                    "type": 1
                },
                {
                    "tag": "convolutional neural network",
                    "type": 1
                },
                {
                    "tag": "long short-term memory",
                    "type": 1
                },
                {
                    "tag": "machine learning",
                    "type": 1
                },
                {
                    "tag": "neural networks",
                    "type": 1
                },
                {
                    "tag": "older adults",
                    "type": 1
                }
            ],
            "collections": [],
            "relations": {},
            "dateAdded": "2019-03-11T22:12:36Z",
            "dateModified": "2019-03-11T22:12:36Z"
        }
    }
]