scholarly journals Predicting falls in community-dwelling older adults: a systematic review of prognostic models

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e044170
Author(s):  
Gustav Valentin Gade ◽  
Martin Grønbech Jørgensen ◽  
Jesper Ryg ◽  
Johannes Riis ◽  
Katja Thomsen ◽  
...  
Keyword(s):  
Older Adults ◽  
High Risk ◽  
Prediction Models ◽  
Data Extraction ◽  
Area Under The Curve ◽  
Risk Of Bias ◽  
Community Dwelling ◽  
Prognostic Models ◽  
Eligibility Criteria ◽  
Community Dwelling Older Adults

ObjectiveTo systematically review and critically appraise prognostic models for falls in community-dwelling older adults.Eligibility criteriaProspective cohort studies with any follow-up period. Studies had to develop or validate multifactorial prognostic models for falls in community-dwelling older adults (60+ years). Models had to be applicable for screening in a general population setting.Information sourceMEDLINE, EMBASE, CINAHL, The Cochrane Library, PsycINFO and Web of Science for studies published in English, Danish, Norwegian or Swedish until January 2020. Sources also included trial registries, clinical guidelines, reference lists of included papers, along with contacting clinical experts to locate published studies.Data extraction and risk of biasTwo authors performed all review stages independently. Data extraction followed the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Risk of bias assessments on participants, predictors, outcomes and analysis methods followed Prediction study Risk Of Bias Assessment Tool.ResultsAfter screening 11 789 studies, 30 were eligible for inclusion (n=86 369 participants). Median age of participants ranged from 67.5 to 83.0 years. Falls incidences varied from 5.9% to 59%. Included studies reported 69 developed and three validated prediction models. Most frequent falls predictors were prior falls, age, sex, measures of gait, balance and strength, along with vision and disability. The area under the curve was available for 40 (55.6%) models, ranging from 0.49 to 0.87. Validated models’ The area under the curve ranged from 0.62 to 0.69. All models had a high risk of bias, mostly due to limitations in statistical methods, outcome assessments and restrictive eligibility criteria.ConclusionsAn abundance of prognostic models on falls risk have been developed, but with a wide range in discriminatory performance. All models exhibited a high risk of bias rendering them unreliable for prediction in clinical practice. Future prognostic prediction models should comply with recent recommendations such as Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis.PROSPERO registration numberCRD42019124021.

scholarly journals A scoping review protocol to map the evidence on self-reported health and neuroticism in older adults

2021 ◽  
Vol 15 ◽  
Author(s):  
Meire Cachioni ◽  
Vanessa Alonso ◽  
Gabriela Cabett Cipolli ◽  
Ruth Caldeira de Melo ◽  
Constança Paúl
Keyword(s):  
Older Adults ◽  
Intervention Studies ◽  
Reference Lists ◽  
Data Extraction ◽  
Community Dwelling ◽  
Eligibility Criteria ◽  
Data Extraction Form ◽  
Review Protocol ◽  
Reported Health ◽  
Community Dwelling Older Adults

OBJECTIVE: To identify the evidence on self-reported health and neuroticism in older adults. METHODS: Indexed literature published in English, Spanish and Portuguese will be systematically searched and retrieved from 10 databases; reference lists from included studies will be manually searched. Two authors will independently screen titles, abstracts, and full texts against the eligibility criteria. A customized data extraction form will be used to perform data extraction of the included studies, which will be: studies written in English, Portuguese, and Spanish; studies of older adults aged 55 years or over (mean age is 60 years at least); studies of community-dwelling older adults; studies that evaluated both self-reported health and personality; studies that evaluated self-reported health and personality with validated instruments; observational, review, and intervention studies. RESULTS: The results will be presented in a tabular format, accompanied by a narrative summary.

scholarly journals Applications of Artificial Intelligence and Machine Learning in Diagnosis and Prognosis of COVID-19 infection: A systematic review

2021 ◽  
Vol 10 (1) ◽  
pp. 93
Author(s):  
Mahdieh Montazeri ◽  
Ali Afraz ◽  
Mitra Montazeri ◽  
Sadegh Nejatzadeh ◽  
Fatemeh Rahimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Prediction Models ◽  
External Validation ◽  
Area Under The Curve ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Discriminative Performance ◽  
Internal Validation ◽  
Diagnostic Models

Introduction: Our aim in this study was to summarize information on the use of intelligent models for predicting and diagnosing the Coronavirus disease 2019 (COVID-19) to help early and timely diagnosis of the disease.Material and Methods: A systematic literature search included articles published until 20 April 2020 in PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases. The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. Studies were critically reviewed for risk of bias using prediction model risk of bias assessment tool.Results: We gathered 1650 articles through database searches. After the full-text assessment 31 articles were included. Neural networks and deep neural network variants were the most popular machine learning type. Of the five models that authors claimed were externally validated, we considered external validation only for four of them. Area under the curve (AUC) in internal validation of prognostic models varied from .94 to .97. AUC in diagnostic models varied from 0.84 to 0.99, and AUC in external validation of diagnostic models varied from 0.73 to 0.94. Our analysis finds all but two studies have a high risk of bias due to various reasons like a low number of participants and lack of external validation.Conclusion: Diagnostic and prognostic models for COVID-19 show good to excellent discriminative performance. However, these models are at high risk of bias because of various reasons like a low number of participants and lack of external validation. Future studies should address these concerns. Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models is needed. 

scholarly journals Social support and depression among community dwelling older adults in Asia: a systematic review

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e026667 ◽  
Author(s):  
Tengku Amatullah Madeehah Tengku Mohd ◽  
Raudah Mohd Yunus ◽  
Farizah Hairi ◽  
Noran N Hairi ◽  
Wan Yuen Choo
Keyword(s):  
Social Support ◽  
Systematic Review ◽  
Older Adults ◽  
Observational Studies ◽  
Community Dwelling ◽  
Eligibility Criteria ◽  
Cross Sectional ◽  
Support Measures ◽  
Received Social Support ◽  
Community Dwelling Older Adults

ObjectivesThis review aims to: (1) explore the social support measures in studies examining the association between social support and depression among community-dwelling older adults in Asia and (2) the evidence of association.DesignA systematic review was conducted using electronic databases of CINAHL, PubMed, PsychINFO, Psychology and Behavioural Sciences Collection, SocINDEX and Web of Science for articles published until the 11th of January 2018.Eligibility criteriaAll observational studies investigating the association between social support and depression among community-dwelling older adults in Asia were included.ParticipantsOlder adults aged 60 years and more who are living in the community.Exposure measuresSocial support.Outcome measuresDepression.ResultsWe retrieved16 356 records and screened 66 full-text articles. Twenty-four observational studies were included in the review. They consisted of five cohort studies and 19 cross-sectional studies. Social support was found to be measured by multiple components, most commonly through a combination of structural and functional constructs. Perceived social support is more commonly measured compared with received social support. Good overall social support, having a spouse or partner, living with family, having a large social network, having more contact with family and friends, having emotional and instrumental support, good support from family and satisfaction with social support are associated with less depressive symptoms among community-dwelling older adults in Asia.ConclusionsThere were 20 different social support measures and we applied a framework to allow for better comparability. Our findings emphasised the association between good social support and decrease depression among older adults. Compared with western populations, family support has a greater influence on depression among community-dwelling older adults in Asia. This indicates that the family institution needs to be incorporated into designed programmes and interventions when addressing depression in the Asian context.Trialregistration numberCRD42017074897.

scholarly journals Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e035045
Author(s):  
Morris Ogero ◽  
Rachel Jelagat Sarguta ◽  
Lucas Malla ◽  
Jalemba Aluvaala ◽  
Ambrose Agweyu ◽  
...  
Keyword(s):  
Systematic Review ◽  
Low Income ◽  
Assessment Tool ◽  
Data Extraction ◽  
Model Development ◽  
Risk Of Bias ◽  
Prognostic Models ◽  
Risk Scores ◽  
Eligibility Criteria ◽  
Bias Assessment

ObjectivesTo identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).DesignSystematic review of peer-reviewed journals.Data sourcesMEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.Eligibility criteriaWe included model development studies predicting in-hospital paediatric mortality in LMIC.Data extraction and synthesisThis systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. The risk of bias assessment was conducted using Prediction model Risk of Bias Assessment Tool (PROBAST). No quantitative summary was conducted due to substantial heterogeneity that was observed after assessing the studies included.ResultsOur search strategy identified a total of 4054 unique articles. Among these, 3545 articles were excluded after review of titles and abstracts as they covered non-relevant topics. Full texts of 509 articles were screened for eligibility, of which 15 studies reporting 21 models met the eligibility criteria. Based on the PROBAST tool, risk of bias was assessed in four domains; participant, predictors, outcome and analyses. The domain of statistical analyses was the main area of concern where none of the included models was judged to be of low risk of bias.ConclusionThis review identified 21 models predicting in-hospital paediatric mortality in LMIC. However, most reports characterising these models are of poor quality when judged against recent reporting standards due to a high risk of bias. Future studies should adhere to standardised methodological criteria and progress from identifying new risk scores to validating or adapting existing scores.PROSPERO registration numberCRD42018088599.

scholarly journals Technological interventions for loneliness and social isolation among older adults: a scoping review protocol

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Andrew Wister ◽  
Ian Fyffe ◽  
Eireann O’Dea
Keyword(s):  
Older Adults ◽  
Social Isolation ◽  
Scoping Review ◽  
English Language ◽  
Data Extraction ◽  
Community Dwelling ◽  
Technological Interventions ◽  
Information And Communication ◽  
Evaluation Trial ◽  
Community Dwelling Older Adults

Abstract Background Loneliness and social isolation are prevalent public health concerns among community-dwelling older adults. One approach that is becoming an increasingly popular method of reducing levels of loneliness and social isolation among older adults is through technology-driven solutions. This protocol outlines a research trajectory whereby a scoping review will be initiated in order to illustrate and map the existing technological approaches that have been utilized to diminish levels of loneliness and social isolation among community-dwelling older adults aged 60 years or older. We will address the question: what are the most common and less used technological approaches to reduce loneliness and social isolation among community-dwelling older adults? Methods A scoping review of Academic Search Premier, AGEline, Global Health, MEDLINE, PsycINFO, and Web of Science databases will take place using our search terms including the following: loneliness, social isolation, older adults, elderly, Aged, Aged 80 and over, program, evaluation, trial, intervention, technology, computer, information and communication technology, internet, and robot. The initial electronic search will be supplemented by reviewing the reference lists and review articles to identify any missing studies. To meet study inclusion criteria, intervention studies had to pertain to community-dwelling adults aged 60 years or older, include technological interventions, include loneliness and/or social isolation as outcome variables, and be written in the English language. Two parallel independent assessments of study eligibility will be conducted for the title, abstract, and full-text screens. Any disagreement will be resolved by consensus and a third reviewer consulted to make a decision if consensus is not achieved initially. Finally, the amalgamation of results will be an iterative process whereby reviewers will refine the plan for presenting results after data extraction is completed so that all of the contents of the extraction may be included in the results. Discussion The information gleaned in this scoping review will be essential to understand the degree to which technological interventions influence social isolation and loneliness among older adults and identify gaps for further research.

scholarly journals Simple scoring algorithm to identify community-dwelling older adults with limited health literacy: a cross-sectional study in Taiwan

BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e045411
Author(s):  
Wen-Hsuan Hou ◽  
Ken N Kuo ◽  
Mu-Jean Chen ◽  
Yao-Mao Chang ◽  
Han-Wei Tsai ◽  
...  
Keyword(s):  
Older Adults ◽  
Health Literacy ◽  
High Risk ◽  
Health Information ◽  
Cross Sectional Study ◽  
Community Dwelling ◽  
Sectional Study ◽  
Cross Sectional ◽  
Scoring Algorithm ◽  
Community Dwelling Older Adults

ObjectiveHealth literacy (HL) is the degree of individuals’ capacity to access, understand, appraise and apply health information and services required to make appropriate health decisions. This study aimed to establish a predictive algorithm for identifying community-dwelling older adults with a high risk of limited HL.DesignA cross-sectional study.SettingFour communities in northern, central and southern Taiwan.ParticipantsA total of 648 older adults were included. Moreover, 85% of the core data set was used to generate the prediction model for the scoring algorithm, and 15% was used to test the fitness of the model.Primary and secondary outcome measuresPearson’s χ2 test and multiple logistic regression were used to identify the significant factors associated with the HL level. An optimal cut-off point for the scoring algorithm was identified on the basis of the maximum sensitivity and specificity.ResultsA total of 350 (54.6%) patients were classified as having limited HL. We identified 24 variables that could significantly differentiate between sufficient and limited HL. Eight factors that could significantly predict limited HL were identified as follows: a socioenvironmental determinant (ie, dominant spoken dialect), a health service use factor (ie, having family doctors), a health cost factor (ie, self-paid vaccination), a heath behaviour factor (ie, searching online health information), two health outcomes (ie, difficulty in performing activities of daily living and requiring assistance while visiting doctors), a participation factor (ie, attending health classes) and an empowerment factor (ie, self-management during illness). The scoring algorithm yielded an area under the curve of 0.71, and an optimal cut-off value of 5 represented moderate sensitivity (62.0%) and satisfactory specificity (76.2%).ConclusionThis simple scoring algorithm can efficiently and effectively identify community-dwelling older adults with a high risk of limited HL.

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