scholarly journals Identifying Consumers Who Search for Long-Term Care on the Web: Latent Class Analysis (Preprint)

2018 ◽  
Author(s):  
Darren Liu ◽  
Takashi Yamashita ◽  
Betty Burston
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Long Term Care ◽  
The Internet ◽  
Class Analysis ◽  
Market Segments ◽  
Term Care ◽  
Web Based ◽  
Internet Users

BACKGROUND Because the internet has become a primary means of communication in the long-term care (LTC) and health care industry, an elevated understanding of market segmentation among LTC consumers is an indispensable step to responding to the informational needs of consumers. OBJECTIVE This exploratory study was designed to identify underlying market segments of the LTC consumers who seek Web-based information. METHODS Data on US adult internet users (n=2018) were derived from 2010 Pew Internet and America Life Project. Latent class analysis was employed to identify underlying market segments of LTC Web-based information seekers. RESULTS Web-based LTC information seekers were classified into the following 2 subgroups: heavy and light Web-based information seekers. Overall, 1 in 4 heavy Web-based information seekers used the internet for LTC information, whereas only 2% of the light information seekers did so. The heavy information seekers were also significantly more likely than light users to search the internet for all other health information, such as a specific disease and treatment and medical facilities. The heavy Web-based information seekers were more likely to be younger, female, highly educated, chronic disease patients, caregivers, and frequent internet users in general than the light Web-based information seekers. CONCLUSIONS To effectively communicate with their consumers, providers who target Web-based LTC information seekers can more carefully align their informational offerings with the specific needs of each subsegment of LTC markets.

scholarly journals Identifying Consumers Who Search for Long-Term Care on the Web: Latent Class Analysis

JMIR Aging ◽  
10.2196/10763 ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. e10763 ◽  
Author(s):  
Darren Liu ◽  
Takashi Yamashita ◽  
Betty Burston
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Long Term Care ◽  
Class Analysis ◽  
Term Care ◽  
The Web

scholarly journals The Impacts Of Socio-Economic Challenge On Long-Term Physical and Mental Health In Retirement Age

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 1057-1058
Author(s):  
Ilan Kwon ◽  
Sojung Park ◽  
BoRin Kim ◽  
ByeongJu Ryu
Keyword(s):  
Mental Health ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Later Life ◽  
The United States ◽  
Class Analysis ◽  
Physical And Mental Health ◽  
Term Care ◽  
The Impact

Abstract Despite consistent evidence on the negative effect of social and economic challenges on health, little is known about the pattern of economic difficulties people experience and the impact of those challenging patterns on long-term health in later life. This study used the national data, Mid Life in the United States (MIDUS 3 in 2013-2014), to identify the different patterns of socio-economic challenges that older Americans (50-64 ages old) experienced during the Recession in 2008 and to examine the impact of past challenging experiences on physical and mental health in their later life. Socio-economic challenges included twenty-six items such as losing or moving a job, missing rent, selling or losing a home, bankruptcy, having debts, and cutting spending. We conducted the latent class analysis and regression while controlling other social determinant factors (e.g., education, employment status, poverty, etc.). The latent class analysis result found five patterns during the Recession: people who experienced various difficulties during the Recession, who moved their jobs, who experienced financial difficulties, who bought a home with decreased debts, and who experienced no difficulty. Compared to people with no challenging experience, those who needed to move their jobs but could make debt off during the Recession reported physically healthier, but not mentally healthier in later life. Interestingly, among this group, women reported more long-term physical health problems than men. The findings suggest the close connection between physical and mental health and the importance of long-term care for mental health among older adults in recovering from socio-economic challenges.

Multimorbidity Profiles in German Centenarians: A Latent Class Analysis of Health Insurance Data

2017 ◽  
Vol 31 (4) ◽  
pp. 580-594 ◽  
Author(s):  
Paul Gellert ◽  
Petra von Berenberg ◽  
Thomas Zahn ◽  
Julia Neuwirth ◽  
Adelheid Kuhlmey ◽  
...  
Keyword(s):  
Health Insurance ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Oldest Old ◽  
Class Analysis ◽  
Health Insurance Data ◽  
Term Care ◽  
Insurance Data ◽  
Geriatric Multimorbidity

Objectives: Multimorbidity in centenarians is common; although investigations of the prevalence of morbidity in centenarians are accumulating, research on profiles of co-occurrence of morbidities is still sparse. Our aim was to explore profiles of comorbidities in centenarians. Method: Health insurance data from 1,121 centenarians comprising inpatient and outpatient diagnoses from the past 5 years (2009-2013) were analyzed using latent class analysis with adjustments for sex, age, hospitalization, and long-term care. Results: Four distinct comorbidity profiles emerged from the data: 36% of centenarians were categorized as “age-associated”; 18% had a variety of comorbidities but were not diabetic were labeled “multimorbid without diabetes”; 9% were labeled “multimorbid with diabetes”; and 36% “low morbidity.” Conclusion: Patterns of comorbidities describe the complexity of geriatric multimorbidity more appropriately than an approach focused on a single disease. The profiles described by this specific research may inform clinicians and health care planners for the oldest old.

Design and Development of a Web-based Delirium Preventive Application for Long-term Care Facilities: A Methodological Study (Preprint)

2020 ◽  
Author(s):  
Kyoung Ja Moon ◽  
Chang-Sik Son ◽  
Jong-Ha Lee ◽  
Mina Park
Keyword(s):  
Risk Factors ◽  
Long Term Care ◽  
Cloud System ◽  
Evidence Based ◽  
Methodological Study ◽  
Term Care ◽  
Web Based ◽  
Care Facilities ◽  
Delirium Prevention

BACKGROUND Long-term care facilities demonstrate low levels of knowledge and care for patients with delirium and are often not properly equipped with an electronic medical record system, thereby hindering systematic approaches to delirium monitoring. OBJECTIVE This study aims to develop a web-based delirium preventive application (app), with an integrated predictive model, for long-term care (LTC) facilities using artificial intelligence (AI). METHODS This methodological study was conducted to develop an app and link it with the Amazon cloud system. The app was developed based on an evidence-based literature review and the validity of the AI prediction model algorithm. Participants comprised 206 persons admitted to LTC facilities. The app was developed in 5 phases. First, through a review of evidence-based literature, risk factors for predicting delirium and non-pharmaceutical contents for preventive intervention were identified. Second, the app, consisting of several screens, was designed; this involved providing basic information, predicting the onset of delirium according to risk factors, assessing delirium, and intervening for prevention. Third, based on the existing data, predictive analysis was performed, and the algorithm developed through this was calculated at the site linked to the web through the Amazon cloud system and sent back to the app. Fourth, a pilot test using the developed app was conducted with 33 patients. Fifth, the app was finalized. RESULTS We developed the Web_DeliPREVENT_4LCF for patients of LTC facilities. This app provides information on delirium, inputs risk factors, predicts and informs the degree of delirium risk, and enables delirium measurement or delirium prevention interventions to be immediately implemented with a verified tool. CONCLUSIONS This web-based application is evidence-based and offers easy mobilization and care to patients with delirium in LTC facilities. Therefore, the use of this app improves the unrecognized of delirium and predicts the degree of delirium risk, thereby helping initiatives for delirium prevention and providing interventions. This would ultimately improve patient safety and quality of care. CLINICALTRIAL none

Symptom profiles following combat injury and long-term quality of life: a latent class analysis

2021 ◽  
Author(s):  
Andrew J. MacGregor ◽  
Amber L. Dougherty ◽  
Edwin W. D’Souza ◽  
Cameron T. McCabe ◽  
Daniel J. Crouch ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Combat Injury ◽  
Symptom Profiles

scholarly journals What matters to me – a web-based preference elicitation tool for clients in long-term care: a user-centred design

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Catharina M. van Leersum ◽  
Albine Moser ◽  
Ben van Steenkiste ◽  
Marion Reinartz ◽  
Esther Stoffers ◽  
...  
Keyword(s):  
Long Term Care ◽  
Preference Elicitation ◽  
Term Care ◽  
Web Based ◽  
User Centred Design ◽  
What Matters

scholarly journals OP0314 LONG TERM TRAJECTORIES OF CHRONIC WIDESPREAD PAIN: A 21-YEAR PROSPECTIVE COHORT LATENT CLASS ANALYSIS

2019 ◽  
Author(s):  
Katarina Aili ◽  
Paul Campbell ◽  
Zoe A Michaleff ◽  
Victoria Y. Strauss ◽  
Kelvin Jordan ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Prospective Cohort ◽  
Latent Class ◽  
Chronic Widespread Pain ◽  
Widespread Pain ◽  
Class Analysis

Giving Long-term Care Residents a Passport to the World With the Internet

2010 ◽  
Vol 25 (3) ◽  
pp. 193-197 ◽  
Author(s):  
Debra Parker Oliver ◽  
Elaine Wittenberg-Lyles ◽  
George Demiris ◽  
David Oliver
Keyword(s):  
Long Term Care ◽  
The Internet ◽  
Term Care ◽  
The World
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