scholarly journals eHealth Practices in Cancer Survivors With BMI in Overweight or Obese Categories: Latent Class Analysis Study (Preprint)

2020 ◽  
Author(s):  
Annie Wen Lin ◽  
Sharon H Baik ◽  
David Aaby ◽  
Leslie Tello ◽  
Twila Linville ◽  
...  
Keyword(s):  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Health Information ◽  
Latent Class ◽  
Electronic Device ◽  
Ethnic Disparities ◽  
Sociodemographic Characteristics ◽  
Class Analysis ◽  
Class Membership ◽  
Health Related

BACKGROUND eHealth technologies have been found to facilitate health-promoting practices among cancer survivors with BMI in overweight or obese categories; however, little is known about their engagement with eHealth to promote weight management and facilitate patient-clinician communication. OBJECTIVE The objective of this study was to determine whether eHealth use was associated with sociodemographic characteristics, as well as medical history and experiences (ie, patient-related factors) among cancer survivors with BMI in overweight or obese categories. METHODS Data were analyzed from a nationally representative cross-sectional survey (National Cancer Institute’s Health Information National Trends Survey). Latent class analysis was used to derive distinct classes among cancer survivors based on sociodemographic characteristics, medical attributes, and medical experiences. Logistic regression was used to examine whether class membership was associated with different eHealth practices. RESULTS Three distinct classes of cancer survivors with BMI in overweight or obese categories emerged: younger with no comorbidities, younger with comorbidities, and older with comorbidities. Compared to the other classes, the younger with comorbidities class had the highest probability of identifying as female (73%) and Hispanic (46%) and feeling that clinicians did not address their concerns (75%). The older with comorbidities class was 6.5 times more likely than the younger with comorbidities class to share eHealth data with a clinician (odds ratio [OR] 6.53, 95% CI 1.08-39.43). In contrast, the younger with no comorbidities class had a higher likelihood of using a computer to look for health information (OR 1.93, 95% CI 1.10-3.38), using an electronic device to track progress toward a health-related goal (OR 2.02, 95% CI 1.08-3.79), and using the internet to watch health-related YouTube videos (OR 2.70, 95% CI 1.52-4.81) than the older with comorbidities class. CONCLUSIONS Class membership was associated with different patterns of eHealth engagement, indicating the importance of tailored digital strategies for delivering effective care. Future eHealth weight loss interventions should investigate strategies to engage younger cancer survivors with comorbidities and address racial and ethnic disparities in eHealth use.

scholarly journals eHealth Practices in Cancer Survivors With BMI in Overweight or Obese Categories: Latent Class Analysis Study

JMIR Cancer ◽  
10.2196/24137 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e24137
Author(s):  
Annie Wen Lin ◽  
Sharon H Baik ◽  
David Aaby ◽  
Leslie Tello ◽  
Twila Linville ◽  
...  
Keyword(s):  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Health Information ◽  
Latent Class ◽  
Electronic Device ◽  
Ethnic Disparities ◽  
Sociodemographic Characteristics ◽  
Class Analysis ◽  
Class Membership ◽  
Health Related

Background eHealth technologies have been found to facilitate health-promoting practices among cancer survivors with BMI in overweight or obese categories; however, little is known about their engagement with eHealth to promote weight management and facilitate patient-clinician communication. Objective The objective of this study was to determine whether eHealth use was associated with sociodemographic characteristics, as well as medical history and experiences (ie, patient-related factors) among cancer survivors with BMI in overweight or obese categories. Methods Data were analyzed from a nationally representative cross-sectional survey (National Cancer Institute’s Health Information National Trends Survey). Latent class analysis was used to derive distinct classes among cancer survivors based on sociodemographic characteristics, medical attributes, and medical experiences. Logistic regression was used to examine whether class membership was associated with different eHealth practices. Results Three distinct classes of cancer survivors with BMI in overweight or obese categories emerged: younger with no comorbidities, younger with comorbidities, and older with comorbidities. Compared to the other classes, the younger with comorbidities class had the highest probability of identifying as female (73%) and Hispanic (46%) and feeling that clinicians did not address their concerns (75%). The older with comorbidities class was 6.5 times more likely than the younger with comorbidities class to share eHealth data with a clinician (odds ratio [OR] 6.53, 95% CI 1.08-39.43). In contrast, the younger with no comorbidities class had a higher likelihood of using a computer to look for health information (OR 1.93, 95% CI 1.10-3.38), using an electronic device to track progress toward a health-related goal (OR 2.02, 95% CI 1.08-3.79), and using the internet to watch health-related YouTube videos (OR 2.70, 95% CI 1.52-4.81) than the older with comorbidities class. Conclusions Class membership was associated with different patterns of eHealth engagement, indicating the importance of tailored digital strategies for delivering effective care. Future eHealth weight loss interventions should investigate strategies to engage younger cancer survivors with comorbidities and address racial and ethnic disparities in eHealth use.

scholarly journals Eating behaviour, physical activity, TV exposure and sleeping habits in five year olds: a latent class analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Molly Mattsson ◽  
Deirdre M. Murray ◽  
Mairead Kiely ◽  
Fergus P. McCarthy ◽  
Elaine McCarthy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Eating Behaviour ◽  
Overweight And Obesity ◽  
Class Analysis ◽  
Class Membership ◽  
Active Play ◽  
Food Avoidance ◽  
Cluster Membership

Abstract Background Diet, physical activity, sedentary behaviours, and sleep time are considered major contributory factors of the increased prevalence of childhood overweight and obesity. The aims of this study were to (1) identify behavioural clusters of 5 year old children based on lifestyle behaviours, (2) explore potential determinants of class membership, and (3) to determine if class membership was associated with body measure outcomes at 5 years of age. Methods Data on eating behaviour, engagement in active play, TV watching, and sleep duration in 1229 5 year old children from the Cork BASELINE birth cohort study was obtained through in-person interviews with parent. Latent class analysis was used to identify behavioural clusters. Potential determinants of cluster membership were investigated using multinomial logistic regression. Associations between the identified classes and cardio metabolic body measures were examined using multivariate logistic and linear regression, with cluster membership used as the independent variable. Results 51% of children belonged to a normative class, while 28% of children were in a class characterised by high scores on food avoidance scales in combination with low enjoyment of food, and 20% experienced high scores on the food approach scales. Children in both these classes had lower conditional probabilities of engaging in active play for at least 1 hour per day and sleeping for a minimum of 10 h, and higher probability of watching TV for 2 hours or more, compared to the normative class. Low socioeconomic index (SEI) and no breastfeeding at 2 months were found to be associated with membership of the class associated with high scores on the food avoidance scale, while lower maternal education was associated with the class defined by high food approach scores. Children in the class with high scores on the food approach scales had higher fat mass index (FMI), lean mass index (LMI), and waist-to-height ratio (WtHR) compared to the normative class, and were at greater risk of overweight and obesity. Conclusion Findings suggest that eating behaviour appeared to influence overweight and obesity risk to a greater degree than activity levels at 5 years old. Further research of how potentially obesogenic behaviours in early life track over time and influence adiposity and other cardio metabolic outcomes is crucial to inform the timing of interventions.

scholarly journals Mental disorders pattern in staff of a military unit in Iran: the role of metabolic syndrome on latent class membership

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Abbas Abbasi-Ghahramanloo ◽  
Mohammadkarim Bahadori ◽  
Esfandiar Azad ◽  
Nooredin Dopeykar ◽  
Parisa Mahdizadeh ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Mental Disorders ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Adult Population ◽  
Class Analysis ◽  
Class Membership ◽  
Anthropometric Measures ◽  
Military Unit

Abstract Introduction Mental disorders are among the most prevalent health problems of the adult population in the world. This study aimed to identify the subgroups of staff based on mental disorders and assess the independent role of metabolic syndrome (MetS) on the membership of participants in each latent class. Methods This cross-sectional study was conducted among 694 staff of a military unit in Tehran in 2017. All staff of this military unit was invited to participate in this study. The collected data included demographic characteristics, anthropometric measures, blood pressure, biochemical parameters, and mental disorders. We performed latent class analysis using a procedure for latent class analysis (PROC LCA) in SAS to identify class membership of mental disorders using Symptom Checklist-90. Results Three latent classes were identified as healthy (92.7%), mild (4.9%), and severe (2.4%) mental disorders. Having higher age significantly decreased the odds of belonging to the mild class (adjusted OR (aOR = 0.21; 95% confidence interval (CI): 0.05–0.83) compared to the healthy class. Also, obesity decreased the odds of membership in mild class (aOR = 0.10, 95% CI: 0.01–0.92) compared to healthy class. On the other hand, being female increased the odds of being in severe class (aOR = 9.76; 95% CI: 1.35–70.65) class in comparison to healthy class. Conclusion This study revealed that 7.3% of staff fell under mild and severe classes. Considering educational workshops in the workplace about mental disorders could be effective in enhancing staff’s knowledge of these disorders. Also, treatment of comorbid mental disorders may help reduce their prevalence and comorbidity.

scholarly journals Patterns of comorbidities in hospitalised cancer survivors for palliative care and associated in-hospital mortality risk: A latent class analysis of a statewide all-inclusive inpatient data

2019 ◽  
Vol 33 (10) ◽  
pp. 1272-1281 ◽  
Author(s):  
Lan Luo ◽  
Wei Du ◽  
Shanley Chong ◽  
Huibo Ji ◽  
Nicholas Glasgow
Keyword(s):  
Risk Factors ◽  
Palliative Care ◽  
Hospital Mortality ◽  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Cancer Patients ◽  
Mortality Risk ◽  
Latent Class ◽  
Class Analysis ◽  
Hospital Deaths

Background: At the end of life, cancer survivors often experience exacerbations of complex comorbidities requiring acute hospital care. Few studies consider comorbidity patterns in cancer survivors receiving palliative care. Aim: To identify patterns of comorbidities in cancer patients receiving palliative care and factors associated with in-hospital mortality risk. Design, Setting/Participants: New South Wales Admitted Patient Data Collection data were used for this retrospective cohort study with 47,265 cancer patients receiving palliative care during the period financial year 2001–2013. A latent class analysis was used to identify complex comorbidity patterns. A regression mixture model was used to identify risk factors in relation to in-hospital mortality in different latent classes. Results: Five comorbidity patterns were identified: ‘multiple comorbidities and symptoms’ (comprising 9.1% of the study population), ‘more symptoms’ (27.1%), ‘few comorbidities’ (39.4%), ‘genitourinary and infection’ (8.7%), and ‘circulatory and endocrine’ (15.6%). In-hospital mortality was the highest for ‘few comorbidities’ group and the lowest for ‘more symptoms’ group. Severe comorbidities were associated with elevated mortality in patients from ‘multiple comorbidities and symptoms’, ‘more symptoms’, and ‘genitourinary and infection’ groups. Intensive care was associated with a 37% increased risk of in-hospital deaths in those presenting with more ‘multiple comorbidities and symptoms’, but with a 22% risk reduction in those presenting with ‘more symptoms’. Conclusion: Identification of comorbidity patterns and risk factors for in-hospital deaths in cancer patients provides an avenue to further develop appropriate palliative care strategies aimed at improving outcomes in cancer survivors.

scholarly journals A primer to latent profile and latent class analysis

2021 ◽  
Author(s):  
Johannes Bauer
Keyword(s):  
Individual Differences ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Class Membership ◽  
External Variables ◽  
Response Profiles ◽  
Person Centered Approach ◽  
Conceptual Overview ◽  
Latent Profile

This chapter gives an applied introduction to latent profile and latent class analysis (LPA/LCA). LPA/LCA are model-based methods for clustering individuals in unobserved groups. Their primary goals are probing whether and, if so, how many latent classes can be identified in the data, and to estimate the proportional size and response profiles of these classes in the population. Moreover, latent class membership can serve as predictor or outcome for external variables. Substantively, LPA/LCA adopt a person-centered approach that is useful for analyzing individual differences in prerequisites, processes, or outcomes of learning. The chapter provides a conceptual overview of LPA/LCA, a nuts-and-bolts discussion of the steps and decisions involved in their application, and illustrative examples using freely available data and the R statistical environment.

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