BACKGROUND: Lifestyle has become a crucial modulator in the management of diabetes and is intimately linked with the development and exacerbation of comorbid depression. The study aimed to analyze lifestyle patterns and their impact on depression in individuals with diabetes and to explore the role of the Dietary Inflammatory Index (DII) in the relationship between lifestyle patterns and depression.
METHODS: Data was attained from the National Health and Nutrition Examination Survey (NHANES) between 2009 and 2020. A latent class analysis (LCA) was performed on 3,009 diabetic adults based on lifestyle behaviors. A generalised linear model (GLM) was employed to analyse the effects of different lifestyle patterns on depression. The mediation effect model was utilised to examine the relationship between lifestyle patterns, DII and Patient Health Questionnaire-9 (PHQ-9) scores.
RESULTS: The cohort was divided through LCA into unhealthy lifestyle (44.53%), unhealthy but non-alcohol use (48.06%) and healthy but smoking (7.41%) groups of lifestyle behaviors, the unhealthy but non-alcohol use group was identified as a risk factor for depression (OR = 1.379, 95%CI = 1.095 ~ 1.735, P = 0.006). The DII partially mediated the relationship between the unhealthy but non-alcohol use group and PHQ-9, and fully mediated the relationship between the healthy but smoking group and PHQ-9, with effect coefficients of - 0.018 (95%CI: -0.044 ~ - 0.001) and - 0.035 (95%CI: -0.083 ~ - 0.001).
CONCLUSIONS: Lifestyle patterns significantly influence the occurrence of depression among diabetes patients. The dietary inflammation plays a varying mediating role between different lifestyle patterns and depression. Restricting pro-inflammatory diets or encouraging anti-inflammatory diets, combined with the promotion of healthy lifestyle practices, may be an effective method for preventing and alleviating symptoms of depression among patients with diabetes.

BACKGROUND: Older adults with varying patterns of multimorbidity may require distinct types of care and rely on informal caregiving to meet their care needs. This study aims to identify groups of older adults with distinct, empirically-determined multimorbidity patterns and compare characteristics of informal care received among estimated classes.
METHODS: Data are from the 2011 National Health and Aging Trends Study (NHATS). Ten chronic conditions were included to estimate multimorbidity patterns among 7532 individuals using latent class analysis. Multinomial logistic regression model was estimated to examine the association between sociodemographic characteristics, health status and lifestyle variables, care-receiving characteristics and latent class membership.
RESULTS: A four-class solution identified the following multimorbidity groups: some somatic conditions with moderate cognitive impairment (30%), cardiometabolic (25%), musculoskeletal (24%), and multisystem (21%). Compared with those who reported receiving no help, care recipients who received help with household activities only (OR = 1.44, 95% CI 1.05-1.98), mobility but not self-care (OR = 1.63, 95% CI 1.05-2.53), or self-care but not mobility (OR = 2.07, 95% CI 1.29-3.31) had greater likelihood of being in the multisystem group versus the some-somatic group. Having more caregivers was associated with higher odds of being in the multisystem group compared with the some-somatic group (OR = 1.09, 95% CI 1.00-1.18), whereas receiving help from paid helpers was associated with lower odds of being in the multisystem group (OR = 0.36, 95% CI 0.19-0.77).
CONCLUSIONS: Results highlighted different care needs among persons with distinct combinations of multimorbidity, in particular the wide range of informal needs among older adults with multisystem multimorbidity. Policies and interventions should recognize the differential care needs associated with multimorbidity patterns to better provide person-centered care.

Latent classification model is a class of statistical methods for identifying unobserved class membership among the study samples using some observed data. In this study, we proposed a latent classification model that takes a censored longitudinal binary outcome variable and uses its changing pattern over time to predict individuals\' latent class membership. Assuming the time-dependent outcome variables follow a continuous-time Markov chain, the proposed method has two primary goals: (1) estimate the distribution of the latent classes and predict individuals\' class membership, and (2) estimate the class-specific transition rates and rate ratios. To assess the model\'s performance, we conducted a simulation study and verified that our algorithm produces accurate model estimates (ie, small bias) with reasonable confidence intervals (ie, achieving approximately 95% coverage probability). Furthermore, we compared our model to four other existing latent class models and demonstrated that our approach yields higher prediction accuracies for latent classes. We applied our proposed method to analyze the COVID-19 data in Houston, Texas, US collected between January first 2021 and December 31st 2021. Early reports on the COVID-19 pandemic showed that the severity of a SARS-CoV-2 infection tends to vary greatly by cases. We found that while demographic characteristics explain some of the differences in individuals\' experience with COVID-19, some unaccounted-for latent variables were associated with the disease.

OBJECTIVE: To identify subgroups of patients with distinct chemotherapy-induced vomiting (CIV) profiles; determine how these subgroups differ on several demographic, clinical, and symptom characteristics; and evaluate factors associated with chemotherapy-induced nausea and CIV profiles.
UNASSIGNED: Adult patients (N = 1,338) receiving cancer chemotherapy.
UNASSIGNED: Data were collected on demographic, clinical, and symptom characteristics. Differences among subgroups of patients with distinct CIV profiles were evaluated using parametric and nonparametric tests.
RESULTS: Three CIV profiles (None, Decreasing, and Increasing) were identified. Compared with the None class, Decreasing and Increasing classes were more likely to have lower household income and a higher comorbidity burden, as well as to report higher rates of dry mouth, nausea, diarrhea, depression, anxiety, sleep disturbance, morning fatigue, and pain interference.
CONCLUSIONS: Clinicians need to assess common and distinct risk factors for CIV and chemotherapy-induced nausea.

UNASSIGNED: Internet hospitals, online health communities, and other digital health APPs have brought many changes to people\'s lives. However, digital health resources are experiencing low continuance intention due to many factors, including information security, service quality, and personal characteristics of users.
UNASSIGNED: We used cross-sectional surveys and structural equation modeling analysis to explore factors influencing user willingness to continue using digital health resources.
UNASSIGNED: Information quality (β = 0.31, p < 0.05), service quality (β = 0.19, p < 0.05), platform reputation (β = 0.34, p < 0.05), and emotional support (β = 0.23, p < 0.05) have significant positive effects on user value co-creation behavior. Additionally, user trust and perceived usefulness could mediate the association between user value co-creation behavior and continuance intention, with mediation effects of 0.143 and 0.125, respectively. User involvement can positively moderate the association between user value co-creation behavior and user trust (β = 0.151, t = 2.480, p < 0.001). Also, user involvement can positively moderate the association between value co-creation behavior and perceived usefulness (β = 0.103, t = 3.377, p < 0.001).
UNASSIGNED: The keys to solving the problem of low continuance intention are improving the quality and service level of digital health resources, and promoting users\' value co-creation behavior. Meanwhile, enterprises should build a good reputation, create a positive communication atmosphere in the community, and enhance user participation and sense of belonging.

BACKGROUND: Pregnant persons with opioid use disorder (OUD) face a multitude of comorbid conditions that may increase the risk of adverse drug and health outcomes. This study characterizes typologies of comorbidities among pregnant persons with OUD and assesses the associations of these typologies with hospitalizations in the first year postpartum.
METHODS: A cohort of pregnant persons with OUD at delivery in 2018 were identified in a Pennsylvania statewide hospital dataset (n = 2055). Latent class analysis assessed 12 comorbid conditions including substance use disorders (SUDs), mental health conditions, and infections. Multivariable logistic regressions examined the association between comorbidity classes and hospitalizations (all-cause, OUD-specific, SUD-related, mental health-related) during early (0-42 days) and late (43-365 days) postpartum.
RESULTS: A three-class model best fit the data. Classes included low comorbidities (56.9% of sample; low prevalence of co-occurring conditions), moderate polysubstance/depression (18.4%; some SUDs, all with depression), and high polysubstance/bipolar disorder (24.7%; highest probabilities of SUDs and bipolar disorder). Overall, 14% had at least one postpartum hospitalization. From 0 to 42 days postpartum, the moderate polysubstance/depression and high polysubstance/bipolar disorder classes had higher odds of all-cause and mental health-related hospitalization, compared to the low comorbidities class. From 43 to 365 days postpartum, the high polysubstance/bipolar disorder class had higher odds of all-cause hospitalizations, while both the high polysubstance/depression and moderate polysubstance/bipolar disorder classes had higher odds of SUD-related and mental health-related hospitalizations compared to the low comorbidities class.
CONCLUSIONS: Findings highlight the need for long-term, multidisciplinary healthcare delivery interventions to address comorbidities and prevent adverse postpartum outcomes.

UNASSIGNED: Understanding chronic disease prevalence, patterns, and co-occurrence is pivotal for effective health care planning and disease prevention strategies. In this paper, we aimed to identify the clustering of major non-communicable diseases among Indian adults aged ≥50 years based on their self-reported diagnosed non-communicable disease status and to find the risk factors that heighten the risk of developing the identified disease clusters.
UNASSIGNED: We utilised data from the nationally representative survey Study on Global AGEing and Adult Health (SAGE Wave-2). The eligible sample size was 6298 adults aged ≥50 years. We conducted the latent class analysis to uncover latent subgroups of multimorbidity and the multinomial logistic regression to identify the factors linked to observed latent class membership.
UNASSIGNED: The latent class analysis grouped our sample of men and women >49 years old into three groups - mild multimorbidity risk (41%), moderate multimorbidity risk (30%), and severe multimorbidity risk (29%). In the mild multimorbidity risk group, the most prevalent diseases were asthma and arthritis, and the major prevalent disease in the moderate multimorbidity risk group was low near/distance vision, followed by depression, asthma, and lung disease. Angina, diabetes, hypertension, and stroke were the major diseases in the severe multimorbidity risk category. Individuals with higher ages had an 18% and 15% higher risk of having moderate multimorbidity and severe multimorbidity compared to those in the mild multimorbidity category. Females were more likely to have a moderate risk (3.36 times) and 2.82 times more likely to have severe multimorbidity risk.
UNASSIGNED: The clustering of diseases highlights the importance of integrated disease management in primary care settings and improving the health care system to accommodate the individual\'s needs. Implementing preventive measures and tailored interventions, strengthening the health and wellness centres, and delivering comprehensive primary health care services for secondary and tertiary level hospitalisation may cater to the needs of multimorbid patients.

Previous research identified four patterns of negative emotional eating in American and Chinese university students and proposed future directions (e.g., exploring potential differences in emotion regulation across patterns and replicating the patterns in a general, non-student population). Furthermore, prior research has not explored group differences in muscularity-oriented eating disorder symptomatology or psychosocial impairment. Therefore, the present study addressed these gaps in a sample of general Chinese adults, further testing group differences in typical and muscularity-oriented eating disorder symptomatology, psychosocial impairment, and emotion regulation difficulties across patterns of negative emotional eating. A total of 600 Chinese adults were recruited. Latent class analysis (LCA) was used. Results replicated the four patterns of negative emotional eating in previous research, including non-emotional eating (non-EE), emotional over- and under-eating (EOE-EUE), emotional over-eating (EOE), and emotional under-eating (EUE). Significant class differences were identified in eating disorder symptomatology, psychosocial impairment, and emotion regulation difficulties. Specifically, individuals with EOE and EOE-EUE patterns exhibited higher eating disorder symptomatology, higher psychosocial impairment, and more emotion regulation difficulties than those with non-EE and EUE patterns. Therefore, these two classes (i.e., EOE and EOE-EUE), especially the poorly researched EOE-EUE group, should be further examined to elucidate research and clinical applications. Furthermore, findings underscore the role of emotion regulation difficulties in further describing the differences across these negative emotional eating patterns, which can be considered in future interventions for reducing negative emotional eating.

OBJECTIVE: There is limited knowledge regarding the progression or consistency of symptoms in OSA over time. Our objective was to examine the changes in symptom subtypes and identify predictors over a span of 5 years.
METHODS: Data of 2,643 participants of the Sleep Heart Health Study with complete baseline and 5-year follow-up visits were analyzed. Latent Class Analysis on 14 symptoms at baseline and follow-up determined symptom subtypes. Individuals without OSA (AHI<5) were incorporated as a known class at each time point. Multinomial logistic regression assessed the effect of age, sex, body mass index (BMI) and AHI on specific class transitions.
RESULTS: The sample consisted of 1,408 women (53.8%) and mean (SD) age 62.4 (10.5) years. We identified four OSA symptom subtypes at both baseline and follow-up visits: minimally symptomatic, disturbed sleep, moderately sleepy, and excessively sleepy. Nearly half (44.2%) of the sample transitioned to a different subtype; transitions to moderately sleepy were the most common (77% of all transitions). A five-year older age was associated with a 50% increase in odds to transit from excessively sleepy to moderately sleepy [OR (95% CI: 1.52 (1.17, 1.97)]. Women had 1.97 times higher odds (95% CI: 1.21, 3.18) to transition from moderately sleepy to minimal symptoms. A 5-unit increase in BMI was associated with 2.39 greater odds (95% CI: 1.30, 4.40) to transition from minimal symptoms to excessively sleepy. Changes in AHI did not significantly predict any transitions.
CONCLUSIONS: The symptoms of OSA may fluctuate or remain stable over time. Knowledge of symptom progression in OSA may support clinicians with treatment decisions.

BACKGROUND: Health disparities between people who are African American (AA) versus their White counterparts have been well established, but disparities among AA people have not. The current study introduces a systematic method to determine subgroups within a sample of AA people based on their social determinants of health.
METHODS: Health screening data collected in the West Side of Chicago, an underserved predominantly AA area, in 2018 were used. Exploratory latent class analysis was used to determine subgroups of participants based on their responses to 16 variables, each pertaining to a specific social determinant of health.
RESULTS: Four unique clusters of participants were found, corresponding to those with \"many unmet needs\", \"basic unmet needs\", \"unmet healthcare needs\", and \"few unmet needs\".
CONCLUSIONS: The findings support the utility of analytically determining meaningful subgroups among a sample of AA people and their social determinants of health. Understanding the differences within an underserved population may contribute to future interventions to eliminate health disparities.