Multiple Unhealthy Behaviors Share Equivalent Profiles of Readiness for Change in Patients with Type 2 Diabetes.
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ABSTRACT:
SUBMITTER: Salinas Martinez AM
PROVIDER: S-EPMC8037007 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundInformation technologies (IT) are increasingly implemented in type 2 diabetes (T2D) treatment as a resource for remotely supported health care. However, possible pitfalls of introducing IT in health care are generally overlooked. Specifically, the effectiveness of IT to improve health care may depend on the user's readiness for health technology.ObjectiveWe aim to investigate readiness for health technology in relation to mental well-being, sociodemographic, and disease-related characteristics among individuals with T2D.MethodsIndividuals with T2D (aged ≥18 years) who had been referred to self-management education, exercise, diet counseling, smoking cessation, or alcohol counseling completed a questionnaire survey covering (1) background information, (2) the 5-item World Health Organization Well-Being Index (WHO-5), (3) receptiveness to IT use in physical activity, and (4) the Readiness and Enablement Index for Health Technology (READHY), constituted by dimensions related to self-management, social support, and eHealth literacy. Individuals were divided into profiles using cluster analysis based on their READHY scores. Outcomes included differences across profiles in mental well-being, sociodemographic, and disease-related characteristics.ResultsParticipants in the study were 155 individuals with T2D with a mean age of 60.2 (SD 10.7) years, 55.5% (86/155) of which were men and 44.5% (69/155) of which were women. Participants were stratified into 5 health technology readiness profiles based on the cluster analysis: Profile 1, high health technology readiness; Profile 2, medium health technology readiness; Profile 3, medium health technology readiness and high level of emotional distress; Profile 4, medium health technology readiness and low-to-medium eHealth literacy; Profile 5, low health technology readiness. No differences in sociodemographic and disease-related characteristics were observed across profiles; however, we identified 3 vulnerable subgroups of individuals: Profile 3 (21/155, 13.5%), younger individuals (mean age of 53.4 years, SD 8.9 years) with low mental well-being (mean 42.7, SD 14.7) and emotional distress (mean 1.69, SD 0.38); Profile 4 (20/155, 12.9%), older individuals (mean age 66.3 years, SD 9.0 years) with less IT use (50.0% used IT for communication) and low-to-medium eHealth literacy; and Profile 5 (36/155, 23.2%) with low mental well-being (mean 43.4, SD 20.1) and low readiness for health technology.ConclusionsImplementation of IT in health care of individuals with T2D should be based on comprehensive consideration of mental well-being, emotional distress, and readiness for health technology rather than sociodemographic and disease-related characteristics to identify the individuals in need of social support, self-management education, and extensive IT support. A one-size-fits-all approach to IT implementation in health care will potentially increase the risk of treatment failure among the most vulnerable individuals.
| S-EPMC7525399 | biostudies-literature
Project description:Individuals with obesity often experience unfair treatment because of their body weight. Such experiences are associated with binge eating, but less is known about its association with other eating-related behaviors and whether these relations are specific to discrimination based on weight or extend to other attributions for discrimination. The present research uses a large national sample (N = 5129) to examine whether weight discrimination is associated with diet and meal rhythmicity, in addition to overeating, and whether these associations generalize to nine other attributions for discrimination. We found that in addition to overeating, weight discrimination was associated with more frequent consumption of convenience foods and less regular meal timing. These associations were generally similar across sex, age, and race. Discrimination based on ancestry, gender, age, religion, and physical disability were also associated with overeating, which suggests that overeating may be a general coping response to discrimination. Unfair treatment because of body weight is associated with unhealthy eating-related behaviors, which may be one pathway through which weight discrimination increases risk for weight gain and obesity.
| S-EPMC4866877 | biostudies-other
Project description:BACKGROUND:In silico functional genomics have become a driving force in the way we interpret and use gene expression data, enabling researchers to understand which biological pathways are likely to be affected by the treatments or conditions being studied. There are many approaches to functional genomics, but a number of popular methods determine if a set of modified genes has a higher than expected overlap with genes known to function as part of a pathway (functional enrichment testing). Recently, researchers have started to apply such analyses in a new way: to ask if the data they are collecting show similar disruptions to biological functions compared to reference data. Examples include studying whether similar pathways are perturbed in smokers vs. users of e-cigarettes, or whether a new mouse model of schizophrenia is justified, based on its similarity in cytokine expression to a previously published model. However, there is a dearth of robust statistical methods for testing hypotheses related to these questions and most researchers resort to ad hoc approaches. The goal of this work is to develop a statistical approach to identifying gene pathways that are equivalently (or inversely) changed across two experimental conditions. RESULTS:We developed Equivalent Change Enrichment Analysis (ECEA). This is a new type of gene enrichment analysis based on a statistic that we call the equivalent change index (ECI). An ECI of 1 represents a gene that was over or under-expressed (compared to control) to the same degree across two experiments. Using this statistic, we present an approach to identifying pathways that are changed in similar or opposing ways across experiments. We compare our approach to current methods on simulated data and show that ECEA is able to recover pathways exhibiting such changes even when they exhibit complex patterns of regulation, which other approaches are unable to do. On biological data, our approach recovered pathways that appear directly connected to the condition being studied. CONCLUSIONS:ECEA provides a new way to perform gene enrichment analysis that allows researchers to compare their data to existing datasets and determine if a treatment will cause similar or opposing genomic perturbations.
| S-EPMC7041296 | biostudies-literature
Project description:This study aims at identifying the association between physical activity (PA) and sedentary behavior (SB) patterns during adolescents on the future increase in BMI and risk of diabetes during young adulthood. A total of 3,717 participants aged 11 to 21 at baseline who completed Waves I (1994-1995), II (1996), III (2001-2002), and IV (2008) surveys of the National Longitudinal Study of Adolescent Health (Add Health) were analyzed. Physical activity and sedentary behavior patterns were assessed using an interviewer-administered questionnaire at Waves I, II, and III. A participant was classified as having diabetes at Wave IV according to WHO guidelines. The k-means cluster analysis was used to identify the number of PA and SB patterns assessed using interviewer-administered questionnaire. The k-means cluster analysis identified three clusters; 575 (15.5%), 2,140 (57.6%), and 1,002 (27.0%) participants belonged to the low PA high SB (LPAHSB), the LPALSB, and the HPALSB cluster respectively. Relative to the LPALSB cluster, the HPALSB cluster had lower increase in BMI from Wave III to Wave IV (P?=?0.03), whereas the difference between LPAHSB cluster and LPALSB cluster was not significant (P?=?0.09). The odds of developing diabetes at Wave IV was significant for the LPAHSB cluster (OR?=?1.69, 95% CI?=?1.04, 2.75) but not significant for the HPALSB cluster (OR?=?0.87, 95% CI?=?0.52, 1.47) relative to the LPALSB cluster. To conclude, PA but not SB during adolescence predicted change in BMI during young adulthood. SB but not PA during adolescence predicted type 2 diabetes during young adulthood.
| S-EPMC4207744 | biostudies-literature
Project description:OBJECTIVE:To measure the effect of motor vehicle crash (MVC) involvement and readiness to change drinking and driving behaviors on subsequent driving and drinking behaviors among injured emergency department (ED) patients who use alcohol at harmful levels. METHODS:This was a secondary analysis of a randomized controlled trial of injured ED patients who screened positive for harmful alcohol use, who at recruitment reported driving in the past 12 months and received at least one of the intended intervention sessions (brief behavioral intervention versus attention placebo control; N = 407). Outcome variables were as follows: (1) change in 6 impaired driving behaviors and (2) report of MVCs and traffic violations in the 12 months following recruitment; predictor variables were as follows: (1) treatment assignment, (2) MVC involvement at recruitment, and (3) baseline readiness to change alcohol use and drinking and driving. RESULTS:Modeling of change in the 6 impaired driving variables indicated that neither the recruitment visits being MVC related nor baseline readiness to change alcohol use and drinking and driving behaviors predicted greater changes in impaired driving over time. Baseline reports of past moving traffic violations and the ED visit being MVC related predicted a greater likelihood of each behavior at 12 months following study recruitment. CONCLUSIONS:This study and others have demonstrated that ED patients with harmful alcohol use are willing to engage in behavioral interventions directed at changing risky behaviors. However, this study did not demonstrate that patients considered having the potential to be more engaged with the intervention because their ED visit was MVC related and/or they had expressed intent to change their risky alcohol use and drinking and driving behaviors were more likely to change these risky behaviors.
| S-EPMC6168940 | biostudies-literature
Project description:PURPOSE OF REVIEW:Type 2 diabetes (T2D), which accounts for the vast majority of diabetes cases, is essentially a diagnosis of exclusion in current clinical practice. Therefore, it is not surprising that T2D is heterogenous in terms of patients' clinical presentation, disease course, and response to treatment. This review summarizes published attempts to improve diabetes subclassification, with a particular focus on the role of genetics. RECENT FINDINGS:A handful of diabetes subclassification schemas have been proposed using clinical data (patient characteristics and laboratory values), with some subgroups associated with distinct management trends or complication risks. However, phenotypically driven classifications suffer from dependencies on time of variable measurement and are not readily linked to disease mechanism. Germline genetic data, in contrast, are essentially unchanged over a person's lifetime and rooted in mechanism. Clustering of T2D genetic loci has identified at least five groupings of loci representing mechanisms of disease that may aid in deconstructing heterogeneity of T2D, but further work is needed to determine clinical utility. Exciting progress in subclassification of diabetes has demonstrated initial steps in deconstructing disease heterogeneity. Incorporation of genetics into classification schemas will require additional research but has the potential to improve our understanding and management of T2D, both as a single disease and as a part of an integrated metabolic disease network.
| S-EPMC6620259 | biostudies-literature
Project description:BackgroundHealth behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are leading risk factors for noncommunicable chronic diseases and play a central role in limiting health and life satisfaction. To date, however, health behaviors tend to be considered separately from one another, resulting in guidelines and interventions for healthy aging siloed by specific behaviors and often focused only on a given health behavior without considering the co-occurrence of family, social, work, and other behaviors of everyday life.ObjectiveThe aim of this study is to understand how behaviors cluster and how such clusters are associated with physical and mental health, life satisfaction, and health care utilization may provide opportunities to leverage this co-occurrence to develop and evaluate interventions to promote multiple health behavior changes.MethodsUsing cross-sectional baseline data from the Canadian Longitudinal Study on Aging, we will perform a predefined set of exploratory and hypothesis-generating analyses to examine the co-occurrence of health and everyday life behaviors. We will use agglomerative hierarchical cluster analysis to cluster individuals based on their behavioral tendencies. Multinomial logistic regression will then be used to model the relationships between clusters and demographic indicators, health care utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. In addition, we will conduct network community detection analysis using the clique percolation algorithm to detect overlapping communities of behaviors based on the strength of relationships between variables.ResultsBaseline data for the Canadian Longitudinal Study on Aging were collected from 51,338 participants aged between 45 and 85 years. Data were collected between 2010 and 2015. Secondary data analysis for this project was approved by the Ottawa Health Science Network Research Ethics Board (protocol ID #20190506-01H).ConclusionsThis study will help to inform the development of interventions tailored to subpopulations of adults (eg, physically inactive smokers) defined by the multiple behaviors that describe their everyday life experiences.International registered report identifier (irrid)DERR1-10.2196/24887.
| S-EPMC8235290 | biostudies-literature
Project description:Type I diabetes (T1D) is an autoimmune disease characterized by destruction of insulin-producing β-cells in the pancreas. Although several islet cell autoantigens are known, the breadth and spectrum of autoantibody targets has not been fully explored. Here the luciferase immunoprecipitation systems (LIPS) antibody profiling technology was used to study islet and other organ-specific autoantibody responses in parallel. Examination of an initial cohort of 93 controls and 50 T1D subjects revealed that 16% of the diabetic subjects showed anti-gastric ATPase autoantibodies which did not correlate with autoantibodies against GAD65, IA2, or IA2-β. A more detailed study of a second cohort with 18 potential autoantibody targets revealed marked heterogeneity in autoantibody responses against islet cell autoantigens including two polymorphic variants of ZnT8. A subset of T1D subjects exhibited autoantibodies against several organ-specific targets including gastric ATPase (11%), thyroid peroxidase (14%), and anti-IgA autoantibodies against tissue transglutaminase (12%). Although a few T1D subjects showed autoantibodies against a lung-associated protein KCNRG (6%) and S100-β (8%), no statistically significant autoantibodies were detected against several cytokines. Analysis of the overall autoantibody profiles using a heatmap revealed two major subgroups of approximately similar numbers, consisting of T1D subjects with and without organ-specific autoantibodies. Within the organ-specific subgroup, there was minimal overlap among anti-gastric ATPase, anti-thyroid peroxidase, and anti-transglutaminase seropositivity, and these autoantibodies did not correlate with islet cell autoantibodies. Examination of a third cohort, comprising prospectively collected longitudinal samples from high-risk individuals, revealed that anti-gastric ATPase autoantibodies were present in several individuals prior to detection of islet autoantibodies and before clinical onset of T1D. Taken together, these results suggest that autoantibody portraits derived from islet and organ-specific targets will likely be useful for enhancing the clinical management of T1D.
| S-EPMC3448600 | biostudies-literature
Project description:The predictive value of the Readiness to Change Questionnaire (RTCQ) for subsequent drinking was evaluated in 499 women. These women had medical problems potentially exacerbated by alcohol use and were enrolled in an intervention study. Correlates and predictors of stage of change were analyzed. Results indicated that the categorical application of the RTCQ did not predict drinking in the 6-12 months after enrollment. Preliminary findings support rescoring the RTCQ into a continuous measure. Following this conversion, situational risks factors for drinking were examined as potential mediators of readiness to change (RTC). Heightened risk for alcohol consumption during argument or boredom was found to attenuate the association between one's RTC and later drinking. Finally, medical condition moderated the association of RTC on later drinking; women with diabetes, infertility, or osteoporosis drank the most in the contemplation stage. In contrast, hypertensive women drank more when action oriented to change. The implications for treating risk drinking in women are discussed.
| S-EPMC3072060 | biostudies-literature
Project description:Aims/hypothesisWe used recently confirmed type 2 diabetes gene regions to investigate the genetic relationship between type 1 and type 2 diabetes, in an average of 7,606 type 1 diabetic individuals and 8,218 controls, providing >80% power to detect effects as small as an OR of 1.11 at a false-positive rate of 0.003.MethodsThe single nucleotide polymorphisms (SNPs) with the most convincing evidence of association in 12 type 2 diabetes-associated gene regions, PPARG, CDKAL1, HNF1B, WFS1, SLC30A8, CDKN2A-CDKN2B, IGF2BP2, KCNJ11, TCF7L2, FTO, HHEX-IDE and THADA, were analysed in type 1 diabetes cases and controls. PPARG and HHEX-IDE were additionally tested for association in 3,851 type 1 diabetes families. Tests for interaction with HLA class II genotypes, autoantibody status, sex, and age-at-diagnosis of type 1 diabetes were performed with all 12 gene regions.ResultsOnly PPARG and HHEX-IDE showed any evidence of association with type 1 diabetes cases and controls (p = 0.004 and p = 0.003, respectively; p > 0.05 for other SNPs). The potential association of PPARG was supported by family analyses (p = 0.003; p (combined) = 1.0 x 10(-4)). No SNPs showed evidence of interaction with any covariate (p > 0.05).Conclusions/interpretationWe found no convincing genetic link between type 1 and type 2 diabetes. An association of PPARG (rs1801282/Pro12Ala) could be consistent with its known function in inflammation. Hence, our results reinforce evidence suggesting that type 1 diabetes is a disease of the immune system, rather than being due to inherited defects in beta cell function or regeneration or insulin resistance.
| S-EPMC2738846 | biostudies-literature