Change in self-rated general health is associated with perceived illness burden: a 1-year follow up of patients newly diagnosed with type 2 diabetes

Servant leadership, brand love, and work ethic: important predictors of general health in workers in the education sector

Background

Building a path aimed at the wellbeing of workers in the education sector is the fundamental basis to encourage quality education. To fill the gap in knowledge and address this aspect by understanding the behavior of the study population, it was proposed as with the objective of determining if servant leadership, brand love and work ethic predict the general health in workers.

Methods

A non-probability sampling was applied for convenience. For this purpose, a sample of 509 workers from Peru was submitted to study, who completed a questionnaire consisting of: scale of servant leadership, work ethic, GHQ-12 and brand love. By applying a quantitative method using a structural equation modeling partial least squares approach.

Results

The present study demonstrated that the three constructs (servant leadership, brand love, and work ethic) predict the general health of workers in a positive and significant way, in a sample of Peruvian workers in the education sector. Furthermore, the results suggest that these factors can be used to improve the health of employees in educational institutions in Peru and possibly in other contexts as well.

Conclusion

Given these results and after knowing the solidity of the predictions, the importance of promoting general health in workers in the education sector.

Sociodemographic and clinical correlates of diabetes self-efficacy in adults with type 2 diabetes and comorbid heart failure

Heart failure (HF) is a comorbidity that complicates type 2 diabetes mellitus (T2D) management and increases the chance of death. However, little is known concerning factors related to diabetes self-efficacy in comorbid HF. This secondary data analysis was aimed at describing sociodemographic and clinical correlates of diabetes self-efficacy in adults with T2D and comorbid HF. A correlational design was used to analyze cross-sectional baseline data from a randomized study of 180 participants that tested a 6-month integrated self-care intervention targeting adults with concomitant HF and T2D. Participants were enrolled from one of four large urban-tertiary hospitals in Atlanta, GA, during 2010-2013. Data were collected from medical records and self-report. We used stepwise multiple linear regressions to examine variables associated with diabetes self-efficacy. The participants' mean age was 58.1 ± 10.7 years and the majority were male (n = 118; 65.6%) and African American (n = 119; 66.1%). Good self-rated health and presence of implantable cardioverter-defibrillator (ICD) had significant positive relationships with diabetes self-efficacy, while taking both oral antiglycemic medication and insulin, history of depression, cardiac pacemaker, and taking digitalis were negatively related. These variables collectively explained 22.4% of the variation in diabetes self-efficacy. One study implication is that using self-rated health provides a quick, patient-centered assessment to evaluate patient health status. Further studies are warranted to ascertain the pathways linking ICD, pacemaker, and digitalis treatment with diabetes self-efficacy.

The effect of structured personal care on diabetes symptoms and self-rated health over 14 years after diabetes diagnosis

AIMS

To explore the effect of structured personal care on diabetes symptoms and self-rated health over 14 years after diabetes diagnosis while patients are gradually diagnosed with other chronic conditions (multimorbidity).

METHODS

Post hoc analysis of the Danish randomized controlled trial Diabetes Care in General Practice including 1381 patients newly diagnosed with type 2 diabetes. The effect of structured personal care compared with routine care on diabetes symptoms and self-rated health was analysed 6 and 14 years after diagnosis with a generalized multilevel Rasch model.

RESULTS

Structured personal care reduced the overall likelihood of reporting diabetes symptoms at the end of the intervention (OR 0.79; 95% CI: 0.64-0.97), but this effect was not explained by glycaemic control or multimorbidity. There was no effect of the intervention on diabetes symptoms after 14 years or on self-rated health after 6 years or 14 years.

CONCLUSIONS

Structured personal care had a beneficial effect on diabetes symptoms 6 years after diagnosis, but not on self-rated health at either follow up point. To optimally manage patients over time it is important to supplement clinical information by information provided by the patients.

A competing-risk-based score for predicting twenty-year risk of incident diabetes: the Beijing Longitudinal Study of Ageing study

Few risk tools have been proposed to quantify the long-term risk of diabetes among middle-aged and elderly individuals in China. The present study aimed to develop a risk tool to estimate the 20-year risk of developing diabetes while incorporating competing risks. A three-stage stratification random-clustering sampling procedure was conducted to ensure the representativeness of the Beijing elderly. We prospectively followed 1857 community residents aged 55 years and above who were free of diabetes at baseline examination. Sub-distribution hazards models were used to adjust for the competing risks of non-diabetes death. The cumulative incidence function of twenty-year diabetes event rates was 11.60% after adjusting for the competing risks of non-diabetes death. Age, body mass index, fasting plasma glucose, health status, and physical activity were selected to form the score. The area under the ROC curve (AUC) was 0.76 (95% Confidence Interval: 0.72-0.80), and the optimism-corrected AUC was 0.78 (95% Confidence Interval: 0.69-0.87) after internal validation by bootstrapping. The calibration plot showed that the actual diabetes risk was similar to the predicted risk. The cut-off value of the risk score was 19 points, marking mark the difference between low-risk and high-risk patients, which exhibited a sensitivity of 0.74 and specificity of 0.65.

Evaluating Digital Health Interventions: Key Questions and Approaches

Digital health interventions have enormous potential as scalable tools to improve health and healthcare delivery by improving effectiveness, efficiency, accessibility, safety, and personalization. Achieving these improvements requires a cumulative knowledge base to inform development and deployment of digital health interventions. However, evaluations of digital health interventions present special challenges. This paper aims to examine these challenges and outline an evaluation strategy in terms of the research questions needed to appraise such interventions. As they are at the intersection of biomedical, behavioral, computing, and engineering research, methods drawn from all of these disciplines are required. Relevant research questions include defining the problem and the likely benefit of the digital health intervention, which in turn requires establishing the likely reach and uptake of the intervention, the causal model describing how the intervention will achieve its intended benefit, key components, and how they interact with one another, and estimating overall benefit in terms of effectiveness, cost effectiveness, and harms. Although RCTs are important for evaluation of effectiveness and cost effectiveness, they are best undertaken only when: (1) the intervention and its delivery package are stable; (2) these can be implemented with high fidelity; and (3) there is a reasonable likelihood that the overall benefits will be clinically meaningful (improved outcomes or equivalent outcomes at lower cost). Broadening the portfolio of research questions and evaluation methods will help with developing the necessary knowledge base to inform decisions on policy, practice, and research.

Prediction of the 20-year incidence of diabetes in older Chinese: Application of the competing risk method in a longitudinal study

The competing risk method has become more acceptable for time-to-event data analysis because of its advantage over the standard Cox model in accounting for competing events in the risk set. This study aimed to construct a prediction model for diabetes using a subdistribution hazards model.We prospectively followed 1857 community residents who were aged ≥ 55 years, free of diabetes at baseline examination from August 1992 to December 2012. Diabetes was defined as a self-reported history of diabetes diagnosis, taking antidiabetic medicine, or having fasting plasma glucose (FPG) ≥ 7.0 mmol/L. A questionnaire was used to measure diabetes risk factors, including dietary habits, lifestyle, psychological factors, cognitive function, and physical condition. Gray test and a subdistribution hazards model were used to construct a prediction algorithm for 20-year risk of diabetes. Receiver operating characteristic (ROC) curves, bootstrap cross-validated Wolber concordance index (C-index) statistics, and calibration plots were used to assess model performance.During the 20-year follow-up period, 144 cases were documented for diabetes incidence with a median follow-up of 10.9 years (interquartile range: 8.0-15.3 years). The cumulative incidence function of 20-year diabetes incidence was 11.60% after adjusting for the competing risk of nondiabetes death. Gray test showed that body mass index, FPG, self-rated heath status, and physical activity were associated with the cumulative incidence function of diabetes after adjusting for age. Finally, 5 standard risk factors (poor self-rated health status [subdistribution hazard ratio (SHR) = 1.73, P = 0.005], less physical activity [SHR = 1.39, P = 0.047], 55-65 years old [SHR = 4.37, P < 0.001], overweight [SHR = 2.15, P < 0.001] or obesity [SHR = 1.96, P = 0.003], and impaired fasting glucose [IFG] [SHR = 1.99, P < 0.001]) were significantly associated with incident diabetes. Model performance was moderate to excellent, as indicated by its bootstrap cross-validated discrimination C-index (0.74, 95% CI: 0.70-0.79) and calibration plot.Poor self-rated health, physical inactivity, being 55 to 65 years of age, overweight/obesity, and IFG were significant predictors of incident diabetes. Early prevention with a goal of achieving optimal levels of all risk factors should become a key element of diabetes prevention.

Diagnostic labelling influences self-rated health. A prospective cohort study: the HUNT Study, Norway

BACKGROUND

Studies have shown an independent association between poor self-rated health (SRH) and increased mortality. Few studies, however, have investigated any possible impact on SRH of diagnostic labelling.

OBJECTIVE

To test whether SRH differed in persons with known and unknown hypothyroidism, diabetes mellitus (DM) or hypertension, opposed to persons without these conditions, after 11-year follow-up.

METHODS

Prospective population-based cohort study in North-Trøndelag County, Norway, HUNT2 (1995-97) to HUNT3 (2006-08). All inhabitants aged 20 years and older were invited. The response rate was 69.5% in HUNT2 and 54.1% in HUNT3. In total, 34144 persons aged 20-70 years were included in the study population. The outcome was poor SRH.

RESULTS

Persons with known disease had an increased odds ratio (OR) to report poor SRH at follow-up; figures ranging from 1.11 (0.68-1.79) to 2.52 (1.46-4.34) (men with hypothyroidism kept out owing to too few numbers). However, in persons not reporting, but having laboratory results indicating these diseases (unknown disease), no corresponding associations with SRH were found. Contrary, the OR for poor SRH in women with unknown hypothyroidism and unknown hypertension was 0.64 (0.38-1.06) and 0.89 (0.79-1.01), respectively.

CONCLUSIONS

Awareness opposed to ignorance of hypothyroidism, DM and hypertension seemed to be associated with poor perceived health, suggesting that diagnostic labelling could have a negative effect on SRH. This relationship needs to be tested more thoroughly in future research but should be kept in mind regarding the benefits of early diagnosing of diseases.