Medication adherence during adjunct therapy with statins and ACE inhibitors in adolescents with type 1 diabetes

OBJECTIVE: Suboptimal adherence to insulin treatment is a main issue in adolescents with type 1 diabetes. However, to date, there are no available data on adherence to adjunct noninsulin medications in this population. Our aim was to assess adherence to ACE inhibitors and statins and explore potential determinants in adolescents with type 1 diabetes. RESEARCH DESIGN AND METHODS:There were 443 adolescents with type 1 diabetes recruited into the Adolescent Type 1 Diabetes Cardio-Renal Intervention Trial (AdDIT) and exposed to treatment with two oral drugs—an ACE inhibitor and a statin—as well as combinations of both or placebo for 2–4 years. Adherence was assessed every 3 months with the Medication Event Monitoring System (MEMS) and pill count. RESULTS: Median adherence during the trial was 80.2% (interquartile range 63.6–91.8) based on MEMS and 85.7% (72.4–92.9) for pill count. Adherence based on MEMS and pill count dropped from 92.9% and 96.3%, respectively, at the first visit to 76.3% and 79.0% at the end of the trial. The percentage of study participants with adherence ≥75% declined from 84% to 53%. A good correlation was found between adherence based on MEMS and pill count (r = 0.82, P < 0.001). Factors associated with adherence were age, glycemic control, and country. CONCLUSIONS: We report an overall good adherence to ACE inhibitors and statins during a clinical trial, although there was a clear decline in adherence over time. Older age and suboptimal glycemic control at baseline predicted lower adherence during the trial, and, predictably, reduced adherence was more prevalent in subjects who subsequently dropped out

Genes versus lifestyles: Exploring beliefs about the determinants of cognitive ageing

Objectives: Genetic and lifestyle factors contribute to cognitive ageing. This study explored people’s beliefs about determinants of cognitive ageing and whether those beliefs were associated with engagement in potentially beneficial behaviours. Methods: Data were collected through a UK-wide survey of people aged 40 and over. Responses from 3,130 individuals (94.0% of the survey sample) were analysed using chi-square tests of independence, principal component analysis and ANCOVAs. Results: Most respondents (62.2%) believed genes and lifestyle contribute equally to age-related changes in cognitive skills. Respondents who believed genetic factors were more influential were less likely to expect cognitive skills might be improved or maintained with age, less sure what behaviours might be associated with brain health, and less likely to engage in behaviours comprising mental challenge/novelty supported as beneficial for brain health. Conclusion: Our results indicate a need for clearer messaging highlighting the role of lifestyle factors for brain health

Genes versus lifestyles: Exploring beliefs about the determinants of cognitive ageing

Genetic and lifestyle factors contribute to cognitive ageing. However, the extent to which the public attribute changes in thinking skills to either genetic or lifestyle factors is largely unknown. This may be important if it impacts engagement in activities deemed beneficial to thinking skills. This study, therefore, explored people’s beliefs about determinants of cognitive ageing and whether those beliefs were associated with engagement in potentially beneficial behaviours. Data were collected through a United Kingdom-wide survey of people aged 40 and over. Participants completed questions about their beliefs regarding cognitive ageing, and specifically the extent to which they believed lifestyle or genetic factors influence those changes, and their engagement in specific behaviours that may be cognitively beneficial. Responses from 3,130 individuals (94.0% of the survey sample) were analysed using chi-square tests of independence, principal component analysis and ANCOVAs to investigate whether their attribution of genetic or lifestyle determinants were associated with their beliefs about cognitive ageing and their participation in brain health-related behaviours. Most respondents (62.2%) believed genes and lifestyle contribute equally to age-related changes in cognitive skills. Respondents who believed genetic factors were more influential were less likely to expect cognitive skills might be improved or maintained with age, less sure what behaviours might be associated with brain health, and less likely to engage in behaviours comprising mental challenge/novelty supported as beneficial for brain health. From this United Kingdom-wide survey about beliefs regarding potential determinants of cognitive ageing, some of our respondents’ views were not aligned with the findings from ageing research. It is important for the public to know how to keep their brains healthy. Our results indicate a need for clearer messaging highlighting the role of lifestyle factors for brain health

Measuring Activity Engagement in Old Age: An Exploratory Factor Analysis

A growing body of literature suggests that higher engagement in a range of activities can be beneficial for cognitive health in old age. Such studies typically rely on self-report questionnaires to assess level of engagement. These questionnaires are highly heterogeneous across studies, limiting generalisability. In particular, the most appropriate domains of activity engagement remain unclear. The Victoria Longitudinal Study-Activity Lifestyle Questionnaire comprises one of the broadest and most diverse collections of activity items, but different studies report different domain structures. This study aimed to help establish a generalisable domain structure of the Victoria Longitudinal Study-Activity Lifestyle Questionnaire. The questionnaire was adapted for use in a sample of UK-based older adults (336 community-dwelling adults aged 65–92 with no diagnosed cognitive impairment). An exploratory factor analysis was conducted on 29 items. The final model retained 22 of these items in a six-factor structure. Activity domains were: Manual (e.g., household repairs), Intellectual (e.g., attending a public lecture), Games (e.g., card games), Religious (e.g., attending religious services), Exercise (e.g., aerobics) and Social (e.g., going out with friends). Given that beneficial activities have the potential to be adapted into interventions, it is essential that future studies consider the most appropriate measurement of activity engagement across domains. The factor structure reported here offers a parsimonious and potentially useful way for future studies to assess engagement in different kinds of activities