Psychological Stress Among Risks for Alzheimer\u27s Disease

This fact sheet defines stress, stressors and reaction to them, what stress does to our brain and body, and how stress effects can accumulate over time. It also includes tips on reducing the effects of stress on the brain

Late-Life Depression

This fact sheet describes late-life depression, including what it is, how common it is, risk factors, where to get help, treatments, suicide risks, and how depression relates to dementia

A Healthy Brain: Use It or Lose It!

Challenging your muscles makes for strong healthy muscles, and challenging your brain cells makes for a strong healthy brain. Build up your brain’s cognitive reserve – create a well-connected, high-capacity brain – through overall healthy behaviors (diet and exercise) and making mentally challenging activities part of your daily life. The more options your brain has to get its signals sent, the better able it is to handle areas of damage. A well-connected brain may be better able to delay or prevent diseases like Alzheimer’s

Maximizing the Quality of Life for Persons with Dementia

This fact sheet show how through simple adjustments to the environment and by providing opportunities for meaningful activities or pleasant experiences, caregivers can play a key role in maximizing the quality of life that people with dementia can maintain despite their impairments

Dementia Risk: Is It Nature or Nurture?

Alzheimer’s is a complex disease for which there is no known method for reversing its effects and no known cure for those who develop it. While a large part of the risk for developing AD comes from genes we inherit, there is evidence suggesting that an even greater part of our risk comes from our “environment,” and more specifically, our lifestyle

Making Lifestyle Choices to Reduce Late-Life Depression Risk

Depression is a complex condition involving insufficient levels of brain chemicals, life stressors, thought processes, behaviors, and coping mechanisms. Medications and counseling therapy are effective in treating depression, and research suggests that several lifestyle practices also help to protect us from depression, and speed up our recovery if we do experience it. These behaviors, such as being socially connected, eating healthy and exercising, cost little to nothing but can have profound benefits on our overall quality of life and help us to stay depression-free

Evaluation of gene-based family-based methods to detect novel genes associated with familial late onset Alzheimer disease

AbstractGene-based tests to study the combined effect of rare variants towards a particular phenotype have been widely developed for case-control studies, but their evolution and adaptation for family-based studies, especially for complex incomplete families, has been slower. In this study, we have performed a practical examination of all the latest gene-based methods available for family-based study designs using both simulated and real datasets. We have examined the performance of several collapsing, variance-component and transmission disequilibrium tests across eight different software and twenty-two models utilizing a cohort of 285 families (N=1,235) with late-onset Alzheimer disease (LOAD). After a thorough examination of each of these tests, we propose a methodological approach to identify, with high confidence, genes associated with the studied phenotype with high confidence and we provide recommendations to select the best software and model for family-based gene-based analyses. Additionally, in our dataset, we identified PTK2B, a GWAS candidate gene for sporadic AD, along with six novel genes (CHRD, CLCN2, HDLBP, CPAMD8, NLRP9, MAS1L) as candidates genes for familial LOAD.</jats:p

The Empowering Role of Mobile Apps in Behavior Change Interventions: The Gray Matters Randomized Controlled Trial

BACKGROUND: Health education and behavior change programs targeting specific risk factors have demonstrated their effectiveness in reducing the development of future diseases. Alzheimer disease (AD) shares many of the same risk factors, most of which can be addressed via behavior change. It is therefore theorized that a behavior change intervention targeting these risk factors would likely result in favorable rates of AD prevention. OBJECTIVE: The objective of this study was to reduce the future risk of developing AD, while in the short term promoting vascular health, through behavior change. METHODS: The study was an interventional randomized controlled trial consisting of subjects who were randomly assigned into either treatment (n=102) or control group (n=42). Outcome measures included various blood-based biomarkers, anthropometric measures, and behaviors related to AD risk. The treatment group was provided with a bespoke “Gray Matters” mobile phone app designed to encourage and facilitate behavior change. The app presented evidence-based educational material relating to AD risk and prevention strategies, facilitated self-reporting of behaviors across 6 behavioral domains, and presented feedback on the user’s performance, calculated from reported behaviors against recommended guidelines. RESULTS: This paper explores the rationale for a mobile phone–led intervention and details the app’s effect on behavior change and subsequent clinical outcomes. Via the app, the average participant submitted 7.3 (SD 3.2) behavioral logs/day (n=122,719). Analysis of these logs against primary outcome measures revealed that participants who improved their high-density lipoprotein cholesterol levels during the study duration answered a statistically significant higher number of questions per day (mean 8.30, SD 2.29) than those with no improvement (mean 6.52, SD 3.612), t(97.74)=−3.051, P=.003. Participants who decreased their body mass index (BMI) performed significantly better in attaining their recommended daily goals (mean 56.21 SD 30.4%) than those who increased their BMI (mean 40.12 SD 29.1%), t(80) = −2.449, P=.017. In total, 69.2% (n=18) of those who achieved a mean performance percentage of 60% or higher, across all domains, reduced their BMI during the study, whereas 60.7% (n=34) who did not, increased their BMI. One-way analysis of variance of systolic blood pressure category changes showed a significant correlation between reported efforts to reduce stress and category change as a whole, P=.035. An exit survey highlighted that respondents (n=83) reported that the app motivated them to perform physical activity (85.4%) and make healthier food choices (87.5%). CONCLUSIONS: In this study, the ubiquitous nature of the mobile phone excelled as a delivery platform for the intervention, enabling the dissemination of educational intervention material while simultaneously monitoring and encouraging positive behavior change, resulting in desirable clinical effects. Sustained effort to maintain the achieved behaviors is expected to mitigate future AD risk. TRIAL REGISTRATION: ClinicalTrails.gov NCT02290912; https://clinicaltrials.gov/ct2/show/NCT02290912 (Archived by WebCite at http://www.webcitation.org/6ictUEwnm

Cognitive-behavioural therapy (CBT) for renal fatigue (BReF) : a feasibility randomised-controlled trial of CBT for the management of fatigue in haemodialysis (HD) patients

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/INTRODUCTION: Fatigue is one of the most common and disabling symptoms in end-stage kidney disease, particularly among in-centre haemodialysis patients. This two-arm parallel group feasibility randomised controlled trial will determine whether a fully powered efficacy trial is achievable by examining the feasibility of recruitment, acceptability and potential benefits of a cognitive-behavioural therapy (CBT)-based intervention for fatigue among in-centre haemodialysis patients. METHODS: We aim to recruit 40 adult patients undergoing in-centre haemodialysis at secondary care outpatient dialysis units, who meet clinical levels of fatigue. Patients will be randomised individually (using a 1:1 ratio) to either a 4-6 weeks' CBT-based intervention (intervention arm) or to a waiting-list control (control arm). The primary feasibility outcomes include descriptive data on numbers within each recruiting centre meeting eligibility criteria, rates of recruitment, numbers retained postrandomisation and treatment adherence. To assess the potential benefits of the cognitive-behavioural therapy for renal fatigue intervention, secondary self-report outcomes include measures of fatigue severity (Chalder Fatigue Questionnaire), fatigue-related functional impairment (Work and Social Adjustment Scale), sleep quality (Pittsburgh Sleep Quality Index), depression (Patient Health Questionnaire-9) and anxiety (Generalised Anxiety Disorder-7). Changes in fatigue perceptions (Brief Illness Perception Questionnaire), cognitive and behavioural responses to fatigue (Cognitive and Behavioural Responses to Symptoms Questionnaire), sleep hygiene behaviours (Sleep Hygiene Index) and physical activity (International Physical Activity Questionnaire-short form) will also be explored. These self-report measures will be collected at baseline and 3 months postrandomisation. Nested qualitative interviews will be conducted postintervention to explore the acceptability of the intervention and identify any areas in need of improvement. The statistician and assessor will be blinded to treatment allocation. ETHICS AND DISSEMINATION: A National Health Service (NHS) Research Ethics Committee approved the study. Any amendments to the protocol will be submitted to the NHS Committee and study sponsor. TRIAL REGISTRATION NUMBER: ISRCTN91238019;Pre-results.Peer reviewe