A novel, multidomain, primary care nurse-led and mHealth-assisted intervention for dementia risk reduction in middle-aged adults (HAPPI MIND): study protocol for a cluster randomised controlled trial

INTRODUCTION

Middle-aged multidomain risk reduction interventions targeting modifiable risk factors for dementia may delay or prevent a third of dementia cases in later life. We describe the protocol of a cluster randomised controlled trial (cRCT), HAPPI MIND (Holistic Approach in Primary care for PreventIng Memory Impairment aNd Dementia). HAPPI MIND will evaluate the efficacy of a multidomain, nurse-led, mHealth supported intervention for assessing dementia risk and reducing associated risk factors in middle-aged adults in the Australian primary care setting.

METHODS AND ANALYSIS

General practice clinics (n≥26) across Victoria and New South Wales, Australia, will be recruited and randomised. Practice nurses will be trained to implement the HAPPI MIND intervention or a brief intervention. Patients of participating practices aged 45-65 years with ≥2 potential dementia risk factors will be identified and recruited (approximately 15 patients/clinic). Brief intervention participants receive a personalised report outlining their risk factors for dementia based on Australian National University Alzheimer's Disease Risk Index (ANU-ADRI) scores, education booklet and referral to their general practitioner as appropriate. HAPPI MIND participants receive the brief intervention as well as six individualised dementia risk reduction sessions with a nurse trained in motivational interviewing and principles of behaviour change, a personalised risk reduction action plan and access to the purpose-built HAPPI MIND smartphone app for risk factor self-management. Follow-up data collection will occur at 12, 24 and 36 months. Primary outcome is ANU-ADRI score change at 12 months from baseline. Secondary outcomes include change in cognition, quality of life and individual risk factors of dementia.

ETHICS AND DISSEMINATION

Project approved by Monash University Human Research Ethics Committee (ID: 28273). Results will be disseminated in peer-reviewed journals and at healthcare conferences. If effective in reducing dementia risk, the HAPPI MIND intervention could be integrated into primary care, scaled up nationally and sustained over time.

TRIAL REGISTRATION NUMBER

ACTRN12621001168842.

Multi-domain prognostic models used in middle-aged adults without known cognitive impairment for predicting subsequent dementia

BACKGROUND

Dementia, a global health priority, has no current cure. Around 50 million people worldwide currently live with dementia, and this number is expected to treble by 2050. Some health conditions and lifestyle behaviours can increase or decrease the risk of dementia and are known as 'predictors'. Prognostic models combine such predictors to measure the risk of future dementia. Models that can accurately predict future dementia would help clinicians select high-risk adults in middle age and implement targeted risk reduction.

OBJECTIVES

Our primary objective was to identify multi-domain prognostic models used in middle-aged adults (aged 45 to 65 years) for predicting dementia or cognitive impairment. Eligible multi-domain prognostic models involved two or more of the modifiable dementia predictors identified in a 2020 Lancet Commission report and a 2019 World Health Organization (WHO) report (less education, hearing loss, traumatic brain injury, hypertension, excessive alcohol intake, obesity, smoking, depression, social isolation, physical inactivity, diabetes mellitus, air pollution, poor diet, and cognitive inactivity). Our secondary objectives were to summarise the prognostic models, to appraise their predictive accuracy (discrimination and calibration) as reported in the development and validation studies, and to identify the implications of using dementia prognostic models for the management of people at a higher risk for future dementia.

SEARCH METHODS

We searched MEDLINE, Embase, PsycINFO, CINAHL, and ISI Web of Science Core Collection from inception until 6 June 2022. We performed forwards and backwards citation tracking of included studies using the Web of Science platform.  SELECTION CRITERIA: We included development and validation studies of multi-domain prognostic models. The minimum eligible follow-up was five years. Our primary outcome was an incident clinical diagnosis of dementia based on validated diagnostic criteria, and our secondary outcome was dementia or cognitive impairment determined by any other method.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened the references, extracted data using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and assessed risk of bias and applicability of included studies using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We synthesised the C-statistics of models that had been externally validated in at least three comparable studies.  MAIN RESULTS: We identified 20 eligible studies; eight were development studies and 12 were validation studies. There were 14 unique prognostic models: seven models with validation studies and seven models with development-only studies. The models included a median of nine predictors (range 6 to 34); the median number of modifiable predictors was five (range 2 to 11). The most common modifiable predictors in externally validated models were diabetes, hypertension, smoking, physical activity, and obesity. In development-only models, the most common modifiable predictors were obesity, diabetes, hypertension, and smoking. No models included hearing loss or air pollution as predictors. Nineteen studies had a high risk of bias according to the PROBAST assessment, mainly because of inappropriate analysis methods, particularly lack of reported calibration measures. Applicability concerns were low for 12 studies, as their population, predictors, and outcomes were consistent with those of interest for this review. Applicability concerns were high for nine studies, as they lacked baseline cognitive screening or excluded an age group within the range of 45 to 65 years. Only one model, Cardiovascular Risk Factors, Ageing, and Dementia (CAIDE), had been externally validated in multiple studies, allowing for meta-analysis. The CAIDE model included eight predictors (four modifiable predictors): age, education, sex, systolic blood pressure, body mass index (BMI), total cholesterol, physical activity and APOEƐ4 status. Overall, our confidence in the prediction accuracy of CAIDE was very low; our main reasons for downgrading the certainty of the evidence were high risk of bias across all the studies, high concern of applicability, non-overlapping confidence intervals (CIs), and a high degree of heterogeneity. The summary C-statistic was 0.71 (95% CI 0.66 to 0.76; 3 studies; very low-certainty evidence) for the incident clinical diagnosis of dementia, and 0.67 (95% CI 0.61 to 0.73; 3 studies; very low-certainty evidence) for dementia or cognitive impairment based on cognitive scores. Meta-analysis of calibration measures was not possible, as few studies provided these data.

AUTHORS' CONCLUSIONS

We identified 14 unique multi-domain prognostic models used in middle-aged adults for predicting subsequent dementia. Diabetes, hypertension, obesity, and smoking were the most common modifiable risk factors used as predictors in the models. We performed meta-analyses of C-statistics for one model (CAIDE), but the summary values were unreliable. Owing to lack of data, we were unable to meta-analyse the calibration measures of CAIDE. This review highlights the need for further robust external validations of multi-domain prognostic models for predicting future risk of dementia in middle-aged adults.

Effectiveness of Interventions Targeting Treatable Traits for the Management of Obstructive Airway Diseases: A Systematic Review and Meta-Analysis

BACKGROUND

The management of obstructive airway diseases (OADs) is complex. The treatable traits (TTs) approach may be an effective strategy for managing OADs.

OBJECTIVE

To determine the effectiveness of interventions targeting TTs for managing OADs.

METHODS

Ovid Embase, Medline, CENTRAL, and CINAHL Plus were searched from inception to March 9, 2022. Studies of interventions targeting at least 1 TT from pulmonary, extrapulmonary, and behavioral/lifestyle domains were included. Two reviewers independently extracted relevant data and performed risk-of-bias assessments. Meta-analyses were performed using random-effects models. Subgroup and sensitivity analyses were carried out to explore heterogeneity and to determine the effects of outlying studies.

RESULTS

Eleven studies that used the TTs approach for OAD management were identified. Traits targeted within each study ranged from 13 to 36. Seven controlled trials were included in meta-analyses. TT interventions were effective at improving health-related quality of life (mean difference [MD] = -6.96, 95% CI: -9.92 to -4.01), hospitalizations (odds ratio [OR] = 0.52, 95% CI: 0.39 to 0.69), all-cause-1-year mortality (OR = 0.65, 95% CI: 0.45 to 0.95), dyspnea score (MD = -0.29, 95% CI: -0.46 to -0.12), anxiety (MD = -1.61, 95% CI: -2.92 to -0.30), and depression (MD = -2.00, 95% CI: -3.53 to -0.47).

CONCLUSION

Characterizing TTs and targeted interventions can improve outcomes in OADs, which offer a promising model of care for OADs.