Reducing dementia risk in mid-life: evaluating a dementia risk profiler and on-line support environment. The In-MINDD feasibility RCT
Dementia is a major challenge for individuals and health systems. While age/genetics are important, increasing evidence shows mid-life risk factors are associated with later dementia risk. These include smoking; obesity; hyperlipidaemia; hypertension; decreased physical activity, plus CVD, diabetes and depression. Digital interventions targeting these risk factors might help people reduce their dementia risk. In-MINDD developed an on-line risk profiler, based on 12 risk factors. This calculated an overall dementia risk score, presented as a Lifestyle for Brain Health (LIBRA) profile, with three components: “Keep This Up”; “Room for Improvement”; “Continue to Manage”. An on-line support environment helped individuals target areas for change and set goals. We aimed to assess the feasibility and acceptability of this intervention to support individuals’ target dementia risk.
To establish parameters of recruitment, effect and acceptability, a feasibility RCT with embedded qualitative process evaluation was conducted in four European primary care settings – France, Ireland, the Netherlands and Scotland. Participants, aged 40-60 years, were randomised to the In-MINDD intervention or control arm. Profiler data were collected at baseline, with intervention group given their LIBRA profile and access to the support environment. All participants re-completed the profiler after 6 months. Primary outcome measure: change in “Keep This Up” LIBRA score. 56 participants were interviewed at baseline or completion to explore their views of dementia risk, In-MINDD and their progress in trying to reduce dementia risk.
451 participants were randomised. Mean age was 52.7 years; more were female and married/co-habiting. Overall, 31.3% were obese; 37.3% had raised cholesterol; almost 50.0% had record of hypertension in medical notes; 13.0% diagnosis of CVD. About 20.0% smoked and over 40.0% drank several times a week. Medical history and risk factors were similar across both arms of trial. Mean LIBRA “Keep This Up” score improved slightly more in the In-MINDD intervention arm (mean change = 1.8 (SD 8.6)) than in control arm (mean change = 1.1 (9.7)); mean difference = 0.638 (p=0.638, 95% CI -1.389 – 2.664). Some risk factors improved more in intervention arm, including drinking, diet and physical activity. Qualitative interviews suggest participants found information on dementia risk helpful and liked the profiler, but lack of time was often cited as reason for not using on-line support environment. Participants in both arms reported targeting diet and exercise as areas for change.
Results show that a digital health intervention targeting mid-life risk factors for dementia is feasible and acceptable. Lack of statistical significance may be because feasibility trial was not adequately powered to detect difference or because it would be better to focus on key behaviours such as drinking, diet and physical activity. Few had prior awareness of role of modifiable risk factors; so knowing about the trial may have contaminated control group.