MinderCare: protocol for a mixed-methods evaluation of a digitally enabled dementia care service

Introduction and aims

Dementia is a growing public health challenge affecting millions of people worldwide. It is a progressive condition that increases the risk of infections, falls, hospital admissions, dependence in activities of daily living, safety issues such as wandering, care home transfers, and death. New ways of supporting people living with dementia (PLWD) at home are urgently needed. We describe the MinderCare study which evaluates a digitally enabled care model that integrates low-burden sensor-based remote monitoring within a nurse-led clinical service.

Methods and analysis

In this mixed-methods study, we will recruit 100 people with confirmed or suspected dementia living at home and deploy the Minder remote monitoring system for at least 12 months. A detailed characterisation of the cohort will be obtained, including cognition, frailty, participant and carer wellbeing, functioning, and quality of life. The feasibility, acceptability, sustainability, and resource requirements of the service will also be assessed. Low-cost sensors provide information about behaviour, environment and physiology from the home. Machine-learning algorithms have been used to develop digital biomarkers of infection, sleep, night-time behaviours, daily activities and routines, and the effects of clinical events and treatment. These will be assessed through clinical reports of sensor-derived data that include anomaly alerts provided to the clinical teams. Algorithms will be assessed for their clinical utility and acceptability. The comparative-effectiveness component will be designed as a target trial emulation using linked electronic health-record data to construct a time-indexed external usual-care control cohort. The primary comparative outcome will be Days Alive and Out of Hospital (DAOH) over 12 months from the activation-index date, with healthcare utilisation, costs, institutionalisation and mortality assessed as secondary outcomes. DAOH and estimated MinderCare effects will also be examined across prespecified strata of baseline inpatient utilisation.

Ethics and dissemination

Ethical approval has been granted by the North East – Newcastle and North Tyneside 2 Research Ethics Committee, and the study has received confirmation of capacity and capability by the Imperial College Healthcare NHS Trust. Study findings will be disseminated to patients, health and social care professionals, and policymakers through peer-reviewed publications and conference presentations.

Study registration number: ISRCTN14997677 and NIHR portfolio CPMSID 63023.

Strengths and limitations of this study

• This study evaluates a digitally enabled remote monitoring model for people living with dementia that integrates passive in-home sensors and algorithm-derived digital biomarkers to detect clinically relevant changes such as infection risk, behavioural disturbance and physiological deterioration.

• The study is embedded within the North West London Integrated Care System, allowing evaluation of a remote monitoring service implemented within routine NHS and social care pathways in a large and socio-demographically diverse population.

• The mixed-methods design integrates continuous sensor data, standardised clinical assessments, linked electronic health records and qualitative interviews to examine algorithm performance alongside service feasibility, acceptability and sustainability.

• The comparative-effectiveness component is structured as a target trial emulation using linked electronic health records, with explicit specification of eligibility, treatment strategies, activation-index date, follow-up, outcomes, causal estimand and analysis plan.

• As a non-randomised target trial emulation using an externally matched comparator, the study may remain susceptible to residual confounding, particularly from unmeasured factors such as carer engagement, home suitability, willingness to accept monitoring and referral-route effects; attrition due to death, care home transition, and variability in home environments or device connectivity may also affect data completeness and interpretation.