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| ID | Type | Description | Link |
|---|---|---|---|
| IRAS ID: 289028 | Other Identifier | Health Research Authority |
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| Name | Class |
|---|---|
| University of Oxford | OTHER |
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The overall aim of this study is to find out if people with cognitive difficulties will wear and use different types of digital technology, and if they will allow data from that technology and their clinical profile to be collected. Participants will be patients in Essex Memory clinic and their partners/carers. The digital technology used will include a smartwatch, a sleep headband and two smartphone applications, which have been selected as part of the Early Detection of Neurodegenerative Disease (EDoN) initiative. The investigators will also investigate how the digital data can be analyzed together with routinely captured clinical data using machine learning models, a complex type of statistical analysis.
The aim of the wider EDoN initiative is to combine digital and clinical data to develop machine learning models which can predict individuals' risk of developing dementia decades before the onset of symptoms.
Study Design & Methods of Data Collection: This study is designed as an observational, pilot and feasibility study. Recruitment will be over 18 months with clinical data collected at baseline and up to 5 years follow up, and digital data collected at baseline, 3, 6, 9 and 12 months.
Setting: Essex Memory Clinics within the Essex Partnership University NHS Foundation Trust which serves older adults living in greater London and Essex areas.
Participants: The investigators aim to recruit a minimum of 100 participants, comprising of a 3:1 ratio of patients to controls respectively (refer to section 6 for eligibility criteria). Participants will include patients of the memory clinic with cognitive complaints, mild cognitive impairment or dementia, and their partners/carers/family members/friends as controls. As this is a feasibility study, this sample is not based on a power calculation, but findings will inform future work in this area. Sampling will follow a convenience strategy, appropriate for a feasibility study.
Methods: Overall feasibility of implementing digital tools within a clinical population will be evaluated through a combination of direct usage data from the devices and apps and via questionnaires. Digital tools (Fitbit, Dreem headband, Mezurio app, Longevity app) will be provided to participants at a baseline visit at the participant's home.
The broad, but not exclusive, areas of interest will include:
Digital Tools: In order to develop disease-specific fingerprint models, it is necessary to collect a wide range of measures which may be affected by early disease processes. Therefore, a combination of digital tools will be utilised, including remote active and passive smartphone-based assessments and remote passive data collection with devices (e.g., a smartwatch and an EEG headband). These technologies will be targeted at functions with a direct relationship to the brain-regions first affected by dementia-related pathology in addition to using digital devices to measure new signals of interest. The investigators will aim to keep the participant burden as low as possible by mainly including passive measures (e.g., monitoring sleep EEG) and by limiting the amount of time participants are engaged in active assessments (e.g., a smartphone-based memory test) to approximately 10 minutes per day.
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| Measure | Description | Time Frame |
|---|---|---|
| Mean proportion (%) of scheduled digital data capture visits fully completed for each digital technology (Fitbit Charge 4/5, Dreem 2/3 headband, Mezurio smartphone app, and Longevity smartphone app) at each timepoint | The investigators will assess the feasibility of capturing digital data from digital technologies that have been selected as part of the EDoN initiative. This will be achieved by assessing the mean proportion (%) of scheduled digital data capture visits fully completed for each digital technology (Fitbit Charge 4/5, Dreem 2/3 headband, Mezurio smartphone app, and Longevity smartphone app) at each timepoint. In addition, a subset of participants who consented to the main study will be invited to participate in a qualitative sub-study to explore the perspectives of people with cognitive impairment and their partners, carers or family members on the use of digital devices included in the EDoN toolkit. | 0, 3, 6, 9, and 12 months |
| Mean proportion (%) of scheduled Mezurio data capture visits fully completed at each timepoint for individuals randomised to 14- and 28-day Mezurio schedules | One of the digital technologies being evaluated/utilised in the study is a cognitive testing application called 'Mezurio' created by Dr Chris Hinds at the University of Oxford. Participants will be asked to install Mezurio on their smartphone at baseline and complete a range of short tasks over a period of either 14 or 28 consecutive days during the 12 month study period. Participants on the 14 day schedule will repeat the assessments at 3 month intervals; participants on the 28 day schedule will repeat the assessments at 6 month intervals. Participants will be allocated to each assessment period randomly. The investigators will assess the mean proportion (%) of scheduled Mezurio data capture visits fully completed at each timepoint for individuals randomised to 14- and 28-day Mezurio schedules. | 0, 3, 6, 9, and 12 months |
| Measure | Description | Time Frame |
|---|---|---|
| Incident subjective cognitive decline (SCD), mild cognitive impairment (MCI), or dementia | In participants who are patients (recruited from memory clinics), the investigators will record new diagnoses that represent a progression/worsening of cognitive decline. For example, in patients with SCD, the investigators will record incident MCI/dementia, whereas for patients with MCI, only incident dementia will be recorded. In participants who are relatives/carers (i.e., healthy controls), the investigators will record any new cognitive diagnoses, including SCD, MCI and/or dementia. These data will be used to achieve the broader aims of the study (please see Study Description for details). |
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Inclusion Criteria:
Exclusion Criteria:
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Participants will include patients of Essex Memory Clinics within the Essex Partnership University NHS Foundation Trust which serves older adults living in greater London and Essex areas. Participants will be patients with cognitive complaints, mild cognitive impairment or dementia, and their partners/carers/family members/friends as controls.
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| Name | Affiliation | Role |
|---|---|---|
| Zuzana Walker, MD | University College, London | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| St Margarets Hospital | Epping | Essex | CM16 6TN | United Kingdom |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40893385 | Derived | Wilson S, Beswick E, Morrell R, Bhogal S, Tolley C, Whitfield T, Wing K, Mc Ardle R, Hassan N, Walker Z, Slight S. Acceptability of wearable technology for the early detection of dementia-causing diseases: perspectives from the CODEC II cohort. BMC Digit Health. 2025;3(1):55. doi: 10.1186/s44247-025-00191-3. Epub 2025 Aug 29. |
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Digital device data, demographic data and some clinical data.
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Q1 2027 for five years
Bona fide researchers
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| ID | Term |
|---|---|
| D003704 | Dementia |
| D060825 | Cognitive Dysfunction |
| D020187 | REM Sleep Behavior Disorder |
| D004194 | Disease |
| ID | Term |
|---|---|
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D019965 | Neurocognitive Disorders |
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Plasma, Serum, Cellular Fraction
| 12 months |
| D001523 | Mental Disorders |
| D003072 | Cognition Disorders |
| D020923 | REM Sleep Parasomnias |
| D020447 | Parasomnias |
| D012893 | Sleep Wake Disorders |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |