Music Attuned Technology for Care via eHealth – MATCH (Study 3.2)
Music Attuned Technology for Care via eHealth – MATCH: Determining Digital Markers of Agitation (Study 3 - Stage 2)
The University of Melbourne
50 participants
Apr 17, 2023
Interventional
Conditions
Summary
An eHealth solution – Music Attuned Technology for Care via eHealth (MATCH) – was developed to support family carers of people living with dementia to use music intentionally to support care. To create scalable solutions for the growing number of people living with dementia, we developed a minimal viable product (the MATCH app) for the HOME setting, which will be adapted for the RAC setting in study 3. MATCH represents a paradigm shift in music and dementia technology because it will: a) embed training programs that guide FCs and professional care staff in the strategic use of music; b) use sensor technology to capture behavioural markers to interpret agitation levels and auto-suggest music using algorithms that learn preferences of the person living with dementia and then suggest music they may like (recommender system); c) be able to continuously adapt the music to match and attune to arousal levels and reduce agitation; and d) be accessible to culturally and linguistically diverse groups (training videos will be available in multiple languages). Stage 2 of this study aims to explore digital markers, using wearable sensor devices, as best indicators of agitation and other symptoms to inform the development of an AI system that can detect changes in agitation in response to music and refine music recommendations to reduce agitation symptoms in residents living with dementia. We expect that data from the sensor devices will be able to support the detection and better management of agitation symptoms in people living with dementia.
Eligibility
Inclusion Criteria12
- Resident (full-time, 24 hours/day) at a participating RAC
- Documented diagnosis of dementia (any aetiology)
- Dementia Severity Rating Scale score >18
- NPI-NH: Severity score >6
- Has given written informed consent (or verbal assent with written consent by proxy for those legally unable to provide consent themselves)
- Presence of at least 1 agitation symptom occurring at least several times a day (rated on the CMAI)
- Able to tolerate wearing sensors, based on care staff clinical knowledge about the resident
- Does not have severe aggression (NPI-NH score is less than 3 on agitation and aggression scale)
- Does not have a severe medical illness and/or life-threatening condition, as assessed by study doctors
- No known allergy to components in the Empatica (anodized aluminum, polycarbonate, SUS316L, 304 stainless steel, polyurethane; see https://support.empatica.com/hc/en-us/articles/115000195083-Embrace-materials)
- Does not have delirium
- Has no or low risk of pressure injuries (rated on Braden Risk Assessment score of 13 or higher)
Exclusion Criteria6
- Residents for whom there is a risk associated with them wearing the sensors
- Residents who refuse to wear the sensors
- Residents with hyperorality, who frequently place items in their mouth
- Residents whose mental state may be impacted by wearing the sensors (as assessed by care staff)
- Residents with known paranoid or delusional ideas
- Residents for whom the RAC staff overseeing their care believes participation would be contraindicated
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Interventions
This study will be conducted in residential aged care (RAC) facilities in Victoria, Australia. We will fit consenting residents with wearable sensors and collect sensor data over a 10-day observation period (2 x 5-day observations separated by a 2-week break). Residents will be asked to wear the sensor devices for up to 8 hours per day. During the first observation, the research team will observe participants to record episodes of agitation. During the second observation, the research team will continue to observe participants to record episodes of agitation. In addition, researchers will play manualised playlists of familiar/preferred and/or neutral music when a participant displays agitation symptoms and record any changes in agitation symptoms in response to the music. Five separate music playlists (5-10 songs per playlist, 1 playlist per day) will be curated by a music therapist prior to commencement of the study. Song choices will be based on information collected from the resident, their family and/or care staff regarding the resident’s familiar/preferred music, as well as any unpreferred music, specific songs to avoid, or past negative responses to music. Playlists will be created using Spotify and will be played on a wireless Bluetooth speaker. The playlists will be played in their entire duration unless participants respond negatively when the music is played (i.e. if agitation symptoms escalate or residents express wanting the music to stop), in which case the MATCH researcher will stop the music. Negative responses to music will also be recorded as adverse events. Data from this study will be used to determine the feasibility of using the different sensors from a clinical and technical perspective, as well as to develop the agitation detection algorithms by comparing clinical notes to sensor readings. Further, data from clinical notes, sensor readings and music metadata will be used to inform the development of an AI system to refine music recommendations to reduce agitation in residents living with dementia. The observation periods will be separated by a 2-week break in which researchers will develop models for agitation detection based on data from the first 5-day observation. Data from the second 5-day observation period will be used to test these models.
Locations(1)
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ACTRN12623000350628