CiC – Effect of medication on mobility in people with PD
Research type
Research Study
Full title
Confidence in Concept (CiC) - Translating digital healthcare to enhance clinical management: evaluating the effect of medication on mobility in people with Parkinson’s disease (PD).
IRAS ID
295771
Contact name
Alison Yarnall
Contact email
Sponsor organisation
Newcastle Joint Research Office
ISRCTN Number
ISRCTN13156149
Duration of Study in the UK
0 years, 11 months, 30 days
Research summary
Lay summary of study results: Why we did this study People with Parkinson’s disease often need to take medicines (such as levodopa) several times a day. Symptoms can change during the day depending on when medication is taken and how well it is working. In routine care, clinicians usually only see people occasionally, so it can be difficult to understand day-to-day symptom changes and how medication timing affects movement in everyday life.
What we aimed to do
CiC-PD explored whether an integrated remote monitoring approach was feasible to use in people with Parkinson’s and could help us understand medication taking (“adherence”), its relationship and its impact on motor function (movement). We also aimed to develop new ways of turning wearable sensor data into meaningful information about motor “states” (for example, times when symptoms are well controlled versus times when symptoms worsen or dyskinesia occurs).Who took part and what was done:
Across the CiC-PD study we recruited and assessed a total of 55 people with Parkinson’s. Participants came to the Clinical Ageing Research Unit, and we collected demographic, general and Parkinson’s specific clinical assessments.
We then asked participants to use a combination of wearables (small devices worn on the lower back and on the wrist) and mobile technologies (smartphone with a newly developed App linked to a smartwatch to remind participants to take medications at the prescribed times and allow participants to input any extra medication intake) for 7 days, in daily life, to collect mobility data and medication adherence data. We also asked them to recorded symptoms/complications using diaries and to fill out a usability questionnaire.What we found and how participants found the remote monitoring system:
1. Remote monitoring was feasible and generally acceptable: (https://gbr01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpmc.ncbi.nlm.nih.gov%2Farticles%2FPMC10050691%2F&data=05%7C02%7Cwestminster.rec%40hra.nhs.uk%7Cf685b9a55f6b4a0fe37708de67ce94e7%7C8e1f0acad87d4f20939e36243d574267%7C0%7C0%7C639062334120250534%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=yekXgLSJDnl4TPr%2FBPyOiKqITgHuTUkRM1pyfn%2BKdj8%3D&reserved=0)
• Participants’ adherence to wearing/using each device was above 70% (73–97%), showing that a week of remote monitoring was practical for most people.
• Overall usability was positive: 17 out of 30 participants rated the system above 75% (average ~89% among those participants). Older age was linked to lower usability, and feedback highlighted practical improvements needed (particularly around smartwatch design/technical issues).
2. Wearable data could help identify motor states (e.g., on/off and dyskinesias) using algorithms. Using wearable movement data and participants’ diary entries as reference labels, we tested approaches to automatically classify motor states in daily life (https://gbr01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fieeexplore.ieee.org%2Fdocument%2F10171868&data=05%7C02%7Cwestminster.rec%40hra.nhs.uk%7Cf685b9a55f6b4a0fe37708de67ce94e7%7C8e1f0acad87d4f20939e36243d574267%7C0%7C0%7C639062334120260584%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=t7xBTPui7Hw64Rb50VIAwths3Bx9ep5OZd0wXyioBrU%3D&reserved=0):
• In a machine-learning analysis using the lower-back sensor, we could classify ON/OFF/DYSKINESIA with ~84% accuracy, and ON vs OFF with ~95% accuracy (in a subset of participants with suitable labelled data).
• In a related study, we developed an energy-based composite index from wearable data. Preliminary results showed OFF states identified with ~98% accuracy, plus evidence that the computed index related to clinical scores. https://gbr01.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftrack.pstmrk.it%2F3ts%2Fieeexplore.ieee.org%252Fdocument%252F10561000%2FNBTI%2F8kjDAQ%2FAQ%2F669a460e-d663-4a1f-a2eb-63c9f37fd421%2F2%2Fnwi7YU3BtM&data=05%7C02%7Cwestminster.rec%40hra.nhs.uk%7Cf685b9a55f6b4a0fe37708de67ce94e7%7C8e1f0acad87d4f20939e36243d574267%7C0%7C0%7C639062334120270610%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=HbmWzkc3e4YejHTilidEywXlU%2BxCQo8YdKwQck5Ee2k%3D&reserved=0
3. New methods were developed to detect dyskinetic events and improve explainability (https://gbr01.safelinks.protection.outlook.com/?url=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-031-83472-1_4&data=05%7C02%7Cwestminster.rec%40hra.nhs.uk%7Cf685b9a55f6b4a0fe37708de67ce94e7%7C8e1f0acad87d4f20939e36243d574267%7C0%7C0%7C639062334120280405%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=SaesToB2we9UlhhER2zaTa7KTIcSr1iyrS6wOHgZdCE%3D&reserved=0):
We also developed an approach to detect dyskinetic events using multivariate time-series methods designed to be more explainable (i.e., able to describe human-readable patterns that relate to dyskinesia). In one evaluation step, an explainable model using estimated medication “active component” levels and sensor-derived information achieved 94.43% accuracy for distinguishing dyskinetic events from periods of relative well-being.
4. Selective real-world walking digital mobility outcomes changed after medication intake (Paper submitted and under review in Gerontology)
• We examined whether real-world walking measures collected using wearable sensors changed after people took their Parkinson’s medication. We focused on walking measures such as step length and walking speed, as well as broader measures like total walking time and number of walking bouts.
• Participants were analysed in two groups: those who only took levodopa, and those who took a combination of dopaminergic medications.
Among the participants with usable data, we found that step length and walking speed improved after medication intake in both groups. However, the timing of these improvements differed. In people taking only levodopa, changes were not seen until around 2–2.5 hours after medication intake, whereas in people taking a combination of dopaminergic medications, improvements were seen earlier, within 30–90 minutes after intake.
• Other walking measures did not show consistent or reliable changes following medication.COVID-19 has reinforced the need for tools for remote patient management; ultimately these tools will transform future clinical care and research. Wearable technology (e.g. body worn devices, smartwatches) has the potential to address this need.
In Parkinson’s disease (PD), motor symptoms (e.g. balance and mobility problems) are disabling and can be improved with medication (e.g. Levodopa). Levodopa is prescribed in multiple doses over the course of a day and therefore timing is critical for alleviating symptoms. Medication response can be highly variable across people with PD leading to fluctuating symptoms over the course of the day, compromising mobility and quality of life. Adhering to a complex medication regime is hard and understanding fluctuations in response to medication is almost impossible to evaluate through clinic visits, recall and diaries.Understanding medication adherence and its effect on motor function during everyday life would ensure effective patient management, by allowing clinicians to use this information to adapt medication regimes (dose and frequency) to provide optimal treatment.
This project aims to collect data, over a week, with a wearable multi-component “system” for remote monitoring which uses mobility data obtained from the individual to improve management and optimise treatment effects in people with PD.
30 people with PD will be recruited and assessed once (single visit) over a week: in addition to real-world walking data collected with a wearable device (placed on the lower back), contextual information (e.g. weather) will be captured via a smartphone and adherence to medication regimes will be collected via a smartwatch.This study will therefore use a wearable multi-component “system” (smartphone, smartwatch and wearable device) to collect real-world data to assess medication adherence, quantify mobility outcomes and create reliable data-driven models to demonstrate that digital measures of mobility can monitor and predict response to medication.
REC name
London - Westminster Research Ethics Committee
REC reference
21/PR/0469
Date of REC Opinion
21 May 2021
REC opinion
Further Information Favourable Opinion