Measurement and Impact of Physical Impairment in Amyotrophic Lateral Sclerosis: Use of Digital Technologies - A Precision ALS Project
Trial ID: NCT06820008
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11-21
Completion date: 2027-12-01
Sponsor: University of Dublin, Trinity College
Collaborators: Beaumont Hospital
Conditions: Amyotrophic Lateral Sclerosis; Motor Neurone Disease
Locations: Beaumont Hospital
Countries: Ireland
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
Amyotrophic Lateral Sclerosis (ALS), the most common form of Motor Neuron Disease (MND), is a neurodegenerative disease. At present there are limited treatment options for this disease which progressively affects physical function, i.e., the ability to speak, breathe, walk, and perform activities of daily living. ALS is a rare disease, and can present differently amongst individuals, therefore global collaboration is vital to have enough participants in studies to evaluate the effects of new treatments more precisely.

There are now many novel technologies that measure physical function which could be used in research studies to allow people with ALS to participate, in-part from home, knowing that they have access to the best clinical trials but with minimal time and travel burden. Their accuracy and the ability/willingness of people with ALS to use them need to be evaluated before they are accepted.

One of the traditional measurements used in research is called the ALS Functional Rating Scale -revised (ALSFRS-r) but this measurement has been criticised for being unable to pick up small changes and, in the digital age, outdated. The primary aim of this study is to develop a digital toolkit for more accurate measurement of physical aspects of ALS. It will test new technologies that measure physical function (i.e., walking, speech, swallow, strength, respiration (breathing), dexterity) that can be used by people with ALS in their own home.

This study has two aims:

Firstly, to test a selection of new digital technologies (in the form of devices, online systems and applications used on smartphones/electronic tablets) that measure physical function (e.g., walking, speech, strength) and assess whether the technologies are easy to use and acceptable both to people with ALS/MND and healthcare professionals.

Secondly, to measure how good technologies are at picking up changes in physical function over time and how they compare to older measures that are usually employed by clinicians.

This study will recruit 60 people with the ALS form of MND who attend a MND clinic in Dublin, Ireland. The study will run from November 2024 to December 2027 approximately. During this period, each participant will be asked to take part for a duration of 12 months.

The study will compare measurements of physical function collected by a researcher in the traditional way, with new ways of measuring the same functions, using technologies that can be used at home by the person with ALS. The experience of people using the technologies at home will be evaluated with interviews and questionnaires.

Over the 12-month duration, participants will be assessed in person by members of the research team on 3 occasions. These assessments will be carried out in the clinic setting or can be completed at the participant's home instead if needed.

In between the in-person assessments, participants will also do assessments in their own home every week using technologies, either independently or with telephone or video support from a researcher. The technologies that the participant use at home will be matched to the ones that were assigned to them for the in-person assessment at the beginning. Participants will use only technologies that are suited to them. The researcher will talk to participants about which technologies are suitable for them and which they are comfortable to use.

Participants coded data collected using the new technologies will be analyzed using established methods and newer methods such as artificial intelligence (AI). AI refers to the ability of computers and digital devices to learn and simulate human intelligence. Machine learning, a field within AI, analyses large data sets to develop models that improve as more data is added. Analysis of participants coded data will be for research purposes only and will not be used for their medical care.

Ultimately this study will create new knowledge on the role of technology in physical measurement in MND and how it can be successfully used in future studies to help find effective treatments.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of ALS made by a Neurologist; possible, lab-supported probable, probable or definite as per the revised El Escorial Criteria.
* Enrolled in the Precision ALS prospective study.
* Ability to understand participation requirements and provide informed consent to undergo assessments and for data to be used.
* Willingness to use a smartphone or tablet and to download and use study apps.
* Age \>18 years
* Home access to a stable broadband internet connection and willingness to use this for data collection.

Exclusion Criteria:

* Diagnosis of a neurological disease other than ALS, active dementia or psychiatric illness that would limit ability to follow the assessment schedule of the study.
* Other medical condition that would compromise the patient's safety or limit their participation in this study.

Outcomes:
Completion of 12-month follow up period; System Usability Scale