Download Introduction: Each year approximately 5,000 people in the U.S. are

Introduction: Each year approximately 5,000 people in the U.S. are diagnosed with
ALS, a progressive and fatal neurological disease. Onset of ALS typically manifests in
one of three patterns – bulbar onset, spinal onset, and mixed. In bulbar onset ALS, the
disease may announce itself by way of dysarthria, dysphagia, or both. Spinal onset
ALS, in contrast, typically presents with limb weakness that may be lateralized to one
side or involve some combination of the upper and lower extremities. A third type, mixed
ALS, presents with elements of both bulbar and spinal onset. Average length of survival
for those with bulbar onset is 1-2 years, while for those with spinal onset survival
estimates are 2-5 years [1].
Regardless of its presentation, research indicates that up to 90% of people with ALS
(pALS) will experience communication problems during the course of their disease and
many will be unable to speak by the time of their death [2, 3, and 4]. Use of
augmentative and alternative communication (AAC) options has achieved significant
penetrance in this population pALS wish to maintain a full range of communicative
functions (e.g., writing, speaking, and access to the internet and social media)
throughout their disease. Research indicates that pALS accept AAC in a variety of
forms and use it to sustain communication across contexts [2, 5] as it helps to mitigate
the effects of speech deterioration which some pALS describe as the most devastating
aspect of the disease [8].
Although both the need and desire to communicate remain intact during the course of
the disease, as motor impairment increases, communication becomes more effortful.
Moreover, in certain situations even the most basic communicative acts such as
obtaining a caregiver’s attention and communicating an urgent need such as “I need to
be suctioned” can be challenging.
Despite the AAC options available for use by pALS, critical communication needs
remain. Eye-tracking systems only function when the patient is positioned directly in
front of them and the device is properly calibrated for that position. It is not practical for
a patient to always be positioned in front of the device, yet a patient needs the ability to
communicate at all times. Similarly, the mount for an eye-tracking system is often
moved out of the way when a pALS goes to bed. Non-invasive ventilatory support is
critical for many pALS and this equipment may further obstruct use of an eye-tracking
based system.
In the sleep environment, whether the pALS is in bed or in another commonly visited
location (e.g., living room), there is a need for a way to summon a caregiver’s attention
and communicate both the nature and the urgency of one’s need. For instance, the
need for mechanical suctioning may be most urgent at some point during the night
whereas indicating to a caregiver that help turning over is needed may be important but
less urgent.
As part of a larger project the aim of which is to develop a robust communication system
for pALS, the work reported here addresses determining vocabulary needs for people
with late-stage ALS so they can use a virtual communication display primarily when in
bed.
One approach to sampling the specific vocabulary needs of this population when they
are bed-bound and/or wake from sleep is to survey their caregivers for the most
frequently requested type of help (e.g., ‘I need to go to the bathroom’) and have the
caregivers rank items in terms of perceived priority. This is likely to yield a sample
reflecting what caregivers deem important.
Another approach to sampling these specific vocabulary needs is to target the end
users of such a system, i.e., pALS, who have intimate knowledge of both their priorities
and the associated levels of urgency.
Aim: The aim of this cross-sectional study is to collect vocabulary to be used by people
with late stage ALS via a virtual communication display. This study will employ two
types of data collection methods: synchronous and asynchronous. Synchronous data
collection will involve a survey circulated to caregivers at a local ALS support group.
Asynchronous data will be collected from the OHC PatientsLikeMe™.
Method: Currently in the early stages of data collection, the ALS caregiver support
group is eager to participate. To sample directly from the population of interest,
asynchronous survey-based data will be collected via the online health community
(OHC) PatientsLikeMe™. PatientsLikeMe is an OHC designed for people with lifealtering illnesses. One of the largest groups of participants is pALS who use the OHC to
share their experiences, find others like them who are matched on demographic and
clinical characteristics, and learn from aggregated data reports [7, 9]. Current estimates
suggest that 7,000 people with ALS participate in this OHC which supports research by
allowing researchers to filter people according to demographic and disease-related
variables.
Despite the limitations inherent in using an OHC as a portal for data collection such as
the potential for selection bias and the volunteer effect (i.e., the non-representative
nature of the internet population [6]), it allows access to people with late-stage ALS who
would otherwise be unable to participate in this research.
Analysis of each vocabulary sample will be conducted to determine categories
identified, items within each category, and the relative priority assigned to each item.
Importantly, ‘item’ in this sense refers to a word or phrase. Discrepancy analysis will be
conducted to determine variance between the samples. Subsequently, cross-validation
of samples will be conducted according to the original method of data collection for each
group (i.e., caregivers will synchronously review the data collected from the pALS and
pALS will asynchronously review data collected from the caregivers).
Results & Conclusion: Final analysis should yield a corpus of approximately three to
five categories with three to five items per category. Methods for constraining the
sample with concrete data on the PatientsLikeMe site will be used. Results and
conclusions will be contextualized to discuss the ecological validity of the resulting
corpus, and both the process and the product of these methods of data collection.
References
1. http://www.alsa.org/about-als/
2. Ball, L., Beukelman, D., & Pattee, G. (2004). Acceptance of augmentative and
alternative communication technology by persons with amyotrophic lateral
sclerosis. Augmentative and Alternative Communication, 20(2), 113-122.
3. Ball, L., Fager, S., & Fried-Oken, M. (2012). Augmentative and alternative
communication for people with progressive neuromuscular disease. Physical
Medicine & Rehabilitation Clinics of North America, 23, 689-699.
4. Brownlee, A. & Bruening, L.M. (2012). Methods of communication at end of life
for the person with amyotrophic lateral sclerosis. Topics in Language Disorders,
32 (2), 168-185.
5. Doyle, M., & Phillips, B. (2001). Trends in augmentative and alternative
communication use by individuals with amyotrophic lateral sclerosis.
Augmentative and Alternative Communication, 17(3), 167-178.
6. Eysenbach, G., & Wyatt, J. (2002). Using the internet for surveys and health
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7. Frost, JH, & Massagli, MP. (2008).Social uses of personal health information
within PatientsLikeMe, an online patient community: what can happen when
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Research, 10(3), e15.
8. Hecht, M., Hillemacher, T., Grasel, E., Tigges, S., Winterholler, M., Heuss, D.,
Neundorfer, B. (2002). Subjective experience and coping in ALS. ALS and Other
Motor Neuron Disorders, 3, 225-232.
9. Wicks, P., Massagli, M., Frost, J., Brownstein, C., Okun, S., Vaughan, T.,
Bradley, R. & Heywood, J. (2010). Sharing health data for better outcomes on
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