Digital Health in The Fight Against Alzheimer's Disease

Over 25 million people worldwide live with dementia, of which AD occupies the majority. The total healthcare cost for the treatment of AD in 2020 is estimated at $305 billion. Digital Health has the potentially to radically change management of patients with Dementia.

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Sep 6, 2022
I have been a weekly virtual companion with B, a 70s-year-old retiree, for almost a year. As a multilinguistic and literary scholar, he had extensive experience working in many academic departments. In our first conversation, he was enthusiastic about sharing his life on a farm far away from the country. Little does one know that, in most meetings afterward, he rehearsed the story with the same excitement all over again. B was diagnosed with Alzheimer's disease (AD), a disease that has been hovering for over one hundred years, yet we have found no cure.
Over 25 million people worldwide live with dementia, of which AD occupies the majority [1]. The total healthcare cost for the treatment of AD in 2020 is estimated at $305 billion [2], and this number will be booming as the world’s population over 65 will likely double by 2050 [3]. With the giant step forward of advanced technology and machine learning, we light up the hope of curing the disease of the century.

Digital Biomarkers (DBs): Early Diagnostic Potential and Care Improvement

“Digital biomarkers are a set of characteristics and properties collected from digital health technology, which is measured as an indicator of the biological, pathological process and the response to any exposure or intervention which can be therapeutic.” - The Food and Drug Administration (FDA)
Drug development for neurodegenerative disorders takes longer, costs more, and is less likely to be approved [4]. The traditional measures of AD are highly inconsistent and subjective, leading to ineffective phase 3 trials of medications. Moreover, our current cognitive tests fail to address non-cognitive changes, which can help detect a neurodegenerative disease 10 to 15 years before their effective diagnosis [5].
By recording and analyzing DBs, mobile and wearable digital technology may overcome such limitations and pave the way for detecting AD cost-effectively and promptly. Here are why:
  • The extensive network of consumers, and it continues to grow
  • The just-in-time access to information
  • The rapid development of onboard sensors
  • The low-cost investment compared to other health services

Figure 1: Health Internet of Things Tools, Measures, and Devices [6]

notion image
Source: (Mumtaz et al., 2022)
The diagnosis of AD also puts a massive informal care burden on family, relatives, friends, and neighbors. A combination of DBs and technology can proactively support patients and their caregivers in this longitudinal care.

Table 1: Digital Health Technology in AD Care*

Purpose(s)
Digital Biomarker(s)
Technology Example
Fall Detection
Patients with AD have a higher risk of falls [7]Major hospitalization cause [8]
Movement and position of body
Apple Watch
Wandering Prevention
Common behavior in AD
Location
Global Positioning System (GPS)
Real-Time Stress Monitoring
Up to 50% in patients with AD have depression [9]
Heart RateGalvanic Skin ResponseBody Temperature
Most smartwatches
  • This is a non-exhaustive list

Opportunities for Virtual Clinical Trials (VCTs)

Traditional clinical trials are slow, costly, and inefficient [10]. The traveling requirement of study protocols creates unnecessary hurdles for research participants, especially those with AD. Alzheimer’s Disease Research Centers (ADRCs) reported that Black Americans carried more risk factors for AD [11], but believe it or not, the majority of research enrollees were White [12].
VTCs can leverage digital health technologies to consistently collect objective data and adverse events, encourage participant enrollment (eg, online consent form, time travel reduction), and measure real-time clinical endpoints. Most importantly, VCTs can recruit enrollees living far from a center or even in different countries. Results generated from such samples will provide excellent generalizability and apply to various institutions across the globe.
“Remember: We are on the way to finding solutions for a disease, not a disease for a specific population.”
The less need for funding enables us to conduct many VCTs in a shorter time, generating enormous information. In this seeming "golden age" of machine learning, we can analyze and "learn" such data to improve our understanding of AD and open the door to the final answer.

Conclude

The failures of numerous traditional clinical trials and the inevitable growing healthcare burden call for a new approach to AD. Although the benefits of digital health have become quite evident, it does not come without challenges, including patient confidentiality, the global healthcare systems' differences, and regulatory policy. But when we realize the "Why," the "How" will undoubtedly follow along.

References

  1. Qiu, C., Kivipelto, M., & von Strauss, E. (2009). Epidemiology of Alzheimer's disease: occurrence, determinants, and strategies toward intervention. Dialogues in clinical neuroscience, 11(2), 111–128. https://doi.org/10.31887/DCNS.2009.11.2/cqiu
  1. Wong W. (2020). Economic burden of Alzheimer disease and managed care considerations. The American journal of managed care, 26(8 Suppl), S177–S183. https://doi.org/10.37765/ajmc.2020.88482
  1. US Census Bureau. (2016, March 28). An Aging World: 2015. Census.gov. https://www.census.gov/library/publications/2016/demo/P95-16-1.html ‌
  1. Adams, C. P., & Brantner, V. V. (2006). Estimating the cost of new drug development: is it really 802 million dollars?. Health affairs (Project Hope), 25(2), 420–428. https://doi.org/10.1377/hlthaff.25.2.420
  1. Albers, M. W., Gilmore, G. C., Kaye, J., Murphy, C., Wingfield, A., Bennett, D. A., Boxer, A. L., Buchman, A. S., Cruickshanks, K. J., Devanand, D. P., Duffy, C. J., Gall, C. M., Gates, G. A., Granholm, A. C., Hensch, T., Holtzer, R., Hyman, B. T., Lin, F. R., McKee, A. C., Morris, J. C., … Zhang, L. I. (2015). At the interface of sensory and motor dysfunctions and Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association, 11(1), 70–98. https://doi.org/10.1016/j.jalz.2014.04.514
  1. Mumtaz, A., Talha Nazir MD, Shahid, H., Muhammad, Filzah Faheem MD, & Junaid Kalia MD. (2022, June 13). Digital Biomarkers in Neurology. Neurology Pocketbook; Neurology Pocketbook. https://neuropkbk.neurocare.ai/digital-biomarkers-in-neurology ‌
  1. Dev, K., Javed, A., Bai, P., Murlidhar, Memon, S., Alam, O., & Batool, Z. (2021). Prevalence of Falls and Fractures in Alzheimer's Patients Compared to General Population. Cureus, 13(1), e12923. https://doi.org/10.7759/cureus.12923
  1. Voisin, T., Andrieu, S., Cantet, C., Vellas, B., & REAL.FR Group (2010). Predictive factors of hospitalizations in Alzheimer's disease: a two-year prospective study in 686 patients of the REAL.FR study. The journal of nutrition, health & aging, 14(4), 288–291. https://doi.org/10.1007/s12603-010-0063-4
  1. Chi, S., Yu, J. T., Tan, M. S., & Tan, L. (2014). Depression in Alzheimer's disease: epidemiology, mechanisms, and management. Journal of Alzheimer's disease : JAD, 42(3), 739–755. https://doi.org/10.3233/JAD-140324
  1. Fogel D. B. (2018). Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemporary clinical trials communications, 11, 156–164. https://doi.org/10.1016/j.conctc.2018.08.001
  1. Lennon, J. C., Aita, S. L., Bene, V., Rhoads, T., Resch, Z. J., Eloi, J. M., & Walker, K. A. (2021). Black and White individuals differ in dementia prevalence, risk factors, and symptomatic presentation. Alzheimer's & dementia : the journal of the Alzheimer's Association, 10.1002/alz.12509. Advance online publication. https://doi.org/10.1002/alz.12509
  1. Turner, B. E., Steinberg, J. R., Weeks, B. T., Rodriguez, F., & Cullen, M. R. (2022). Race/ethnicity reporting and representation in US clinical trials: a cohort study. Lancet Regional Health. Americas, 11, 100252. https://doi.org/10.1016/j.lana.2022.100252

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