Finalist: Augmenting wearable-metrics with outcome data for fatigue-tracking among long-haulers by Shashaank Vatttikuti

Posted by Health+ Long COVID (Admin) 7 months ago Posted in Healthathon Finalists

Augment wearable-measures and scores, that lack personalized outcome data to validate their utility, with performance measures used in fatigue-risk management as well as other relevant measures such as mood.

Project Case Study

What problem was the team trying to solve? Why did you choose that particular problem? How did you use federal information and datasets in the course of the project?

The development of the LongHauler Companion Platform by PhysioForecast was motivated by several factors: 1) Health+ Long COVID Human-Centered Design Report (ref. 1) highlighting the need for better data tracking, 2) interest within longhauler (LH) (and related ME/CFS) communities in current wearable consumer-scores (ref. 2), and 3) a compelling in-depth longhauler interview further underscoring the need for such a platform that addresses:

1. Improved Long COVID data tracking: provide longhaulers with better data for personal monitoring and support services – aligned with the Health+ Report Opportunity Area 5: Structural Capacity,

2. Accommodation guidance: By providing comprehensive data tracking, we offer insight that for facilitating accommodation strategies - aligned with Opportunity Area 3, importance of accommodation guidance,

3. Facilitating telehealth: accessible platform provides holistic measures for remote monitoring thus facilitating telehealth services - aligned with Opportunity Area 6 emphasizing the importance of telehealth.

Our initial focus is on mental fatigue. Assessing, tracking, and eventually predicting mental fatigue remains a challenge. For instances, individuals have difficulty reporting the total effects of chronic sleep loss (ref.3), and sleep-measure reported-fatigue relationships in ME/CFS are mixed (ref. 4). Stimulant medications like modafinil may provide short-term relief but mask accruing brain fatigue. There is a lack of objective data on meaningful outcomes. These challenge how to track and thus manage fatigue for individuals and providers. These are areas of research but what can we offer to longhaulers now?  

A first step to intervention is good data. That is what this proposal aims to address. To provide an accessible platform to collect data in the real world for both individuals with Long COVID (or ME/CFS) and their support system, as well as clinical researchers.

How did you approach your project? Who was involved in the project? What experiences did team members bring to the table?

Dr. Shashaank Vattikuti, Founder of PhysioForecast, performed the entirety of this. He is a quantitative physician-scientist with work in neuroscience/mental health and fatigue science (NIH, DoD). He has a background in computational modeling and building digital tools. He has also struggled with poor sleep for much of his life.

We took a two-pronged approach: 1) seeking input from those with or who support Long COVID, and 2) development of the platform based on prior work and input from (1). We reached out through social media, one-on-one requests, and the Long COVID Alliance. Received three positive replies from longhaulers and conducted one interview, in time for this report.

Interview: Adult female neuropsychologist with Long COVID since early 2023. Ongoing tiredness and mental fatigue, needing rest by mid-evening. Tried NAC and nicotine with some success. Interested in tracking data, found PF LH platform appealing for comprehensive measures and mental tests, and holistic view. Would “help with (her) sanity”. Wants data sharing with providers. Would like to inform work-team but through provider, based on this data. Supports having a user-forum. Will use platform on phone/computer, and desires updates and to support development. Potential interest in smartwatch/ring.

Based on initial considerations above and this target-user feedback, we are designing the platform with some key elements:

1. Holistic - capture a whole picture of the individual across time by including - wearable measures (automatic tracking but need better personal-validation), short mental and physical performance tests (well-tested for decades in fatigue (ref. 5,6) research), longhauler symptoms and self-reported energy/mood, real world (RW) outcomes (user-defined outcomes), RW contributing factors (potential user-defined contributing factors like stressors and interventions);

2. Environment – track informative external factors (e.g., local night/day, ambient light, pollen count, weather);

3. Personal – add custom measures for individuals, health providers, researchers, others;

4. Accessible - web-app works with common devices (computers/phones) and public computers via secured access;

5. Social support - share data with health providers, managers, etc. and feedback through user forum; and

6. Cutting-edge – PhysioForecast advances predictive models using this type of data - platform facilitates adding this functionality.

What obstacles did you encounter along the way?

Feedback from target-users was slow. However, given the positive feedback, including future use and support, we anticipate more input as we move forward.

This post was edited on Sep 19, 2024 by Health+ Long COVID