Wearable Device for Long COVID

Overseen By Romina Foster-Bonds

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Scripps Translational Science Institute

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 2 Jurisdictions This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 1

Duration: 3 months

100500 Participants Needed

Trial Summary

What is the purpose of this trial?

To further characterize Long COVID-19 by collecting data from individuals who already own wearable devices or are provided with a wearable device along with basic and enhanced educational materials to determine if both can improve Long COVID-19 symptom management and post-exertional malaise.

Will I have to stop taking my current medications?

The trial protocol does not specify whether you need to stop taking your current medications, so it's unclear. However, you should discuss this with the trial coordinators or your doctor.

What data supports the effectiveness of the wearable device treatment for Long COVID?

Research shows that wearable activity trackers can help improve physical activity and other health outcomes in hospitalized adults and those with chronic conditions. These devices have been used successfully in managing conditions like COPD and diabetes, suggesting they might also help people with Long COVID by encouraging more activity and better self-management. ^{1 2 3 4 5}

Is the wearable device generally safe for humans?

The research does not provide specific safety data for wearable devices, but they are widely used by many people for tracking health indicators without reported safety concerns. ^{6 7 8 9 10}

How does the wearable device treatment for Long COVID differ from other treatments?

The wearable device for Long COVID is unique because it continuously monitors key physiological parameters like body temperature, heart rate, and oxygen levels, allowing for early detection and ongoing management of symptoms. Unlike traditional treatments, this approach emphasizes personal healthcare monitoring and can be used remotely, providing a novel way to manage Long COVID symptoms. ^{11 12 13 14 15}

Eligibility Criteria

This trial is for adults over 18 with a self or physician diagnosis of Long COVID, ME/CFS (chronic fatigue syndrome), or POTS (rapid heartbeat when standing). Participants must own or be willing to use a wearable device for symptom management and agree to share data regularly.

Inclusion Criteria

It's unclear whether this criterion is an exclusion or inclusion criteria. Can you please provide more details?

I have been diagnosed with Long COVID, ME/CFS, or POTS.

Agrees to wear the device throughout the study period, share the data with the study, and sync data at least weekly

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Baseline Assessment

Participants complete baseline assessments and consent to participate in the trial

1 week

1 visit (virtual)

Treatment

Participants receive wearable devices and educational materials to manage Long COVID-19 symptoms

3 months

Monthly virtual check-ins

Follow-up

Participants are monitored for changes in symptom severity and quality of life

9 months

Quarterly virtual surveys

Treatment Details

Interventions

• Wearable device

Trial Overview The study aims to understand Long COVID better by using wearable devices. Participants will receive educational materials and use their own or provided wearables to track symptoms and see if this helps manage post-exertional malaise.

Participant Groups

Group I: Treatment: study provided wearables Experimental Treatment 1 Intervention

Following consent and the completion of the baseline assessments the treatment group will be prompted to order their device and receive basic education on symptom management along with the enhanced education on wearable devices.

Group II: Treatment: self provided wearables Experimental Treatment 1 Intervention

Following consent and the completion of the baseline assessments the treatment group will receive basic education on symptom management along with the enhanced education on wearable devices.

Group III: Control: study provided wearables Experimental Treatment 1 Intervention

Following consent and the completion of the baseline assessments the control group will receive basic education on symptom management. At the 3 month timepoint they will be prompted to order their device and receive enhanced education that will provide information on how wearable devices can be used to monitor and manage symptoms.

Group IV: Control: self provided wearables Experimental Treatment 1 Intervention

Following consent and the completion of the baseline assessments the control group will receive basic education on symptom management. At the 3 month timepoint they will be receive enhanced education that will provide information on how wearable devices can be used to monitor and manage symptoms.

Who Is Running the Clinical Trial?

Scripps Translational Science Institute

Lead Sponsor

Trials

53

Recruited

572,000+

Findings from Research

Consumer-wearable activity trackers like Fitbit and Jawbone show high validity in counting steps, with laboratory studies reporting strong correlations (CC > 0.80) between tracker-assessed and actual steps.

However, these trackers tend to underestimate energy expenditure and overestimate sleep metrics when compared to more accurate methods like polysomnography, indicating that while they are reliable for step counting, their accuracy for other health metrics may vary.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Systematic review of the validity and reliability of consumer-wearable activity trackers. Evenson, KR., Goto, MM., Furberg, RD.[2022]

Interventions using wearable activity trackers during hospitalization significantly increased overall physical activity levels and reduced sedentary behavior among 1911 participants across various medical conditions, showing a standardized mean difference of 0.35 for activity and a reduction of 35.46 minutes per day in sedentary time.

Patients using these trackers also experienced improvements in physical function, with a standardized mean difference of 0.27, indicating that these devices can enhance recovery during hospital stays, although they did not significantly affect pain, mental health, length of stay, or readmission rates.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Interventions Using Wearable Activity Trackers to Improve Patient Physical Activity and Other Outcomes in Adults Who Are Hospitalized: A Systematic Review and Meta-analysis. Szeto, K., Arnold, J., Singh, B., et al.[2023]

Wearable activity tracking devices have the potential to significantly enhance patient engagement in managing chronic diseases, but their effectiveness in improving health outcomes over the long term remains unclear due to a lack of comprehensive studies.

For successful integration of these devices into patient care, it is crucial to address issues such as data validity, patient motivation, safety, privacy, and the need for personalized user experiences that consider individual differences in understanding health data.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient-centered activity monitoring in the self-management of chronic health conditions. Chiauzzi, E., Rodarte, C., DasMahapatra, P.[2022]

A study involving 32 older adults (ages 52-84) with chronic illnesses found that 73% planned to purchase a wearable activity tracker after testing, indicating high acceptance and perceived usefulness of these devices for promoting physical activity.

Participants preferred trackers like Fitbit and Misfit over simple pedometers due to better accuracy and features, highlighting the importance of usability and support for older adults in using technology effectively.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation. Mercer, K., Giangregorio, L., Schneider, E., et al.[2022]

Wearable technology, such as pedometers and accelerometers, shows potential in monitoring physical activity in COPD patients, with a reported increase of about 1,821 daily steps on average when using these devices, although results varied widely across studies.

Despite some promising findings, the current evidence is limited and inconsistent, with technical issues reported in several studies, highlighting the need for more robust and well-designed research before these technologies can be routinely recommended for COPD management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The use of wearable technology to monitor physical activity in patients with COPD: a literature review. Pericleous, P., van Staa, TP.[2020]

In a study of 10,866 users of the 'Amazfit Health' app and Huami wearable devices, only 10.7% were identified as having a high risk of developing cardiovascular disease (CVD).

The average age of users was 50.7 years, with a body mass index of 24.6 kg/m², indicating that while the user base is relatively middle-aged, the majority do not fall into the high-risk category for CVD.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Cardiovascular Disease Risk Stratification in Wrist Wearable Devices and e-Health App Users: A Large-Scale Retrospective Study. Chen, E., Jiang, J., Zhou, J., et al.[2022]

The dataset includes detailed information on 423 wearable fitness trackers and smartwatches released before July 2017, providing a comprehensive overview of the market during that period.

The data collected includes key attributes such as release year, company name, and supported sensors, which can help researchers analyze trends and advancements in wearable technology.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Dataset of fitness trackers and smartwatches to measuring physical activity in research. Henriksen, A., Woldaregay, AZ., Muzny, M., et al.[2022]

The study assessed the acceptability of an instrumented shoe insole for monitoring walking speed in frail older adults, involving 35 participants over 3 months, and found mixed results: while usability was rated positively (4.3 out of 6), reliability was rated low (2.1 out of 6).

Despite some technical issues leading to a 25% dropout rate, the majority of participants reported comfort and daily use of the insole, indicating that while the device is generally acceptable, improvements are needed for reliability.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Smart Shoe Insole to Monitor Frail Older Adults' Walking Speed: Results of Two Evaluation Phases Completed in a Living Lab and Through a 12-Week Pilot Study. Piau, A., Steinmeyer, Z., Charlon, Y., et al.[2022]

Women are 2.3 times more likely to use wearable activity trackers compared to men, indicating a gender difference in the adoption of these health-monitoring devices.

Individuals with a higher Body Mass Index (BMI) are more likely to use tracker devices, with a 5.2% increase in odds for each 1-point increase in BMI, suggesting that tracker usage may be more common among those who are overweight or obese.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Health Habits and Wearable Activity Tracker Devices: Analytical Cross-Sectional Study. Tricás-Vidal, HJ., Lucha-López, MO., Hidalgo-García, C., et al.[2022]

In a 12-week pilot study with 10 participants who had chronic medical conditions, the use of wearable activity trackers led to an average weight loss of 5.97 lbs and a 9.2% decrease in LDL cholesterol, indicating positive health outcomes.

Participants reported increased well-being and confidence in their ability to be active, suggesting that trackers not only provided motivation and education about their activity levels but also reinforced their commitment to physical activity.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Can a Free Wearable Activity Tracker Change Behavior? The Impact of Trackers on Adults in a Physician-Led Wellness Group. Gualtieri, L., Rosenbluth, S., Phillips, J.[2022]

A new self-quarantine system is proposed that combines wearable technology with contact tracing to ensure compliance and prevent the spread of COVID-19 by individuals who may otherwise not adhere to quarantine rules.

The system is designed to be cost-effective, lightweight, and safe for reuse, utilizing edge artificial intelligence for monitoring and reporting, which enhances the effectiveness of quarantine measures.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

COVID-19 Mandatory self-quarantine wearable device for authority monitoring with edge AI reporting & flagging system. Lim, WJ., Abdul Ghani, NM.[2022]

Wearable devices are transforming personal healthcare by enabling continuous monitoring of physiological parameters, which is crucial for early detection of COVID-19 and managing long COVID symptoms.

Various innovative devices, such as smartwatches and nano-patches, can track vital signs like heart rate and oxygen levels, providing valuable data for both patients and healthcare providers in managing health conditions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Role of Wearable Sensing Technology to Manage Long COVID. Khondakar, KR., Kaushik, A.[2023]

The PCovNet+ framework, developed for smartwatches and fitness trackers, effectively monitors Resting Heart Rate (RHR) anomalies related to COVID-19, achieving a high precision of 0.993 and detecting infections in 74% of subjects, including presymptomatic cases.

This deep learning model, which combines a convolutional neural network and long short-term memory network, demonstrates significant potential as a secondary diagnostic tool for continuous health monitoring and contact tracing, especially in the context of future pandemics.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection. Abir, FF., Chowdhury, MEH., Tapotee, MI., et al.[2023]

The use of smart wearable Internet of Medical Things (IoMT) devices can help track biomarkers for COVID-19 detection, potentially aiding in the management of the virus spread as communities reopen.

Machine learning algorithms can analyze data from these wearables to identify symptoms of SARS-CoV-2 infection and assess the risk of virus transmission, providing a systematic approach to controlling outbreaks.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Perspective: Wearable Internet of Medical Things for Remote Tracking of Symptoms, Prediction of Health Anomalies, Implementation of Preventative Measures, and Control of Virus Spread During the Era of COVID-19. Mehrdad, S., Wang, Y., Atashzar, SF.[2023]

The proposed intelligent tool combines a phone application with a wearable device to enhance contact tracing and predict COVID-19 clusters, addressing limitations like insufficient network coverage found in existing methods.

By utilizing advanced IoT features for data analysis and visualization, this tool aims to improve public health management and could be beneficial in managing future global health crises.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science. Jahmunah, V., Sudarshan, VK., Oh, SL., et al.[2023]

References

1. England pubmed.ncbi.nlm.nih.gov

Systematic review of the validity and reliability of consumer-wearable activity trackers. [2022]

2. United States pubmed.ncbi.nlm.nih.gov

Interventions Using Wearable Activity Trackers to Improve Patient Physical Activity and Other Outcomes in Adults Who Are Hospitalized: A Systematic Review and Meta-analysis. [2023]

3. England pubmed.ncbi.nlm.nih.gov

Patient-centered activity monitoring in the self-management of chronic health conditions. [2022]

4. Canada pubmed.ncbi.nlm.nih.gov

Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation. [2022]

5. New Zealand pubmed.ncbi.nlm.nih.gov

The use of wearable technology to monitor physical activity in patients with COPD: a literature review. [2020]

6. United States pubmed.ncbi.nlm.nih.gov

Cardiovascular Disease Risk Stratification in Wrist Wearable Devices and e-Health App Users: A Large-Scale Retrospective Study. [2022]

7. England pubmed.ncbi.nlm.nih.gov

Dataset of fitness trackers and smartwatches to measuring physical activity in research. [2022]

8. Canada pubmed.ncbi.nlm.nih.gov

A Smart Shoe Insole to Monitor Frail Older Adults' Walking Speed: Results of Two Evaluation Phases Completed in a Living Lab and Through a 12-Week Pilot Study. [2022]

9. Switzerland pubmed.ncbi.nlm.nih.gov

Health Habits and Wearable Activity Tracker Devices: Analytical Cross-Sectional Study. [2022]

10. Canada pubmed.ncbi.nlm.nih.gov

Can a Free Wearable Activity Tracker Change Behavior? The Impact of Trackers on Adults in a Physician-Led Wellness Group. [2022]

11. Germany pubmed.ncbi.nlm.nih.gov

COVID-19 Mandatory self-quarantine wearable device for authority monitoring with edge AI reporting & flagging system. [2022]

12. Switzerland pubmed.ncbi.nlm.nih.gov

Role of Wearable Sensing Technology to Manage Long COVID. [2023]

13. England pubmed.ncbi.nlm.nih.gov

PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection. [2023]

14. Switzerland pubmed.ncbi.nlm.nih.gov

Perspective: Wearable Internet of Medical Things for Remote Tracking of Symptoms, Prediction of Health Anomalies, Implementation of Preventative Measures, and Control of Virus Spread During the Era of COVID-19. [2023]

15. United States pubmed.ncbi.nlm.nih.gov

Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science. [2023]