Can consumer wearables support outpatient health monitoring for patients with post-acute infection syndromes? A systematic umbrella review,2026,Kaplan

[Dolphin]

Can consumer wearables support outpatient health monitoring for patients with post-acute infection syndromes? A systematic umbrella review of accuracy, validity, and clinical utility data

Author summary Millions of people worldwide live with long-lasting symptoms after viral infections, including Long COVID. Many are encouraged to track their daily symptoms, activity, and physical limits to better understand their health and avoid setbacks. Consumer wearables—such as smartwatches...

journals.plos.org

Can consumer wearables support outpatient health monitoring for patients with post-acute infection syndromes? A systematic umbrella review of accuracy, validity, and clinical utility data​

• Deanna M. Kaplan ,
• Nicole Kessler,
• Nicole S. Pozzo,
• Caroline Y. Doyle,
• Page Dickey,
• Nicholas A. Giordano,
• Asmita Lehther,
• Saad Maan,
• Jennifer S. Mascaro,
• Ariel McDowall,
• Shenita Peterson,
• Himani Sirsi,
• Roman Palitsky

Can consumer wearables support outpatient health monitoring for patients with post-acute infection syndromes? A systematic umbrella review of accuracy, validity, and clinical utility data​

• Deanna M. Kaplan,
• Nicole Kessler,
• Nicole S. Pozzo,
• Caroline Y. Doyle,
• Page Dickey,
• Nicholas A. Giordano,
• Asmita Lehther,
• Saad Maan, …

• Published: June 8, 2026
• https://doi.org/10.1371/journal.pdig.0001124

Abstract​

An estimated 65 million individuals around the world have experienced Post-Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) and an additional 17–24 million are living with other post-acute infection syndromes.

Current frontline treatment recommendations for these heterogenous, multisystemic conditions emphasize self-monitoring and non-progressive rehabilitation of activity and exertion.

Consumer health wearables can potentially support symptom, activity, and exertion tracking; however, the strength of the scientific evidence supporting their use in this context is unclear.

This pre-registered systematic umbrella review aimed to address this gap by synthesizing review-level evidence for the accuracy, validity, and clinical utility of consumer wearable–assessed biometrics relevant to PASC and related syndromes.

Following PRISMA guidelines, 3,988 records were screened, with 42 reviews meeting inclusion criteria spanning more than 30 brands and over 150 distinct device series.

This umbrella review characterized quality and risk of bias, synthesized and evaluated accuracy evidence for 17 biometrics, and evaluated clinical utility outcomes.

Findings revealed highly variable review quality; substantial heterogeneity in device performance across biometrics, populations, and settings; and limited evidence for clinical utility.

Two biometrics—heart rate measurement and atrial fibrillation detection—had comparably stronger support.

Strengths and limitations to the current evidence base are identified, along with recommendations for informed patient use and the further research needed to responsibly support the integration of consumer wearables into outpatient care pathways.

A living umbrella review repository is provided on the Open Science Framework so that findings can be updated as new, review-level evidence for emergent technologies becomes available.

Author summary​

Millions of people worldwide live with long-lasting symptoms after viral infections, including Long COVID.

Many are encouraged to track their daily symptoms, activity, and physical limits to better understand their health and avoid setbacks.

Consumer wearables—such as smartwatches and fitness trackers—offer a convenient way to do this, but it is not yet clear whether the science behind these devices is strong enough to support this recommendation.

In this study, we reviewed and summarized review-level evidence on how accurate and clinically useful wearable-based measures are for health concerns relevant to Long COVID and other post-infection conditions.

We examined thousands of articles and identified 42 reviews covering many brands, device types, and wearable features.

Overall, we found inconsistent device accuracy across groups and settings, and limited evidence for clinical benefit.

Two areas—heart rate measurement and atrial fibrillation detection—showed comparatively stronger support. Because technology evolves quickly, we created this project as a living review that can be updated as new evidence and new devices emerge.

By outlining what is known, where the gaps are, and what kinds of research are most needed, our goal is to help researchers, clinicians, and patients make informed decisions about when and how wearables might be useful in outpatient care.

 

[Utsikt]

Current frontline treatment recommendations for these heterogenous, multisystemic conditions emphasize self-monitoring and non-progressive rehabilitation of activity and exertion.

So not rehabilitation?
The risk of bias assessment just lists the total scores, so there’s no way to check their judgements. Not a very good start.

Findings revealed highly variable review quality; substantial heterogeneity in device performance across biometrics, populations, and settings; and limited evidence for clinical utility.

Two biometrics—heart rate measurement and atrial fibrillation detection—had comparably stronger support.

I couldn’t find any actual evidence of utility. There were studies confirming that wrist-worn HR monitors like Apple watches are fairly accurate for measuring HR.

These were all of the measurements:

Biometrics.​

Data were extracted for the following biometrics: heart rate (HR), heart rate variability (HRV), blood pressure, atrial fibrillation detection, other arrhythmia detection, EKG, oxygen saturation (O2sat), V02Max, skin temperature, electrodermal activity (EDA; also sometimes called galvanic skin response or GSR), steps taken, flights of stairs climbed, energy expenditure (EE, estimated amount of energy burned by the body), total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO).

I have to say that I struggle to understand whar values this review adds. None of these measurements would qualify as «health monitoring» except for in the broadest sense possible.

I know ResetME is using step count, but that’s on a group level where you expect life-altering changes if the treatment works. So it’s a very removed proxy of health that’s at least better than just self-report because nobody can sustain very high increases without actually being better.