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For a progressive web app (PWA), mobile performance is the experience people actually get—not a single Lighthouse score or an old PWA audit badge. Use Core Web Vitals to judge real-user outcomes, lab tests to find likely causes, and production field data to verify whether a change helped.
Which metrics define mobile performance?
Start with the three Core Web Vitals: Largest Contentful Paint (LCP) for loading, Interaction to Next Paint (INP) for responsiveness, and Cumulative Layout Shift (CLS) for visual stability. Chrome’s guidance uses these thresholds:
| Metric | What it reflects | Good | Needs improvement | Poor |
|---|---|---|---|---|
| LCP | When the page’s likely main content, such as a heading or hero image, appears | ≤ 2.5 seconds | > 2.5–4.0 seconds | > 4.0 seconds |
| INP | Responsiveness across user interactions | ≤ 200 milliseconds | > 200–500 milliseconds | > 500 milliseconds |
| CLS | Unexpected movement of page content | ≤ 0.10 | > 0.10–0.25 | > 0.25 |
These are the thresholds documented by Chrome for Developers’ PageSpeed Insights guidance. PageSpeed Insights (PSI) evaluates field results at the 75th percentile. A page passes the Core Web Vitals assessment when all three are good; if there is insufficient INP data, PSI may assess the other two instead.
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How do lab tests and field data differ?
Lab tests are controlled and repeatable, which makes them useful during development and when tracking down regressions. Field data reflects actual visits, including the mix of devices, networks, background processes, and user actions. The methods answer different questions: a lab run helps investigate why a page may be slow; field measurement shows how it performs for observed users.
| Approach | Best used for | What it cannot establish by itself |
|---|---|---|
| Lab testing with Lighthouse or browser developer tools | Reproducing a load under defined conditions and inspecting bottlenecks before or after a change | The full range of real-user devices, networks, and interactions |
| Field measurement through CrUX, PSI, or real-user monitoring | Assessing production experiences over an observed population and reporting window | Why a result changed without additional diagnostics; results also depend on having enough observed data |
Google’s Web Vitals guidance puts the distinction plainly: “Only field measurement can accurately capture the complete picture.” Lighthouse cannot directly measure an interaction metric in a simulated run because it lacks real user input. Total Blocking Time (TBT) can serve as a lab proxy for First Input Delay (FID), but that does not make it a direct measurement of field INP.
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How to check Core Web Vitals for a PWA
- During development: run Lighthouse or use browser developer tools on representative mobile page types. Use repeatable conditions to find issues and check whether a code or layout change causes a regression. Treat the results as diagnostics, not a prediction of every user’s experience.
- After release: enter the page URL in PSI and inspect its field data alongside Lighthouse diagnostics. PSI’s field data comes from the Chrome User Experience Report (CrUX); its lab diagnostics come from Lighthouse.
- Interpret the field window: PSI reports the 75th percentile and aggregates field data over the previous 28 days, with results refreshed daily. Record the date and window when comparing results; a day-to-day change can reflect the rolling population and period, not just a recent code edit.
- Make comparisons specific: where the available data allows, state the percentile, device class, geography, page type, and time period. A result without those details can conceal meaningful differences in the users represented.
- Measure real interactions when needed: use real-user measurement or analytics for production behavior that a controlled load cannot capture, especially interaction responsiveness. Ensure comparisons use the same population and reporting window where possible.
PSI is useful for a page-level view, while CrUX represents eligible Chrome user experiences rather than every visitor to a site. If the page does not have sufficient field data, the absence of a PSI field result is not proof that the page is fast or slow; use lab diagnostics and collect production measurements appropriate to your audience.
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What should SPA teams report about route transitions?
For a single-page application (SPA), say whether a reported result covers a full page navigation or a transition between routes. Those are different experiences, and historically, navigation-focused measurement could miss what users encounter after client-side routing.
In an August 2026 update, web.dev reported that Chrome 151 added APIs for measuring Core Web Vitals across SPA route transitions. Tool adoption was just beginning, CrUX integration had no announced timetable, and equivalent support was not yet available in other browser engines. These details describe the status reported at that time, not a guarantee of current support. Check the current web.dev SPA Core Web Vitals FAQ before relying on route-transition data, and disclose the browser and measurement method used.
Does passing an old PWA Lighthouse audit prove a PWA is fast?
No. Chrome’s guidance on PWA audits in Lighthouse says that PWA-specific Lighthouse testing is deprecated and directs developers to current PWA documentation. An older audit can be useful historical context, but passing it is not a current, comprehensive performance benchmark or an installability guarantee. Fast loading on mobile networks remains an important baseline concern; assess it with current metrics and measurements instead of treating a legacy badge as proof.
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What can a published PWA performance case study show?
YouTube’s 2022 mobile-web case study describes using CrUX to identify real-user problems and Lighthouse to diagnose its watch page in the lab. The team reported work on lazy loading and code modernization; its case study said 76% of its mobile website URLs passed Core Web Vitals thresholds after its investment. That is a company-reported result for YouTube’s site, not an independent experiment or a forecast for another PWA. The useful lesson is the complementary method—field data to find user-facing problems, lab diagnostics to investigate them—not a promised outcome.
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