research paper pdf summarizer

AI PDF Summarizer

AI PDF Summarizer can summarize long PDF documents in seconds. It can convert PDFs to text, allowing you to ask your PDF. The best PDF Summary tool helps you save time and learning faster and better.

Spending too much time on lengthy PDF files?

Say goodbye to time-consuming PDF summaries with NoteGPT's PDF Summary tool. AI PDF Summarizer is free online tool saves you time and enhances your learning experience. The PDF Summarizer can convert PDFs to text page by page to and summarize Large PDFs into concise summaries and PDF to mind map with just one click.

How does AI PDF Summarizer work?

The AI PDF Summarizer has great features:

Convert PDF to text

Instantly Convert PDF to Text with a single click, you can copy and download easily.

AI That Summarizes PDF

Let AI quickly summarize long PDFs into short, clear summaries with key points.

Free Online PDF Summary Tool

NoteGPT's pdf summary tool is a free online tool requiring no payment or install.

How to Use AI PDF Summarizer

Easily using AI PDF Summarizer in three simple steps:

1. Upload Your PDF

Drag and drop your PDF file into the AI PDF Summarizer, or enter the PDF URL. You can also upload directly from Google Drive (coming soon).

2. Select Your Desired Feature

Choose from a range of AI-powered features such as summarizing the PDF document, extracting PDF to text, ask your PDF or chat PDF with AI, translating PDF content, generating mind maps from PDF, or read PDF with the AI PDF Reader.

3. Save and Share

Save the summarized content, extracted text, or generated mind maps. You can also share your results with friends or colleagues for collaborative learning and discussion.

Welcome to AI PDF Summarizer

Welcome to AI PDF Summarizer, your go-to free online tool for summarizing, translating, and understanding PDF documents with ease.

Why choose AI PDF Summarizer?

AI PDF Summarizer offers 9 features:

Drag-and-Drop Upload and URL Input

Easily drag and drop your PDF files into the PDF Summarizer or input the PDF URL. Future updates will include integration with Google Drive.

Read PDF documents with AI PDF Reader

Preview your PDF files online and read PDFs using AI to better understand the content, AI PDF reader to learn PDFs better for you.

Extract PDF to Text with PDF Extractor

Use AI to extract the text content from your PDF files, which can then be copied and downloaded.

AI Translation of PDF Content

Translate the entire PDF or specific pages with AI, making it easy to comprehend the document's content.

AI Summarization of Long PDFs

Save time by having AI summarize your long PDFs, providing you with summaries, key points, outlines, and essential questions.

AI PDF to Mind Map

For better visual understanding, AI PDF Summarizer can generate editable and downloadable mind maps from your PDFs in seconds.

Chat PDF with AI, Ask Your PDFs

Engage in conversations with AI to directly ask questions about your PDFs, making your study sessions more efficient.

Save PDFs and Share to your Friends

Support for saving and sharing your PDF study content allows you to share your learning materials with friends.

Extensive Prompt Library for PDF Summarizer

An extensive prompt library is available to summarize, explain, rewrite, or ask anything with PDFs and make your study sessions more targeted and effective.

Frequently Asked Questions

Is ai pdf summarizer free to use, how do i upload a pdf file to ai pdf summarizer, what is the maximum size of pdf files that pdf summary can summarize, can i extract text from my pdf files, is it possible to ai translate pdf content, how does ai pdf summarizer help with long pdfs, can i generate mind maps from my pdfs, can i preview my pdf files before summarizing, can i chat with ai about my pdf content, can i save and share my summarized content, what is the pdf prompt library used for, is ai pdf summarizer a safe tool, user reviews with ai pdf summarizer.

"AI PDF Summarizer is a game-changer for my research. It saves me hours by summarizing long articles and extracting key points efficiently."

"As a student, this tool has been invaluable. The AI PDF Reader helps me understand complex papers better, and the summarization feature is fantastic for exam prep."

"I love the ease of use with PDF Summary tool. Dragging and dropping files or entering a URL makes it so simple. The translation feature is a bonus for our international documents."

" PDF Summarizer is an excellent tool for educators. I can quickly get summaries of lengthy reports and share key insights with my students."

"The mind map generation feature is incredible. It helps me visualize and organize ideas from various PDFs seamlessly. Highly recommend it! best pdf to mind map!"

"This PDF Summary tool has made reviewing lengthy legal documents much more manageable. The ability to extract PDF to text and summarize content is a huge time-saver."

"Being able to chat with AI about PDF content is a fantastic feature. I can ask PDF for my questions. It makes finding specific information within technical documents so much easier."

"The AI PDF Summarizer's translation capability is extremely useful for medical papers in different languages. It helps me stay updated with global research."

"AI PDF Summarizer's prompt library is very effective. It guides me to focus on the most relevant sections of financial reports quickly and accurately."

Get PDF Summary with AI in Seconds

Experience a new level of efficiency and unlock the full potential of your PDF documents using AI PDF Summarizer.

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PDF Summarizer

Summarize lengthy pdf documents swiftly and accurately with hix writer's free summary generator..

Quick Guide to Using HIX Writer's PDF Summarizer

Getting to the essence of lengthy PDF documents becomes a breeze with HIX Writer's PDF Summarizer. Here's how you can make use of this innovative tool:

• 1. Begin by uploading your PDF files using the 'Upload File' button.
• 2. Next, define your preferred format for the summary. Choose 'Paragraph' for a flowing narrative or 'Bullet Points' for quick and concise insights.
• 3. Finally, hit 'Summarize' to receive a brief version of your document. You can also click 'Chat with File' and instruct the AI further.

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Understanding the Power of HIX Writer's PDF Summarizer

Immerse yourself in the technological marvel that is HIX Writer's PDF Summarizer. This AI-infused tool skilfully decodes and distills voluminous PDF documents into succinct summaries, preserving all vital information. With its ability to convert lengthy, intricate text into digestible material, it is an indispensable tool for everyone seeking efficiency and speed in information processing.

The potential uses of this tool cross numerous fields:

Students: Imagine having to review a pile of documents for a project or thesis. With HIX Writer’s PDF Summarizer, students can quickly distill the key ideas and concepts from books, lecture notes, essays, and more. The days of spending long hours reading through extensive academic content are over.

Researchers/Academics: For those who constantly delve into lengthy reports and research papers, HIX Writer’s PDF Summarizer is a game-changer. It enables swift identification of relevant studies, key findings, and key information, all while saving a substantial amount of time.

Journalists and Writers: Time is of the essence in journalism and writing. With the ability to quickly summarize source materials and reference documents, crafting stories and articles becomes far more efficient and manageable with HIX Writer’s PDF Summarizer.

Lawyers: Legal professionals often face the daunting task of going through complex legal files, reports, and case studies. HIX Writer’s PDF Summarizer simplifies this process by providing concise summaries, enabling lawyers to focus on the most critical pieces of information.

Compelling Advantages of Using HIX Writer's PDF Summarizer

HIX Writer's PDF Summarizer, one of the best PDF summarizers available, provides an unmatched toolset that ensures a superior and effortless summarizing experience. These advantages aren't just a matter of convenience; they can revolutionize your information processing practices.

Here's a look at why it's a must-have tool:

Power of Advanced AI: HIX Writer's PDF Summarizer deploys leading-edge artificial intelligence technology to streamline massive documents into concise, readable summaries. This powerful AI analyzes, comprehends, and retains the key information, making it easier for users to digest and understand complex content.

Efficiency in Time and Cost: In the fast-paced world where every second and penny counts, HIX Writer's PDF Summarizer stands as a beacon of efficiency. The tool swiftly processes information, delivering the essence of extensive PDFs in a fraction of the time it would take to read them. Moreover, it presents a cost-effective solution, enabling users to optimize their resources by reducing the budget spent on information processing.

Versatility Across Various Subjects: HIX Writer's PDF Summarizer offers incredible flexibility in handling a wide array of topics and subjects. Whether the document involves intricate scientific research, detailed business reports, or dense legal documents, our tool can efficiently distill the main points, making it a reliable companion across multiple domains.

High-Quality, Professional Summaries: HIX Writer's PDF Summarizer goes beyond merely reducing text length - it crafts high-quality, professional summaries. It maintains the integrity of the original document, capturing the crucial points in a condensed format while preserving the original narrative's tone and context. This ensures that users receive a polished, refined summary that can directly be used for their specific purposes.

The Impact of HIX Writer's PDF Summarizer at a Glance

Our Tools Can Summarize PDF Files, and More

Explain it to a 5th grader, continue writing, linkedin summary generator, story summarizer, real estate bio generator, professional bio generator, free spell checker, sentence shortener, 1. what kind of pdf files can be summarized using hix writer's pdf summarizer.

Our tool can handle PDF files that contain pure text or are OCR scannable. However, text embedded within images may not be processed. Keep in mind the tool accepts PDF files up to 10MB in size and can process content within 200K characters, which is approximately 40,000 words.

2. Is there any cost associated with using HIX Writer's PDF Summarizer?

Users can access HIX Writer's PDF Summarizer free of charge, with a basic account offering a weekly limit of 3,000 words in generated summaries. If you need to process a larger volume, HIX Writer provides subscription plans with additional word credits suitable for academic or commercial use.

3. How precise is the summary generated by HIX Writer's PDF Summarizer?

Built on advanced AI algorithms and up-to-date databases, HIX Writer's PDF Summarizer delivers accurate summaries. It is proficient in extracting core ideas, critical statements, essential data, and other key information, ensuring the original content's essence is preserved.

4. Can I trust HIX Writer's PDF summarizer?

HIX Writer's PDF summarizer stands out as one of the most reliable free AI summary generators at your disposal. It provides you with the capability to condense extensive PDFs into brief bullet points or comprehensive paragraphs within mere seconds.

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Enhance your writing process with HIX Writer. Whether you're crafting fact-based articles, humanizing AI text, or rewriting, summarizing, and translating your content, HIX Writer provides the tools you need.

AI PDF SUMMARIZER

Summarize pdfs in just a few clicks.

No time to read 50-page PDFs? Let our AI do it for you. Our smart AI reads the details so you don't have to. Summarize any PDF in seconds and get the key points fast.

AI PDF Summarizer

Upload a PDF file and receive an accurate summary.

Trusted by 500,000+ marketers

Ways to use our AI PDF summarizer

Summarize pdfs and distill key points.

PDFs like research papers, industry reports or medical studies can be dry and hard to digest. Use our PDF summarizer to extract key points and dig deeper by asking specific questions.

Learn new topics through PDF summaries

Upload PDFs of topics you wish to get good at and ask for layman summaries or explanations. Clarify points you’re unsure about to deepen your understanding.

Analyze and highlight observations

Get our PDF summarizer to analyze and tell a story with facts and numbers. Derive conclusions and come up with discussion points.

How our AI PDF summarizer works

Say goodbye to tedious hours spent poring over PDF documents. Now, you can instantly uncover the key insights of any PDF document with our fast and accurate PDF summarizer so you can get to the important work quickly.

Upload your PDF file

Once you upload your PDF file, you’ll get a quick summary, along with suggested prompts.

Expand your summary or pick a focus

You can ask our AI PDF summarizer to expand the summary or write concise paragraphs of subtopics within the file. ‍ Sample prompt: Write a longer summary. Include any important figures or examples.

Summarize in your preferred format

Turn the summary paragraph into bullet points, split the summary into distinct sections, or ask our AI to use your preferred format. ‍ Sample prompt: Create a summary in bullet points.

Analyze tables, facts, and figures within your file

Get our PDF summarizer to extract key insights from specific tables or figures and share ideas on how to mitigate or improve. ‍ Sample prompt: Analyze table x, share insights and suggest action items

Make our PDF summarizer work for you

Quickly get the answers you need.

Turn the most complex PDF documents into concise summaries with just a few clicks. Instantly generate summaries that are accurate and comprehensive.

Cut down on reading time

Save time by automating repetitive tasks. Our PDF summarizer can quickly and accurately summarize any PDF, allowing you to focus on more important tasks and regain control of your day.

Accurately condense long texts

Don't let big documents bog you down. With PDF Summarizer's advanced algorithms, you can trust that you're receiving a concise summary that accurately represents the entirety of the original PDF.

Discover a better way to write

We're just scratching the surface on what you need to write five-star content for your Product Detail Pages (PDPs).

Your brand's voice

Need active voice versus passive voice? Titles and bullets? Or avoid competitors' keywords? We get our models to learn your needs and your voice.

Generate in bulk

Generate all your product descriptions at once. Grab a cup of coffee while our product description generator does the heavy lifting for you.

Syncs to your PIM

All your product information synced with a touch of the button. Keep your product details aligned with a single source of truth.

Edit freely

A built in editor right at your fingertips. We're also compatible with all your grammar checking browser extensions and tools.

Multiple languages

Speak the languages that your customers do and expand your market footprint. We support over 25 languages and counting.

AI that learns

The more you use Hypotenuse AI, the more it learns your preferences, the better it gets. It's the intelligent assistant you always wanted.

Ready to try our PDF summarizer?

Get your PDF documents summarized in less time. Generate concise points that you can use for your reports and presentations.

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The best pdf summarizer tool to condense your reading.

Ever feel overwhelmed by the sheer volume of reading you have to get through for work, or just your own curiosity? Between long-form articles, research papers, and books, the amount of information out there seems endless. The good news is there are tools that can help. A PDF summarizer is designed specifically for condensing all that content into the key highlights so you can grasp the essence without getting bogged down in the details.

Why You Need a PDF Summarizer

A PDF summarizer tool can save you hours of time and frustration. Let's face it, most of us have limited time to read the tons of documents, reports, and books required to stay on top of things.

Using an automatic PDF summarizer allows you to get the key highlights from long, dense documents in minutes instead of hours. You'll be able to extract the main ideas, concepts, and conclusions without wading through pages of fluff and filler.

What are the Benefits of PDF Summarizer

A PDF summarizer tool can save you a ton of time and effort. Here are some of the major benefits of using one:

• It condenses lengthy PDFs into shorter, more concise summaries. This means you can grasp the key ideas, concepts and information in a fraction of the time.
• It highlights the most important sentences and passages. The summarizer uses algorithms to determine significance and relevance, so you know you're focusing on the critical parts.
• It can summarize at different levels of depth. Want just the key highlights? Choose a shorter summary length. Need more details? Select a longer summary. You get to pick what depth of information you need.
• It's fast and efficient. In just a few seconds, a PDF summarizer can condense even lengthy documents into the essentials you need to know. No more wasting time skimming and scanning for the highlights!

Using a PDF summarizer tool is a simple way to make your reading more effective and efficient. If you regularly deal with longer PDF documents, a good summarizer can save you hours of time and frustration. The benefits to both your productivity and your sanity are huge!

How AI Generates a PDF Summary

AI-based PDF summarizers use machine learning models trained on massive datasets to analyze the text and images in PDF files and generate an abstractive summary.

How AI Reads PDFs

AI systems convert the PDF into raw text and images that their algorithms can interpret. The text is analyzed to identify the key topics, concepts and ideas.

The AI looks for semantic links and connections between sentences and sections to determine what's most important. It identifies subjects, verbs, objects and modifiers to understand how ideas relate. Just like humans, the AI makes inferences and forms a mental model of what the document is about based on what it reads.

From there, the AI rewrites the key points and main ideas in its own words to generate a short, coherent summary. The summary aims to capture the essence and primary substance of the source content, omitting unnecessary or redundant information. The result is a high-level overview of the PDF's most significant details, conclusions and messages.

Using AI to analyze and summarize PDF documents helps people save time reading and get to the heart of the content quickly. The technology is improving all the time, getting faster, smarter and better at pinpointing and rephrasing what really matters in any PDF file.

So now you have a few excellent options to choose from to help you distill those lengthy PDFs into bite-sized summaries you can quickly digest. Any of these summarizer tools would be a great pick to help you cut through the clutter and get to the heart of what really matters in those big reports or studies you need to get through. Give one a try and see how much time it saves you and how much more engaged you feel with the material. You might just become an evangelist for how PDF summarization can change the way you read and learn. And with technology steadily improving summarization capabilities, these tools will only get better at delivering the essence of the content in a fraction of the time. The future of efficient reading is here—embrace it!

What is a PDF summarizer?

PDF summarizer is an online tool that can automatically analyze the content of a PDF document and generate a summary of the most important details for you.

Why is summarizing important?

Summarizing can help you identify the key details from a longer piece of content in a more time-efficient way. It allows you to get the important points without having to read through unnecessary details. Summarizing is useful for anyone looking for a quick overview or understanding without reviewing everything in depth.

How do PDF summarizers work?

A PDF summarizer is a tool that automatically analyzes the content of a PDF document and generates a condensed version highlighting the key points. It's a quick way to get the gist of lengthy PDFs without having to spend a lot of time reading through everything yourself.

How accurate is a PDF summarizer?

The summaries are analyzed and condensed accurately using artificial intelligence. This allows it to handle large volumes of text across many topics with precision.

How can I make a PDF more readable?

One easy option is to adjust the font size. Most PDFs allow you to enlarge or reduce the text to find a comfortable size for viewing. You can also adjust line spacing and margins if the text is too cramped. This helps separate lines for easier scanning.

Aside from PDFs, which types of files can be summarized?

A summarizer tool can process a variety of text-based file types. Besides PDFs, the Hypotenuse AI Summarizer can also take in:

• DOCX: This is the default file format for Microsoft Word, a very commonly-used text processing program. Many documents, particularly those of a professional or academic nature, are created and shared in this format.
• TXT: These are plain text files that contain unformatted text. They're created by Notepad on Windows and by TextEdit on Mac. They're often used for notes, instructions, and other simple documents.

Is Hypotenuse AI’s PDF Summarizer free to use?

You can summarize 2 files for free during the trial. To keep using it without limits, you'll need to upgrade your subscription to an Individual or Teams plan.

What is the best PDF summarizer?

When it comes to PDF summarizers, Hypotenuse AI makes it the best option for quickly and accurately summarizing PDF files. It allows users to get the most important information from lengthy PDFs—without you having to scour through walls of text.

Ready to try out your PDF summarizer?

Get started with our set of AI tools to get the most out of your content.

Use AI to summarize scientific articles and research papers in seconds

Send a document, get a summary. It's that easy.

If GPT had a PhD

• Unlimited Summaries
• Summarize articles up to 200,000 words.
• 5 Figure and table analysis with AI
• Unlimited Chat Messages
• Unlimited article searches
• Import and summarize references with the click of a button
• 30,000 words summarized
• 5 Figures or Tables analyzed with AI
• 100 Chat Messages
• Maximum document length of 200,000 words
• Unlimited bulk summaries
• Unlimited chat messages per month
• Unlimited figure and table analysis with AI
• 1,000 documents indexed for semantic search

AI Summarizer

Summarize Any PDF or Document Instantly

Used by professionals in

Used by over 1,000,000+ professionals & researchers in

AI Summary for any document

Sharly ai summarizer tool is powered by artificial intelligence and provides concise summaries of any content, including pdf, general documents, articles, audio files, or presentations. no more shifting through pages of information or listening to lengthy recordings to extract key points. just upload your files and let the ai do the work. the ai summary generator reads through the file, identifies the main themes and the most essential details, and produces a resume that captures the essence of the content. with sharly's ai summarizer you turn the tedious process of summarizing into a thing of the past..

Key Features of Sharly AI Summarizer

Accurate ai summary.

Sharly's AI summary tool is capable of discerning key points from complex content accurately. Whether you're dealing with a research paper, a business report or the recording of a meeting, you can rely on Sharly to bring text to a comprehensive and accurate summary.

Time-Efficient Summarization

Using Sharly's AI to summarize text, articles, documents or audio files greatly cuts down the time you spend on reading or listening to lengthy content. This saved time can then be redirected to other productive tasks, boosting your overall efficiency.

Multi-Format Support

Sharly AI summary generator supports various document file formats, including PDF, PDF with OCR, .docx, .doc, .txt, .csv, .rtf, .odt, .ods, .odp. .pptx and .html files. It also supports audio and multimedia files such as .mp3, .mp4, .wav, .webm, .flac, .oga, .ogg and .mpeg.

Speed up Learning Processes for Academics and Students

Postgraduate students, overwhelmed by vast academic materials, turned to sharly ai for efficient study. the ai summarizer quickly condensed complex papers into clear summaries, enabling them to grasp key concepts and theories swiftly. this tool significantly saved time and enhanced understanding, proving invaluable for navigating their rigorous coursework and research, thus improving their overall academic experience and productivity..

Sharly AI’s Advantages for Summary Generator

Efficient information extraction.

Rapidly condense large volumes of text into digestible AI summaries.

Cross-Document Analysis

Advanced data analytics.

Summarize and compare points across multiple PDF or other document types.

Customizable Summaries

Sharly offers flexibility in summary length and focus, tailored to your specific needs.

Secure and Confidential

Ensures the privacy and security of all uploaded documents.

User-Friendly Interface

Simple, intuitive interface allowing for easy upload and quick summarization of documents.

Integration with Popular Tools

Seamless integration with common workplace tools for straightforward access and use in various professional environments.

Citations and Page Number

Automatically includes relevant citations and page numbers in summaries, making it easier to reference and locate original content.

Custom Behavior

Offers customizable summarization options, allowing you to tailor the focus and depth of summaries to their specific needs and preferences.

How to create AI summaries?

Which file formats does Sharly's AI summarizer support?

How quickly can Sharly's AI summarize content?

Does Sharly's AI summarizer maintain the quality of the original content?

Is it possible to use Sharly's AI summarizer for multiple content at once?

"Sharly improved our customer support, enabling us to provide faster, more accurate assistance; our customers couldn't be happier."

Sarah Mitchell

Director of Customer Support at SupportPro Innovations

"Sharly AI has transformed my research workflow. It efficiently summarizes complex texts, enhancing my focus on critical neuroscience analysis"

Ethan Parker

Research Assistant in Computational Neuroscience

Try Sharly AI for Summary

Start for free — upgrade anytime.

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• Compress PDF
• PDF Converter
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AI PDF Summarizer

• Translate PDF
• Delete PDF Pages
• Extract PDF Pages
• PDF Annotator
• Number Pages
• Watermark PDF
• PDF to Word
• PDF to Excel
• Word to PDF
• Excel to PDF
• Protect PDF
• Flatten PDF
• PDF Scanner
• › AI PDF Summarizer

Add PDF , image , Word , Excel , and PowerPoint files

Supported formats:

With AI PDF, you can utilize the powers of artificial intelligence to summarize PDFs for free! The interactive chat function lets you request specific information to be summarized and presented to you in a matter of seconds. AI PDF Summarizer lets you understand document contents without having to read through every page.

• Chat with a PDF for free, without sign-up
• TLS encryption for secure file processing
• An AI assistant for instant PDFs summaries

How To Use AI PDF Summarizer

• Import or drag & drop your file to our AI PDF Summarizer.
• Review the summary that’s instantly generated.
• Ask AI additional questions about the PDF in the prompt box.

Join over 600,000 people saving time

Summarize, analyze and organize your research

Summarize anything

Understand complex research

Organize your knowledge

AI powered tools built specifically for academic papers

From undergrad to postgrad and beyond.

Researchers

“It would normally take me 15mins – 1 hour to skim read the article but with Scholarcy I can do that in 5 minutes.”

Omar Ng , Masters student ‍ @omarng

‍ It’s time to revolutionize your research workflow

So, you have texts coming at you from every angle and need to articulate your understanding of them tomorrow? We’ve been there…

Summarize any paper, article or textbook.

You can summarize videos too! Scholarcy converts long complex texts into interactive summary flashcards, which highlight key information.

We’re compatible!

Import any file, from anywhere. Whether you're browsing articles online, have a chapter downloaded, or a folder of PDFs and Word docs.

Enhance your summaries

Change the summary to match your reading style with our Enhance feature. Choose from a single sentence to a researcher level overview.

Jump to key findings with Spotlight

We’ll take you straight to the important points, key concepts, and contributions. 

Critically evaluate complex texts more easily

Smart highlighting and analyzing features guide you to important sections of text and help you interpret them.

Find order from chaos

Our Flashcards provide a structured, consistent format to read and explore any text from, whether you’re reading just one article or 20.

Highlight, annotate and take notes

Never lose another flash of inspiration. Add notes while you read and pick up right where you left off.

Explore new concepts and terms as you go

We’ll point you to further reading and show you how the article compares to earlier work.

Keep track of your knowledge

Never lose another text. Scholarcy is the perfect tool for saving, organising and getting a quick refresher of your reading.

Save summaries and never lose another paper

Generate and save flashcards to your library even while browsing and reading on the go.

Keep track of important details

Store all of your references, figures and tables and easily find them again.

Refresh your memory

Quickly remind yourself of the key facts and findings before a lecture or meeting with your supervisor.

Synthesize your insights. Export to other apps.

Export your flashcards to a range of file formats that are compatible with lots of research and productivity apps.

Export summaries to a range of formats

Learn more about your texts and how they compare, or connect by exporting to Excel, PKMS and more.

Import directly from Zotero

Convert your Zotero library into Scholarcy Flashcards for more efficient article screening.

Generate bibliographies in a click

Export your flashcards to your favourite citation manager or generate a one-click, fully formatted bibliography in Word.

Apply what you’ve learned. Write that magnum opus 🤌

Transform all that knowledge you’ve built up into a perfectly articulated argument.

Introduction

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PDF Summarizer Powered by AI.

No credit card required.

Unlock the Power of Your Documents

Discover, summarize, translate, and interact with your PDFs like never before. hey | docs is the simplest way to harness the power of AI to summarize your PDFs and interact with the content.

• Easy to Read Beautifully Formatted AI Responses Responses support bullet points, headers, tables, bold text, and more.
• Long-term Memory Infinite Chat History Scroll through your entire chat history and never lose your context.
• Fast and Accurate The Latest AI Models Get accurate and fast responses, with additional context on related topics not included in the document. PDFs summarized like never before.

How It Works

Whether it's a dense legal contract, a detailed research paper, or crucial lecture notes, hey | docs makes understanding your documents effortlessly intuitive.

• Step 1 Sign Up for an Account Start out with a FREE 1 day trial or choose our paid plan .
• Step 2 Upload your PDFs Securely upload any PDF document to get started. We'll process your file and make it ready for you to chat in seconds.
• Step 3 Summarize your PDF It's that simple. Extract the most relevant information, summarize complex texts, and get answers without the manual search.

Perfect for Ambitious Professionals and Academics

Join others who are already enhancing their productivity with hey | docs . Simplify your studies, research, and professional life with an AI that reads between the lines so you don't have to.

• Contracts Legal Professionals Navigate through extensive legal documents swiftly to find the precise information you need.
• Lecture Notes, Research Papers Academia Turn your lecture notes into an interactive Q&A session to better prepare for exams and papers. Digest scholarly articles and research papers without sifting through pages of data.
• Reports and Memos Business Professionals Quickly extract critical points from reports and memos to make informed decisions faster. Summarize and extra key information from resumes.

Sign up now for a free 1 day trial.

Unlimited PDFs upload limit

Unlimited messages/month

Mobile-friendly interface

High-quality responses

Infinite chat history

Multilingual

Research Paper Summarizer

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AHelp PDF Summarizer

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If you’ve ever felt swamped by the mountain of PDFs waiting to be read for a class or a project, you’re not alone. Students and professors often lean on PDFs because they keep the look and feel of the original documents no matter where you open them, from your laptop to your tablet. But let’s be honest, the sheer volume of info you need to get through can be daunting.

What Is an AI PDF Summarizer?

PDF documents, especially in academic settings, can be dense and packed with complex information, often extending to hundreds of pages and citations. With such an amount of data to analyze, efficiently extracting the most important information can be a time-consuming task. Students and professors typically have tight schedules, with multiple deadlines and projects, making it impractical to read every document in detail.

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Facing the task of managing never-ending reading materials can be overwhelming, whether you’re a college student tired from assignments and citations or a professional with tight work deadlines. Often, the sheer volume of documents, from academic papers to KPI reports, can consume valuable time and energy that could be better spent on other important tasks or studies.

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Integrating a PDF summarizer into your routine is simple and effective. It reduces the stress associated with heavy reading loads and frees up time for critical thinking and application of the material. Whether for school or work, a PDF summarizer can significantly enhance your productivity and make your reading tasks more manageable and enjoyable.

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Is there a PDF Summarizer tool?

Of course! You can check out the AHelp PDF Summarizer. It's designed to help users quickly extract the essential points from lengthy PDF documents. This way, you can save time and effort by getting concise summaries of complex texts without needing to read through every page.

How do you summarize a PDF?

To summarize a PDF, you can use the AHelp PDF Summarizer. Simply upload your PDF (or docx) file to the tool, and it will analyze the content using advanced algorithms. The summarizer identifies key information and presents it in a shortened form.

Is there an AI that summarizes PDF for free?

Yes, the AHelp PDF Summarizer is a free AI tool that summarizes PDF documents. It uses artificial intelligence to analyze and condense text and then provides users with brief, accessible summaries of long documents without any cost.

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• Managers have adjusted to new rules, emphasizing flexibility to respond rapidly to changes in the market and competition.
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• The traditional value of strategic positioning is perceived as static and obsolete due to the belief that rivals can readily replicate any market position.
• The focus on operational effectiveness has led to the proliferation of management tools and techniques, resulting in significant operational improvements, but with limited sustainable profitability.
• The first story emphasizes the importance of trusting in life's journey, following intuition, and connecting the dots, even when the path forward seems unclear.
• The second story highlights the experience of being fired from Apple, experiencing rejection, and eventually finding a new creative path and success with NeXT and Pixar.
• The third story focuses on the significance of embracing mortality, making important life choices, and following one's heart without being trapped by external expectations.
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• Buffett's 3-step productivity strategy involves asking his personal pilot, Mike Flint, to write down his top 25 career goals.
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• Buffett argues that these uncircled items are considered "Avoid-At-All-Cost lists," and they only receive attention once they've succeeded with the top 5.

Text is from https://hbr.org/1996/11/what-is-strategy

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This study has demonstrated that higher relative humidity and wind speed, and lower atmospheric pressure, were associated with increased pain severity in people with long-term pain conditions

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There were 9695 hazard periods included in the analysis for the final 2658 participants, matched to 81,727 control periods in 6431 participant-months

Time spent outside did not have a significant interaction with relative humidity or wind speed , nor did it lead to significant associations for temperature when conducting analyses stratified by time spent outside (Supplementary Table 3 )

This study has demonstrated that higher relative humidity and wind speed , and lower atmospheric pressure, were associated with increased pain severity in people with long-term pain conditions

The ‘worst’ combination of weather variables would increase the odds of a pain event by just over 20% compared to an average day

We showed that Cloudy participants were largely representative of a population reporting chronic-pain symptoms,[13] proportionally fewer participants at both extremes of age were recruited

The odds of a pain event was 12% higher per one standard deviation increase in relative humidity (9 percentage points) ( OR 1.119 (1.084–1.154), compared to 4% lower for pressure ( OR 0.958 (0.930–0.989) and 4% higher for wind speed ( OR 1.041 (1.010–1.073) (11 mbar and 2 m s−1, respectively)

The analysis has demonstrated significant relationships between relative humidity , pressure, wind speed and pain, with correlations remaining even when accounting for mood and physical activity

This study used a smartphone app to collect data from 2658 patients with chronic pain over 15 months, finding significant relationships between pain and relative humidity, pressure, and wind speed, with correlations remaining even when accounting for mood and physical activity.

Key findings

The study found significant relationships between pain and relative humidity , pressure, and wind speed , with correlations remaining even when accounting for mood and physical activity . Relative humidity had the strongest association with pain, and temperature the least.

The study found significant relationships between relative humidity , pressure, wind speed , and pain, with correlations remaining even when accounting for mood and physical activity .

The objective of the study was to examine the relationship between local weather and daily pain in people living with long-term pain conditions using a smartphone app.

The study used a smartphone app to collect daily data from participants over 15 months, including pain symptoms, mood, physical activity , and weather data from nearby weather stations.

The study used a case-crossover design, where participants served as their own control, eliminating confounding by time-invariant factors. Weather data were obtained by linking hourly smartphone GPS data to the nearest of 154 possible United Kingdom Met Office weather stations.

The study uses a case-crossover design and statistical analysis, including the calculation of odds ratios using the following equation: n Odds Ratio = exp(βT(temperature - μT) + βRH( relative humidity - μRH) + βwsp( wind speed - μwsp) + βP(pressure - μp)).

The study found significant relationships between pain and relative humidity , pressure, and wind speed , with correlations remaining even when accounting for mood and physical activity . The odds of a pain event were higher with an increase in relative humidity and wind speed , and lower with an increase in atmospheric pressure.

The study found that the odds ratio for a pain event increased with relative humidity , pressure, and wind speed . The results remained significant even when accounting for mood and physical activity .

Conclusions

The study demonstrated that higher relative humidity and wind speed , and lower atmospheric pressure, were associated with increased pain severity in people with long-term pain conditions. The effect of weather on pain was not fully explained by its day-to-day effect on mood or physical activity .

The study concludes that there is a significant relationship between weather and pain, and that understanding this relationship is important for patients with chronic pain .

Front matter

How the weather affects the pain of citizen scientists using a smartphone app William G . Dixon 1,2,3*, Anna L . Beukenhorst 1, Belay B . Yimer[1], Louise Cook[1], Antonio Gasparrini 4,5, Tal El-Hay 6, Bruce Hellman 7, Ben James[7], Ana M. Vicedo-Cabrera[4], Malcolm Maclure[8], Ricardo Silva 9,10, John Ainsworth 2, Huai Leng Pisaniello1,[11], Thomas House 12,13, Mark Lunt 1, Carolyn Gamble3,[14,15], Caroline Sanders[14,15], David M. Schultz 16, Jamie C. Sergeant 1,3,17,18 and John McBeth1,[3,18]

Patients with chronic pain commonly believe their pain is related to the weather. Scientific evidence to support their beliefs is inconclusive, in part due to difficulties in getting a large dataset of patients frequently recording their pain symptoms during a variety of weather conditions. Smartphones allow the opportunity to collect data to overcome these difficulties. Our study Cloudy with a Chance of Pain analysed daily data from 2658 patients collected over a 15-month period . The analysis demonstrated significant yet modest relationships between pain and relative humidity, pressure and wind speed, with correlations remaining even when accounting for mood and physical activity . This research highlights how citizen-science experiments can collect large datasets on real-world populations to address long-standing health questions. These results will act as a starting point for a future system for patients to better manage their health through pain forecasts .

INTRODUCTION

Weather has been thought to affect symptoms in patients with chronic disease since the time of Hippocrates over 2000 years ago.[1] Around three-quarters of people living with arthritis believe their pain is affected by the weather .[2,3] Many report their pain is made worse by the cold, rain , and low atmospheric pressure. Others report that their pain is made worse by warmth and high humidity. Despite much research examining the existence and nature of the weather–pain relationship,[4] there remains no scientific consensus. Studies have failed to reach consensus in part due to their small sample sizes or short durations (commonly fewer than 100 participants or one month or less); by considering a limited range of weather conditions; and heterogeneity in study design (e.g. the populations studied, methods for assessing pain, assumptions to determine the weather exposure, and statistical analysis techniques).[5–11] Resolving this question requires collection of high-quality symptom and weather data on large numbers of individuals. Such data also need to include other factors potentially linked to daily pain variation and weather , such as mood and amount of physical activity . Collecting this kind of multi-faceted data in large populations over long periods of time, however, has been difficult.

The increasing uptake of smartphones offers new and significant opportunities for health research.[12] Smartphones allow the integration of data collection into daily life using applications (apps). Furthermore, embedded technologies within the smartphones, such as the Global Positioning System ( GPS ), can be used to link the data collection to specific locations. We created Cloudy with a Chance of Pain,[13,14] a national United Kingdom smartphone study, to collect a large dataset to examine the relationship between local weather and daily pain in people living with longterm pain conditions.

The study app was downloaded by 13,207 users over the 12month recruitment period (Figs 1 and 2a) with recruitment from all 124 UK postcode areas. A total of 10,584 participants had complete baseline information and at least one pain entry, with 6850 (65%) participants remaining in the study beyond their first week and 4692 (44%) beyond their first month (Fig. 2b) . Further description of engagement clusters is provided in Supplementary Table 2 and Supplementary Figs 1–3. A total of 2658 participants had at least one hazard period matched to a control period in the same month (Fig. 3) and were included in the final analysis. There were 9695 hazard periods included in the analysis for the final 2658 participants, matched to 81,727 control periods in 6431 participant-months. A total of 1235 participants contributed one month, and the remaining 1423 participants contributed 2–15 months.

The final cohort was active for a median of 165 days (interquartile range, IQR 84–245) with symptoms submitted on an average of 73% of all days. Cohort members were

How sƟff did you feel on waking No sƟffness Very severely this morning?

predominantly female (83%), had a mean age of 51 years (standard deviation 12.6), and had a range of different pain conditions, predominantly arthritis (Supplementary Table 1 ). The median number of weather stations associated with each participant during the course of their active data-collection period was 9 (IQR 4–14) with a maximum of 82 stations, indicating how mobile participants were during the course of the study and the importance of accounting for the weather at different locations over the course of the study. As an illustration of the structure of the data, the proportion of participants reporting a pain event was plotted as a heat map per calendar day for the study period (Fig. 4), aligned with the average United Kingdom weather data for the same time period. On any given day during the study, about 1–6% of participants had a pain event. At the start of the study, most participants believed in an association between weather and their pain (median score 8 out of 10, IQR 6–9). The demographics, health conditions and baseline beliefs of the 2658 participants included in the analysis were representative of the 10,584 participants who downloaded the app and provided baseline information (Supplementary Table 2 ).

The multivariable case-crossover analysis including the four state weather variables demonstrated that an increase in relative humidity was associated with a higher odds of a pain event with an OR of 1.139 (95% confidence interval 1.099–1.181) per 10 percentage point increase, as was an increase in wind speed with an OR of 1.02 (1.005–1.035) per 1 m s−1 increase ( Table 1 ). The odds of a pain event was lower with an increase in atmospheric pressure with an OR of 0.962 (0.937–0.987) per 10-mbar increase. Temperature did not have a significant association with pain ( OR 0.996 (0.985–1.007) per 1 °C increase). The odds of a pain event was 12% higher per one standard deviation increase in relative humidity (9 percentage points) (OR 1.119 (1.084–1.154), compared to 4% lower for pressure (OR 0.958 (0.930–0.989) and 4% higher for wind speed (OR 1.041 (1.010–1.073) (11 mbar and 2 m s−1, respectively) . Of the four weather variables, relative humidity had the strongest association with pain , and temperature the least, evidenced by the estimated relative importance of the variables and their standardized odds ratios ( Table 1 , Supplementary Table 4 ). Similar effect sizes were seen when each variable was examined in univariable analyses . Precipitation was not associated with an increased odds of a pain event ( OR 0.996 (0.989–1.003) per 1 mm daily rainfall amount) (Supplementary Table 5 ). Exploratory analyses considered time spent outside by including an interaction term with temperature, relative humidity , and wind speed . Time spent outside did not have a significant interaction with relative humidity or wind speed , nor did it lead to significant associations for temperature when conducting analyses stratified by time spent outside (Supplementary Table 3 ). It thus was not included in the final model.

The model was then expanded to include mood and physical activity on the day of interest, included as binary variables ( Table 1 ), resulting in a modest reduction in the point estimates for all weather variables. Mood was strongly and independently associated with pain events ( OR 4.083 (3.824–4.360) for low mood versus good mood), whereas there was no significant association with physical activity ( OR 0.939 (0.881–1.002) for high versus low activity).

This multivariable regression model output represents the effect of one weather variable while all else remains constant. In reality, a single weather variable rarely changes in isolation while others remain unchanged . To illustrate the composite effect of the weather variables on the odds of reporting pain, an OR was calculated for each day using the coefficients of our multivariable model and daily UK mean weather values. Figure 5 demonstrates there is significant variability in the odds of a pain event for any given value of each weather variable. For example, at a temperature of 8 °C, the odds of a pain event varied from around npj Digital Medicine (2019) 105

0.8–1.2, depending on the other state variables in the weather that day.

Other factors such as day of the week (Supplementary Table 6 ), lagged weather values (Supplementary Table 7 ) and changes in weather variables from the previous day were tested. Mondays, Thursdays, and Saturdays (ORs 1.14, 1.14, and 1.29, respectively) had higher odds of pain compared to Sundays, but adjusting for the day of the week did not alter the effect of the four main weather variables . Except for relative humidity (1-day lag and 2day lag), no significant associations were observed between lagged weather variables and pain events . Including change in weather from yesterday showed a minor effect of changing relative humidity ( OR 1.005 (1.001–1.009) per 10 percentage point increase), whereas the effects of today’s relative humidity and pressure remained unchanged (Supplementary Table 8 ). Stratification by disease led to a loss of statistical power and largely inconclusive results, although relative humidity appeared to have a stronger association with pain in patients with osteoarthritis (Supplementary Table 9 , Supplementary Fig. 4). Stratification by the number of pain sites also showed no meaningful difference (Supplementary Table 10 ). After stratifying by participants’ prior beliefs about their weather–pain relationship, relative humidity remained associated with pain in all participants although the association with pressure was only seen in those with a strong prior belief (Supplementary Table 11 ).

This study has demonstrated that higher relative humidity and wind speed , and lower atmospheric pressure, were associated with increased pain severity in people with long-term pain conditions . The most significant contribution was from relative humidity . The effect of weather on pain was not fully explained by its day-to-day effect on mood or physical activity . The overall effect sizes, while statistically significant, were modest. For example, the ‘worst’ combination of weather variables would increase the odds of a pain event by just over 20% compared to an average day . Nonetheless, such an increased risk may be meaningful to people living with chronic pain .

In addition to investigating the weather–pain relationship, we successfully conducted a national smartphone study that delivered on the promise of how consumer technology can support health research.[12,15] This study recruited over 10,000 participants throughout the United Kingdom, sustained daily self-reported data over many months,[13] and showcased the value of passively collected GPS data. Prior large smartphone studies have retained npj Digital Medicine (2019) 105

only around one in ten participants for seven days or less.[16,17] In contrast, our study retained 65% of participants for the first seven days, and 44% for the first month, with over 2600 participants contributing to the analysis having provided data for many months of the study.[13,14] An important success factor was strong public involvement in early setup and piloting, as well as participants’ interest in weather as a possible pain trigger.[14] The study design has resolved problems of prior weather–pain studies such as small populations,[5,7] short follow-up,[3,8] surrogate pain outcomes,[11] the absence of possible causal pathway variables such as mood, and assumptions about where participants were located and thus the weather to which they were exposed.[18,19]

Overcoming these obstacles produced a large dataset that allowed us to tease out subtle relationships between weather and pain .

There are potential limitations to this study. First, the reduction in participant numbers from over 10,000 with baseline data to the final 2658 participants with at least one within-month risk set raises questions about generalisability . Importantly, the characteristics of those included in the analysis were similar to the initial

10,000 participants, other than being slightly older (mean age 51 versus 48 years old). In a prior analysis, we showed that Cloudy participants were largely representative of a population reporting chronic-pain symptoms,[13] although proportionally fewer participants at both extremes of age were recruited. However, we would not expect middle-aged recruits to differ in their relationship between weather and pain from older or younger participants, and thus such selection factors would not invalidate our results. Second, the study was advertised to participants with a clear research question . It is possible that only people with a strong belief in a weather–pain relationship participated, generating an unrepresentative sample. However, the percentage of participants who believed in the weather –pain relationship was similar to prior studies,[20] and we did not see selective attrition of people who reported no weather–pain beliefs.[13] The within-person design would, regardless, mean that participants who drop out early would not introduce bias from time-invariant characteristics. Third, the lack of blinding raises possible information bias where observed weather could influence participants’ symptom reporting. Our baseline questionnaire demonstrated that rain and cold weather were the most common pre-existing beliefs. If a reporting bias were to exist, we would expect higher pain to be reported at times of colder weather . Our findings—including the absence of an association with either temperature or rainfall—cannot be explained by such a reporting bias. Fourth, pain reporting is subjective, meaning one participant’s “moderate” might equate to npj Digital Medicine (2019) 105

someone else’s “severe”. The within-person case-crossover analysis meant we compared moments when an individual’s score increased by a meaningful amount to a control period for that same person. Fifth, we chose to model the weather using daily averages. It is possible that other findings may be hidden if the association between weather and pain was with other metrics of weather , such as the daily maximum, minimum, or range, or even if the changes in weather on hourly time scales affect participants’ pain. Sixth, the findings from this United Kingdom study cannot necessarily be extrapolated to different climates where the weather is different . Seventh, our population-wide analysis assumed that all participants have the same weather –pain relationship. Different diseases may have different sensitivities to pain and, even within disease, participants may be affected differently. Our decision to use the whole chronic-pain population in our primary analysis means the overall associations with weather variables may be combinations of strong, weak and absent causal effects, thereby underestimating the most important associations. Notable differences were not seen after stratification by pain condition , although the power to detect any differences was reduced because of smaller sample sizes. Lastly, the inclusion of repeated events per person required us to consider within-subject dependence which, if not accounted for, would lead to bias.[21] Our outcome was based on changes in pain (a two or more category increase), which meant events rarely occurred on consecutive days , thereby ensuring a time gap between recurrent events and the avoidance of bias.

Understanding the relationship between weather and pain is important for several reasons . First, this study validates the perception of those who believe that their pain is associated with the weather . Second, given we can forecast the weather days in advance, understanding how weather relates to pain would allow pain forecasts . Patients could then plan activities and take greater control of their lives. Finally, understanding the relationship between weather and pain might also allow better understanding of the mechanisms for pain and thus allow the development of new and more effective interventions for those who suffer with pain .

In summary, our large national smartphone study has successfully supported the collection of daily symptoms and high-quality weather data , allowing examination of the relationship between weather and pain. The analysis has demonstrated significant relationships between relative humidity, pressure, wind speed and pain, with correlations remaining even when accounting for mood and physical activity .

Patient involvement has been important throughout the study , from inception to interpretation of the results. Co-author C.G. is a patient partner and co-applicant, while a patient and public involvement group of seven additional members has supported the study, meeting eight times in total . During the feasibility study,[14] patients positively influenced the wording and display of questions within the app . C.G. and other members of the Patient and Public Involvement Group were involved in media broadcasts at study launch and subsequent public engagement activities, explaining why the research question was important to them and relevant to patients with long-term pain conditions.[22] They have supported the interpretation of findings and the development of dissemination plans for the results, ensuring the results reach study participants, patient organizations and the general public.

We recruited participants through local and national media (television, radio, and press) and social media from 20 January 2016 to 20 January 2017. To participate in the study, participants needed to (i) be living with long-term (>3 months) pain conditions, (ii) be aged 17 years or older, (iii) be living in the United Kingdom, and (iv) own an Android or Apple iOS smartphone. Interested participants were directed to the study website (www.cloudywithachanceofpain.com) where they could check their eligibility, learn about the study, and download the uMotif app (Fig. 1). After downloading the study app, participants completed an electronic consent form and a baseline questionnaire including demographic information (sex, year of birth, first half of postcode), anatomical site(s) of pain, underlying pain condition(s), baseline medication use, and beliefs about the extent to which weather influenced their pain on a scale of 0–10, including which weather condition(s) were thought to be most associated with pain. Participants were then invited to collect daily symptoms for six months, or longer if willing. Each day, the app alerted participants to complete ten items at 6:24 p.m. (Fig. 1). The ten items were pain severity, fatigue, morning stiffness, impact of pain, sleep quality, time spent outside, waking up feeling tired, physical activity , mood, and well-being. Each data

Univariable (single weather variable only) Odds ratio (95% CI)

Multivariable (all weather variables only) Odds ratio (95% CI)

Multivariable (weather plus activity and mood) Odds ratio (95% CI)

Temperature Per 1 °C Per 1 s.d. (4.8 °C) Relative humidity Per 10% Per 1 s.d. (8.6%) Pressure Per 10 mbar Per 1 s.d. (11.1 mbar) Wind speed Per 1 m s–1 Per 1 s.d. (2.1 m s–1) High activity Low mood

1.011 (0.995–1.027) 1.022 (0.990–1.056) 0.939 (0.881–1.002) 4.083 (3.824–4.360)

High activity—Top three categories: 30 min or more of light or strenuous activity per day, or less than 30 min of strenuous activity Low mood—Bottom three categories: ‘depressed’, ‘feeling low’ or ‘not very happy’ s.d. standard deviation

Distribution of weather variables: Temperature: range −4.9 to 25.9 °C, s.d. 4.8 °C Relative humidity: range 43.8–100%, s.d. 8.6% Pressure: range 966–1044.8 mbar, s.d. 11.1 mbar Wind speed: range 0–21.5 m s−1, s.d. 2.1 m s−1 item had five possible labeled ordinal responses. For example, in response to the question “How severe was your pain today?”, possible responses were “no pain”, “mild pain”, “moderate pain”, “severe pain” or “very severe pain”. The data were analysed using a case-crossover design where, for each participant, exposure during days with a pain event (“hazard periods”) were compared to “control periods” without a pain event in the same month.[23] Pain events were defined as a two-or-more category increase in pain from the preceding day, consistent with more stringent definitions of a clinically important difference[24] (Fig. 3). Data collection ended on 20 April 2017.

Cohort selection Participants were included in the final cohort for analysis if they fulfilled the following criteria: (1) downloaded the app; (2) provided consent; (3) completed the baseline questionnaire; and (4) contributed at least one pain event and matched control period in the same month (see below). During exploratory analysis, it was apparent that people reported higher pain levels in the first ten days following recruitment (perhaps due to calibration or regression to the mean). Therefore, the first ten days were excluded from the formal analysis. However, even if the first ten days were included, they had a negligible effect on the results (Supplementary Table 12 ).

The total person-days in study was calculated for each participant as the number of days between their first and last day of entering pain data. The number of person-days on which a pain score was entered was summed per participant, presented as a proportion of the total person-days in study, and averaged across the population. The geographical distribution of recruitment was described as the number of UK postcode areas represented (out of a maximum of 124).[25] The movement of participants during the study was described as the median number of weather stations associated with each participant during their data-collection period.

Ethical approval

Ethical approval was obtained from the University of Manchester Research Ethics Committee (ref: ethics/15522) and from the NHS IRAS (ref: 23/NW/

0716). Participants were required to provide electronic consent for study inclusion. Further details are available elsewhere.[13,14]

Weather data were obtained by linking hourly smartphone GPS data to the nearest of 154 possible United Kingdom Met Office weather stations. Where GPS data were missing, we used significant location imputation. (For details, see supplement). Local hourly weather data were obtained from the Integrated Surface Database ( ISD ) of NOAA (http://www.ncdc. noaa.gov/isd), which includes hourly observations from UK Met Office weather stations.

Given the latitude–longitude coordinates of a participant location, the haversine distance to every Met Office weather station was calculated. The nearest station to the given location was selected, conditional on the distance being less than 100 km and the station having four weather variables (temperature, pressure, wind speed , and dewpoint temperature) available at that time. If all stations with the required weather data exceeded the maximum distance (100 km), the location was left unlinked and the observation was excluded from the analysis.

The significant location imputation approach for handling missing hourly GPS data had three stages.[26] First, the participant’s observed location data were ordered by the frequency that the locations were visited. Second, the locations were spatially clustered using Hartigan’s Leader Algorithm[27] with a threshold of 0.5 km. Third, missing locations during weekdays were replaced by the centroid of the participant’s most visited cluster for weekdays and missing locations during weekends were replaced by centroid of the participant’s most visited cluster for weekends.

Recruitment and duration of follow-up were presented as a graph of cumulative recruitment and active participants, with participation ending at the last symptom entry. Retention in the study was also presented as a survival probability against time since recruitment, with participants censored when they were no longer eligible for follow-up. Eligible follow-up time ranged from 90 days (for those recruited on 20 January 2017) to 456 days (for those recruited on 20 January 2016). Engagement of npj Digital Medicine (2019) 105

mbar. The day associated with the lowest estimated odds of a pain event was when the temperature was 7 °C, relative humidity was 67%, wind speed 4.5 m s−1 and pressure 1030 mbar participants was further described through clustering of engagement states, which has been described in detail elsewhere.[13] Following recruitment, individuals were labeled as engaged if they reported any of the ten symptoms on a given day. A first-order hidden Markov model was used to estimate the levels of engagement of participants by assuming three latent engagement states: high, low, and disengaged. Clusters were defined according to different probabilities of transitioning between high engagement, low engagement and disengagement during the study. Retention of active participants was also presented stratified by engagement cluster, and in the subset of participants who contributed to the final analysis.

Days without pain events were only control periods if they were eligible to have a two-or-more category increase (i.e. the preceding day’s pain was lower than “severe”), thus fulfilling the exchangeability assumption for the case-crossover study design.[28] With this design, participants serve as their own control, eliminating confounding by time-invariant factors. Each month per participant with at least one hazard and one control period formed a risk set. Conditional logistic regression was used to estimate the odds ratio ( OR ) for a pain event for four state weather variables (temperature, relative humidity , pressure, and wind speed ). The condition logistic regression model was implemented with the assumption that the possible recurrent events (hazard periods) within a person are independent conditional on the subject-specific variables and other observed timevarying covariates. Further, we make sure that there is no overlap between case and control periods. Our assumption is reasonable given the time gap between subsequent events.

Each weather variable was included in univariable models and then all four were included in a multivariable analysis. Each weather variable was represented as a daily average per participant for the hazard or control period, with results presented as an OR for a pain event in response to a one-unit increase for temperature and wind speed (°C and meter per second, respectively) or a ten-unit increase for relative humidity and pressure (percentage points and millibar, respectively). Standardized odds ratios of each weather variable were also calculated. The relative importance of the four state weather variables was estimated by summing the Akaike weights.[29] In all models, the preceding day’s pain score was included as it influenced the likelihood of a pain event the following day. The model was expanded to include mood and physical activity on the day of interest, included as binary variables. Time spent outside was considered as a possible effect modifier by including an interaction term with temperature, relative humidity , and wind speed . A directed acyclic graph is included in the supplementary material (Supplementary Fig. 5).

Sensitivity analyses were conducted to examine the effect of precipitation, day of the week, possible lag between weather and pain, change in weather from the day before the hazard or control day, disease type, sites of pain (single versus multiple sites) and prior beliefs in the weather–pain relationship. Respecting patients’ perspectives, we decided our primary analysis would focus on the whole chronic-pain population and our analyses of disease-specific associations would be secondary. We also reran the analysis including the first 10 days.

Daily pain-event estimates Estimated odds ratio for a pain event per day compared to the average weather day were calculated using the following equation: n

Odds Ratio 1⁄4 exp; βTðtemperature À μTÞ þ βRH 10 relative humidity À μRH

ÁÉ pressure À μp þ βwsp wind speed À μwsp þ βP 10 where βT = coefficient for temperature from final βRH = coefficient for relative humidity , βwsp = coefficient for wind speed , βP = coefficient for pressure, and μT = mean temperature, μRH = mean relative humidity , μwsp = mean wind speed , and μp = mean pressure model, of the daily UK means over the study period.

The predicted odds ratios of a pain event, relative to the average weather day, were plotted for all days within our study period for each of the four state weather variables. Statistical analyses were performed using R 3.3.0.30

22. Cloudy with a Chance of Pain on BBC North West Tonight. https://www.youtube.

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

com/watch?v=YUdtKGr49GY. Accessed 14 Oct 2019 (2016). 23. Maclure, M. The case-crossover design: a method for studying transient effects on the risk of acute events. Am. J. Epidemiol. 133, 144–153 (1991).

24. Olsen, M. F. et al Pain relief that matters to patients: systematic review of DATA AVAILABILITY

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

empirical studies assessing the minimum clinically important difference in acute pain. BMC Med. 15, 35. https://doi.org/10.1186/s12916-016-0775-3 (2017). 25. BPH postcodes. A brief guide to UK postcodes. https://www.bph-postcodes.co. uk/guidetopc.cgi (2018).

26. Isaacman, S., Becker, R., Martonosi, M., Rowland, J., & Varshavsky, A. Identifying

CODE AVAILABILITY important places in people’s lives from cellular network data sibren. Proc. of 9th Int. Conf. Pervasive Comput. 1–18 (2011).

Data management and analyses were performed in R 3.3.0. Code may be available on

27. Hartigan, J. A. Clustering Algorithms. (Wiley, New York, 1975).

reasonable request.

28. Mittleman, M. A. & Mostofsky, E. Exchangeability in the case-crossover design. Int.

J. Epidemiol. 43, 1645–1655 (2014).

Received: 22 May 2019; Accepted: 23 September 2019; 29. Burnham, K. P., Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed. (Springer-Verlag, New York, 2003).

30. R Core Team. R: A language and environment for statistical computing, https://www.r-project.org (2018).

ACKNOWLEDGEMENTS

We are grateful for the contributions of our patient and public involvement group throughout the study: Carolyn Gamble, Karen Staniland, Shanali Perara, Simon Stones, Rebecca Parris, Annmarie Lewis, Dorothy Slater and Susan Moore . The study app and website was provided by uMotif Limited (London, UK) . The unique flowerlike ‘motif’ symptom tracking interface is owned by uMotif Limited and protected through EU Design Registrations and a U.S Design Patent . We gratefully acknowledge the National Oceanic and Atmospheric Administration/National Climatic Data Center Integrated Surface Database (https://www.ncdc.noaa.gov/isd) for providing the weather data used in this study. The study was funded by Versus Arthritis (new name for Arthritis Research UK) (grant reference 21225), with additional support from the Centre for Epidemiology (grants 21755 and 20380). A.G. and A.M.V.C. are the recipients of Medical Research Council U.K. grants (MR/M022625/1 and MR/R013349/ 1). H.L.P. is the recipient of the Ken Muirden Overseas Training Fellowship from the Arthritis Australia, an educational research grant funded by the Australian Rheumatology Association. A.B. is supported by a Medical Research Council doctoral training partnership (grant MR/N013751/1). T.H. is supported by the Alan Turing Institute and the Royal Society (grant INF/R2/180067). D.M.S. is partially supported by the Natural Environment Research Council U.K. (grants NE/I005234/1, NE/I026545/1, and NE/N003918/1). R.S. is partially supported by the Alan Turing Institute (grant EP/ N510129/1).

W.G.D. designed the study, acquired funding, supervised and participated in datacollection and content analysis, and wrote the first draft of the manuscript. A.L.B., B.B.Y. and H.L.P. conducted the analysis. L.C. coordinated project management and participant support. A.G., T.E.L., A.V.M.C., M.M., R.S., T.H., M.L., D.M.S., J.C.S. and J. McB. contributed to analysis plans and supervised the analysis. B.H., B.J., J.A., C.G., C.S., D.M.S., J.C.S. and J.McB. contributed to study design. C.S. led qualitative research in the feasibility study and led patient and public involvement. All authors critically reviewed manuscript drafts and approved the final version of the manuscript. W.G.D. is responsible for the overall content as guarantor, and attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

COMPETING INTERESTS

W.G.D. has received consultancy fees from Bayer Pharmaceuticals and Google, unrelated to this study . B.J. and B.H. are co-founders of uMotif . All other authors declare no competing interests .

Supplementary information is available for this paper at https://doi.org/10.1038/ s41746-019-0180-3.

Correspondence and requests for materials should be addressed to W.G.D.

Reprints and permission information is available at http://www.nature.com/ reprints npj Digital Medicine (2019) 105

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.

Open Access This article is licensed under a Creative Commons org/licenses/by/4.0/.

Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative

Patient involvement has been important throughout the study , from inception to interpretation of the results. Co-author C.G. is a patient partner and co-applicant, while a patient and public involvement group of seven additional members has supported the study, meeting eight times in total. During the feasibility study,[14] patients positively influenced the wording and display of questions within the app. C.G. and other members of the Patient and Public Involvement Group were involved in media broadcasts at study launch and subsequent public engagement activities, explaining why the research question was important to them and relevant to patients with long-term pain conditions.[22] They have supported the interpretation of findings and the development of dissemination plans for the results, ensuring the results reach study participants, patient organizations and the general public.

Distribution of weather variables: Temperature: range −4.9 to 25.9 °C, s.d. 4.8 °C Relative humidity : range 43.8–100%, s.d. 8.6% Pressure: range 966–1044.8 mbar, s.d. 11.1 mbar Wind speed : range 0–21.5 m s−1, s.d. 2.1 m s−1 item had five possible labeled ordinal responses. For example, in response to the question “How severe was your pain today?”, possible responses were “no pain”, “mild pain”, “moderate pain”, “severe pain” or “very severe pain”. The data were analysed using a case-crossover design where, for each participant, exposure during days with a pain event (“hazard periods”) were compared to “control periods” without a pain event in the same month.[23] Pain events were defined as a two-or-more category increase in pain from the preceding day, consistent with more stringent definitions of a clinically important difference[24] (Fig. 3). Data collection ended on 20 April 2017.

The study app was downloaded by 13,207 users over the 12month recruitment period (Figs 1 and 2a) with recruitment from all 124 UK postcode areas. A total of 10,584 participants had complete baseline information and at least one pain entry, with 6850 (65%) participants remaining in the study beyond their first week and 4692 (44%) beyond their first month (Fig. 2b). Further description of engagement clusters is provided in Supplementary Table 2 and Supplementary Figs 1–3. A total of 2658 participants had at least one hazard period matched to a control period in the same month (Fig. 3) and were included in the final analysis. There were 9695 hazard periods included in the analysis for the final 2658 participants, matched to 81,727 control periods in 6431 participant-months. A total of 1235 participants contributed one month, and the remaining 1423 participants contributed 2–15 months.

The multivariable case-crossover analysis including the four state weather variables demonstrated that an increase in relative humidity was associated with a higher odds of a pain event with an OR of 1.139 (95% confidence interval 1.099–1.181) per 10 percentage point increase, as was an increase in wind speed with an OR of 1.02 (1.005–1.035) per 1 m s−1 increase ( Table 1 ). The odds of a pain event was lower with an increase in atmospheric pressure with an OR of 0.962 (0.937–0.987) per 10-mbar increase. Temperature did not have a significant association with pain ( OR 0.996 (0.985–1.007) per 1 °C increase). The odds of a pain event was 12% higher per one standard deviation increase in relative humidity (9 percentage points) ( OR 1.119 (1.084–1.154), compared to 4% lower for pressure ( OR 0.958 (0.930–0.989) and 4% higher for wind speed ( OR 1.041 (1.010–1.073) (11 mbar and 2 m s−1, respectively). Of the four weather variables, relative humidity had the strongest association with pain , and temperature the least, evidenced by the estimated relative importance of the variables and their standardized odds ratios ( Table 1 , Supplementary Table 4 ). Similar effect sizes were seen when each variable was examined in univariable analyses . Precipitation was not associated with an increased odds of a pain event ( OR 0.996 (0.989–1.003) per 1 mm daily rainfall amount) (Supplementary Table 5 ). Exploratory analyses considered time spent outside by including an interaction term with temperature, relative humidity , and wind speed . Time spent outside did not have a significant interaction with relative humidity or wind speed , nor did it lead to significant associations for temperature when conducting analyses stratified by time spent outside (Supplementary Table 3 ). It thus was not included in the final model.

Other factors such as day of the week (Supplementary Table 6 ), lagged weather values (Supplementary Table 7 ) and changes in weather variables from the previous day were tested. Mondays, Thursdays, and Saturdays ( ORs 1.14, 1.14, and 1.29, respectively) had higher odds of pain compared to Sundays, but adjusting for the day of the week did not alter the effect of the four main weather variables . Except for relative humidity (1-day lag and 2day lag), no significant associations were observed between lagged weather variables and pain events . Including change in weather from yesterday showed a minor effect of changing relative humidity ( OR 1.005 (1.001–1.009) per 10 percentage point increase), whereas the effects of today’s relative humidity and pressure remained unchanged (Supplementary Table 8 ). Stratification by disease led to a loss of statistical power and largely inconclusive results, although relative humidity appeared to have a stronger association with pain in patients with osteoarthritis (Supplementary Table 9 , Supplementary Fig. 4). Stratification by the number of pain sites also showed no meaningful difference (Supplementary Table 10 ). After stratifying by participants’ prior beliefs about their weather–pain relationship, relative humidity remained associated with pain in all participants although the association with pressure was only seen in those with a strong prior belief (Supplementary Table 11 ).

There are potential limitations to this study. First, the reduction in participant numbers from over 10,000 with baseline data to the final 2658 participants with at least one within-month risk set raises questions about generalisability. Importantly, the characteristics of those included in the analysis were similar to the initial

someone else’s “severe”. The within-person case-crossover analysis meant we compared moments when an individual’s score increased by a meaningful amount to a control period for that same person. Fifth, we chose to model the weather using daily averages. It is possible that other findings may be hidden if the association between weather and pain was with other metrics of weather , such as the daily maximum, minimum, or range, or even if the changes in weather on hourly time scales affect participants’ pain. Sixth, the findings from this United Kingdom study cannot necessarily be extrapolated to different climates where the weather is different. Seventh, our population-wide analysis assumed that all participants have the same weather –pain relationship. Different diseases may have different sensitivities to pain and, even within disease, participants may be affected differently. Our decision to use the whole chronic-pain population in our primary analysis means the overall associations with weather variables may be combinations of strong, weak and absent causal effects, thereby underestimating the most important associations. Notable differences were not seen after stratification by pain condition , although the power to detect any differences was reduced because of smaller sample sizes. Lastly, the inclusion of repeated events per person required us to consider within-subject dependence which, if not accounted for, would lead to bias.[21] Our outcome was based on changes in pain (a two or more category increase), which meant events rarely occurred on consecutive days , thereby ensuring a time gap between recurrent events and the avoidance of bias.

The reduction in participant numbers from over 10,000 with baseline data to the final 2658 participants with at least one within-month risk set raises questions about generalisability. The characteristics of those included in the analysis were similar to the initial

W.G.D. has received consultancy fees from Bayer Pharmaceuticals and Google, unrelated to this study. B.J. and B.H. are co-founders of uMotif. All other authors declare no competing interests

The study was funded by Versus Arthritis (new name for Arthritis Research UK) (grant reference 21225), with additional support from the Centre for Epidemiology (grants 21755 and 20380)

A.G. and A.M.V.C. are the recipients of Medical Research Council U.K. grants (MR/M022625/1 and MR/R013349/ 1)

H.L.P. is the recipient of the Ken Muirden Overseas Training Fellowship from the Arthritis Australia, an educational research grant funded by the Australian Rheumatology Association

A.B. is supported by a Medical Research Council doctoral training partnership (grant MR/N013751/1)

T.H. is supported by the Alan Turing Institute and the Royal Society (grant INF/R2/180067)

D.M.S. is partially supported by the Natural Environment Research Council U.K. (grants NE/I005234/1, NE/I026545/1, and NE/N003918/1)

R.S. is partially supported by the Alan Turing Institute (grant EP/ N510129/1)

Data and code

Further information on research design is available in the Nature Research Reporting Summary linked to this article

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request

Supplementary information is available for this paper at https://doi.org/10.1038/s41746-019-0180-3

Reprints and permission information is available at http://www.nature.com/reprints npj Digital Medicine (2019)[105]

Supplementary information is available for this paper at https://doi.org/10.1038/s41746-019-0180-3 .

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