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UX Case Study: Calm Mobile App

By Sean McGowan

Welcome to the first UX case study of 2018! Codal hopes you have had a fantastic start to the new year. For those who have followed along with our long-running UX Case Study series , welcome back, and to new readers, welcome aboard.

For those who are just joining us: UX Case Study is a regular series written by UX design agency Codal , where we conduct in-depth examinations of different mobile apps. An exercise in scrutiny, we place apps under a microscope and critically examine them through a user experience lens, acknowledging both their successes and failures.

The ultimate goal is to view UX holistically and examine how different design techniques coalesce to work as a whole in a functioning application. To kick off the new year, we have decided to study the 2017 winner of Apple’s “App Of The Year” award: Calm .

Calm is a mindfulness and mental health app that offers free exercises and programs that help users do everything from meditating and improving sleep to reducing stress and increasing focus. Offering a range of ASMR-like readings, ambient music, and relaxing nature sounds, Calm aims to help their users become more mindful through daily sessions and regular practice.

As always, I will be conducting my analysis using an iPhone 7 with the latest iOS release, and the most updated version of Calm as well.

Before I even open Calm, its icon sets the stage for the app’s aesthetic motifs and visual language. Set against a soft, azure gradient, the app’s name is written in a spindly, naturalistic typeface that evokes tranquillity and serenity.

When I tap the icon, Calm’s splash screen appears, though “splash” seems too strong a word. It is more of a single drop of water-the words “take a deep breath” imposed upon the same soothing blue gradient used for the app’s icon. I oblige, though I hope it is not to prepare me for what is going to be a lengthy and tedious onboarding process.

Expecting the standard “sign up / login” screen, I am instead greeted with a display that asks me to choose my top goals. Here, Calm shows off the benefits of using its programs, offering everything from “Build Self-Esteem” to “Increase Happiness”.

This is both an excellent marketing strategy and a savvy UX move-the app is offering the user value upfront, providing a reason why I should sign up for Calm. Moreover, it does it not by listing features or functions, but by end results. It does not say “over 100+ programs and exercises”. It promises the resulting benefits: better sleep, less anxiety, reduced stress.

Assigning each goal a distinct colour and icon is an aesthetically pleasing choice as well. I tap a few goals and select “Continue”.

I am brought to the sign-up/login screen. Calm uses a standard template for this standard display, offering sign-up functions with both email and Facebook. The most striking design feature of the screen is easily the picturesque background. We see that omnipresent gradient as the sky, coupled with a mountain terrain and a tranquil body of water.

I select sign up via email and am brought to another conventional screen in the onboarding process. There is not much to mention here, other than the fact Calm once again demonstrates value by offering a reason why the user should create a profile-to track and save progress.

After creating an account, Calm serves up two interstitials between account creation and the home screen. The first: an offer to unlock the full version of Calm with a seven-day trial period, and the laundry list of features that accompany it.

The feature list is not much help to me for now- I am not familiar with in-app jargon like ‘Sleep Stories’ or ‘Daily Calm’ – and I do not know if I will even enjoy the app yet. However, to be fair, they are offering a free trial, so I am sure it does convert some users that want to begin with the full app experience.

The second interstitial is a classic prompt to enable Calm to send notifications. Long-time readers will know of my personal pet peeve with these untimely interruptions, especially when they do not reveal what I will be notified or why I should enable them.

Home Screen / Tutorial

With the onboarding process complete, I am brought to Calm’s home screen, a modest, unadorned page with the same picturesque backdrop as the login. In the upper corners of the screen are two icons, one which appears to be the “my profile” page, and the other which I am unsure of.

The app’s primary navigation lies across the screen’s bottom, and is only three options: Music, Meditate and Sleep. The meditate option is given priority, as evidenced by its larger size and placement in the centre. An overlay appears above it that reads “Start your journey” here.

In-app tutorials are a hotly debated topic in mobile app design, with some designers claiming that if your app needs one, you have already failed. While that is an admirable ideal for a UX designer to hold, I personally do not subscribe to it (though I am sure some of Codal’s UXers would disagree with me).

As it stands, Calm’s in-app tutorial is relatively painless, essentially guiding me along the platform’s fundamental user journey, rather than taking the time to illustrate and explain every feature and function. Displayed below, the tutorial takes me the beginner’s meditation program, “7 Days of Calm”.

All-in-all, it is only four overlays, and it works well in efficiently immersing me in Calm’s first program. Some UX designers may disagree with me here, but I think Calm’s in-app tutorial is a strong design choice.

As indicated by the navigation bar, Calm offers three primary features for mindfulness: Meditate, Music, and Sleep. While all three contain different programs for different goals, they also all rely on the same playback functionality, making this interface the perfect closer to our UX case study . Another reason Calm’s playback feature deserves scrutiny: it is unlike any standard player you have probably ever used.

The most striking feature is its lack of features. Subscribing to a minimalist strategy, Calm’s player has only two main buttons: play/pause and stop. There is a necessary volume control, and a “favourite” function too, but other than that, there is not much else to write.

There is a jarring lack of a fast-forward function (Calm does not want you skipping through its programs), and even the reverse feature can only be used in fifteen-second intervals. This lack of granularity is rare for playback mechanics, but for Calm it makes sense.

Calm’s player is a rare example of taking distinct features away to improve the user’s experience. The user should not be able to fast-forward or rewind incessantly-this is a meditation app.

Final Verdict

I only used Calm for about three or four days (I guess meditation is not really my thing), but overall I was pleased with the user experience. Its content well-produced and the app’s commitment to a card-based design keeps it well-organised and easy to traverse.

While I did not notice it during my initial use when I plugged in my headphones the app began to play nature sounds while I was interfacing with it, which was a nice touch as well.

Overall, it is easy to see why Calm was named Apple’s 2017 App of the Year. Their calming, meditative feel extends from their programs and content to envelop the entire user experience. Traversing Calm feels seamless as if you are floating in the lake that adorns the app’s home screen.

Want to learn more?

If you’re interested in mobile UX, then taking the course on Mobile User Experience , which includes templates you can use in your own projects. If, on the other hand, you’d like to…

learn all the details of Usability Testing
get easy-to-use templates
learn how to properly quantify the usability of a system/service/product/app/etc
learn how to communicate the result to your management

… then consider to take the online course Conducting Usability Testing .

Lastly, if you want to brush up on the basics of UX and Usability, the online course on User Experience could provide you with the necessary knowledge. Good luck on your learning journey!

(Lead image: Depositphotos – affiliate link . All other photos in this article are screenshots taken during usage of the Calm app.)

About Sean McGowan

Sean is a technical researcher & writer at Codal , authoring blog posts on topics ranging from UX design to the Internet of Things. Working alongside developers, designers, and marketers, Sean helps support the writing team to ensure Codal produces engaging web content of the highest quality. When not writing about the latest innovations in app design, Sean can be found cooking, watching old movies, or complaining about the shortcomings of his favorite Philadelphia sports teams.

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Last updated on April 3rd, 2024

How Calm Built a $2B Media, Content Marketing, & YouTube SEO Empire

Article's Content

Calm is valued at more than $2 billion. The leading meditation app boasts:

120 million downloads
More than 8.1 million backlinks
Over 207,200 organic visits a month
More than $23 million in in-app purchase revenue in 2021

It’s no wonder Calm has attracted sterling 5-star reviews from over 1.5 million users .

We took a closer look at Calm’s marketing engine and found that it’s earned the trust and money of over 4 million customers through three major areas of focus: branding, content, and YouTube SEO.

This case study is a deep dive into Calm’s marketing engine. We’ll explore the strategies Calm used to dominate the meditation industry, including how it uses content and YouTube SEO to drive growth.

Ready? Let’s go. 🚀

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– Brian Dean, Founder of Backlinko (acquired by SEMRush)

How Calm Grew To Dominate The Meditation Industry

Here’s a fact about Calm that you might not know…

One of Calm’s co-founders, Alex Tew, is the brains behind the internet sensation: The Million Dollar Homepage .

This project’s success set Alex up to launch more internet projects—including Calm, which is the most successful. Calm started in 2011 as a website called donothingfor2minutes.com :

The idea was to get people to pause, stare at the screen, listen to the sound of the waves, and relax for two minutes. All without touching their mouse. At the end of the two minutes, a prompt asked users to like the Facebook page and sign up.

In just two weeks, the Facebook page had over 300,000 likes and the site had 100,000 sign-ups.

Leveraging the website’s success, Alex Tew and Michael Acton Smith co-founded Calm.

Headspace, a competitor that was founded two years before Calm, was already making waves in the UK and Silicon Valley. Calm had a tough time competing because it was understaffed and underfunded . It wasn’t until 2017 that Calm started to see some real success:

Won Apple’s App of the Year award in 2017
Named #1 meditation app in 2019, outshining Headspace

So even though Headspace already dominated the market, Calm overtook Headspace in revenue five years after launch, despite its small team.

Content played a major role in winning users and investors over.

Cofounder Michael Acton Smith said they increased premium content prices to increase revenue and attract potential investors. It worked: Calm raised $218 million in funding . That’s the power of content—its ability to turn readers into paying customers .

Let’s review Calm’s strategy for creating content that converts.

How Calm Uses Content To Drive Traffic & Backlinks

First, let’s take a look at these numbers:

Calm’s meditation tool and rich content have attracted over 8 million backlinks. Creating assets that attract backlinks from high-authority websites is an SEO tactic that drives visibility and generates organic traffic.

We found that Calm’s free tools were the most highly linkable assets, followed by definitions, stats, and research. These assets win prospects over and boost organic ranking on search engines.

For example, Calm dominates the SERP for the keyword “feelings wheel,” which has over 52,000 monthly searches. This high search volume attracts over 21,000 monthly visitors to Calm’s blog post .

The blog post has also generated over 400 backlinks from high-authority websites like Indeed and MSN. The piece directly targets Calm’s audience—people who are stressed out and looking for ways to relax.

Headlines are another way Calm captures prospects’ attention in the SERPs. Take this blog post, for example:

Calm captures attention by using “9” to tell readers there are different ways to solve the problem and includes the keyword “help a friend with anxiety,” which has 600 monthly searches. As a result, the headline attracts over 1,000 visits each month.

To create content that attracts and engages readers, then turns them into customers, do these :

Know your audience
Craft attention-grabbing headlines
Leverage the power of emotion
Use short paragraphs instead of large chunks of text
Use a font style and size that the average person can read clearly
Include visuals to break up text and explain your points
Add value before selling
Include testimonials and case studies
Choose a strong call-to-action

Calm also leveraged influencer collaboration to boost its credibility and authority in the space. Let’s see how they do it.

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How Calm Uses YouTube SEO to Increase Organic Reach

YouTube is the world’s second-largest search engine , with over 5 billion videos viewed daily. The platform generates 3 billion monthly searches, presenting B2B and B2C brands an opportunity to provide relevant content for searchers.

YouTube SEO is a fascinating, underrated tactic. Brands like MasterClass and Salesforce leverage YouTube to increase their organic reach and educate prospects. Calm is not left out of the mix.

Like Masterclass, Calm relies more on YouTube than Google as a search engine. It’s no wonder YouTube generates over 50% of Calm’s organic social traffic:

The Calm team optimized YouTube content by leveraging a combination of high-intent niche keywords to boost ranking and visibility on YouTube’s results page. The results are staggering:

More than 111,232,904 views on Calm’s videos
713,000 subscribers who rely on the channel to reduce stress and sleep better

What made Calm prioritize YouTube SEO over traditional search?

Well, for starters, it was an insight the team uncovered. They noticed that most Calm users were active between 9 PM and 11 PM. Anyone using the app at that time probably isn’t trying out a meditation exercise to start their day. Most are likely trying to relax their minds and get some sleep. Since YouTube is the largest search engine after Google, it’s only logical that Calm invested in creating YouTube videos around high-intent keywords like those that people search when they can’t sleep.

For example, the keyword “mindfulness meditation” has over 28,000 monthly searches. When you do a quick search on YouTube for this term, you’ll see three of Calm’s videos dominating the results page in quick succession:

Notice how each of these videos has over a million views. This is directly related to the power that YouTube videos have in the SERP:

Each month, there are more than 90,500 searches for mindful meditation. Calm created two of the three videos that appear at the top of the ‘videos’ section in the SERP. The Be Present video has an impressive 14 million views. Even though most of these videos are up to three years old, they still generate hundreds of views per hour. Talk about treating content as an investment .

Calm also has video categories optimized for different audiences, including:

Calm Stories
Soothing sounds
Take a deep breath
Calm Sleep Stories
Daily Calm | 10-Minute Meditations
Meditation for Beginners with Tamara Levitt

Some videos in these categories have earned as many as 20 million views.

The most popular YouTube video on Calm’s channel is an 8-hour video, “ Rainstorm Sounds for Relaxing, Focus or Deep Sleep | Nature White Noise .” People have viewed this video 24 million times, with an average of 14,000 daily views.

The conversion work on each of Calm’s videos is brilliant. The rainstorm sounds video features Calm’s logo prominently throughout the entire video, and the YouTube description is optimized to immediately drive people to sign up for the app.

Calm repeats this across other videos in its library.

The comments sections of Calm’s YouTube videos are also filled with people being nice to each other. One commenter wrote: “This is the only rain sound I’ve found without loud lightning and tinder noises. It really helps me sleep at night.” When you scroll down the comment sections, you’ll see tons of people sharing how much Calm sleep stories and video library has helped them find peace and sleep better.

Calm’s Media Strategy: Leveraging Celebrity Influence

Since 2015, it’s estimated that there are more than 2,500 different meditation apps in the App Store. That’s a lot of competition for Calm. But Calm stands out by thinking outside the box and investing heavily in brand differentiation.

For example, when the founder of Calm described their vision for the product early on, it was described as “ Nike for the mind. ”

Nike is known for collaborating with top athletes and creating emotionally driven content. Now, Calm is doing the same—partnering with celebrities to gain user trust and boost conversions. Some celebrities Calm collaborates with include:

Lebron James
Harry Styles
Mandy Moore
Stephen Fry
Michael Buble

This gives the app mass market adoption and establishes credibility. This is what Calm looks like today:

Calm is quickly evolving from a meditation app into a mindfulness & wellness media company. Calm isn’t just for meditating anymore. It’s now filled with mindfulness & sleep content:

The most genius part of Calm’s partnerships is getting the celebrities to develop content. For example, in the series with LeBron James, he preps users before the big game by sharing his mental frameworks and strategies for getting into the zone.

Calm has also leveraged celebrity influence through its Sleep Stories .

Celebrities like Stephen Fry, Matthew McConaughey, LeBron James, Leona Lewis, and others share original or fiction stories to help listeners sleep better. This one of Stephen Fry reading a story has generated almost 15 million views so far:

Mathew McConaughey’s sleep story, “Wonder,” has reportedly been streamed over 11 million times .

Celebrity power boosts Calm’s authority, credibility, and awareness. For example, if LeBron James tweets about his sleep story with his audience, his massive following will rush over to listen and sleep to the sound of his voice.

The secret sauce is leveraging celebrities whose influence boosts Calm’s brand awareness and makes its website more linkable. Some of Calm’s pages with the most backlinks are from celebrity partnerships.

While we admire Calm’s strategy, we aren’t asking you to invest millions of dollars in celebrity partnerships. Instead, you can partner with experts in your niche. Here’s how to do it:

First, draw up a list of experts and put them on a table like this:

Then rank them according to follower count, authority, or any metric that impacts your bottom line.

Finally, determine the connection between you to these experts:

That means if you already have a relationship with some of the experts on the list, you can reach out to them faster than those you don’t know personally. However, an expert you know might know another expert on the list. You can leverage that connection to reach those you don’t have relationships with yet.

When you complete this step, you can craft your pitch and reach out to them for a mutually beneficial partnership.

What Calm Can Do Better

Calm has done a great job evolving from a meditation app to a wellness and mindfulness media company. The team has even created Calm Business to serve other businesses.

A brilliant move for Calm would be to take a page out of Masterclass’s SEO book and build content that goes above and beyond YouTube. That means:

Creating more original linkable blog assets
Publishing informational blog posts on wellness and mindfulness topics
Leveraging more webinars to generate awareness
Repurposing content for social media platforms
Leveraging reactive storytelling and intent-based marketing

Doing these has the potential to 10x Calm’s current results and drive business growth long-term.

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Other reads on this topic, what’s generative engine optimization (geo) & how to do it, the strategy behind stripe’s million dollar page | vol 181, a million dollar stripe page: why some content is worth $$$.

Introduction

The following case study is a conceptual UX design improvement made to the Calm app. For those of you that have not heard of Calm, it is an app for practising daily mental well-being. Primarily it is used for meditation and as a sleep aid.

This case study will focus on Sleep Mode. Sleep Mode is where the apps sleep aid content lives. The majority of the content here is soothing music, sounds and Sleep Stories. What are Sleep Stories? Bedtime stories for adults. The stories are read by a variety of narrators with a suitably soothing voice, which help people get to sleep. Some narrators are well known, such as Stephen Fry and Matthew McConaughey. Here is an example of a Sleep Story. This might sound a little strange to you but for people who struggle to sleep, aids that work are extremely valuable. Calm also includes meditation music and a variety of masterclasses, also for practising daily mental wellbeing.

Calm is big. According to TechCrunch Calm has over 50 million downloads and almost 2 million paid subscribers which make it the market leader not only in the mental health category, but the entire health and fitness category. Its direct competitor is Headspace , although Calm is currently the clear market leader (Source: App Annie ).

Design Thinking Process

I used the Design Thinking Process for this case study. This process has many forms, I took inspiration from the below sources:

Don Norman’s book “Design in Everyday Things”.
The British Design Councils’ framework for Design Thinking and the double diamond process.
A podcast featuring IDEO leaders Suzanne Gibbs Howard and Coe Leta Stafford.

Based on these sources my design process took the following shape. I began with an idea, or awareness of an issue that I could solve. I explored this through research, then defined the problem as accurately as possible. With the problem well defined I set about creating solutions and testing these until I could deliver the right solution. This process was not linear. At certain points I learned something new about the problem/solution which meant I needed to start over. The below is an outline of this process, the phases and subsections within each.

Finding the Right Problem

Taking time to think a little deeper about the issue and how to articulate it in the most accurate way will save time. Having worked in an agency I know getting a real understanding of the client’s problem is often a challenge. There is inherently a lot of room for bad communication and mis-understanding on both sides. Typically issues fall into two brackets:

The wrong problem - The focus is on the symptoms of the underlying problem.
Too many problems - No clear priority. “These 10 things are all high priority”.

Of course as this is a conceptual brief; I would be choosing a problem to start with. I’m most familiar with Sleep Stories so I am focusing on that. I have always felt that finding a Sleep Story is not as easy as it should be. I thought this was a good place to start. The library of Sleep Stories is growing all the time.The amount of content has outgrown how the app was initially designed. For example, at the time of writing there is no search bar*. Users have to select a category such as “Fiction” or “Nature” and scroll through that list until they find something suitable.

*(as I was creating this Case Study Calm did introduce a basic search function that spans across the entire app, similar to Headspace. However in my opinion its functionality still does not reflect human centered design, therefore I believe this Case Study is still fully relevant).

At this point I defined the problem as:

"It takes too much time and effort for people to find a Sleep Story to their taste.”

This was my starting point and hypothesis which I used to frame my research. I began my research by finding out who the users are.

Audience statistics are not readily available from Calm, but by using Facebook’s Audience Insights tool I was able to learn about people interested in Meditation which I could use as a proxy for Calm users. I’ll call this audience “Meditators” from here on. Based on the Facebook data I learned more about their demographics and socio-economics.

Likely to be 25 years old and above.
Meditators are slightly more likely to be women. 56% of Meditators are female, compared to Facebook’s total female user base being 44% female.
Well educated. 75% more likely to have a postgrad compared to the average Facebook user.
In jobs likely to be well paid. This audience over indexes for jobs like “Government employees”, “Healthcare and medical services”, “Business and finance”.

Using this information I selected people to interview for further research.

To learn more about this audience and the problem I was trying to solve, I used several research techniques such as Observation, Interviews and Secondary Research. I’ve outlined my methodology below, the main findings follow.

Methodology

Observation .

Watching users use in their actual environment. Don Norman (Author of The Design of Everyday Things) says you should follow people into the shower if you have to! Luckily I was able to ask some friends who used the app if I could observe them. To get people comfortable in this unusual setting, I had them do several tasks until they relaxed. I then paired two people together to perform the same tasks and had them explain to each other their thoughts while using the app. This normally makes people more comfortable, and can squeeze more detail from participants.

Interviews

I designed an interview that would explore the problem in more detail, while also I was indeed looking at the right problem. I purposefully did interviews early in the process as I wanted a better understanding of the problem before looking through secondary research.

Secondary Research

I researched online sentiment for the App, such as online reviews and social comments. I also wanted to see what the best in the field of search and discovery currently is. l looked not only at only from the Wellbeing category players such as Headspace, but others too such as Netflix, Spotify and Asos.

Observation

FINDING - Everyone had the same issue. It took too long to find something to their taste. Each user had different tastes. They might want a specific narrator or length. They often couldn’t remember the name of the narrator, but they could remember the topic. It was clear it took too long to search through the categories, there are too many Sleep Stories. They were all trying to find another way to sort through the library. There was a lot of looking around each list, and revisiting lists, or forgetting where stuff was.

FINDING - Calm is actually really important to people. People with trouble getting to sleep every night, will gladly pay for the premium service if it means they can get to sleep in 20 minutes instead of 90 minutes. Calm is an essential tool for these people and can make a big difference in their lives. The Sleep Mode needs to be a seamless and enjoyable user experience.

FINDING - Finding new content is important. Sleep Stories lose their effectiveness over time. Some people only like certain types of narrators. They need to be able to discover content similar to what they like.

FINDING - Some people could take longer than one Sleep Story to get to sleep. Once the Sleep Story ends, these people found themselves waking up, or losing any progress they had made towards sleep. They’d have to unlock their phones and find another Sleep Story, further waking them up. They wanted a sleep timer function for the Sleep Stories. Similar to the sleep timer function in Apple’s Podcast app. Their favourites list should play on shuffle once their initial Sleep Story had completed, until the selected time limit elapsed.

FINDING - There are over 4,000 reviews on the App Store and they are mostly positive. They are the number one in health and fitness. After combing through for complaints I found some, mainly about the price and the very limited free version. The UX issues I could find were about search functions. As one user puts it - “The app isn’t as slick and fresh as it used to be. It feels bloated now with too much content.“

Competitors

I downloaded the Headspace app. It has a similar problem; their version of Sleep Stories are in one list. However they do have a search function for the entire app spanning across all sections. After testing it to find specific Sleep Stories, I found that it had limitations. For example I couldn’t search by narrator or length.

Best in class examples for search

FINDING - Spotify has several search capabilities that I thought might be useful for Calm to adapt.

Recently Listened To.
Discover Weekly - Music you have not heard before that is tailored to your taste.
Release Radar - New music from artists you like.

Spotify has many more search capabilities that could arguably be incorporated into Calm however Spotify has a much larger library of content which makes the case for such a powerful search function. Calm, for now, has a smaller library and by its nature has different requirement priorities.

FINDING - The great thing about Netflix’s search engine is how they have tagged up their content into a network in which all similar content is smartly connected. This means the search results will return similar content to what you may be looking for, which is useful for when the movie you want is not available on Netflix. I presume this was born out of necessity in their early days when the library would have been limited. For example you can put in “Lock Stock and Two Smoking Barrels” and the search result will show not only that title, but also other titles in that genre, other Jason Stahtham movies (Actor), and other Guy Ritchie (Director) movies.

FINDING - Online Fashion Retailers such as ASOS have a functionality allowing users to overlay searches on top of each other. For example, you can search by brand and sort by cost. This could be really powerful for Calm, one could search for a narrator, and then sort this list by length.

Re-defining the problem

At this point I wanted to go back to the initial problem I had started with. I had learned so much about the problem that I was able to re-define it.

Initial problem definition:

Re-definition:

“Calm’s Sleep Mode search capabilities need to be designed around how their users look for that content. The experience should be simple and effortless.”

This redefinition helped me stay focused on the problem throughout the rest of the design thinking process (“develop” and “deliver”).

Requirements List

With the research findings and re-defined problem, I created a requirements list:

“Recently Listened To” category
“Recommended for you” - I decided to combine two Spotify functions into one, Discover Weekly/Release Radar. Meaning there would be a list displaying new content from your favourite artists, and similar content to your favourite Sleep Stories. There isn’t enough new content on a frequent basis to have Release Radar for Calm, currently. (Calm have just released an update with a similar function)
Sort by Long/Short - The research showed that this was something uniquely required for Calm. Some users, when really struggling to switch off and get sleep, needed longer Sleep Stories.
More powerful search bar, similar to Netflix. This means tagging each Sleep Story by author, narrator and topic. Enabling search results pages sorted by those tagged keywords.
Sort by New/Old - The interviews showed that people really valued new content. Listening to old stuff didn’t have the same magical sleepiness effect.
Layered searches - I wanted to layer searches on top of eachother, similar to many online fashion retailers.
Sleep Timer - Set a timer for the Sleep Stories to stop. Their favourites list should play on shuffle once their initial Sleep Story had completed, until the selected time limit elapsed.

Finding the Right Solution

Based on the requirements list, I began sketching out User Flows. The output of this section looks really simple. It actually took me a lot of time. There has been a lot written about how useful user flows actually are. Could I have just jumped into sketching out some ideas? Yes and no. Distilling the problem down into what the user flow needed to be did help me later in the process and stay focused on the outcome I wanted. Particularly while I was wire-framing. Once I had the sketch down, I built it in Figma. As I said - it doesn’t look like much but it acted as a source of truth to keep me focused on the right solution.

Wireframing

I began sketching my ideas for what the solution would look like. As you can see in the images below, some of the initial sketches became very busy looking. The app is beautifully put together and the artwork for each Sleep Story should still be showcased. This is a challenge on mobile where space is scarce. After a few attempts I realised I didn’t want to detract from the visual experience. I actually ended up staying very close to the existing great UI.

The UI I was happiest with was the below “Option A” (right). This design allowed the new affordances to be integrated seamlessly into the existing UI. Users can tap on a search function to use it. Once it is selected it will have a “pressed” state signifying it is in use. Users tap again to change, or a new search function to overlay. After sketching it, I built this out in Figma. I also built a second option (Option B) for user testing and feedback.

The following case study is a conceptual UX design improvement made to the Calm app. For those of you that have not heard of Calm, it is used for practising daily mental well-being in the form of meditation and sleep aides.

This case study will focus on Sleep Stories. What are Sleep Stories? Bedtime stories for adults. The stories are read by a variety of narrators with a suitably soothing voice, which help people get to sleep. Some narrators are well known, such as Stephen Fry and Matthew McConaughey. Here is an example of a Sleep Story.↗ The idea of Sleep Stories might sound a little strange to you, but for many people who struggle to switch off and get to sleep, they are extremely useful.

Calm is big. According to TechCrunch↗ Calm has over 50 million downloads and almost 2 million paid subscribers which make it the market leader not only in the mental health category, but the entire health and fitness category. Its direct competitor is Headspace↗ , although Calm is currently the clear market leader (Source: App Annie↗ ).

The British Design Councils’ framework for Design Thinking↗ and the double diamond process.
A podcast featuring IDEO leaders↗ Suzanne Gibbs Howard and Coe Leta Stafford.

My process was to began with an awareness of an issue. I explored this through research, then defined the problem as accurately as possible. With the problem well defined I set about exploring solutions and testing these until I could deliver the right one. This process was not linear. At certain points I learned something which meant I needed to start over.

Of course as this is a conceptual brief; I would be choosing a problem to start with. I’m most familiar with Sleep Stories so I am focusing on that. I have always felt that finding a Sleep Story is not as easy as it should be. The library of Sleep Stories is growing all the time.The amount of content has outgrown how the app was initially designed. For example, at the time of writing there is no search bar*. Users have to select a category such as “Fiction” or “Nature” and scroll through that list until they find something suitable. See below video for demonstration.

*(as I was creating this case study Calm introduced a basic search function. In my opinion its functionality does not reflect human centered design, and I believe this case study is still relevant).

"It takes too much time and effort for people to find a Sleep Story.”

Audience statistics are not readily available from Calm, but by using Facebook’s Audience Insights tool↗ I was able to learn about people interested in Meditation which I could use as a proxy for Calm users. I’ll call this audience “Meditators” from this point forward. Based on the Facebook data I learned a few key points about this audience:

Likely to be 25 years old and above .
Meditators are slightly more likely to be women . 56% of Meditators are female, compared to Facebook’s total female user base being 44% female.
Well educated . 75% more likely to have a postgrad compared to the average Facebook user.
In jobs likely to be well paid . This audience over indexes for jobs like “Government employees”, “Healthcare and medical services”, “Business and finance”.

Using this information I selected people to interview for further research through Observation, Interviews and some Secondary Research. I’ve outlined my methodology below, the main findings follow.

Watching users use in their actual environment. Don Norman (Author of The Design of Everyday Things) says you should follow people into the shower if you have to. Luckily I was able to ask some friends who used the app if I could observe them. To get people comfortable in this unusual setting, I had them do several tasks until they relaxed. I then paired two people together to perform the same tasks and had them explain to each other their thoughts while using the app. This normally makes people more comfortable, and can encourage more detail from participants.

FINDING - There are over 4,000 reviews on the App Store and they are mostly positive. After combing through for complaints I found some, mainly about the price or how limited the free version is. The UX issues I could find were about search functions. As one user puts it - “The app isn’t as slick and fresh as it used to be. It feels bloated now with too much content.“

FINDING - The great thing about Netflix’s search engine is how they have tagged up their content. This means the search results will return similar content to what you may be looking for, which is useful for when the movie you want is not available on Netflix. I presume this was born out of necessity in their early days when the library would have been very limited. For example you can put in “Lock Stock and Two Smoking Barrels” and the search result will show not only that title, but also other titles in that genre, other Jason Stahtham movies (Actor), and other Guy Ritchie (Director) movies.

‍ ‍ ‍ FINDING - Online Fashion Retailers such as ASOS have a functionality allowing users to overlay searches on top of each other. For example, you can search by brand and sort by cost. This could be really useful in Calm. One could search for a narrator, and then sort this list by length.

At this point I wanted to go back to the initial problem I had started with. I had learned more about the problem that I was able to re-define it.

Based on the requirements list, I began sketching out possible User Flow Charts for the changes to the search function. The output of this section looks really simple. It actually took me a lot of time. There has been a lot written about how useful user flows actually are. Could I have just jumped into sketching out some ideas? Yes and no. Distilling the problem down into what the user flow needed to be did help me later in the process and stay focused on the outcome I wanted. Particularly while I was wire-framing. Once I had the sketch down, I built it in Figma. As I said - it doesn’t look like much but it acted as a source of truth to keep me focused on the right solution.

I began sketching my ideas for what the solution would look like for the new search function. As you can see in the images below, some of the initial sketches became very busy looking. The app is well designed for the Sleep Story artwork to be showcased and I didn't want any changes to get in the way. This is a challenge on mobile where space is scarce. I stayed very close to the existing UI.

I was happiest with was the below “Option A” (right). This design allowed the new affordances to be integrated seamlessly into the existing UI. The new search functions would be added as identical buttons from the existing design. Users can tap on a search function to use it. Once it is selected it will have a “pressed” state signifying it is in use. Users tap again to change, or a new search function to overlay. After sketching it, I built this out in Figma. I also built a second option (Option B) for user testing and feedback.

With regards to the Sleep Timer I went straight to designing in Figma as it was simple and I had a clear idea of what I wanted.

Prototyping

The interaction animations were designed to blend into the app’s existing UI. The different filters would be “pressed” to show they are in use, similar to how a selected category would in the existing UI. The slider bar was also still perfectly suitable for this update. The ability to layer several search types (e.g. keywords, length, new) seemed intuitive and simple to me. I was ready to have people try it.

Testing and Feedback

Using the wire frames and prototype, I had users test the new functionality and gathered their feedback to consider for future iterations of this design.

I showed wireframe options “A” and “B” wire frames to users. Their feedback was a unanimous preference for option “A”. Option “A” had not reduced the visual impact of the artwork for each Sleep Story, while housing all search functionalities in an intuitive way.
One piece of feedback was to add text to the search bar to inform users what kind of search queries were possible.
Another suggestion was to have an option to bring up recent search settings. I’ve seen this on AirBnB. The app will save your search settings so they are available to you for next time you log in if you wish to use it again.

Recommendations for Future Iterations.

I would like to get feedback on a "recent searches" feature as seen in the Airbnb example in the feedback section. This feature would increase the speed at which users can browse their favourite types of Sleep Stories.

I would also be interested to see if the learnings here can be applied for the other parts of the app such as the guided meditations, music and masterclasses. The ability to find content is important in each part, but should mirror the respective human needs. In a future iteration, I might look at the app as a whole and see if a single search solution is possible. Calm recently put a search function that spans the entire apps library. It is a basic text search function, without the ability to sort, filter or combine searches in the way that I have outlined. There might be a solution that can span the entire app and fit the human needs of each section, but at the moment I don't think what is available works well enough.

That brings an end to this case study. Thank you for reading. If you'd like to reach out and talk, my contact details are below.

Userflow Chart

How Calm Increased Retention 3X After Switching to Amplitude

Discovering a retention hook, why amplitude over mixpanel.

Using Amplitude’s behavioral cohort analysis, Calm found that users who set a Daily Reminder had 3x the retention of other users.

Increase in retention

Apple app store best app of the year, app for meditation and sleep.

Calm is a mindfulness meditation app that brings clarity and peace of mind to their users. With a solid user base on iOS and Android, Calm needed to understand how current features were driving retention and what they could do to improve it. They worked with Amplitude to analyze their user behavior data and discover ways to improve Calm’s retention.

We use Amplitude to track top line metrics, but the real power of it is going deep.

Calm built a Daily Reminder feature that allowed users to set a reminder for their daily meditation session, but the reminder feature was buried deep on the Settings page of the app. Very few users, less than 1%, were finding and using reminders. Calm hadn’t given the reminders feature much thought for several months, but on a hunch, used Amplitude’s behavioral cohorting feature to measure the retention of users who set a reminder, compared to those who didn’t.

To their surprise, they found an almost 3x increase in retention for users who set Daily Reminders. With such a small sample size of users, they couldn’t know whether this was a causal relationship. It could be that the power users of their app, who would have been well-retained anyways, were the ones digging into the Settings page and finding the Reminders feature.

Experiment: Does Prompting Users to Set Daily Reminders Increase Retention?

So, Calm ran an experiment on a portion of their new users where they made Daily Reminders more prominent. In the test, after a user completes their first meditation session, they are shown a screen encouraging them to set a daily reminder.

Result: Increase Retention by 3x

Calm found that people who set a reminder from the new prompt had an equal boost in retention to the users who had previously found the reminder feature on their own, indicating that the relationship between daily reminders was causative, not just correlative.

In addition, 40% of users who saw the prompt went on to set a daily reminder, so the new prompt provides a big boost to overall new user retention. Based on these results, Calm plans to roll out the new reminder prompt to all users in the next app update.

You feel like Amplitude is on your side. They want you to have success and get results, as opposed to just, ‘Hey, here’s your analytics platform.’

Prior to Amplitude, Calm was using Mixpanel for analytics. According to Tyler Sheaffer, Calm’s lead iOS Engineer, “We were just not tracking stuff because it would be too expensive. And that’s a bad way to think about what to track.”

Calm was initially drawn to Amplitude based on their scalability and advanced behavioral features—said Alex Tew, CEO of Calm, “there’s really no incentive not to track as many things as possible.” Alex quickly saw that there was much more to be gained from switching to Amplitude, saying, “It’s actually a much better product than Mixpanel.”

“I also think there’s something more that you get,” Alex said. “With the continual improvement of the platform, support from our Success Manager, and even the pricing structure—you feel like Amplitude is on your side. They want you to have success and get results, as opposed to just, ‘Hey, here’s your analytics platform.’”

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Headspace vs. Calm: A Mindful Competition

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Ayelet Israeli

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Headspace vs. Calm: A Mindful Competition By: Ayelet Israeli and Anne Wilson

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Becoming happier and healthier – a Calm case study

Discover how Calm use Postcards to market their brand offline.

May 26, 2023
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We’re thrilled to have recently chatted with our client Calm , to discover more about their brand journey over the last 10 years. And as Mental Health Awareness Month draws to a close, it serves as a great reminder that ‘checking in’ both with yourself and with those around you should be practiced year-round.

Read on to discover how we’ve supported Calm in their mission to ‘support everyone on every step of their mental health journey’.

The mental health gap

According to the World Health Organisation , there’s been a 13% rise in mental health conditions in the last decade. Not only does this have an effect on family relationships and work – but it also impacts global development. Two of the most common mental health conditions, depression and anxiety, cost the global economy $1 trillion each year. Yet despite this, the global median of government health expenditure going towards mental health is less than 2%.

Calm has been supporting the mental health and wellness journey of millions of people across the world for over 10 years. And as the population becomes bigger and busier, there is an increased need to carve out mindfulness moments to help counter these worrying global statistics.

Calm’s mission is clear and in this article we discuss some of their offline marketing tactics for keeping their brand front of mind in a growing industry.

Adapting to growth

Calm is the leading mental health brand, on a mission to support everyone on every step of their mental health journey.

The #1 app for sleep, meditation and relaxation – Calm’s transformational content supports users across seven languages in more than 190 countries. And it goes without saying that when they approached MOO to help support their continued growth we jumped at the chance to be part of the journey.

“MOO is a nimble and efficient partner for us”.

A calm spokesperson stated; “Our partnership with MOO first started in a focused capacity on specific projects and then expanded as our needs grew”. They added, “As a company, it’s always important to adapt to new and innovative ways of working and MOO is a nimble and efficient partner for us – enabling us to not only work quickly but creatively”.

In 2022, the global mental health apps market size was valued at $5.2 billion, and according to Deloitte this growth remains on an upward trajectory. Additionally, they highlight that as many as 20,000 mental health apps may exist today, so creating standout – both on and offline – is key to fueling continued expansion.

If you’re a primarily online business, creating memorable offline experiences comes with its challenges. Whether it’s brand packaging, a personalized Flyer or a simple Business Card . Offline communications are a chance to build trust and a more personal connection with your customers (and future clients).

Without a physical product, offline marketing can take on many forms. But the most important part is creating something that people can hold onto. Something that is a tangible reminder of what you do, and a touchpoint to keep your business front of mind. And when it comes to new customer acquisition in a crowded marketplace, leaving a lasting impression is paramount.

Offline design solutions

At MOO we support Calm on a number of different branding projects, but most notably we work on editable Postcard designs for their B2B networking events. These events include the Mental Health Forum in Chicago and The Employee Healthcare Conference in San Diego and New York. These large-scale events host hundreds of companies looking for ways to help support the mental health of their employees and teams.

‘ Calm Business ’ is Calm’s enterprise-level solution aimed at helping employees improve their mental health to stay productive in the workplace. Over 3,000 organizations around the world choose Calm as their mental health partner, benefitting from various events, webinars, workshops and unlimited access to Calm content.

Initially Calm’s designers came to us with a vision to create an editable Postcard template that would display unique discount codes. Their requirements for this can be anywhere between hundreds or even thousands of unique codes, depending on the scale of each event.

“Postcards have been a great means of direct engagement with our customers”.

A Calm spokesperson said; “Postcards allow us to create different use cases for multiple projects all at once – seamlessly and efficiently. In fact, Postcards have been a great means of direct engagement with our customers.”

Gianna, Calm’s dedicated MOO account manager works closely with the Calm team, and said; “We set Calm up with editable Postcard templates that accommodate recipients anywhere. Our Printfinity tool allows for dozens of unique codes, text, or images to be uploaded to an editable template at once.”

MaryAnn, our Production Artist who works closely on the account added; “Unique promotional Postcards are a great way to utilize Printfinity. It is a simple and sustainable way to get the most out of your design without having to print unnecessary duplicates”.

Working directly with both their MOO account manager and our Design Services team has enabled Calm to produce a catalog of flexible, editable product templates across a broad spectrum of use cases. Their spokesperson commented; “We work with MOO frequently across several work streams at Calm, which enables us to offer everything from giveaway collateral for national television talk shows, gifting content for VIPs. and even networking and ‘leave-behind’ materials at important conferences and events”.

But having a dedicated account manager and creative artwork support aren’t the only benefits of working with the team here at MOO. Calm is a member of our Business Advanced plan which supports the complex needs of larger, more established businesses. This plan also includes custom printed samples, employee self-ordering options, and access to our offline catalog and MOO business platform too. It really is everything in one place!

When describing what it’s like to work with MOO a Calm spokesperson added; “Whether our focus is in engaging and forging relationships with our current partnerships portfolio, fostering employee engagement through internal events and custom collateral, or networking with potential customers within the B2B landscape – MOO makes the process seamless and easy. And they’ve grown to become a valued partner of ours over the years.”

If you’re looking for new ways to adapt and grow your business with our premium marketing materials , then fill in the form below and a friendly account manager will be in touch!

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Headspace vs. Calm: A Mindful Competition

By: Ayelet Israeli, Anne Wilson

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Publication Date: May 3, 2021
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By 2021, the mindfulness app wars reached their apex. Over 2,000 meditation apps were available to consumers, but two apps, Headspace and Calm, dominated the space, jointly holding about 70% of the total market. Headspace had established itself as the approachable educator for novice meditators. Headspace also invested heavily in athletics partnerships, business-to-business services, and in creating Headspace Health, which they hoped would gain FDA approval and become the first prescription meditation app. Meanwhile, Calm had earned a reputation as the soothing sleep app. Calm was also known for its numerous partnerships with A-list celebrities such as Matthew McConaughey, Harry Styles, and Idris Elba. Indeed, Calm was focused on becoming the leader in "calmtainment" and making a home for itself in Hollywood. What does the future hold for the rivalry between Calm and Headspace? Will both brands continue to co-dominate the mindfulness and meditation space or will one ultimately prevail? What should the brands do to maintain their co-dominance or edge out the other and claim the number one spot?

Learning Objectives

This case provides students with the opportunity to understand how marketing communications can be orchestrated to support a brand image and strategic brand positioning. Moreover, it provides students with the chance to learn how e-commerce and new technologies can be used to disrupt marketplaces that traditionally rely on in-person experiences and service models. The case provides an overview of how Headspace and Calm have respectively carved out differentiated spaces for themselves as the two leading mindfulness and meditation apps, how they sustained that positioning over time, and how they developed their offerings to continue to serve and grow the market. This core discussion allows students to uncover insights about how to synthesize and coordinate marketing communications and answer important questions such as: How do each of the elements of a brand (e.g. name, aesthetic, strategic partnerships, characters, tone of voice, etc.) support and further brand image and positioning? How can one effectively add new features and distribute unique content while sustaining the brand's image? How does brand positioning affect pricing decisions and vice versa?

May 3, 2021 (Revised: May 17, 2022)

Discipline:

Geographies:

North America, United Kingdom, United States

Industries:

B2B e-commerce, B2C e-commerce, E-commerce industry, Marketing industry, Media, entertainment, and professional sports, Mobile and social application industry, Mobile devices and personal computers

Harvard Business School

521102-PDF-ENG

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Headspace or Calm: Which One Will Help You Achieve Mindfulness?

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In today’s high-stress, fast-paced world, finding a moment of tranquility can often seem daunting. Mental health concerns like anxiety and sleep disorders are rising, and people are increasingly looking for natural ways to manage these issues. Among various techniques, meditation stands out for its simplicity and effectiveness. This ancient art has been redefined and brought to our fingertips through the power of technology, specifically through meditation apps: Headspace and Calm. These two apps have dominated the mindfulness and meditation space for years, jointly holding about 70% of the total market. They have also won numerous awards and accolades for their quality, design, and impact.

But how do these two apps compare? Which one is better for you? In this blog post, we will review the features, content, and price of the apps Headspace and Calm and help you decide which suits your needs and preferences better.

Headspace: The pioneer of mindfulness on your phone

Woman meditating while wearing headphones

Practicing meditation with Headspace

Headspace was the first prescription meditation app launched in 2010 by Andy Puddicombe , a former Buddhist monk whose mission was to make meditation accessible and appealing to everyone. Headspace’s story is a core discussion in our “Headspace vs. Calm” case study, as the app has grown into one of the leading mindfulness apps in the world, having over 70 million users spanning over 190 countries.

Aside from the vast array of specific meditations, Headspace offers other features to enhance its users’ overall well-being. ‘The Wake Up’ is a daily video series designed to inspire and set a positive tone for the day. ‘Move Mode’ combines physical activity with mindfulness, offering workouts, stretches, and mindful walks.

The Scientific Case for Meditation and Mindfulness

Employees at Google and Roche who used Headspace for 8 weeks experienced a 46% drop in depression and a 31% drop in anxiety, according to a published study.

Headspace is often ranked as the best meditation app, and it’s not hard to see why. It has a simple, soothing interface and various meditations for different levels and goals.

Try HeadSpace.com

To cater to its users who experience difficulty in sleeping, Headspace provides ‘Sleep Mode’, a selection of soothing sleep apps, sounds, music, stories, and meditations designed to help users relax and drift off to sleep. ‘Focus Mode’ is another unique feature that includes playlists, soundscape scenes, and timers to help increase productivity and focus. For the younger audience, ‘Headspace for Kids’ offers engaging meditations designed specifically for children of different ages.

Headspace Plus, the app’s subscription service, grants unlimited access to all the content and features available, thus providing a wider scope for exploring in-person experiences.

In the mindfulness app wars of Headspace and Calm, the two leading mindfulness apps continue to offer services that meet the diverse needs of their users. While both brands continue co-dominating the market, they use new technologies and business-to-business services and occupy differentiated spaces. Headspace continues to invest heavily in its approach of being an educator and a clinically backed tool for mental health improvement.

As they forge ahead, it remains to be seen which of these two giants will ultimately prevail. However, these two apps’ increasing popularity may indicate a growing acceptance and integration of mindfulness and meditation practices into everyday life, which can only be a positive trend.

Calm: The Award-Winning app for relaxation and sleep

Introduced in 2012 by Michael Acton Smith and Alex Tew, Calm was intended to be a simple, beautiful app that helps people unwind and sleep better. This vision has materialized impressively, as Calm has become one of the most popular wellness apps globally, with over 100 million downloads and various awards to its name, such as Apple’s App of the Year in 2017 and Google Play’s Editor’s Choice of 2018 , while it is not the first prescription meditation app.

Calm’s brand positioning in the Headspace vs. Calm war for the mindfulness prescription meditation app space is distinct in its focus on relaxation and sleep. Its main feature is its library of guided meditations, covering various themes like stress, anxiety, gratitude, and self-care. These meditations are narrated by various voices, even including A-list celebrities such as Matthew McConaughey, Harry Styles, and Idris Elba, creating a sense of novelty and intrigue. Suitable for beginners and experienced meditators, these sessions range from 3 to 25 minutes.

In addition to guided meditations, Calm offers features to enhance relaxation and improve sleep quality. ‘Sleep Stories’ are bedtime tales read in soothing voices, designed to lull you into a peaceful sleep. The soothing sleep app also provides a selection of relaxing music tracks composed by artists and sound therapy experts and a variety of natural soundscapes that create a peaceful atmosphere conducive to meditation or sleep.

One unique feature in Calm’s offering is the ‘Masterclass’ series: audio courses taught by experts on various well-being-related topics. ‘Calm Body’ offers gentle stretches and exercises to release tension and relax the body, while ‘Calm Kids’ includes stories, meditations, and music designed for children of different ages.

Calm Premium, the app’s subscription service, allows users unlimited access to all available content and features.

Calm’s strategic positioning is in its entertainment-based approach to meditation. Employing celebrities, engaging stories, and music aims to make meditation enjoyable, thereby attracting and retaining users who may not be inclined toward traditional techniques for meditation. Dubbed as a leader in “containment,” Calm aims to carve out a niche in Hollywood.

In mindfulness app wars, Calm and Headspace continue co-dominating the meditation space, each having differentiated spaces within the total market. While Calm is not the first prescription meditation app, it invests heavily in partnerships and new features, creating headspace health and focusing on being a soothing sleep app and a sanctuary of relaxation amidst the chaos.

The ultimate business outcome of these mindfulness app wars between Headspace and Calm remains to be seen, but the increasing acceptance of mindfulness practices bodes well for both. Their business partnerships, coordinate marketing communications, strategic brand positioning, strategies to lift the brand image, and commitment to providing mental health services make them leading players in this growing field.

Headspace vs. Calm: A mindful competition of features, content, and price

Both Headspace and Calm are excellent apps that can help you meditate more effectively and improve your well-being. However, they have some differences in their features, content, and price that may influence your choice. Here is a summary of how they compare on these aspects:

As you can see, both meditation apps were available and offered a lot of value for their users. However, depending on your preferences and goals, you may find one app more suitable. For example:

If you want to learn more about meditation techniques and how they can benefit you in different aspects of your life, you may prefer Headspace’s educational approach.

You may prefer Calm’s entertainment approach if you want to enjoy various content and features designed to make meditation enjoyable.

If you want more flexibility and choice in the length of your meditations, you may prefer Headspace’s wider range of options.

If you want to listen to celebrity voices or stories while meditating or falling asleep, you may prefer Calm’s star-studded lineup.

Headspace vs. Calm: Pricing Comparison

Headspace and Calm offer free trials for new users to try their premium content. However, users must pay a subscription fee to access the full range of features and content after the trial period. Here is a comparison of the pricing plans for both apps:

Both apps also offer discounts and promotions occasionally, as well as gift cards and referral programs.

Headspace vs. Calm: Content Comparison

Both the apps Headspace and Calm have a large and diverse library of content that covers various aspects of meditation and mindfulness. However, they also differ in their content’s style, quality, and quantity. Here is a comparison of some of the main types of content for both apps:

Both meditation apps offered guided and unguided meditations for different experience levels, goals, and situations. However, Headspace has more meditations in quantity and variety and more structured courses that teach specific skills and techniques. Calm has fewer meditations but is more flexible and customizable in length and background sounds.

Headspace’s meditations are led by Andy Puddicombe, who has a calm, clear, and friendly voice. He uses simple language to explain the concepts and instructions of meditation, as well as some humor and anecdotes to make it more engaging. He also provides feedback and encouragement throughout the sessions.

Various instructors lead calm meditations with different styles, accents, and personalities. Some users may prefer having more options and diversity in their guides, while others may find it harder to connect or relate to them. Calm’s meditations are also less instructional and more experiential, focusing on guiding the user’s attention rather than explaining the theory or rationale behind meditation.

Both apps offer content to help users fall asleep faster and sleep better. However, Calm has more content in terms of quantity and variety and more features that cater to different preferences and needs for sleep.

Efficacy of the Mindfulness Meditation Mobile App “Calm” to Reduce Stress Among College Students: Randomized Controlled Trial

Jennifer huberty.

1 College of Health Solutions, Arizona State University, Phoenix, AZ, United States

Christine Glissmann

2 Department of Student Affairs, University of Redlands, Redlands, CA, United States

Linda Larkey

3 College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ, United States

Megan Puzia

4 Behavioral Research and Analytics, LLC, Salt Lake City, UT, United States

Associated Data

CONSORT‐EHEALTH checklist (V 1.6.1).

College students experience high levels of stress. Mindfulness meditation delivered via a mobile app may be an appealing, efficacious way to reduce stress in college students.

We aimed to test the initial efficacy and sustained effects of an 8-week mindfulness meditation mobile app—Calm—compared to a wait-list control on stress, mindfulness, and self-compassion in college students with elevated stress. We also explored the intervention’s effect on health behaviors (ie, sleep disturbance, alcohol consumption [binge drinking], physical activity, and healthy eating [fruit and vegetable consumption]) and the feasibility and acceptability of the app.

This study was a randomized, wait-list, control trial with assessments at baseline, postintervention (8 weeks), and at follow-up (12 weeks). Participants were eligible if they were current full-time undergraduate students and (1) at least 18 years of age, (2) scored ≥14 points on the Perceived Stress Scale, (3) owned a smartphone, (4) were willing to download the Calm app, (5) were willing to be randomized, and (7) were able to read and understand English. Participants were asked to meditate using Calm at least 10 minutes per day. A P value ≤.05 was considered statistically significant.

A total of 88 participants were included in the analysis. The mean age (SD) was 20.41 (2.31) years for the intervention group and 21.85 (6.3) years for the control group. There were significant differences in all outcomes (stress, mindfulness, and self-compassion) between the intervention and control groups after adjustment for covariates postintervention (all P <.04). These effects persisted at follow-up (all P <.03), except for the nonreacting subscale of mindfulness ( P =.08). There was a significant interaction between group and time factors in perceived stress ( P =.002), mindfulness (P<.001), and self-compassion ( P <.001). Bonferroni posthoc tests showed significant within-group mean differences for perceived stress in the intervention group ( P <.001), while there were no significant within-group mean differences in the control group (all P >.19). Similar results were found for mindfulness and self-compassion. Effect sizes ranged from moderate (0.59) to large (1.24) across all outcomes. A significant group×time interaction in models of sleep disturbance was found, but no significant effects were found for other health behaviors. The majority of students in the intervention group reported that Calm was helpful to reduce stress and stated they would use Calm in the future. The majority were satisfied using Calm and likely to recommend it to other college students. The intervention group participated in meditation for an average of 38 minutes/week during the intervention and 20 minutes/week during follow-up.

Conclusions

Calm is an effective modality to deliver mindfulness meditation in order to reduce stress and improve mindfulness and self-compassion in stressed college students. Our findings provide important information that can be applied to the design of future studies or mental health resources in university programs.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT03891810","term_id":"NCT03891810"}} NCT03891810 ; https://clinicaltrials.gov/ct2/show/ {"type":"clinical-trial","attrs":{"text":"NCT03891810","term_id":"NCT03891810"}} NCT03891810

Introduction

Elevated stress has been reported in more than 75% of college students (ie, aged 18-33 years) [ 1 - 3 ], and college students often report higher levels of stress than people of other age groups [ 4 , 5 ]. Some studies showed that more than 85% of college students reported feeling overwhelmed [ 4 , 5 ]. This is likely due to the unique set of stressors and demands they experience as they gain autonomy from their parents (ie, move away leaving family and friends), learn to manage finances, balance an increased academic workload and extracurricular activities, and make career choices [ 5 - 7 ]. These demands may lead to increased anxiety, loneliness, depression, sleep disturbance, and even suicidal ideation [ 8 - 10 ].

Stress is associated with a greater likelihood of suicide attempts [ 11 ], which is the second leading cause of death among teens and young adults (aged 15-24 years) [ 12 ]. One report found that 60.8% of college students felt overwhelming anxiety, 38.2% felt so depressed that it was difficult to function, and 10.4% seriously considered suicide [ 13 ]. Studies suggest that untreated mental health is highly prevalent among college students. One study reported that <25% of college students received treatment for any type of mental health disorder (eg, depression or anxiety) within the past year [ 14 ]. Another study reported that only 36% of students with mental health issues had sought treatment [ 15 ]. Many students do not seek treatment partly due to the lack of time, privacy concerns, stigma [ 16 ], lack of emotional openness [ 17 ], and financial constraints [ 18 - 21 ].

Beyond the potential negative impact that elevated stress may have on mental health, stress may also have a negative impact on the physical health of college students and the health behavior they choose during this time [ 22 ]. College students with elevated stress may be more likely to experience poor sleep, increased alcohol consumption, less exercise, and unhealthy eating habits [ 23 - 25 ]. There is an urgent need for effective stress management strategies to address the adverse outcomes of stress and the potential barriers to treatment in college students.

Implementing stress reduction programs on college campuses has become a priority [ 26 , 27 ]. Mindfulness-based interventions, in particular, have become more popular on college campuses recently [ 28 ] and may be an effective strategy to reduce stress in college students [ 29 ]. Mindfulness is defined as the state of being attentive to and aware of what is taking place in the present moment without judgement [ 30 ]. Two of the most popular mindfulness-based interventions are mindfulness-based stress reduction (MBSR) and mindfulness-based cognitive therapy (MBCT) [ 31 , 32 ]. Although effective for stress management, these programs may be rigorous, time consuming, and costly, which may not be ideal for college students.

Mindfulness meditation, a component of MBSR and MBCT, alone has shown to have positive benefits—from reducing stress and anxiety to improving overall well-being [ 29 , 33 ]. The mechanisms of mindfulness meditation are unclear but constructs such as increasing self-compassion and mindfulness have been shown to mediate the effect of meditation programs on stress in a variety of populations [ 34 - 36 ]. Meta-analyses have suggested that self-compassion is strongly related to mental health [ 37 ] and well-being [ 38 ]. However, the stress-reducing effects in college students is less clear. More research is needed to understand what aspects of mindfulness meditation interventions may be working to reduce stress in college students.

Mindfulness meditation may be a practical approach to reduce stress in college students. In a recent narrative synthesis of studies on the effects of mindfulness meditation on stress in college students, 16 of the 22 studies reported significant decreases in self-reported stress [ 29 ]. Significant findings were reported in interventions ranging from 2.25 hours to as much as 30 hours of total required participation in meditation over the course of the intervention (eg, 3-8 weeks) [ 39 - 44 ]. Although these findings are promising, several limitations must be noted, including low adherence rates, lack of control groups, and small sample sizes [ 29 ]. More studies are needed to address the existing limitations of mindfulness meditation interventions and explore more convenient ways to deliver mindfulness meditation in order to encourage participation and increase accessibility among college students.

Approximately 98% of college-aged students use the internet [ 45 ] and 85% own a smartphone [ 46 ]. Many college students have expressed being receptive to online mental health treatments, with one study reporting that college students were more likely to seek help online than face-to-face [ 47 ]. The development of mobile health apps has been increasing exponentially, and the use of apps has been reported to improve the efficiency of health care delivery and the effectiveness of treatment [ 48 ]. It is also important to note that while apps may offer the ability for increased access to health information, remote care, and user autonomy [ 49 ], there is a need to ensure safety of users, as there is no regulatory oversight. App users should consider the quality and accuracy of evidence-based information, qualifications of content developers, medical/health claims [ 50 ], information sharing or data security [ 51 ], informed consent, and user competence (eg, training required to use techniques) [ 49 ]. Independent testing of mobile apps is highly warranted.

Mindfulness meditation mobile apps may be a promising approach to reduce stress and address the barriers for stress management reported by college students. A recent study conducted a search for “mindfulness” mobile apps in Apple iTunes and Google Play Store and found a total of 560 apps that were accessible and in English [ 52 ]. The top mindfulness apps listed in Apple iTunes (in a current search as of April 2019) was Calm (named “2017 app of the year” by Apple, #2 Health & Fitness app, 4.8/5 star reviews), followed by Aura (#6 Health & Fitness app, 4.7/5 star reviews) and Headspace (#9 Health & Fitness app, 4.9/5 star reviews). Similar results were found in the Google Play Store, with the top mindfulness apps being Headspace (#6 Health & Fitness app, 4.5/5 star reviews), followed by Calm (#8 Health & Fitness app, 4.5/5 star reviews) and Deep Calm (#44 Health & Fitness app, 4.5/5 star reviews). Although the popularity of mindfulness apps is increasing, only a few studies have examined the feasibility and efficacy of such mindfulness-based mobile apps for reducing stress among college students.

The purpose of this study was to test the efficacy [ 53 ] of an 8-week mindfulness meditation intervention delivered via a consumer-based mobile app (ie, Calm) as compared to a wait-list control group on stress in college students with elevated stress (≥14 on the Perceived Stress Scale [PSS]). We also explored the feasibility (ie, acceptability and demand) of the intervention delivered via the mobile app and examined the sustained effects (at 12 weeks from baseline) of the intervention on stress, mindfulness, and self-compassion. Additionally, exploratory analyses examined the potential effects of the mindfulness intervention on health behaviors (ie, sleep disturbance, alcohol consumption [binge drinking], physical activity, and healthy eating [fruit and vegetable consumption]).

We hypothesized that college students in the intervention group would have significant improvements in perceived stress as compared to the wait-list control. We also hypothesized that stress, mindfulness, and self-compassion would have sustained effects in the intervention group as compared to the wait-list control. Finally, we hypothesized that the intervention may improve health behaviors. The findings from this study will provide important insights on the potential stress-reducing effects of a consumer-based mindfulness meditation mobile app (ie, Calm) and could be applied to the design of future mindfulness meditation interventions in stressed college students.

Ethics Approval

This study was approved by an Institutional Review Board at a large university in the Southwestern United States (STUDY00006896). All participants provided electronic consent prior to participation in the study. The datasets generated or analyzed during the study are available from the corresponding author upon request.

Study Design

This study was a randomized, wait-list control trial (trial registration: ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT03891810","term_id":"NCT03891810"}} NCT03891810 ) with assessments conducted at baseline, postintervention (8 weeks), and at follow-up (12 weeks). Participants randomized to the intervention group participated in an 8-week mindfulness meditation mobile app intervention of at least 10 minutes per day. Those randomized to the wait-list control group received the intervention after 12 weeks.

Sample Size

The sample size was determined to detect significant change in perceived stress between the intervention and control groups (effect size=0.76) based on previous studies [ 28 , 54 ]. The power analysis showed that 93 participants were sufficient to have 85% statistical power at a two-sided α of 0.05 for significance. Accounting for a 10% dropout rate, the total sample size was planned to be 104 participants.

Recruitment and Selection

Participants were recruited between January and April 2018 via social media (ie, Facebook and Instagram), email listservs, and flyers and by emailing university professors. Interested students were sent an eligibility survey via Qualtrics (ie, online survey database). Participants were eligible for the study if they were a current full-time undergraduate student attending a university in the Southwestern United States and (1) at least 18 years old, (2) scored ≥14 points on the PSS, (4) owned a smartphone, (5) were willing to download the Calm app, (6) were willing to be randomized, and (7) were able to read and understand English. Participants were excluded if they had a current mindfulness practice (ie, practice ≥15 min per day of meditation, yoga, and body scan within the past 6 months) [ 55 ] or were currently using the Calm app or another meditation app. Eligible participants were sent a link via Qualtrics to an online intake video that was approximately 5 minutes long. Participants were required to view the video in its entirety and correctly answer three follow-up questions regarding information presented in the video (eg, how long will you be asked to participate in mediation?). If participants missed a question, they were redirected to watch the video again and answer the questions until all three were answered correctly. Once the questions were answered correctly, participants were sent the informed consent and baseline questionnaire via Qualtrics software (Qualtrics, Provo, UT). After the informed consent and baseline questionnaire was completed, participants were randomized into the intervention group or the wait-list control group.

Randomization and Blinding

Participants were randomized using a list created from an online randomization tool (randomizer.org) that was set to randomize in a ratio of 1:1. After participants completed baseline assessments, a research team member (unblinded) allocated participants to a group using the randomization list. Study staff and participants were unblinded to group allocation.

Intervention Group

After randomization, the intervention group was emailed instructions on how to download the Calm mobile app. Calm is a consumer-based mindfulness meditation mobile app that offers a range of mindfulness meditation practice guide modules that vary in length, instruction, and content. Mindfulness meditation is the practice of moment-to-moment awareness in which the person purposefully focuses on the present without judgement. Vipassana is a technique of mindfulness that explores how the mind influences the body and how the body influences the mind (ie, objective observation of physical sensations in the body) [ 56 ]. The goal is to sharpen concentration, maintain awareness, and develop equanimity by releasing habitual tendencies toward craving and aversion. Calm also integrates some cognitive behavioral therapy (CBT) techniques into the meditation sessions on occasion. The CBT-influenced sessions encourage users to develop awareness of their thoughts, interpretations, and emotional and physiological responses in order to alter their perception of a situation or create a new, more balanced thought process [ 57 ].

Participants can meditate using the “daily Calm” set of guided meditations or may choose from a number of programs offering multiday meditations specific to goals (ie, happiness or self-esteem). Calm also offers other individual guided and unguided (eg, a brief introductory guidance followed by a chosen period of silence or sounds from nature) meditations.

Once downloaded, participants were asked to complete the “7 Days of Calm” program for the first week of the intervention to familiarize themselves with the principles of meditation and to standardize the introductory teaching content. Each day, the “7 Days of Calm” program started with an educational component on a principle of mindfulness meditation (eg, being present, returning to the here and now, and pulling out of autopilot). For each 10-minute session, after a principle was discussed, a related mindfulness meditation exercise was introduced and guided (eg, body scan, breath focus, and loving kindness). After participants completed the “7 Days of Calm” program, they were allowed to choose any meditation they desired. They could choose meditations from the College Collection, which addresses topics such as stress, sleep, self-compassion, and concentration or they could choose another series such as “7 Days of Managing Stress.” During the 8-week intervention, participants were asked to complete at least 10-minutes daily of meditation and could exceed that time limit by choosing additional meditations. If participants were not achieving 30 minutes of meditation per week, they were sent a text reminder to meditate. After the 8-week intervention, participants still had access to calm and could use it at their own leisure for 1 additional month (12 weeks from baseline).

Wait-List Control Group

Participants randomized to the wait-list control group received an email with their group assignment and stating that they would receive access to the Calm app after 12 weeks. They were also asked not to participate in any mindfulness activities (eg, yoga, meditation, and qigong) during this time. After 8 weeks, participants received a Qualtrics link to the postassessment (same surveys that the intervention group received). After 12 weeks, participants were sent a Qualtrics link to the follow-up assessment (same surveys that the intervention group received) and an email with instructions on how to download Calm and the assigned username and password.

Measures and Incentives

Both the intervention and wait-list control groups were administered three surveys—at baseline (week 0), postintervention (week 8), and at follow-up (week 12)—to assess perceived stress, mindfulness, self-compassion, health behaviors, and feasibility outcomes via online surveys (Qualtrics). Data on demographics, mental health history, medication use, and counseling activities were collected at baseline. Participants were incentivized with a US $5 gift card for completing baseline questionnaires, US $10 gift card for completing the postintervention questionnaires, and US $15 gift card for completing follow-up questionnaires. Participants could choose to receive their gift card from Starbucks, Target, or Amazon. Calm memberships were provided to participants for free during the study.

Perceived Stress

Stress was measured using the PSS [ 58 , 59 ]. This scale is a 10-item inventory used for the assessment of perceived stress. The scale measures the degree to which situations are appraised as stressful. The response items are rated on a 5-point Likert scale ranging from 0 (never) to 4 (very often). Scores range from 0 to 40, with higher scores indicating higher levels of perceived stress. The PSS has been shown to be reliable in undergraduate college samples [ 58 , 59 ]; in this study, the alpha coefficient for baseline PSS scores was 0.82.

Mindfulness

Mindfulness was measured using the Five Factor Mindfulness Questionnaire (FFMQ) [ 60 ]. The FFMQ is a 39-item self-report inventory used for the assessment of multiple constructs of mindfulness skills. The inventory assesses five subscales: observing, describing, acting with awareness, nonjudgment of inner experience, and nonreactivity to inner experience. The response items are rated on a 5-point Likert scale ranging from 1 (never or very rarely true) to 5 (very often or always true). The facet scores range from 8 to 40, with the exception of nonreactivity to inner experience, which ranges from 7 to 35. Higher scores indicate higher levels of mindfulness. The five FFMQ subscales have high internal reliability, test-retest reliability, and validity in undergraduate samples [ 60 ]. Consistent with this research, the FFMQ subscale reliability in this study was found to be high (at baseline, alpha coefficients were between 0.80 and 0.92).

Self-Compassion

Self-compassion was measured using the Self-Compassion Survey Short-Form (SCS-SF) [ 61 ]. The SCS-SF is a 12-item survey assessing three subscales: self-kindness versus self-judgment, common humanity versus isolation, and mindfulness versus over-identification. The response items are rated on a 5-point Likert scale ranging from 1 (almost never) to 5 (almost always). Higher scores indicate higher levels of self-compassion. The SCS-SF is a valid and reliable measure to assess self-compassion in college students (α=0.87 in our sample), and previous research has demonstrated near-perfect correlations ( r =0.98) with the long-form version [ 61 ].

Secondary Outcomes

Health behaviors measured included sleep disturbance, alcohol consumption (ie, binge drinking), physical activity, and fruit and vegetable consumption. Sleep disturbance (ie, sleep quality) was measured using the Patient-Reported Outcomes Measurement Information System (PROMIS) (α=0.97 in our sample) [ 62 ]. The Youth Risk Behavior Surveillance (YRBS) survey was used to measure binge drinking, physical activity participation, and healthy eating [ 63 ]. Specifically, items from the YRBS survey inquired about whether participants had consumed alcohol (ie, engaged in binge drinking; for women, consuming four consecutive alcoholic beverages in a 2-hour period; for men, five alcoholic beverages) at any point during the past 7 days, engaged in at least 150 minutes of physical activity during the past 7 days, and eaten five servings of fruits or vegetables on most of the past 7 days.

Feasibility

Feasibility measures included acceptability and demand. Acceptability was measured with a satisfaction survey postintervention (week 8). Demand was measured using adherence (minutes/week) to the meditation intervention. Adherence to meditation was recorded in weekly reports from the Calm informatics team. Reports included the date and time of each meditation participated in, the title of the meditation, and the duration of participation (ie, the time spent viewing the meditation) for each participant.

Statistical Analyses

General linear models (GLMs) were used to examine differences in means between groups at baseline and to test the initial efficacy of the intervention, examining mean differences of change (ie, change scores) for perceived-stress, mindfulness, and self-compassion after 8 weeks of mindfulness meditation between the intervention and control groups after adjustment for covariates (age, gender, and race). To examine the sustained effects, linear mixed models were used to test mean differences for perceived stress, mindfulness, and self-compassion between groups, time, and group×time interaction factors, with adjustment for covariates (age, gender, and race) performed using Bonferroni posthoc tests. GLMs were also used to evaluate changes in sleep disturbances; however, because sleep was exploratory, covariates were not included in these models. The McNemar tests were used to examine changes in other exploratory outcome variables (ie, binge drinking, physical activity, and healthy eating), which were coded dichotomously.

Feasibility potential was examined using adherence to the intervention. Adherence was calculated by averaging the weekly meditation minutes provided by the reports from the Calm informatics team. Acceptability measures and type of meditation were summarized using frequency with percentages. The Cohen d statistic was used to compute effect size. A two-sided α of 0.05 was used to determine statistical significance between groups, and a Bonferroni correction α of 0.016 was used for within-group comparison. All statistical procedures were performed using the IBM Statistical Package for Social Sciences (SPSS) version 24.0 (IBM Corp, Armonk, NY) and Statistical Analysis Systems Software, version 9.4 (SAS Institute, Cary, NC).

Participant Enrollment

A total of 329 Arizona State University students completed the eligibility questionnaire. Of those who met the study eligibility, 37% (n=121) were ineligible and 30% (n=99) did not sign the informed consent. Subsequently, 56 participants were randomized to the intervention group and 53, to the wait-list group, yielding a total of 109 consented/randomized participants. We excluded participants who did not complete the postsurvey (n=18), set up a Calm account (n=2), or meditate at all (n=1), resulting in a total of 88 participants to be included in the analysis ( Figure 1 ).

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Enrollment flow diagram.

Participant Demographics

Baseline characteristics of the study participants are presented in Table 1 . The mean age (SD), adjusted for gender and race, was 20.41 (2.31) years for the intervention group and 21.85 (6.3) years for the control group. The majority of participants were female (79/88, 88%), freshman (27/88, 31%), non-Hispanic (66/88, 77%), and of white race (48/88, 59%). There were no differences in demographic characteristics, mental diagnosis, medication use, and counseling activities between the intervention and control groups (using Chi-square tests, all P >.17).

Baseline characteristics of the study participants (N=88).

a Adjusted for gender and race.

Primary Outcomes (Perceived Stress, Mindfulness, and Self-Compassion)

Table 2 describes the means of perceived stress, mindfulness, and self-compassion between the intervention and control groups at baseline, postintervention, and at follow-up. The baseline mean (SD) scores of self-reported stress (intervention: 23.11 [4.93] vs control: 21.88 [4.94]; P =.25), mindfulness (intervention: 109.41 [17.36] vs control: 113.88 [17.35]; P =.25), and self-compassion (intervention: 29.81 [8.13] vs control: 31.85 [8.2]; P =.25) showed no significant differences between study groups.

Mean scores per study group for perceived stress, mindfulness, and self-compassion.

a PSS: Perceived Stress Scale.

b P <.01.

c FFMQ: Five Facet Mindfulness Questionnaire.

d P <.0001

e P <.05.

f SCS-SF: Self-Compassion Scale - Short Form.

Table 3 summarizes the results of 8 weeks of meditation on changes in stress, mindfulness, and self-compassion, testing the initial efficacy of the intervention compared to wait-list control participants. Participants in the intervention group had a significant reduction in perceived stress compared with the control group (∇=–7.13; P <.001; effect size=1.24). Participants in the intervention group had significant improvements in total mindfulness (∆=19.23; P <.001; effect size=1.11) and all five constructs: observe (∆=3.735; P <.001; effect size=0.67), describe (∆=3.528; P <.001, effect size=0.59), acting with awareness (∆=4.737; P <.001; effect size=0.83), nonjudgment of inner experience (∆=4.938; P <.001; effect size=0.76), nonreactivity to inner experience (∆=3.781; P <.001; effect size=0.92). Participants in the intervention group also had significant improvements in self-compassion (∆=8.223; P <.0001; effect size=0.84) compared to participants in the control group.

Pre-post change scores in perceived stress, mindfulness, and self-compassion between groups (N=88; all P <.001). Scores are adjusted for age, gender, and race.

b FFMQ: Five Facet Mindfulness Questionnaire.

c SCS-SF: Self-Compassion Scale - Short Form

Additional analyses using linear mixed models were used to examine effects of the intervention compared to control over time, including initial and sustained effects. Figure 2 illustrates the means of perceived stress, mindfulness, and self-compassion and the 95% CIs between intervention and control groups across baseline, postintervention, and at follow-up. On an average, participants in the intervention group had significantly decreased perceived stress scores postintervention (8 weeks) as compared to those in the control group, and these changes persisted through follow-up (12 weeks; Figure 2 and Table 2 ). Compared to those in the control group, participants in the intervention group showed significantly increased mindfulness and self-compassion scores postintervention (8 weeks), and these changes persisted through follow-up (12 weeks).

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Effects of mindfulness meditation on perceived stress, mindfulness, and self-compassion.

There was a significant interaction between group and time factors in perceived stress ( P =.002), mindfulness ( P <.001), and self-compassion ( P <.001). Bonferroni posthoc tests showed significant within-group mean differences for perceived stress in the intervention group (baseline and postintervention: P <.001; baseline and follow-up: P <.001), while there were no significant within-group mean differences for perceived stress in the control group (all P >.19). For mindfulness and self-compassion, there were significant within-group mean differences in the intervention group (baseline and postintervention: P <.001; baseline and follow-up: P <.001), while there were no significant within-group mean differences in the control group (all P >.40). In the intervention group, there were no statistical differences in perceived stress, mindfulness, and self-compassion between the postintervention phase and follow-up (all P >.05).

Secondary Outcomes (Sleep Quality, Binge Drinking, Physical Activity Participation, and Healthy Eating)

Linear models indicated that among participants using the mindfulness meditation app, there was a significant decrease in sleep disturbance, as indicated by t scores on PROMIS ( P =.02; effect size=0.79), while there were no changes among control participants ( P =.95; effect size=0.02). However, results from linear mixed models showed that the interaction between the time and intervention groups was nonsignificant, suggesting that sleep improvements did not differ across the two groups ( P =.11; effect size=0.34).

Data from the three YRBS items were used to create dichotomous variables reflecting whether in the past 7 days, participants engaged in binge drinking (ie, consumed four or more [for women] or five or more [for men] alcoholic beverages in a row on at least one day), engaged in 150 minutes of physical activity, and ate five serving of fruits and vegetables per day on most days. Results from the McNemar tests indicate that there were no significant changes in binge drinking for students in either study group (all P >.51) for physical activity or eating fruits and vegetables.

Acceptability (Satisfaction, Enjoyment, Intent to Continue Use)

We assessed acceptability via a satisfaction survey postintervention (week 8) in the intervention group. Approximately, 51% (21/41) of the students indicated that Calm was helpful to very helpful in reducing their stress in the short term, and these findings were similar for the long term (20/41, 49%). Over half of the students said they would use Calm to help reduce stress in the future. Approximately 85% (35/41) of the sample was somewhat to very satisfied using Calm, and 85% (35/41) indicated that they somewhat to very much enjoyed using Calm. About 68% (28/41) of the sample said they were likely to extremely likely to use Calm in the future, and 76% (31/41) would be likely to extremely likely to recommend Calm to other college students.

Demand (Adherence)

Participants in the intervention group engaged in an average of 37.9 (SD 30.5) minutes of meditation per week over the course of the 8-week study. Over half of the participants (23/41, 56%) completed more than 30 minutes per week using the Calm app, with 22% (9/41) of participants meditating more than 60 minutes a week. Approximately one-third (14/41, 34%) of intervention participants continued to meditate during the follow-up period (12 weeks from baseline) and spent an average of 20.4 (SD 23.9) minutes meditating.

Principal Findings

The purpose of this study was to test the efficacy and sustained effects of an 8-week mindfulness meditation intervention delivered using a consumer-based mobile app (ie, Calm) to reduce stress in college students and to examine feasibility components germane to planning the next steps in research. This was one of the first studies to test the efficacy of such a product in undergraduate college students. Our findings demonstrate significant between-group differences on all main outcomes variables including perceived stress, all five factors of mindfulness (observing, describing, acting with awareness, nonjudgment of inner experience, and nonreactivity to inner experience), and self-compassion postintervention. These effects were sustained at follow-up, and effect sizes ranged from moderate (0.59) to large (1.24) across all outcomes. Additionally, Calm was perceived as helpful; many participants enjoyed Calm and were likely to continue using the app. These findings highlight the promise of Calm to reduce stress in college students.

To our knowledge, this study was one of the first to demonstrate the stress-reducing effects of a mindfulness meditation mobile app (ie, Calm) in college students. The magnitude of change in perceived stress was greater in the intervention group than in the control group postintervention and at follow-up. Our findings are similar to those of a recent study that tested whether two mindfulness meditation apps (Headspace and Smiling Mind) led to improvements in mental health as compared to a control group in undergraduate students (N=208) [ 64 ]. Students were asked to meditate for 10 minutes daily for 40 days (10 days of requested use followed by 30 days of discretionary use). Students using Headspace (not Smiling Mind) reported significant reductions in stress and were maintained at follow-up. However, unlike our study, the app usage was measured by self-report, nearly half of all participants never used the app during the open-access period, students did not have elevated levels of stress as part of eligibility criteria, and the effect size was small ( d =0.26). Another study testing 10-20 minutes/day of Headspace use for 30 days among medical students (N=88) reported similar findings on stress compared to a wait-list control group both postintervention (30 days) and at follow-up (60 days) [ 65 ]. App usage in this study was self-reported, and participants were also instructed to send a screenshot to the research team of the minutes meditated report in Headspace. However, this study only reported on the days of app usage rather than minutes meditated and did not report effect sizes. Reporting days of app usage does not reflect the total amount of minutes meditated and does not illustrate how minutes of meditation may vary across days. Our findings are also similar to those of a study testing a newly developed mindfulness app (Wildflowers) on stress compared to a cognitive training control group (app called 2048) in college students (N=86) [ 66 ]. Students were asked to use the Wildflowers app daily for 3 weeks and could choose lessons on meditation or choose from a library of guided meditations. Students using the Wildflowers app had improved mood and reduced stress following each training session. However, the study did not have adequate power, did not report on the number of minutes students had meditated (only sessions completed), and had small effect sizes ( r =–0.08 to –0.37). Although all the aforementioned studies demonstrated a reduction in the number of minutes (or sessions) meditated, ours was the longest intervention (8 weeks) and showed the strongest effect sizes for perceived stress (effect size=1.24).

Although the average minutes of meditation in our study significantly decreased at follow-up, impressively, the reduction in perceived stress remained significant at follow-up (12 weeks from baseline). Similar to findings of other studies, our findings suggest that brief mindfulness meditation interventions delivered via mobile apps show promise in reducing stress in college students, and there may be added benefit for weeks following the intervention period, regardless of the time spent in meditation. Delivering mindfulness meditation may be a more practical approach to reducing stress, as it requires fewer resources (eg, cost, staff, or brick and mortar building), students can participate remotely, and there are fewer time constraints. However, there is a need to independently test and determine the efficacy of mindfulness meditation using mobile apps.

We also demonstrated significant improvements with large effects (effect size=1.11) in overall mindfulness scores and significant improvements with moderate-to-large effects (effect size=0.59-0.92) in all five factors of the FFMQ (observing, describing, acting with awareness, nonjudgment of inner experience, and nonreactivity to inner experience). Yamada and colleagues [ 67 ] implemented a brief 10-minute mindfulness meditation intervention (led by an instructor twice a week) over a 15-week semester in college students. Those participating in the mindfulness group, on an average, increased their mindfulness scores by nearly 9% as compared to the control group (no change). However, investigators did not provide the mindfulness factor scores. Greeson and colleagues [ 42 ] implemented a mindfulness meditation intervention (Koru) in college students and found significant increases in mindfulness and large effect sizes at the end of the 4-week intervention compared to a wait-list control group [ 42 ]. The Koru program includes 75-minute sessions taught in a small group format and also requires 10-minute daily meditation practice. Both the aforementioned studies, and our current study, demonstrate that brief mindfulness meditation interventions have the potential to improve mindfulness scores; however, the aforementioned studies did not include a follow-up period to assess the sustained effects of the intervention. In our study, mindfulness was greater even at the follow-up assessment. More studies are needed to test the sustained effects of brief mindfulness meditation interventions.

We found significant improvements in self-compassion in the intervention group as compared to the wait-list control group after the 8-week intervention with a large effect size (effect size=0.84). Improvements in self-compassion were sustained at follow-up. Greeson and colleagues (2014) also found significant increases with a large effect size in self-compassion, measured by the 26-item Self-Compassion Scale [ 68 ], as compared to a wait-list control group [ 42 ], but did not include a follow-up assessment. Another study implemented a brief mindfulness intervention (based on MBSR) that included 10-minute periods of mindfulness training followed by 5 minutes of discussion across 28 clinical interviewing classes (7 hours of instruction over 10 weeks) in social work students [ 69 ]. In contrast to our findings, this study did not find significant improvements in self-compassion. However, this study was limited by a lack of randomization and a small sample size. Despite these findings, our study and others demonstrate that self-compassion often improves with increasing mindfulness in students [ 29 , 36 , 70 ], but the sustained effects after the intervention period are unclear. Self-compassion has been considered a core quality of mindfulness [ 71 ] and is associated with lower stress and psychological symptoms in addition to greater well-being [ 72 , 73 ]. In one study, mindfulness and self-compassion were mediators in the pathway to emotional well-being in adolescents [ 36 ]. More research investigating the impact of brief mindfulness meditation interventions, especially those delivered via mobile apps, on self-compassion is needed to determine the efficacy of such apps and potential to sustain effects after the intervention period.

Sleep Quality and Health Behaviors

Our findings suggest that Calm may be a promising approach to help improve sleep quality (ie, reduce sleep disturbance) in college students. Similar to our findings, Greeson and colleagues [ 42 ] reported a significant reduction in sleep problems as compared to a wait-list control group after participation in a 4-week mindfulness meditation intervention. However, the intervention was only for 4 weeks and delivered in person. Another study tested the impact of an 8-week internet-based mindfulness training program to improve mental health and sleep in college students and young adults compared to an internet-based cognitive behavioral training program [ 74 ]. Each session lasted 30-45 minutes and both groups experienced significant reductions in sleep disturbance. This study also did not deliver the intervention with a mobile app and had a high attrition rate. One study tested a mindfulness-based health app on mental health and sleep behavior in college students [ 75 ]. Students were asked to use the app 5 days per week for 4 weeks, but there were no significant changes in sleep. There are limited and mixed data available on the impact of mobile meditation apps and sleep outcomes in college students, and there is a need for further exploration of meditation delivered via an app and sleep outcomes in college students.

We did not find an association between mindfulness meditation and changes in alcohol consumption (ie, binge drinking), physical activity, or healthy eating (ie, fruit and vegetable consumption). There are a lack of studies testing the impact of mindfulness meditation mobile apps on health behavior outcomes in college students. One study testing the effects of a mindfulness app on weight and weight-related behaviors reported reductions in emotional eating and uncontrolled eating after engagement in an 11-week intervention compared to a self-monitoring electronic diary control group [ 76 ]. However, this study had low adherence and did not report on fruit and vegetable consumption. A cross-sectional survey administered to college women reported that higher levels of mindfulness were related to healthy eating practices, better sleep quality, and better physical health [ 77 ]. Another cross-sectional survey implemented in adults aged 18-25 years suggested that self-compassion (a potential mechanism of mindfulness) was positively correlated with intentions to engage in health-promoting behaviors (eg, healthy eating, physical activity, good sleep hygiene, and stress management) [ 78 ]. Papies (2017) suggests that mindfulness interventions may be most effective if targeted at a specific behavior (ie, reducing alcohol consumption, healthy eating, and reducing smoking) [ 79 ]. Although mindfulness-based interventions show promise in improving health behaviors, more research in this area is warranted.

Approximately 85% of the sample was satisfied with and found Calm enjoyable. None of the students in our sample indicated that they were dissatisfied with using Calm. A majority said they would use the app in the future or refer it to other college students. There is limited evidence regarding the acceptability of mindfulness meditation mobile apps, specifically in college students. Similar to our data, a study by Donovan and colleagues [ 80 ] suggested high satisfaction with a mobile app (BodiMojo) aiming to teach adolescents (13-22 years old) about mindfulness and self-compassion, with 92% of the sample rating their enjoyment of the program a score of 6 or 7 (1=not at all, 7=very much) [ 80 ]. Another study conducted in the Netherlands by Emmerik and colleagues [ 81 ] implemented a mindfulness-based mobile app (VGZ Mindfulness Coach) to improve mindfulness, general psychiatric symptoms, and quality of life in adults aged ≥18 years [ 81 ]. Participants in the study reported high satisfaction with an average satisfaction score of 4.18 (SD 0.84) on a scale of 1-5 (higher scores indicate higher satisfaction) [ 81 ]. The aforementioned research is similar to our findings and demonstrates high satisfaction among mindfulness-based mobile apps. However, it is difficult to compare the mobile apps against each other because of the various mindfulness components used, differences in application interfaces, usability, and length of programs. Future studies may assess the satisfaction of the various components of behavioral/mental health mobile apps, as studies have reported that satisfaction of these apps may be dependent upon components such as accountability, tracking progress, reminders/push notifications, and convenience [ 82 , 83 ]. This information could be particularly useful for researchers to design/modify new or existing mindfulness meditation mobile apps to be more relatable to the user and potentially attenuate the decline in usage over time.

The average participation in meditation across the 8-week study was 38 minutes/week, which was sufficient to see effects in perceived stress in our sample. The decrease in minutes of meditation practiced by college students over the length of the intervention is not surprising, as college students have a number of barriers that may keep them from participating in daily meditation (eg, time constraints, financial pressure, educational expectations, and interpersonal relationships). In a formative analysis by Lauricella [ 84 ], a majority of undergraduate students who participated in a face-to-face mindfulness meditation intervention followed by a digital session of the same technique preferred the in-person version as compared to the digital practice [ 84 ]. The minority (~25%) that preferred the digital practice found the solitude and confidentiality of this format to be favorable. It is important to understand more about why college students do or do not participate in meditation delivered digitally. Gender differences regarding use of mindfulness-based mobile apps may be important considerations in future studies. Men are often underrepresented in mindfulness interventions, and women may be more engaged in the intervention [ 85 ] and experience greater benefit [ 86 ]. We also provided students with incentives, which may have impacted intervention results and adherence. Future studies may consider replicating this study without the use of incentives to observe real-world participation. Furthermore, many consumer-based mobile apps do not include theory-based strategies for behavior change, which may impact adherence rates [ 87 ]. For example, self-monitoring (ie, the ability to track minutes) and social support are strategies highly associated with adherence to behavior. Calm is a consumer-based app that does include tracking and reminders for participants, but other strategies may be necessary. Future studies could incorporate social support with an app and examine the effects of such an addition on adherence.

It is also important to note that mobile app usage rapidly declines within the first 90 days of downloading the app [ 88 ]. According to a 2018 report by Localytics, 21% of users abandon using an app after only one use, 57% of users stop using the app within the first 4 weeks, and average retention by 90 days is only 29% across all industries (media and entertainment, electronic commerce/retail, travel and lifestyle, business, and technology) [ 88 , 89 ]. Several studies that have investigated barriers to health-related mobile app usage have identified a lack of regulatory oversight, limited evidence-based literature, privacy/security concerns [ 87 ], poor functionality or incompatibility, lack of desired features, abandonment of a health goal [ 90 ], loss of interest, hidden costs, and data entry burden [ 91 ]. Mobile apps have the potential to provide affordable, convenient access, and evidence-based information to a large and broad audience. However, as noted previously, there are several limitations that need to be addressed to improve the quality and efficacy of mobile apps.

Limitations

Although the findings in this study are promising regarding the efficacy of a mindfulness-based mobile app to reduce stress and improve mindfulness and self-compassion among college students, with moderate-to-large effect sizes, several limitations must be noted. First, the sample mostly comprised white, young female adults; therefore, the generalizability of our findings may be limited. In future interventions, it is important to explore efficacy in more diverse samples (eg, race/ethnicity, education level, and chronic disease status) [ 44 , 54 , 92 ]. At the time of the intervention, Calm was only offered in English. Exploration of the efficacy of Calm with non-English speaking populations (and offered in other languages) is warranted. Second, all data collected in the study relied on self-report, which may lead to social desirability or response bias where participants respond in what they believe is a more favorable manner (although the wait-list control condition assists partially in controlling for this bias). We had a wait-list control group and were not able to control for maturation or expectation effects. Future studies warrant determining the best control condition. In our study, we tracked the minutes of meditation and not the frequency of sessions. Other studies have tracked both but only conducted analysis with one or the other [ 65 , 66 ]. Future studies should consider measuring and reporting both minutes and frequency (eg, days/sessions) related to participation. Our estimated sample size of 104 was not achieved (88 included in data analysis), but we did have adequate power because our effect sizes were strong. However, analyses of all secondary outcomes were exploratory and not sufficiently powered to detect an effect, and results for these outcomes should be interpreted with caution. Finally, we did not collect information regarding students’ school schedule, homework load, or testing schedule, and therefore, it is unclear how these factors may have played a role in students’ participation or stress scores.

The findings in this study demonstrate the efficacy of Calm to reduce stress and improve mindfulness and self-compassion in short-term contexts in stressed college students. Although direct comparisons are not possible, it appears that the degree of improvement in response to a smartphone-based mindfulness app may be similar to programs that require in-person attendance with even greater opportunity for convenient compliance and continued use. Students also reported high satisfaction using Calm to reduce stress. Currently, there is limited literature exploring the effectiveness and acceptability of mobile apps for delivering mindfulness meditation. More research in this area is needed to establish efficacy and to explore the degree to which effects are sustained in the short and long term. Particularly, we suggest future independent replications of this work to establish efficacy. The findings presented here provide important information that can be applied to the design of future studies or mental health resources in university programs. Calm may be a cost-effective, convenient, easily disseminated, and enjoyable way to manage stress among college students.

Acknowledgments

This study was supported by Arizona State University Counseling and Health Services.

Abbreviations

Multimedia appendix 1.

Conflicts of Interest: JH is currently the Director of Science at Calm (although she was engaged in this role almost 1 year after the design, collection of data, and analysis of results of the study presented in this paper). None of the authors declare any conflicts of interest.

How Calm Achieved Better Production Stability with EKS on AWS

Chris' senior-level experience with AWS best-practices fast-tracked our company's infrastructure development. His work was a crucial milestone that enabled us to scale our engineering teams and systems in step with our rapid growth. Mark Marcantano, Technical Program Manager at Calm

Calm is really taking leaps forward to ensure their customers have the best possible user experience in terms of stability and performance. Moving to AWS EKS further enabled Calm to focus on product, velocity, and user experience without concerns for the operational overhead and complexities that Kubernetes can introduce. Christopher Stobie, Principal DevOps Engineer at Toptal

The Challenge Calm Faced When an Unexpected Outage Brought Their System Down

Many companies are evolving their IT solutions to move from virtualization to containerized solutions, allowing them to abstract away differences in OS distributions and underlying infrastructures. Kubernetes is an open-sourced container management system that provides mechanisms for deploying, maintaining, and scaling containerized applications, and is the system Calm had put into place for its own operations, using the standard industry tools that existed at the time.

Calm had hired Christopher Stobie, a senior engineer through Toptal’s AWS DevOps practice, in order to supplement their current resources, as they simply didn’t have enough people with the necessary skills to manage the systems they already had in place. On Chris’s 2nd day on the job, what Calm subsequently referred to as the “Great and Terrible Outage” occurred as a result of Etcd corruption in the self-managed k8s control plane rolling the system back to its legacy infrastructure, with catastrophic consequences. “Calm was running Kubernetes entirely by themselves, which is very hard to do,” notes Chris. “But on my 2nd day at Calm, there was a two-day outage, a Kubernetes failure, and the control plane was corrupted and unrecoverable.”

Despite the dire situation, Chris was able to build a new, fully-automated cluster that would be managed by AWS instead of self-managed. He developed the system to run under EKS, creating a whole networking layer as code in Terraform and enabling Calm to be fully functional again. Because of the ease of use of the AWS solution, the migration to EKS only took about three days.

An Immediate Beneficial Outcome

Though prompted by an unexpected emergency situation, the migration had immediate results. The control plane saw improved stability immediately, and the networking overhead within the cluster was significantly reduced. In addition, the source-controlled cluster configuration allowed for quick iterations, and the IAM authorization setup was extremely easy.

The metrics for success that most companies running an IT environment would use – uptime, resiliency, ability to depend on production environments – saw substantial improvement after the switch to EKS. Previously, the cost of downtime alone was significant, with each outage costing approximately $40K per hour as Calm was unable to subscribe users. In the six months since the EKS deployment, networking has become much more reliable, and the speed at which the server returns responses means that DevOps is no longer waiting for auto-complete to come back for suggested deployments.

Bold and Innovative Thinking Pays Off

While the AWS EKS system isn’t the only one in the managed Kubernetes marketplace, it certainly showcases the depth and breadth of AWS technology and expertise. And in selecting EKS as an early adopter, Calm displayed the forward thinking that is a hallmark of the best companies, as they implement technologies that will assure the most seamless client and customer experiences. In this case, Calm recognized early on that they needed extra assistance, and turned to Toptal knowing that Toptal would have the resources needed for such a monumental undertaking. A key lesson here for other companies relates to understanding that technology itself is not enough: while this success would not have been possible without the agile superiority of AWS cloud and technology offerings, the “human talent cloud” with experience in implementation is imperative as well. This combination enabled Calm to institute a robust system that is in full production, a distinction that is relatively unique and gives them an advantage in the marketplace. Now, six months into the EKS rollout, the experience that Calm has had shows that their innovative path is one that is continuing to pay dividends and will do so for some time into the future.

Faster server responses, which saves significant time previously spent waiting for auto-complete.

Reliable networking, saving money by preventing unexpected outages.

Fully-automated cluster managed by AWS, which allows Calm to focus on other priorities.

Download a PDF version of this case study.

Reports & Guides

2024 voice of the workplace report, workforce well-being checklist: 5 tips for establishing healthy workloads, mental health benefits: education industry benchmarking report, stress & burnout tip sheet: the 5 stages of burnout & tips to address them, blueprint for workplace mental health benefits: 4 strategies for success, caregiver mental health support: a win-win for all, recession-proof your benefits: a blueprint for workplace mental health benefits, mindful manager’s checklist: 5 steps to becoming a mindful manager, harvard business review: the battle against workplace stress: how smart organizations are creating healthier environments, infographic: a mindful way out: tackling the stressed-out workplace, 7-day employee sleep challenge, infographic: future of work mental health trends report, 2023 workplace mental health trends report: the future of work, bring calm to your organization.

Connect with our Calm specialists today and see how we can help you prioritize mental health initiatives.

Artificial intelligence is being used in healthcare for everything from answering patient questions to assisting with surgeries and developing new pharmaceuticals.

According to Statista , the artificial intelligence (AI) healthcare market, which is valued at $11 billion in 2021, is projected to be worth $187 billion in 2030. That massive increase means we will likely continue to see considerable changes in how medical providers, hospitals, pharmaceutical and biotechnology companies, and others in the healthcare industry operate.

Better machine learning (ML) algorithms, more access to data, cheaper hardware, and the availability of 5G have contributed to the increasing application of AI in the healthcare industry, accelerating the pace of change. AI and ML technologies can sift through enormous volumes of health data—from health records and clinical studies to genetic information—and analyze it much faster than humans.

Healthcare organizations are using AI to improve the efficiency of all kinds of processes, from back-office tasks to patient care. The following are some examples of how AI might be used to benefit staff and patients:

Administrative workflow: Healthcare workers spend a lot of time doing paperwork and other administrative tasks. AI and automation can help perform many of those mundane tasks, freeing up employee time for other activities and giving them more face-to-face time with patients. For example, generative AI can help clinicians with note-taking and content summarization that can help keep medical records as thoroughly as possible. AI might also help with accurate coding and sharing of information between departments and billing.
Virtual nursing assistants: One study found that 64% of patients are comfortable with the use of AI for around-the-clock access to answers that support nurses provide. AI virtual nurse assistants—which are AI-powered chatbots, apps, or other interfaces—can be used to help answer questions about medications, forward reports to doctors or surgeons and help patients schedule a visit with a physician. These sorts of routine tasks can help take work off the hands of clinical staff, who can then spend more time directly on patient care, where human judgment and interaction matter most.
Dosage error reduction: AI can be used to help identify errors in how a patient self-administers medication. One example comes from a study in Nature Medicine , which found that up to 70% of patients don’t take insulin as prescribed. An AI-powered tool that sits in the patient’s background (much like a wifi router) might be used to flag errors in how the patient administers an insulin pen or inhaler.
Less invasive surgeries: AI-enabled robots might be used to work around sensitive organs and tissues to help reduce blood loss, infection risk and post-surgery pain.
Fraud prevention: Fraud in the healthcare industry is enormous, at $380 billion/year, and raises the cost of consumers’ medical premiums and out-of-pocket expenses. Implementing AI can help recognize unusual or suspicious patterns in insurance claims, such as billing for costly services or procedures that are not performed, unbundling (which is billing for the individual steps of a procedure as though they were separate procedures), and performing unnecessary tests to take advantage of insurance payments.

A recent study found that 83% of patients report poor communication as the worst part of their experience, demonstrating a strong need for clearer communication between patients and providers. AI technologies like natural language processing (NLP), predictive analytics, and speech recognition might help healthcare providers have more effective communication with patients. AI might, for instance, deliver more specific information about a patient’s treatment options, allowing the healthcare provider to have more meaningful conversations with the patient for shared decision-making.

According to Harvard’s School of Public Health , although it’s early days for this use, using AI to make diagnoses may reduce treatment costs by up to 50% and improve health outcomes by 40%.

One use case example is out of the University of Hawaii , where a research team found that deploying deep learning AI technology can improve breast cancer risk prediction. More research is needed, but the lead researcher pointed out that an AI algorithm can be trained on a much larger set of images than a radiologist—as many as a million or more radiology images. Also, that algorithm can be replicated at no cost except for hardware.

An MIT group developed an ML algorithm to determine when a human expert is needed. In some instances, such as identifying cardiomegaly in chest X-rays, they found that a hybrid human-AI model produced the best results.

Another published study found that AI recognized skin cancer better than experienced doctors. US, German and French researchers used deep learning on more than 100,000 images to identify skin cancer. Comparing the results of AI to those of 58 international dermatologists, they found AI did better.

As health and fitness monitors become more popular and more people use apps that track and analyze details about their health. They can share these real-time data sets with their doctors to monitor health issues and provide alerts in case of problems.

AI solutions—such as big data applications, machine learning algorithms and deep learning algorithms—might also be used to help humans analyze large data sets to help clinical and other decision-making. AI might also be used to help detect and track infectious diseases, such as COVID-19, tuberculosis, and malaria.

One benefit the use of AI brings to health systems is making gathering and sharing information easier. AI can help providers keep track of patient data more efficiently.

One example is diabetes. According to the Centers for Disease Control and Prevention , 10% of the US population has diabetes. Patients can now use wearable and other monitoring devices that provide feedback about their glucose levels to themselves and their medical team. AI can help providers gather that information, store, and analyze it, and provide data-driven insights from vast numbers of people. Using this information can help healthcare professionals determine how to better treat and manage diseases.

Organizations are also starting to use AI to help improve drug safety. The company SELTA SQUARE, for example, is innovating the pharmacovigilance (PV) process , a legally mandated discipline for detecting and reporting adverse effects from drugs, then assessing, understanding, and preventing those effects. PV demands significant effort and diligence from pharma producers because it’s performed from the clinical trials phase all the way through the drug’s lifetime availability. Selta Square uses a combination of AI and automation to make the PV process faster and more accurate, which helps make medicines safer for people worldwide.

Sometimes, AI might reduce the need to test potential drug compounds physically, which is an enormous cost-savings. High-fidelity molecular simulations can run on computers without incurring the high costs of traditional discovery methods.

AI also has the potential to help humans predict toxicity, bioactivity, and other characteristics of molecules or create previously unknown drug molecules from scratch.

As AI becomes more important in healthcare delivery and more AI medical applications are developed, ethical, and regulatory governance must be established. Issues that raise concern include the possibility of bias, lack of transparency, privacy concerns regarding data used for training AI models, and safety and liability issues.

“AI governance is necessary, especially for clinical applications of the technology,” said Laura Craft, VP Analyst at Gartner . “However, because new AI techniques are largely new territory for most [health delivery organizations], there is a lack of common rules, processes, and guidelines for eager entrepreneurs to follow as they design their pilots.”

The World Health Organization (WHO) spent 18 months deliberating with leading experts in ethics, digital technology, law, and human rights and various Ministries of Health members to produce a report that is called Ethics & Governance of Artificial Intelligence for Health . This report identifies ethical challenges to using AI in healthcare, identifies risks, and outlines six consensus principles to ensure AI works for the public’s benefit:

Protecting autonomy
Promoting human safety and well-being
Ensuring transparency
Fostering accountability
Ensuring equity
Promoting tools that are responsive and sustainable

The WHO report also provides recommendations that ensure governing AI for healthcare both maximizes the technology’s promise and holds healthcare workers accountable and responsive to the communities and people they work with.

AI provides opportunities to help reduce human error, assist medical professionals and staff, and provide patient services 24/7. As AI tools continue to develop, there is potential to use AI even more in reading medical images, X-rays and scans, diagnosing medical problems and creating treatment plans.

AI applications continue to help streamline various tasks, from answering phones to analyzing population health trends (and likely, applications yet to be considered). For instance, future AI tools may automate or augment more of the work of clinicians and staff members. That will free up humans to spend more time on more effective and compassionate face-to-face professional care.

When patients need help, they don’t want to (or can’t) wait on hold. Healthcare facilities’ resources are finite, so help isn’t always available instantaneously or 24/7—and even slight delays can create frustration and feelings of isolation or cause certain conditions to worsen.

IBM® watsonx Assistant™ AI healthcare chatbots can help providers do two things: keep their time focused where it needs to be and empower patients who call in to get quick answers to simple questions.

IBM watsonx Assistant is built on deep learning, machine learning and natural language processing (NLP) models to understand questions, search for the best answers and complete transactions by using conversational AI.

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UX Case Study: Calm Mobile App
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UX Case Study: Calm Mobile App
UX Case Study: Calm Mobile App
UX Case Study: Calm Mobile App
Calm App Business and Revenue Model Explained

COMMENTS

UX Case Study: Calm Mobile App
Welcome to the first UX case study of 2018!Codal hopes you have had a fantastic start to the new year. For those who have followed along with our long-running UX Case Study series, welcome back, and to new readers, welcome aboard.. For those who are just joining us: UX Case Study is a regular series written by UX design agency Codal, where we conduct in-depth examinations of different mobile apps.
Discover Calm's Billion Dollar Marketing Strategy
Calm was founded in 2012 by Alex Tew & Michael Acton Smith. In 7 years, they grew from Startup to a $1 Billion valuation company. Perhaps best known for becoming the World's First Mental Health ...
How Calm Built a $2B Media, Content Marketing ...
It's no wonder Calm has attracted sterling 5-star reviews from over 1.5 million users. We took a closer look at Calm's marketing engine and found that it's earned the trust and money of over 4 million customers through three major areas of focus: branding, content, and YouTube SEO. This case study is a deep dive into Calm's marketing ...
Unify + Calm. UX Case Study
Partnering with Calm Calm leads the wellness space as the #1 meditation and sleep app. The main reason we chose Calm as a potential partner is because of their expansive library of resources.
UX Design Case Study
The following case study is a conceptual UX design improvement made to the Calm app. For those of you that have not heard of Calm, it is an app for practising daily mental well-being. Primarily it is used for meditation and as a sleep aid. This case study will focus on Sleep Mode. Sleep Mode is where the apps sleep aid content lives.
How Calm Increased Retention 3X After Switching to Amplitude
Calm is a mindfulness meditation app that brings clarity and peace of mind to their users. With a solid user base on iOS and Android, Calm needed to understand how current features were driving retention and what they could do to improve it. They worked with Amplitude to analyze their user behavior data and discover ways to improve Calm's ...
Case study: New feature for the app Calm.
The group, who are studying together at Ironhack Berlin, are developing a project to add a new feature related to food and its relationship with the mind in the Calm application. We start with a video interview where I tell them a little bit about my life, my studies, where I work, my relationship with food, my relaxation methods and my ...
Headspace vs. Calm: A Mindful Competition
Abstract. By 2021, the mindfulness app wars reached their apex. Over 2,000 meditation apps were available to consumers, but two apps, Headspace and Calm, dominated the space, jointly holding about 70% of the total market. Headspace had established itself as the approachable educator for novice meditators. Headspace also invested heavily in ...
Customer Stories
Global Benefits & Well-Being Program Manager at Zendesk. We found that Calm Business is a tangible, easy-to-use benefit with immediate effects. It's really important that we proactively reduce stress and anxiety and offer a preventive mental health tool as a first step for employees who are struggling. That's how I see Calm Business, and I ...
Becoming happier and healthier
The #1 app for sleep, meditation and relaxation - Calm's transformational content supports users across seven languages in more than 190 countries. And it goes without saying that when they approached MOO to help support their continued growth we jumped at the chance to be part of the journey. "MOO is a nimble and efficient partner for us".
Headspace vs. Calm: A Mindful Competition
By 2021, the mindfulness app wars reached their apex. Over 2,000 meditation apps were available to consumers, but two apps, Headspace and Calm, dominated the space, jointly holding about 70% of the total market. Headspace had established itself as the approachable educator for novice meditators. Headspace also invested heavily in athletics partnerships, business-to-business services, and in ...
Headspace or Calm: Which One Will Help You Achieve Mindfulness?
Headspace's story is a core discussion in our "Headspace vs. Calm" case study, as the app has grown into one of the leading mindfulness apps in the world, having over 70 million users spanning over 190 countries. Aside from the vast array of specific meditations, Headspace offers other features to enhance its users' overall well-being.
Calm App Review: Features, Costs, Pros, and Cons
Several studies have examined Calm's benefits and efficacy. For example, in a 2019 study involving 88 college students, Calm reduced stress and improved mindfulness and self-compassion skills.
Use of the Consumer-Based Meditation App Calm for Sleep Disturbances
Introduction. A large body of research suggests that mindfulness has positive effects on both mental and physical health [1-3].In recent years, there has been a noticeable surge in the use of commercially available mindfulness-based smartphone apps [4,5], and there is growing evidence that individuals are using these apps specifically to help improve their health, mood, and sleep [].
Efficacy of the Mindfulness Meditation Mobile App "Calm" to Reduce
The purpose of this study was to test the efficacy of an 8-week mindfulness meditation intervention delivered via a consumer-based mobile app (ie, Calm) as compared to a wait-list control group on stress in college students with elevated stress (≥14 on the Perceived Stress Scale [PSS]). We also explored the feasibility (ie, acceptability and ...
Case Study: How Toptal Helped Calm After a Massive Outage
Download a PDF version of this case study. This business case looks at how Calm, the company with the #1 meditation app, worked with Toptal to implement EKS, after corruption brought on a two-day outage and control plane corruption. The migration led to better stability, reduced networking overhead, and improved metrics such as uptime and ...
Resources
Customer Story. Ogilvy Case Study. Ogilvy partnered with Calm to implement a Mindful Manager Program that resulted in 60% of managers reporting reduced stress.
Headspace vs. Calm: Which App Is Right for You?
The Calm app receives a 4.8 out of 5 rating on the App Store with about 1.5 million ratings submitted. On Google Play, the app receives a 4.3 rating with about 485,000 ratings total.
Clinical Studies
How others found success with Calm. Clinical Studies. ... Customer Story. Ogilvy Case Study. Ogilvy partnered with Calm to implement a Mindful Manager Program that resulted in 60% of managers reporting reduced stress. ... Leveraging a Consumer-Based Product to Develop a Cancer-Specific Mobile Meditation App: Prototype Development Study
Creating an app for better sleep
For example, apps like Calm or Headspace to a good job of attempting to help people build good habits and curb bad ones, if you run to meditation for comfort from the harsh world. One application that I added that may seem out of place is the app tracking application Moment that mainly gauges the usage of a user's activity of the applications ...
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A recent study found microplastics in every human testicle examined, raising significant concerns about the impact on the male reproductive system. People magazine reported that the study ...
Microsoft Advertising marketing case studies
View marketing case studies of how business use Microsoft Advertising products to achieve their business goals.
Reports & Guides
Bring Calm to your organization. Connect with our Calm specialists today and see how we can help you prioritize mental health initiatives. Check out our HR and employee well-being content library of e-books, workplace assessments, strategy development guides, and more.
The Benefits of AI in Healthcare
A recent study found that 83% of patients report poor communication as the worst part of their experience, demonstrating a strong need for clearer communication between patients and providers. AI technologies like natural language processing (NLP), predictive analytics, and speech recognition might help healthcare providers have more effective communication with patients.
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UX Case Study: Calm Mobile App

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How Calm Built a $2B Media, Content Marketing, & YouTube SEO Empire

How Calm Grew To Dominate The Meditation Industry

How Calm Uses Content To Drive Traffic & Backlinks

How Calm Uses YouTube SEO to Increase Organic Reach

Calm’s Media Strategy: Leveraging Celebrity Influence

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Introduction

Design Thinking Process

Finding the Right Problem

Methodology

Interviews

Secondary Research

Observation

Competitors

Best in class examples for search

Finding the Right Solution

Testing and Feedback

Recommendations for Future Iterations.

How Calm Increased Retention 3X After Switching to Amplitude

Increase in retention

Experiment: Does Prompting Users to Set Daily Reminders Increase Retention?

Result: Increase Retention by 3x

Actionable Insights Help Backstage Connect the Right Talent to the Right Opportunity

Enable Teams to Run with Data by Focusing On the Questions That Matter

2021 Datamonster of the Year: ABS-CBN’s Robbin Brillantes

How Lindblad Expeditions Turns Increased Digital Adoption into Once-in-a-Lifetime Experiences

Headspace vs. Calm: A Mindful Competition

About The Author

Ayelet Israeli

Becoming happier and healthier – a Calm case study

The mental health gap

Adapting to growth

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Headspace vs. Calm: A Mindful Competition

Learning Objectives

Headspace or Calm: Which One Will Help You Achieve Mindfulness?

Headspace: The pioneer of mindfulness on your phone

The Scientific Case for Meditation and Mindfulness

Calm: The Award-Winning app for relaxation and sleep

Headspace vs. Calm: A mindful competition of features, content, and price

Headspace vs. Calm: Pricing Comparison

Headspace vs. Calm: Content Comparison

Other Content

Efficacy of the Mindfulness Meditation Mobile App “Calm” to Reduce Stress Among College Students: Randomized Controlled Trial

Christine Glissmann

Linda Larkey

Megan Puzia

Associated Data

Conclusions

Trial Registration

Introduction

Ethics Approval

Study Design

Sample Size

Recruitment and Selection

Randomization and Blinding

Intervention Group

Wait-List Control Group