Unlocking the Power of Sleep: How AI Revolutionizes Digital Sleep Management
This story was originally published at Neurons Lab, on May 29, 2023.
Last summer, Apple added an expansion to the iPhone health app by including additional sleep-tracking features such as sleep stages and metrics.
What does this mean for the digital sleep management market?
From sleep-tracking wearables to sophisticated data analytics, groundbreaking technologies are paving the way for personalized interventions and enhanced sleep experiences.
However, navigating this rapidly evolving market comes with its fair share of challenges. For example, inaccurate sleep tracking, limited personalization, and unexplained recommendations can hinder the effectiveness of digital sleep management solutions. This is why addressing these pain points head-on is crucial and will ensure that your offerings rise above the competition.
In this article, we delve into the staggering growth of this market, the driving forces behind it, and how you can tap into its vast potential.
State of the digital sleep management market
The digital sleep management app market has experienced substantial growth in recent years. Last year, 2022, the market was valued at $16.08 billion USD, with a projected compound annual growth rate (CAGR) of 22.6% from 2023 to 2033.
What is driving the digital sleep management market?
There are a few things to consider:
1. Rising prevalence of sleep disorders: According to the 2019/2020 Phillips Global Sleep Survey, only 55% of adults are satisfied with their sleep.
2. Increased awareness of the importance of quality sleep: The pandemic and remote work have significantly changed our lifestyles and made quality sleep even more important. In the same survey, 58% of surveyors said they would use telehealth for sleep-related concerns in the future.
3. Advancements in wearable technology and data analytics: New and innovative products like smart beds, sleep-tracking devices, and sleep-enhancing apps are emerging to meet user needs for quality sleep. According to McKinsey stats, most of these devices focus on monitoring the quality of sleep, motion, and biosignals.
However, the software could also provide personalized recommendations and tips for improving sleep, making it an attractive tool for individuals looking to improve their overall well-being.
For example, see the following list of companies in the digital sleep management market that have recently caught our attention.
These companies use different monitoring strategies to assess users’ sleep quality, namely:
1. Oura’s wearable ring tracks and analyzes several aspects of the user’s health such as sleep patterns, activity levels, and heart rate variability. Doing so provides personalized insight and recommendations for better overall wellness.
2. Sleep Cycle is a mobile sleep tracker that uses sounds to monitor sleep quality, wake the user up at an appropriate time, and suggest optimized sleep programs. Additionally, the tracker analyzes the correlation between sleep quality and various user events and states.
3. Nanit’s baby video monitor lets parents track their baby’s health, wellness, and development, from sleep and breathing to growth, delivering personalized insight and advice.
4. The next generation of medical-grade sleep diagnostics from Onera Health is a comfortable-to-wear and wireless system for sleep monitoring from the comfort of one’s home.
Companies also offer varied intervention strategies to improve sleep quality:
5. Loóna is an award-winning sleep intervention app that can serve as a healthy wind-down ritual, helping people calm their minds before bed. Loóna is known for its enchanting and immersive experiences called Sleepscapes.
6. Sleepio from Big Health is a digital therapy (Cognitive Behavioral Therapy) program that has been scientifically proven and clinically validated to help people sleep well without pills or potions. Sleepio is a six-week sleep improvement program designed by sleep expert Prof Colin Espie.
7. Eight Sleep’s Pod Mattress is a smart mattress that uses Active Grid technology and an app to regulate temperature. The mattress also tracks sleep, allowing customers to learn more about how they sleep and how to improve their quality of sleep.
8. Asleep develops acoustic-based precise sleep quality tracing methods and deep integration with smart devices and smart home systems to provide a real-time sleep care environment.
Are we aware of digital sleep management’s challenges and pain points?
While the market and the range of products are growing, it is still fairly unsaturated with some research suggesting that this is the area with the greatest unmet consumer need (according to McKinsey research). This fact leaves plenty of room for new innovative solutions.
However, if you’d like to tap into this space, it’s crucial to acknowledge the challenges and pain points that most digital sleep management solutions face.
Here are some key factors to keep in mind:
- Inaccurate sleep tracking (both estimating sleep metrics and sleep staging) and sleep scores may only worsen orthosomnia and the anxiety associated with getting a perfect sleep score.
- Lack of personalization and customization of digital interventions results in low solution effectiveness and low customer engagement.
- Unexplained recommendations with no clear reasoning behind them can make users hesitant to follow those recommendations which can result in low adherence.
- Lack of technology evidence or understanding of how the product works can hinder compliance with recommendations as well.
How do you ensure your digital sleep solution is effective and, most importantly, prove this to the user?
Here, we have you covered. First, we created a list of the main pain points and solutions to consider. If you can check off all the points below, you can be sure that you’re on the right track.
1. Provide accurate sleep assessment
Many sleep-related products offer sleep-tracking features, but if the tracking is inaccurate, this can lead to incorrect recommendations and inefficiency.
Those recommendations should be based on well-thought-out data analysis in order for your digital solution to bring real value to users and influence behavioral and lifestyle changes. This research ideally should combine data from various sensors and sources such as wearable devices, smartphones, and smart beds.
Machine learning (ML) models can be used to identify patterns and trends in this data for accurate sleep-tracking metrics. ML algorithms can also analyze sleep data to measure different stages of sleep, including deep sleep, light sleep, and REM sleep, by analyzing patterns such as changes in heart rate, brain activity, and body movement.
Thus, using a combination of various data sources and ML-powered data analytics could significantly enhance sleep quality measurement and increase the accuracy of sleep staging and metrics estimation.
2. Generate highly personalized recommendations
Everyone’s sleep needs differ, so if a product doesn’t offer customization options or adjust to individual needs, it may be less effective for some users. As a result, these products may face a lack of engagement.
For starters, batch training techniques are perfect for learning from large customer groups and generating personalized care plans for individual users based on their sleep patterns and preferences.
Additionally, reinforcement learning algorithms can make decisions based on feedback from individual users, considering factors such as sleep quality improvements and changes.
Therefore, combining these ML techniques ensures that all the recommendations suggested to a user are highly personalized, ensuring that their unique needs are being addressed.
3. Make sure all the recommendations are explained
You have generated just the right recommendation for a user, but what’s next? Without clear reasoning behind sleep recommendations, users may hesitate to trust them. To increase customer trust and engagement, it is crucial to provide recommendations along with the rationale.
ML-based data analytics can be leveraged here as well to identify and evaluate predictors for sleep recommendations such as behavioral and environmental factors. Regression analysis and clustering algorithms can then evaluate these predictors and determine the most critical factors for optimal sleep outcomes.
To enhance recommendation interpretability, you could utilize advanced language models such as GPT-4 to provide reasonable explanations to users.
This approach ensures that users understand the reasoning behind each recommendation, fostering trust and confidence in the app.
4. Create evidence of the technology (proof-of-work)
It is important that users trust not only their individual sleep program and lifestyle recommendations but the product itself. Lack of technology evidence or an understanding of how the product works can hinder adherence to recommendations as well.
By utilizing data analytics and regression analysis to identify sleep quality patterns and trends, it is possible to demonstrate sleep quality correlation with adherence to recommendations.
By conducting multifactor analysis, comprehensive explanations, and learning can be provided to users.
By gathering and analyzing large amounts of data, you could build a robust statistical proof of technology efficacy, which can be shared with users to instill confidence in the app’s capabilities.
Implementing AWS solutions for enhanced personalization and interoperability in sleep management applications
By integrating AWS services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, and Amazon QuickSight, we can create a robust and scalable infrastructure for a sleep management data analytics platform. This AWS solution supports data ingestion, processing, analysis, and visualization and could enhance personalization and interoperability in sleep management applications.
The integration of a BI dashboard in Amazon QuickSight plays a central role in this solution by providing advanced analytics and visualization features that utilize data from HealthKit. Through this dashboard, users can gain valuable insight into their sleep metrics and overall sleep quality, both on a nightly basis and over longer periods of time.
By visualizing HealthKit data within the BI dashboard, users can effortlessly recognize patterns in their sleep data and understand the primary factors influencing their sleep. These valuable insights empower users to make informed choices regarding their sleep habits, ultimately assisting them in achieving their sleep goals with greater efficacy.
AWS solutions offer numerous benefits for sleep management applications, including
1. Enhanced personalization: By leveraging AI/ML technologies and HealthKit data, you could provide highly personalized sleep plans, tailored sleep recommendations, and customized coaching experiences that align with users’ goals, preferences, and physiological information.
2. Improved interoperability: The seamless integration of multiple AWS services enables smooth data flow across different devices and platforms, fostering a cohesive and unified user experience in sleep management apps.
3. Powerful analytics and visualization: By utilizing the BI dashboard in Amazon QuickSight, you could help your users with a deeper understanding of their sleep progress, patterns, and areas that need improvement.
4. Scalability and flexibility: With AWS infrastructure, you can effortlessly scale up or down your sleep management app based on demand, ensuring consistent performance and cost-efficiency in managing sleep-related data.
5. Data security and compliance: AWS solutions adhere to strict security and compliance standards, ensuring the safeguarding and responsible handling of users’ sensitive sleep and health data within sleep management apps.
By implementing AWS solutions like this, sleep management applications could create a more engaging and personalized experience for users, promoting sustained behavior change and ultimately leading to improved sleep quality.
Wrapping everything up
In conclusion, the digital sleep management app market is booming, driven by the increasing prevalence of sleep disorders and the growing awareness of the importance of quality sleep.
To ensure your app stands out and proves its effectiveness to users, focus on accurate sleep assessment through advanced data analysis and machine learning techniques. Personalized recommendations tailored to individual needs will enhance user engagement and satisfaction. In addition, you can foster trust and confidence in your app by providing explainable recommendations and building a robust statistical proof of the technology’s efficacy.
Ready to integrate AI features into your digital sleep management app? Get started and revolutionize how people manage their sleep for better health and well-being.
Looking for a partner to co-create a disruptive AI solution?
The Neurons Lab team combines industry experts, data scientists, and doctorates in science from the startup ecosystem, all driven by a “speed as a habit” mindset. We match our experience and knowledge to your unique product needs, empowering you to gain a distinct competitive edge in the industry.
Feel free to contact us at info@neurons-lab.com or get in touch for a free consultation.
