Better Sleep at Our Fingertips

With roughly 30% of the U.S. population using consumer health technologies that track sleep, mobile sleep health apps have emerged as a scalable and affordable tool with the potential to improve sleep health for the general population.¹ While wearables and other hardware devices often get the spotlight, the power of mobile apps—driven by behavioral change techniques and personalized advice—should not be overlooked. These apps, which often accompany hardware-based devices, are taking sleep tracking to the next level, guiding users from data collection to actionable sleep improvement strategies grounded in evidence-based behavioral techniques.

According to a 2023 survey from the American Academy of Sleep Medicine, most people who’ve used a wearable device or mobile health app found it to be helpful (77%) and more than two-thirds (68%) even reported changing their behavior because of what they learned from using it.² With some apps harnessing validated novel sleep measurement methods such as sonar or integrating data from wearables, lifestyle products are now equipped with data that was once confined to a clinic or a research study.³

It’s also clear: consumers are seeking sleep advice and solutions on their own, unguided. In fact, a simple review of Google’s search and shopping trends since 2013 show that—perhaps unexpectedly—“sleep” is the most searched health term relative to mental health, exercise, and nutrition. A recent McKinsey report further reported that despite consistently ranking as the second-highest health and wellness priority for consumers, sleep is also the area with the most unmet needs.⁴

Interest is widespread—but guidance is needed for adoption across both the sleep medicine and consumer community.

Leveraging Sleep Data

There have been several published reviews and position statements that have explicitly highlighted the limitations of consumer sleep technology, including mobile apps, primarily for the diagnosis of sleep disorders.⁵ Despite the diagnostic limitations of consumer sleep technology emphasized in these published guidelines, certain sleep apps can serve as a useful adjunct behavioral tool for individuals with sub-clinical sleep problems or a treated sleep disorder. They must include:

• An acceptable degree of measurement accuracy—or integrated data from a device with an acceptable degree of measurement accuracy.
• Evidence-based behavioral sleep improvement features.

Historically, consumer apps initially focused on sleep tracking, simply recording sleep duration, stages, and cumulative measures of sleep quality. In fact, a 2018 systematic review of smartphone applications to support sleep self-management found that only eight of the 73 investigated apps had a feature to intervene or improve sleep.⁶

While data collected may be valuable, many users are left wondering what to do with it —and whether or not smartphone app data is even accurate.⁷ Nonetheless, there are clear use-cases that leverage objective sleep data to help users become more aware of their sleep and daily habits.

1. Data obtained from apps can serve as a powerful tool for opening discussions around the importance of sleep health. For example, SleepScore Labs’ on-demand Doctor’s Report synthesizes retrospective sleep data into a comprehensive PDF report mirroring the format of a typical clinical sleep study report. This allows users to easily download and share the data with their primary care physician.
2. Clinicians can use these objective metrics to substantiate their advice, helping patients understand the tangible impact of their lifestyle on sleep quality. For example, Oura and SleepScore offer an Experiments feature where users can try a sleep hygiene “challenge” (e.g., reduce caffeine intake) and see how it impacts their sleep over time.
3. By visualizing the correlation between sleep and daily habits, users can become more aware of their personal sleep hygiene. The RISE App and Whoop both include a Daily Journals feature allowing a user to tag a certain behavior and see how that behavior is associated with sleep over time through intuitive and engaging data visualizations.

The real value of sleep apps lies not just in their ability to analyze and contextualize hyper-longitudinal sleep data, but also to provide personalized and dynamic recommendations alongside bespoke tools for improving sleep based on that objective data. For example, a recent meta-analysis found that sleep apps produced medium to large effects over controls in improving insomnia and sleep disturbances—offering a scalable tool to manage sleep disturbances.⁸

These apps are no longer just tools for passive data collection; they have the potential to drive behavioral change by leveraging sleep data to suggest practical, evidence-based solutions—all in a highly engaging and dynamic user interface.

Sleep Coaching in the Digital (App) Age

One of the biggest challenges in sleep improvement is that one-size-fits-all recommendations may not work for—or be followed by—many individuals. There is no silver bullet, particularly among poor sleepers without sleep disorders where sleep symptoms are so varied. Sleep is deeply personal and influenced by a wide variety of factors, from individual chronotypes to daily routines and environmental conditions.

For example, a recent study found that there was no one single sleep hygiene factor that was associated with objectively measured sleep.⁹ It was only when sleep hygiene factors were combined as an aggregate score that associations with total sleep time, REM, and overall sleep scores were observed. Healthy sleep behavior, such as those found in static sleep hygiene checklists, should not be defined by one single behavior, but rather by the sum of their parts.

That is why certain consumer sleep apps leverage evidence-based principals from cognitive behavioral therapy for insomnia (CBT-i) to provide personalized and automated sleep coaching that go beyond static sleep hygiene lists. For example, SleepScore’s advice engine, which is backed by a randomized controlled trial showcasing improvements in perceived sleep and stress, leverages a user’s objectively measured sleep data, daily sleep log data, and contextual environmental data to provide users with dynamic and personalized behavioral sleep recommendations and education including bite-sized nuggets related to stimulus control, sleep and circadian hygiene education, cognitive restructuring, relaxation training, and more.¹⁰

Similarly, Samsung’s Galaxy Watch phenotypes users into one of eight “sleep animals” and offers personalized sleep coaching based on the phenotype including missions, checklists, sleep-related articles, meditation guidance, and sleep reports. And beyond coaching, other apps such as Calm and Headspace both offer bespoke and evidence-based relaxation training and mindfulness meditation tools to improve pre-sleep affect, with a randomized controlled trial showing Calm’s efficacy in improving fatigue, daytime sleepiness, and pre-sleep arousal in adults with sleep disturbances.¹¹

It is critical that sleep improvement apps intended for non-clinical populations (i.e., consumers) leverage aspects of CBT-i but don’t claim or attempt to replace it—both for legal and ethical reasons. Digital CBT-i programs, such as Sleepio and Somryst, are prescription-only digital therapeutics that are distinct from consumer sleep technologies and apps. However, certain apps, such as Sleep Reset and Stellar Sleep which combine personalized coaching, education, and tracking, hover somewhere in between CBT-i programs and consumer apps as they are not classified as a digital therapeutic but market their programs to individuals with insomnia.¹²

Nonetheless, among consumer app users with a suspected sleep disorder that are non-responders to behavioral modification, tools should be in place to offer screening options or education on the importance of screening for undiagnosed sleep disorders. For example, both the Samsung Galaxy Watch and Apple Watch have recently received FDA clearance for their sleep apnea detection features.^13-14 On the other hand, the SleepScore App contains three clinically validated self-report screeners for on-demand screening of insomnia, restless legs syndrome, and obstructive sleep apnea.¹⁵

Combatting Sleep Data Anxiety

While sleep apps with robust measurement and evidence-based behavioral features may benefit consumers, one challenge they face is preventing “orthosomnia”—a term coined to describe the anxiety caused by obsessing over sleep data.

In 2022, Amber Carmen Arroy, PhD, and colleagues published a systematic review on the implementation of behavior change techniques in consumer sleep tech and found strong evidence that mobile health applications were indeed effective at improving sleep.⁸ Most unexpected, however, was that regardless of the sleep dimension or measurement assessed, there were no negative effects of interventions on sleep reported across all apps included in the systematic review—an arguably surprising finding given preliminary evidence of orthosomnia among some “quantified-selfers”.¹⁶

If an app simply informs users they’ve had poor sleep without offering a clear path to improvement, it can exacerbate sleep issues rather than solve them. A 2018 study by University of Oxford researchers found that when participants received fake negative sleep data (i.e., showing that they slept worse than they actually did) they had significantly impaired alert cognition and felt sleepier and more fatigued compared with a group that was given positive feedback.¹⁷

To avoid this, apps need to focus on delivering context-sensitive, empathetic, and actionable insights, rather than just raw data. Telling someone they only slept four hours is simply not good enough—apps need to provide clear guidance on how to take action without feeling overwhelmed.

Nonetheless, there may be a subset of the population that should not be using any kind of consumer sleep technology. For example, patients with insomnia who are likely already dealing with cognitive barriers and distorted beliefs about their sleep may not benefit from consumer sleep technology, especially if they’re likely to see objective data that can be negatively reinforcing.

Looking ahead, emerging technologies like personal health language models and generative AI hold the potential to revolutionize how we address these challenges. By leveraging the power of AI, we can better identify who is most likely to benefit from consumer sleep tech, while also offering contextualized, personalized guidance. These tools will enable more precise, tailored sleep health coaching, helping individuals to focus on actionable insights rather than becoming overwhelmed by raw sleep data.

The Future of Mobile Sleep Apps

While this article isn’t focused on generative AI, its growing influence on sleep technology is too important to overlook.

Generative AI is poised to play a transformative role in consumer sleep technology. Google’s recently announced personal health large language model (LLM), for example, showed nearly expert-level performance in personalized sleep health management.¹⁸ While human sleep coaches still slightly outperformed AI in providing recommendations, trained personal health LLMs are closing the gap.

With the ability to continuously learn from wearable data and improve over time, these models show immense potential for long-term sleep coaching. Companies like Thrive AI Health, Oura, and Whoop already have live AI-powered health coaches available, leveraging existing generative AI models to provide personalized insights and recommendations to users.

AI’s ability to deliver context-aware and dynamic recommendations based on a user’s environment could further enhance its relevance and effectiveness in promoting better sleep outcomes.¹⁹ However, challenges remain, including reliance on user input, limitations in contextual understanding, and a lack of transparency in both AI decision-making and their efficacy. These hurdles must be addressed to build user trust and improve engagement.

As generative AI continues to evolve, it holds the promise of reshaping the landscape of sleep health, offering personalized, data-driven insights that could rival traditional human coaching methods.

Conclusion

Mobile sleep health apps are revolutionizing the way we think about scaling improvement for population sleep health. By moving beyond simple tracking—and integrating behavioral change techniques, personalization, and responsible-AI—these apps can guide users toward healthier sleep habits.

As digital health companies lean into evidence-based behavioral tools and continue to iterate their products and validate their efficacy, mobile apps will become an increasingly important part of sleep health management—helping users go beyond tracking to not just understand their sleep, but actively improve it.

By: Elie Gottlieb, PhD

Source: SleepWorld Magazine November/December

References

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