Draft:Sleep app

The earliest sleep-tracking apps appeared in the late 2000s, alongside the broader growth of consumer health technology. Applications such as Sleep Cycle and Sleep as Android gained popularity for offering affordable, user-friendly alternatives to clinical sleep studies. By the 2020s, the sleep app market had expanded to include guided meditation apps like Calm and Headspace, as well as more data-intensive platforms compatible with smartwatches and fitness bands.

Sleep apps vary widely in functionality, but common features include **sleep tracking**, which monitors sleep stages (such as light, deep, and REM) using smartphone accelerometers or data from wearable devices. Some apps employ **smart alarms** intended to wake users during lighter sleep phases, potentially reducing sleep inertia. Others offer **audio content**, such as ambient sounds, white noise, or guided meditations, to facilitate relaxation before bedtime.

Additional tools include **manual or automatic sleep logs**, which can help users identify long-term sleep patterns, and **personalized insights** generated through algorithmic analysis of sleep data. A number of apps incorporate techniques from cognitive behavioral therapy for insomnia (CBT-I), aiming to promote behavioral change. Many also allow integration with smartwatches or fitness trackers, enabling more detailed physiological monitoring, such as heart rate and movement during sleep.

The effectiveness of sleep apps remains a subject of ongoing research. A 2024 meta-analysis of randomized controlled trials concluded that digital sleep interventions delivered via smartphones can moderately improve insomnia symptoms and users’ perceived sleep quality.{{cite journal

| last1=Choi

| first1=Young

| last2=Lee

| first2=Min Ji

| title=Effectiveness of Smartphone Applications for Sleep: A Meta-Analysis

| journal=Sleep Medicine Reviews

| year=2024

| volume=122

| pages=237–244

| doi=10.1016/j.sleep.2024.08.025

| pmid=39213858

}}

However, other studies have questioned the clinical utility of most commercially available apps. A 2023 review found that only a small percentage of sleep apps utilized evidence-based behavior change techniques or underwent scientific validation.{{cite journal

| last1=Ko

| first1=Na-Eun

| last2=Lim

| first2=Hyeyoung

| title=Evaluation of Sleep Mobile Health Applications Using the Behavior Change Technique Taxonomy

| journal=Journal of Medical Internet Research

| year=2023

| volume=21

| issue=6

| pages=757–773

| doi=10.2196/45329

| doi-access=free

| pmid=36628485

| pmc=10330944

}}

Many sleep apps rely on proprietary algorithms that have not been independently reviewed, and their results often do not align with those of clinical sleep assessments such as polysomnography.{{cite journal

| last=Becker

| first=Spencer P.

| title=Sleep Tracking Technology: A Review of Consumer Sleep Apps

| journal=Current Sleep Medicine Reports

| year=2021

| volume=14

| issue=1

| pages=83–86

| doi=10.1007/s40675-021-00213-1

| doi-broken-date=17 June 2025

| pmid=34104344

| pmc=8157780

}}

Privacy is also a concern. Some apps collect and share sensitive user data—including sleep times, habits, and biometric data—with third parties for advertising or analytics purposes, often without clear disclosure.{{cite journal

| last=Papageorgiou

| first=Apostolos

| title=Privacy Risks of mHealth Apps: A Systematic Analysis

| journal=JMIR mHealth and uHealth

| year=2021

| volume=9

| issue=3

| pages=6147–6191

| doi=10.1021/acsnano.1c01146

| pmid=33739822

| pmc=8023021

}}

Additionally, a phenomenon known as orthosomnia—a type of sleep anxiety driven by obsessive self-monitoring—has been reported among some users.{{cite journal

| last=Baron

| first=Kelly G.

| title=Orthosomnia: Are Some Patients Taking the Quantified Self Too Far?

| journal=Journal of Clinical Sleep Medicine

| year=2017

| volume=13

| issue=2

| pages=351–354

| doi=10.5664/jcsm.6472

| pmid=27855740

| pmc=5263088

}} Clinicians have also expressed concerns about the difficulty of integrating consumer sleep data into medical practice, as these devices often lack standardized metrics or clinical accuracy.{{cite journal

| last=Cheng

| first=Penelope

| title=Integration of Consumer Sleep Data in Clinical Practice: Challenges and Opportunities

| journal=Sleep Health

| year=2022

| volume=8

| issue=4

| pages=380–386

| doi=10.1016/j.sleh.2022.05.001

| pmid=35750631

}}

• Mobile health
• Sleep hygiene
• Wearable technology
• Quantified self
• Digital therapeutics

{{reflist}}

• [https://pubmed.ncbi.nlm.nih.gov/?term=sleep+app&sort=date Recent research on sleep apps – PubMed]

{{Draft categories|

:Category:Sleep

:Category:Medical software

:Category:Wearable computers

}}

{{Drafts moved from mainspace|date=May 2025}}