Smartphone Applications to Support Sleep Self-Management

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Study Objectives: Mobile health (mHealth) tools such as smartphone applications (apps) have potential to support sleep self-management. The objective of

this review was to identify the status of available consumer mHealth apps targeted toward supporting sleep self-management and assess their functionalities.

Methods: We searched four mobile app stores (iTunes Appstore, Android Google Play, Amazon Appstore, and Microsoft Appstore) using the terms “sleep”,

“sleep management,” “sleep monitoring,” and “sleep tracking.” Apps were evaluated using the Mobile Application Rating Scale (MARS) and the IMS Institute

for Healthcare Informatics functionality scores.

Results: We identied 2,431 potentially relevant apps, of which 73 met inclusion criteria. Most apps were excluded because they were unrelated to sleep self-

management, simply provided alarm service, or solely played relaxation sounds in an attempt to improve sleep. The median overall MARS score was 3.1 out

of 5, and more than half of apps (42/73, 58%) had a minimum acceptability score of 3.0. The apps had on average 7 functions based on the IMS functionality

criteria (range 2 to 11). A record function was present in all apps but only eight had the function to intervene. About half of the apps (33/73, 45%) collected

data automatically using embedded sensors, 27 apps allowed the user to manually enter sleep data, and 14 apps supported both types of data recording.

Conclusions: The ndings suggest that few apps meet prespecied criteria for quality, content, and functionality for sleep self-management. Despite the

rapid evolution of sleep self-management apps, lack of validation studies is a signicant concern that limits the clinical value of these apps.

Keywords: consumer health informatics, mHealth, self-management, sleep

Citation: Choi YK, Demiris G, Lin SY, Iribarren SJ, Landis CA, Thompson HJ, McCurry SM, Heitkemper MM, Ward TM. Smartphone applications to support

sleep self-management: review and evaluation. J Clin Sleep Med. 2018;14(10):1783–1790.

INTRODUCTION

Sleep that is adequate in quality and duration is essential for

life, health, and well-being. Yet, sleep deciency is quite com-

mon and pervasive in modern society. Sleep deciency is de-

ned as a decit in the quantity or quality of sleep obtained

versus the amount needed for optimal health, performance,

and well-being.”

Sleep deciency most often manifests as re-

current disrupted or fragmented sleep, an inadequate amount

of sleep, or sleep of poor quality from a sleep disorder such as

insomnia or sleep apnea. Sleep deciency associated with poor

sleep quality is common in chronic illness and directly con-

tributes to (1) various problems in carrying out daytime func-

tions, (2) reduced health-related quality of life, (3) increased

health care utilization,

2,3

and (4) is associated with increased

morbidity and mortality.

4,5

Often adults and children with

chronic illness, who report poor quality of life, have substan-

tial sleep deciency.

Self-management is an ongoing, perhaps lifelong, process

with a focus on self-identied needs or problems that require

continual monitoring and enacting appropriate actions that may

require interaction with others including health care providers.

In the eld of interdisciplinary sleep medicine, interventions to

treat sleep deciency are mainly provider initiated. As an al-

ternative to provider-directed interventions, sleep investigators

have tested the use of self-help behavioral interventions based

REVIEW ARTICLES

Smartphone Applications to Support Sleep Self-Management:

Review and Evaluation

Yong K. Choi, MPH

; George Demiris, PhD

; Shih-Yin Lin, PhD, MPH, MM

; Sarah J. Iribarren, PhD, RN

; Carol A. Landis, PhD, RN

;

Hilaire J. Thompson, PhD, RN, ARNP, CNRN, AGACNP-BC

; Susan M. McCurry, PhD

; Margaret M. Heitkemper, PhD, RN

; Teresa M. Ward, PhD, RN

School of Nursing, University of Washington, Seattle, Washington;

School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania

pii: jc-18-00177 http://dx.doi.org/10.5664/jcsm.7396

on the self-management approach.

Although self-management

approaches have shown small to moderate eect sizes,

7–9

much

greater gains could potentially be achieved with the use of

self-management approaches, such as patient-centered self-

monitoring technologies, and strategies that engage individu-

als and families in interaction with providers and/or with other

individuals with insomnia. The advent of developing patient-

centered self-management sleep interventions to support main-

tenance of therapy over time has been considered a “new era”

in the sleep eld.

Sleep self-management interventions that incorporate tech-

nology have the potential to empower patients to improve

health and health care outcomes of individuals who have sleep

deciency. With an uptake of mobile phone ownership among

adults in the United States, a rapid development in mHealth

technologies allows users to self-monitor and visualize their

sleep patterns, symptoms, and behavioral data and aid them in

taking appropriate actions on potentially a daily basis. Several

years ago, Ko et al. provided a general overview of the land-

scape of mobile health apps to support sleep self-management

along with other consumer sleep health technologies including

wearable devices.

About the same time, Ong et al. reviewed

and assessed 51 unique sleep analysis apps available for down-

load.

However, this review was based on the description

available at each developer’s website only as the authors did

not download the apps to conduct a systematic evaluation of

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YK Choi, G Demiris, SY Lin, et al. Review: Smartphone Applications to Support Sleep Self-Management

their quality. To date no studies have systematically assessed

the quality of commercially available apps to support sleep

self-management. To address this gap in the literature, we con-

ducted a thorough review of commercially available mHealth

apps focused specically on sleep self-management. Our ob-

jectives were to (1) identify the current landscape of commer-

cially available sleep self-management related mHealth apps;

(2) describe their characteristics; (3) identify the extent to

which available apps have been rigorously tested; (4) rate the

quality of the apps based on existing rating scales; and (5) pro-

vide recommendations for the design and implementation for

future apps to support sleep self-management.

METHODS

Systematic Search and Selection Criteria

In April 2017, we conducted a thorough review of mHealth apps

across four leading web-based mobile app stores: Apple iTunes

App Store, Android Google Play store, Amazon Appstore, and

Microsoft Appstore. The following search terms were used in

each app store: “sleep,” “sleep management,” “sleep monitor-

ing,” and “sleep tracking.” In the rst round of screening, the

duplicate apps identied from multiple search terms in each

app store were excluded. In the second round, two members of

the research team (YC, SL) conducted a preliminary screening

based on app titles, full market descriptions, and screenshots of

the potential apps to evaluate relevance. Inclusion criteria for

the apps were as follows: (1) focus on sleep self-management

based on user generated data (eg, monitoring or tracking us-

ers’ sleep patterns, providing guidance to improve sleep based

on user generated data); (2) must be able to be used without

the assistance of a healthcare provider; (3) must be currently

available on the public market; and (4) must be in English.

Because our focus was to assess apps that use user-generated

data, apps that only provided sleep education, tips, or relax-

ation techniques without the use of user-generated data were

not part of this review. More specic exclusion criteria can be

found in Figure 1.

Any discrepancies in ratings of inclusion and exclusion cri-

teria between the team members were discussed until consen-

sus was reached.

The remaining apps were downloaded and reviewed (using

the following platforms: iOS on iPhone 6; Android on Nexus

5x; Amazon Fire Tablet; Windows Phone 8.1 on Lumia 435).

Approximately 25% of the apps were independently evaluated

and rated by both reviewers and the interrater reliability was

high. For the remainder, apps on the Apple iTunes appstore

were reviewed by one member (SL) and apps on the other plat-

forms were reviewed by another (YC).

In reviewing the apps, 75 additional apps that did not meet the

inclusion criteria upon closer examination were excluded. The

preliminary list of apps to be included was reviewed for dupli-

cated apps identied across multiple platforms and for highly

similar versions of the same app (eg, “lite” or “pro” versions) to

produce the nal list of unique apps (see Figure 1 owchart).

A Microsoft Excel spreadsheet was used to characterize

each app as to its required platform (eg, iOS, Android, etc.),

country developed, cost to download, number of downloads,

rating and number of reviewers contributing to the rating, date

of last update, and primary features. Additionally, a data ex-

traction form was developed using a Research Electronic Data

Capture (REDCap) survey that included the two rating scales

(described in the next paragraph). In the REDCap survey, we

also measured the sleep tracking method of each app (eg, “au-

tomatic,” “manual entry,” “both”). Additionally, we conducted

PubMed searches using the app name of each of the included

apps as the search term to identify peer-reviewed publication

reporting on the app credibility (eg, development using evi-

dence-based intervention, ecacy testing).

Rating Tools

To systematically assess and appraise the apps, the reviewers

used two dierent rating tools: (1) Mobile Application Rat-

ing Scale (MARS) quality score,

and (2) IMS Institute for

Healthcare Informatics app functionality score.

The MARS

rating tool is a 23-item scale developed to systematically as-

sess the quality of mHealth apps (Table 1). The MARS instru-

ment includes an objective app quality section with 19 items

divided into 4 scales: engagement, functionality, esthetics,

and information quality and one subjective quality section

with 4 items evaluating the users’ overall satisfaction. Each

MARS item is rated on a 5-point Likert scale (1 = inadequate,

2 = poor, 3-acceptable, 4 = good, and 5 = excellent). For this

review, we did not rate the MARS item 19 pertaining to app

credibility, because a PubMed search identied only three

validation studies among the included apps.

15 –17

The MARS

rating tool has been previously applied to evaluate diverse

mHealth apps including mindfulness,

weight management,

smoking cessation,

heart failure symptom monitoring,

and

blood alcohol calculation.

The IMS Institute for Healthcare Informatics mobile app

functionality score consists of 7 functionality criteria and 4

functional subcategories

(Table 2). Each app was evaluated

to assess whether each of 11 functionalities exists and a func-

tionality score (0 to 11) was calculated accordingly. The IMS

functionality score is dierent from the MARS functionality

score as it focuses solely on the availability of the functional-

ity (inform, record, display, guide, remind, and communicate),

whereas the MARS functionality score measures the quality

of performance, ease of use, navigation, and gestural design of

the app with a 5-point Likert scale.

Additionally, we assessed the type of data recording into

three categories: automatic tracking (eg, using embedded sen-

sors such as an accelerometer), manual tracking (ie, manual

logging by the user), or both.

Data Analysis

Two reviewers (YC and SL) were trained in the use of the

MARS scale

following the steps presented in the YouTube

training tutorial.

Both reviewers rated 25% of randomly selected apps to eval-

uate interrater reliability for both MARS and the IMS func-

tionality scores. The intraclass correlation coecients were

calculated on all MARS subscales and total score, as well as

for the IMS Institute for Healthcare Informatics functionality

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YK Choi, G Demiris, SY Lin, et al. Review: Smartphone Applications to Support Sleep Self-Management

score. For our analysis, we used a two-way mixed-eects, aver-

age-measures model with absolute agreement.

All statistical

analyses were conducted using R Software.

RESULTS

Descriptive Characteristics

Our search queries in Android Google Play, Apple iTunes,

Amazon Appstore, and Microsoft App stores yielded 2,431

potentially relevant apps, of which 73 unique apps were in-

cluded in our review. The ow diagram (Figure 1) shows

the overview of the selection process and categories for ex-

clusion. After multiple screening iterations, most of the apps

were excluded because they were unrelated to sleep content

(n = 437), focused on simply playing environmental or relax-

ation sounds (n = 642) or providing alarm service (n = 305), or

required other external devices to operate (eg, wearable watch)

(n = 238). Appendix 1 in the supplemental material provides

the full list of the included apps and their characteristics.

Seventy-eight percent of the apps (57/73) were free to down-

load and 22% of the apps (16/73) had costs up to $9.99. When

there were multiple versions of the same app, we chose to

download the paid version only if it added signicant features

such as visualizing the data in dierent graphs. Sixty-six per-

cent of the apps (48/73) had been updated within the past 2

years. We did not include the small number of apps (n = 6)

that had no updates beyond 2012. The average consumer star

rating across all of the apps was 3.8 out of 5 with a range of

0 to 5 (5 being the highest score) for those with a user rating

reported. Approximately 20% of the apps (15/73) had not been

rated by anyone. The number of individual ratings ranged from

0 to more than 25,000. Only Google Appstore provided infor-

mation on the number of downloads per app. Of the 33 apps

Figure 1—Screening process owchart.

CBT = cognitive behavioral therapy.

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with download information, 27% (9/33) had fewer than 5,000

downloads, 27% (9/33) had 5,000 to 50,000, 27% (9/33) had

50,000 to 500,000 downloads, and 18% (6/33) had 500,000 to

500,000,000 downloads.

MARS App Quality Scores

Table 3 presents the four subscale scores (engagement, func-

tionality, esthetics, and information), overall quality score,

and subjective quality score (satisfaction) for the top 20 apps

in the order of descending overall quality. The full list of the

MARS scores for all included apps is found in Appendix 2

in the supplemental material. Approximately 25% of the apps

(19/73) were independently evaluated by both raters, and there

was good interrater reliability (two-way mixed consistency-of-

agreement intraclass correlation = .81, 95% condence inter-

vals .75–.84). Of the 4 subscales, functionality had the highest

median score (3.75) and satisfaction had the lowest (2.25). The

median overall MARS score was 3.1 out of 5, and 58% of the

apps (42/73) had a minimum acceptability score of 3.0.

Overall, Sleep as Android Unlock, Alarm Clock Xtream

& Timer, and Sleep Center Free each had the highest average

MARS total (4.0) followed by Smart Sleep Manager: Alarm

clock & sleep log (3.8), Samsung Health (3.7), and Good Morn-

ing Alarm Clock (3.7).

Functionality

Figure 2 illustrates the functionalities of the apps based on the

seven functionality criteria and four functional subcategories

adapted from the Institute for Healthcare Informatics report.

The gure highlights that all apps had a record function (73/73,

100%). Out of a total of 11 functionalities, the median number

of functionalities was 7; 55% of apps (41/73) had 7 or fewer

functions. Sixty-six apps (90%) were able to provide some

form of graphical representations of user-entered data and 62

apps (85%) had the function to inform. Additionally, there were

56 apps that had the function to instruct (77%), 36 to remind/

alert (49%), 33 to communicate (45%), and 15 to guide (21%).

Among the 73 apps that had the function to record, 72 could

collect and evaluate data, 45 could share the data, but only 8

had the function to intervene by sending alerts or to propose be-

havioral changes based on the collected data. Most of the apps

focused on collecting daily sleep duration but also collected

other information including snoring or sleep sounds, daily

moods, diet, and physical activity. Thirty-three apps (45%) col-

lected data automatically using the embedded sensors, 27 apps

allowed users to manually enter the sleep data, and 14 apps

supported both types of data recording. For automatic data

Table 1—MARS items and subscales criteria.

App Quality

Scoring Criteria Subscales

1. Engagement 1.1 Entertainment

1.2 Interest

1.3 Customization

1.4 Interactivity

1.5 Target group

2. Functionality 2.1 Performance

2.2 Ease of use

2.3 Navigation

2.4 Gestural design

3. Aesthetics 3.1 Layout

3.2 Graphics

3.3 Visual appeal: how good does the app look?

4. Information 4.1 Accuracy of app description

4.2 Goals

4.3 Quality of information

4.4 Quantity of information

4.5 Visual information

4.6 Credibility

4.7 Evidence base

5. Subjective

quality

5.1 Would you recommend this app?

5.2 How many times do you think you would use

this app?

5.3 Would you pay for this app?

5.4 What is your overall star rating of the app?

MARS = Mobile Application Rating Scale.

Table 2—IMS Institute for Healthcare Informatics functionality scoring criteria.

Functionality

Scoring Criteria Description

1. Inform Provides information in a variety of formats (text, photo, video)

2. Instruct Provides instructions to the user (eg, app user guides, instructions to interpret sleep charts)

3. Record Capture user-entered data (eg, manual sleep log, sensor-based automatic sleep log)

3.1 Collect data Able to enter and store health data on individual phone

3.2 Share data Able to transmit health data (eg, export, upload, email sleep data)

3.3 Evaluate data Able to evaluate the entered health data by patient and provider, provider and administrator, or patient and caregiver

3.4 Intervene Able to send alerts based on the data collected or propose behavioral intervention or changes (eg, smart wakeup alarm based on

user sleep data, anti-snoring alerts when snoring is detected)

4. Display Graphically display user-entered data/output user-entered data (eg, sleep trends chart)

5. Guide Provide guidance based on user-entered information, and may further offer a diagnosis, or recommend a consultation with

a physician/a course of treatment (eg, recommendations for improving sleep based on user sleep data)

6. Remind or alert Provide reminders to the user (eg, bedtime notication)

7. Communicate Provide communication between health care providers, patients, consumers, caregivers and/or provide links to social networks (eg,

email or upload sleep data to Facebook)

Total score (0 to 11): one point is assigned to each functionality that is present.

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collection to function correctly, the phone needed to be placed

nearby or on the bed.

Four apps had a total of 11 functionalities (Good Morning

Alarm Clock, SleepRate: Sleep Therapy, Sleep as Android Un-

lock, Samsung Health) followed by 5 apps that had 10 func-

tionalities (MotionX 24/7: Sleeptracker, Sleep Center Free,

WakeMode, Snail Sleep, Instant - Quantied Self).

Sharing data was often supported through syncing data with

a cloud server, exporting as a CSV or PDF le, or sending an

email. Some apps provided a channel to communicate by allow-

ing users to share their sleep-related data with friends and family

through linking with social networking services. The guidance

was often provided to the user by analyzing the sleep data and

providing information such as sleep decit and snoring statistics

(Figure 3). Additionally, some apps monitored the sleep session

using the embedded microphone and alerted the user when snor-

ing was detected by producing gentle sounds or vibration.

Overall App Quality

Cross-comparing the apps that had the highest MARS scores

with and IMS functionality scores, the highest-performing

apps included Sleep Center Free, Good Morning Alarm Clock,

Sleep as Android Unlock, and Samsung Health (Table 4).

Design Recommendations for Developers

A number of key features emerged from analyses of the apps

that could help improve the design of future apps for sleep self-

management. As noted in the previous section on app func-

tionality, all the included apps had some form of sleep data

automatic and/or manual recording function. However, one of

the biggest challenges identied across the apps was the lack

of manual editing functionality, especially for apps that do au-

tomatic tracking of sleep. Many apps did not allow users to

go back and edit the previous log entries. The manual editing

functionality is crucial because automatic tracking based on the

embedded sensors, despite some developer’s marketing claims,

is often inaccurate and the user would want to adjust the logs to

more accurately reect that night’s sleep and identify potential

bias in underestimation or overestimation of sleep.

Figure 2—Functionality of included apps based on IMS

Institute for Healthcare Informatics functionality scores.

Table 3—Top 20 MARS score apps.

App Name Engage Function Aesthetics Information Satisfaction Overall

Sleep as Android Unlock 4.0 4.3 4.3 3.8 3.8 4.0

Alarm Clock Xtreme & Timer 4.2 4.3 4.3 3.5 3.8 4.0

Sleep Center Free 4.2 4.3 4.0 4.0 3.5 4.0

Smart Sleep Manager: Alarm clock & sleep log 4.0 4.3 4.0 3.8 2.8 3.8

Samsung Health 4.0 3.5 4.3 3.8 3.0 3.7

Good Morning Alarm Clock 3.8 4.3 3.7 3.7 3.0 3.7

SnoreLab: Record Your Snoring 3.8 4.3 3.7 3.8 2.8 3.7

TracknShare LITE- A Quantied Self Journal 4.6 4.0 3.7 3.8 2.3 3.7

SnoreClock-Do you snore? 4.0 4.0 4.0 3.8 2.5 3.7

Instant - Quantied Self 4.2 3.8 4.0 3.5 2.8 3.6

MotionX 24/7: Sleeptracker 4.0 4.0 3.7 3.8 2.8 3.6

Sleep Meister - Sleep Cycle Alarm Lite 4.0 4.3 3.7 3.8 2.5 3.6

Alarm Clock - Sleep Cycle 3.6 4.0 3.7 3.0 3.8 3.6

Juice 3.8 3.8 4.0 3.2 3.0 3.5

Argus Health & Calorie Counter 3.8 3.5 4.0 3.2 3.3 3.5

Sleep Well Alarm; Intelligent Alarm Clock 3.4 4.0 4.0 3.8 2.5 3.5

Snail Sleep 3.4 4.0 3.7 3.8 2.8 3.5

Zen Sleep Cycle Alarm Clock Pro - Sleep Smarter 3.8 4.0 4.0 3.5 2.3 3.5

Sleep Science Alarm Clock: smart sleep cycle tracker &

monitor with diary & graphs

3.6 4.0 3.7 3.5 2.8 3.5

Sleep Tracker+ Mood Diary 3.8 3.8 3.7 3.5 2.8 3.5

MARS = Mobile Application Rating Scale.

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Another important feature is the ability to easily document

or annotate information related to sleep such as subjective

sleep quality, diet (eg, caeine, alcohol intake), and exercise.

Allowing users to easily record this information in a structured

format would help when investigating behaviors and habits that

could be aecting sleep. In addition, goal setting is a feature

that was often missing from the included apps. Users could

benet from being able to set up personalized goals for bet-

ter sleep self-management. Having capacity to visualize sleep

data in clear and informative graphs could further help users

easily understand their sleep patterns and facilitate meaningful

interpretations of the data.

A potentially important feature that was missing from the

included apps was user ability to export data. Although some

apps allowed users to upload their data to the cloud servers

that app supported, many apps did not allow users to export

the data for further analysis. Additionally, only a few apps had

a functionality to record sound or noise along with sleep pat-

tern data. Although there were apps specically designed to

capture sleep noise or environmental sounds, it would be more

convenient for users if a sleep app supported capturing sleep

sounds, sleep pattern behavior, and other environmental fac-

tors such as light levels and temperatures.

Ideally, to maximize user experience and satisfaction, future

apps should engage end users in the design process to identify

and address users’ needs and preferences. This would also help

Table 4—Description of top 5 apps combining MARS and IMS functionality scores.

Name Platform Cost, US $ # of Installs

* Star Rating MARS Score IMS Score Tracking Type

Sleep as Android Unlock Google/Amazon 3.99 500,000–1,000,000 5 4.0 11 Both

Sleep Center Free Apple Free N/R NA 4.0 10 Both

Good Morning Alarm Clock Apple/Google Free 1,000,000–5,000,000 4 3.7 11 Automatic

Samsung Health Google Free 100,000,000–500,000,000 4 3.7 11 Manual

Snail Sleep Google/Amazon Free 100,000–500,000 4 3.5 10 Automatic

* = data on number of installs were only available in Google Play. MARS = Mobile Application Rating Scale.

designers of future apps address potential mechanical or visual

limitations that might be important to users across a diverse

spectrum of abilities.

DISCUSSION

In the past several years, smartphone ownership and adoption

has skyrocketed. A recent survey shows that approximately

77% of United States adults owned a smartphone in 2017.

With

the growth of smartphone usage, the number and the variety of

health-promoting apps has increased exponentially. In this re-

view, we identied and evaluated consumer mHealth apps for

sleep self-management and systematically evaluated quality

using validated rating scales. Despite the need for supporting

sleep self-management and the large number of mHealth apps

on the market, our results showed that there are few apps that

scored above average in prespecied criteria for quality, con-

tent, and functionality for sleep self-management. The func-

tionality of the apps was primarily focused on recording and

displaying the user generated sleep data for self-evaluation.

About half of apps (45%) collected data automatically using

the embedded sensors in a smartphone device. Such apps often

instruct a user to connect the phone to the charger and place it

on the sleeping surface or under the pillow to collect data. Using

the data collected, apps provided information on sleep patterns

Figure 3—Screenshots of the selected apps.

Apps display different sleep statistics based on the data collected by the embedded sensor. Left, Sleep as Android Unlock. Right, Good Morning Alarm

Clock.

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YK Choi, G Demiris, SY Lin, et al. Review: Smartphone Applications to Support Sleep Self-Management

(eg, bedtime, wakeup time, and average time in bed) and some

even reported additional sleep parameters including amount of

time in light, deep, and rapid eye movement stage. However, it

is critically important to highlight that such sleep parameters

calculated by the custom algorithms of the sleep self-manage-

ment apps have yet to be successfully validated against results

obtained by polysomnography (PSG), the gold standard. Based

on our PubMed search of included apps, only three apps (Sleep

Time,

MotionX 24/7,

Sleep Cycle

) have been formally eval-

uated for clinical validity comparing the parameters reported

by the apps and those obtained by PSG. The validation studies

have shown that the sleep parameters by the apps poorly corre-

lated with PSG and failed to accurately reect the sleep stages

and thus deemed not useful as a clinical tool.

15 –17

With the rapid evolvement of consumer health technologies,

governmental regulation of mHealth apps has not been able to

adequately keep pace. In 2013, the United States Food and Drug

Administration (FDA) has issued initial guidelines regarding

the type of mobile apps to be considered “medical devices”

to be under stringent regulation.

Although the guideline will

change over time, as of 2018, a sleep self-management app falls

under the category of “lower risk” products for which the FDA

intends to exercise enforcement discretion.

In practice, devel-

opers or companies make claims to benet users’ sleep health

but categorize their products as “lifestyle apps” or “entertain-

ment apps” to circumvent potential liabilities that may arise for

its use for clinical purpose. Nonetheless, the lack of validation

research to demonstrate clinical value from the use of these

apps is a serious concern. The position statement issued by the

American Academy of Sleep Medicine (AASM) emphasizes

this concern and asserts that consumer sleep technology in-

tended for a diagnosis and/or treatment of sleep disorders must

be cleared by the FDA and undergo rigorous testing against

current gold standards.

The potential implications of self-

management or self-treatment protocols informed by inaccu-

rate or invalid data generated by an unvalidated sleep app are

important to consider. The lack of validation also limits sleep

specialists’ ability to recommend or draw conclusions about

their potential eect. The position statement arms that given

the lack of FDA clearance and validation data, consumer sleep

technology tools cannot replace a clinical evaluation and vali-

dated diagnostic instruments.

As the popularity of using consumer sleep self-management

apps along with other sleep tracking devices continues to grow,

sleep specialists and more broadly health care providers will

inevitably have to deal with patients who bring questions re-

lated to data collected by their sleep self-management apps.

Despite the limitations, data generated by the apps may facili-

tate meaningful interactions between patients and providers

and encourage patients to be more active in their sleep care.

However, without concrete clinical evidence and established

guidelines regarding the app use in clinical practice, clinicians

would have to rely on their individual experience and judgment

on how to guide their patients on the use of such apps.

Usability or ease of use of an app is another important as-

pect for widespread adoption. Without a simple and easy-to-

navigate interface, gestural design, and clear instructions,

consumers would struggle to use the features in the app. Sleep

self-management apps target a variety of potential consumers

across a broad age spectrum, physical abilities, and technology

literacy levels. Therefore, designers and developers should pro-

vide dierent interface settings to accommodate a broad range

of consumers. For example, voice-activated interfaces for data

entry and retrieval could make the app more usable for those

with ne motor issues.

The strength of this study is that it systematically applied

and evaluated the quality and the functionalities of the apps

using the MARS rating scale and the IMS functionality score.

Although the use of the MARS scale to evaluate health pro-

moting mobile apps has been done before,

18–22

our study rep-

resents the rst to assess sleep self-management apps. Our

review provides an exhaustive and comprehensive snapshot of

mobile apps for sleep self-management across four major app

stores. One of the limitations of this review is that we did not

include apps that required subscription to external services or

additional tracking devices such as a wearable tness band.

Additionally, we were not able to fully assess all the technical

aspects and accuracy of all features within the apps, especially

features that required long-term data tracking (eg, month-to-

month comparisons).

In conclusion, consumer-targeting apps that support sleep

self-management have the potential to help raise awareness

and promote healthy sleep habits. However, without regulation

and enforcement of clinical validation compliance, these apps

should certainly be used with caution. It is clear that concrete

guidelines and regulation are necessary for safe usage of con-

sumer sleep health technologies in general including speci-

cally mobile sleep apps. In addition, future research should

focus on testing the ecacy of the apps and demonstrating the

magnitude of behavior change with respect to improved sleep

health outcomes. For example, research that compares the ef-

cacy and eventual eectiveness of a sleep self-management

app in a head to head comparison with recommended cognitive

behavioral therapy for insomnia programs would advance the

science of sleep self-management.

ABBREVIATIONS

MARS, Mobile Application Rating Scale

mHealth, mobile health

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SUBMISSION & CORRESPONDENCE INFORMATION

Submitted for publication March 28, 2018

Submitted in nal revised form July 17, 2018

Accepted for publication August 1, 2018

Address correspondence to: Yong K. Choi, University of Washington Box 357266,

Seattle, WA 98195-7266; Tel: (206) 496-2147; Email: yongchoi@uw.edu

DISCLOSURE STATEMENT

This work was performed at the University of Washington and supported by NIH/

NINR, Center for Innovation in Sleep Self-Management (P30NR016585, MPI:

Heitkemper MM & Ward TM). All authors have contributed to this work, reviewed the

submitted manuscript, and approve it for submission. The authors report no conicts

of interest.