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Smartphone Applications to Support Sleep Self-Management: Review and Evaluation

Yong K. Choi, MPH1; George Demiris, PhD2; Shih-Yin Lin, PhD, MPH, MM1; Sarah J. Iribarren, PhD, RN1; Carol A. Landis, PhD, RN1; Hilaire J. Thompson, PhD, RN, ARNP, CNRN, AGACNP-BC1; Susan M. McCurry, PhD1; Margaret M. Heitkemper, PhD, RN1; Teresa M. Ward, PhD, RN1
1School of Nursing, University of Washington, Seattle, Washington; 2School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania

ABSTRACT

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 identified 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 findings suggest that few apps meet prespecified 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 significant concern that limits the clinical value of these apps.

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 deficiency is quite common and pervasive in modern society. Sleep deficiency is defined as a deficit in the quantity or quality of sleep obtained versus the amount needed for optimal health, performance, and well-being.”1 Sleep deficiency most often manifests as recurrent 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 deficiency associated with poor sleep quality is common in chronic illness and directly contributes to (1) various problems in carrying out daytime functions, (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 substantial sleep deficiency.6

Self-management is an ongoing, perhaps lifelong, process with a focus on self-identified needs or problems that require continual monitoring and enacting appropriate actions that may require interaction with others including health care providers. In the field of interdisciplinary sleep medicine, interventions to treat sleep deficiency are mainly provider initiated. As an alternative to provider-directed interventions, sleep investigators have tested the use of self-help behavioral interventions based on the self-management approach.7 Although self-management approaches have shown small to moderate effect sizes,79 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 individuals 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 maintenance of therapy over time has been considered a “new era” in the sleep field.10

Sleep self-management interventions that incorporate technology have the potential to empower patients to improve health and health care outcomes of individuals who have sleep deficiency. 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 landscape of mobile health apps to support sleep self-management along with other consumer sleep health technologies including wearable devices.11 About the same time, Ong et al. reviewed and assessed 51 unique sleep analysis apps available for download.12 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 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 conducted a thorough review of commercially available mHealth apps focused specifically on sleep self-management. Our objectives were to (1) identify the current landscape of commercially 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) provide 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 monitoring,” and “sleep tracking.” In the first round of screening, the duplicate apps identified 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 users' 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 relaxation techniques without the use of user-generated data were not part of this review. More specific exclusion criteria can be found in Figure 1.

Screening process flowchart.

CBT = cognitive behavioral therapy.

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Figure 1

Screening process flowchart.

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Any discrepancies in ratings of inclusion and exclusion criteria between the team members were discussed until consensus 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 platforms 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 duplicated apps identified across multiple platforms and for highly similar versions of the same app (eg, “lite” or “pro” versions) to produce the final list of unique apps (see Figure 1 flowchart).

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 extraction 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, “automatic,” “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 evidence-based intervention, efficacy testing).

Rating Tools

To systematically assess and appraise the apps, the reviewers used two different rating tools: (1) Mobile Application Rating Scale (MARS) quality score,13 and (2) IMS Institute for Healthcare Informatics app functionality score.14 The MARS rating tool is a 23-item scale developed to systematically assess the quality of mHealth apps (Table 1). The MARS instrument 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 identified only three validation studies among the included apps.1517 The MARS rating tool has been previously applied to evaluate diverse mHealth apps including mindfulness,18 weight management,19 smoking cessation,20 heart failure symptom monitoring,21 and blood alcohol calculation.22

MARS items and subscales criteria.

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Table 1

MARS items and subscales criteria.

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The IMS Institute for Healthcare Informatics mobile app functionality score consists of 7 functionality criteria and 4 functional subcategories14 (Table 2). Each app was evaluated to assess whether each of 11 functionalities exists and a functionality score (0 to 11) was calculated accordingly. The IMS functionality score is different from the MARS functionality score as it focuses solely on the availability of the functionality (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.

IMS Institute for Healthcare Informatics functionality scoring criteria.

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Table 2

IMS Institute for Healthcare Informatics functionality scoring criteria.

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Additionally, we assessed the type of data recording into three categories: automatic tracking (eg, using embedded sensors 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 scale13 following the steps presented in the YouTube training tutorial.23

Both reviewers rated 25% of randomly selected apps to evaluate interrater reliability for both MARS and the IMS functionality scores. The intraclass correlation coefficients were calculated on all MARS subscales and total score, as well as for the IMS Institute for Healthcare Informatics functionality score. For our analysis, we used a two-way mixed-effects, average-measures model with absolute agreement.24 All statistical analyses were conducted using R Software.25

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 included in our review. The flow diagram (Figure 1) shows the overview of the selection process and categories for exclusion. 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 relaxation 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 download 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 significant features such as visualizing the data in different graphs. Sixty-six percent 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 information on the number of downloads per app. Of the 33 apps 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, functionality, 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% confidence intervals .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.

Top 20 MARS score apps.

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Table 3

Top 20 MARS score apps.

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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 Morning 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 figure 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%).

Functionality of included apps based on IMS Institute for Healthcare Informatics functionality scores.

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Figure 2

Functionality of included apps based on IMS Institute for Healthcare Informatics functionality scores.

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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 behavioral 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%) collected 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 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 Unlock, Samsung Health) followed by 5 apps that had 10 functionalities (MotionX 24/7: Sleeptracker, Sleep Center Free, WakeMode, Snail Sleep, Instant - Quantified Self).

Sharing data was often supported through syncing data with a cloud server, exporting as a CSV or PDF file, or sending an email. Some apps provided a channel to communicate by allowing 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 deficit and snoring statistics (Figure 3). Additionally, some apps monitored the sleep session using the embedded microphone and alerted the user when snoring was detected by producing gentle sounds or vibration.

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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Figure 3

Screenshots of the selected apps.

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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).

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

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Table 4

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

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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 functionality, all the included apps had some form of sleep data automatic and/or manual recording function. However, one of the biggest challenges identified across the apps was the lack of manual editing functionality, especially for apps that do automatic 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 reflect that night's sleep and identify potential bias in underestimation or overestimation of sleep.

Another important feature is the ability to easily document or annotate information related to sleep such as subjective sleep quality, diet (eg, caffeine, 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 affecting sleep. In addition, goal setting is a feature that was often missing from the included apps. Users could benefit from being able to set up personalized goals for better 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 pattern data. Although there were apps specifically 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 factors 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 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 2017.26 With the growth of smartphone usage, the number and the variety of health-promoting apps has increased exponentially. In this review, we identified 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 prespecified criteria for quality, content, and functionality for sleep self-management. The functionality 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 (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-management 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,15 MotionX 24/7,16 Sleep Cycle17) have been formally evaluated 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 correlated with PSG and failed to accurately reflect the sleep stages and thus deemed not useful as a clinical tool.1517

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.27 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.28 In practice, developers or companies make claims to benefit users' sleep health but categorize their products as “lifestyle apps” or “entertainment 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 intended for a diagnosis and/or treatment of sleep disorders must be cleared by the FDA and undergo rigorous testing against current gold standards.29 The potential implications of self-management or self-treatment protocols informed by inaccurate 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 effect. The position statement affirms that given the lack of FDA clearance and validation data, consumer sleep technology tools cannot replace a clinical evaluation and validated diagnostic instruments.29

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 related to data collected by their sleep self-management apps. Despite the limitations, data generated by the apps may facilitate 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 aspect 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 provide different 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 fine 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 promoting mobile apps has been done before,1822 our study represents the first 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 fitness 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 consumer sleep health technologies in general including specifically mobile sleep apps. In addition, future research should focus on testing the efficacy of the apps and demonstrating the magnitude of behavior change with respect to improved sleep health outcomes. For example, research that compares the efficacy and eventual effectiveness 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.

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 conflicts of interest.

ABBREVIATIONS

MARS

Mobile Application Rating Scale

mHealth

mobile health

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Supplemental Material

Supplemental Material

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