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Although mobile health technologies have been developed for interventions to improve sleep disorders and sleep quality, evidence of their effectiveness remains limited.
A systematic literature review was performed to determine the effectiveness of mobile technology interventions for improving sleep disorders and sleep quality.
Four electronic databases (EBSCOhost, PubMed/Medline, Scopus, and Web of Science) were searched for articles on mobile technology and sleep interventions published between January 1983 and December 2016. Studies were eligible for inclusion if they met the following criteria: (1) written in English, (2) adequate details on study design, (3) focus on sleep intervention research, (4) sleep index measurement outcome provided, and (5) publication in peer-reviewed journals.
An initial sample of 2679 English-language papers were retrieved from five electronic databases. After screening and review, 16 eligible studies were evaluated to examine the impact of mobile phone interventions on sleep disorders and sleep quality. These included one case study, three pre-post studies, and 12 randomized controlled trials. The studies were categorized as (1) conventional mobile phone support and (2) utilizing mobile phone apps. Based on the results of sleep outcome measurements, 88% (14/16) studies showed that mobile phone interventions have the capability to attenuate sleep disorders and to enhance sleep quality, regardless of intervention type. In addition, mobile phone intervention methods (either alternatively or as an auxiliary) provide better sleep solutions in comparison with other recognized treatments (eg, cognitive behavioral therapy for insomnia).
We found evidence to support the use of mobile phone interventions to address sleep disorders and to improve sleep quality. Our findings suggest that mobile phone technologies can be effective for future sleep intervention research.
Sleep disorders are an important public health problem that affects approximately 50 to 70 million people in the United States [
In addition to sleep disorders, insufficient sleep and irregular sleep patterns are also risk factors for obesity [
In 2011, approximately 39% of Americans, 72% of whom were adolescents, used mobile phones immediately before sleeping [
The potential and importance of mHealth technology in health care and health interventions are evident through more than 100,000 health apps in the app store, and a US $26 billion estimated mHealth market size in 2017 [
Although new mHealth technologies are assumed to be able to improve the quality and quantity of sleep, limited examinations of behavioral sleep interventions using mobile phones exist [
This study included articles if they met the following criteria:
Study design: randomized controlled trials (RCTs), pre-post studies, and case-control studies;
Sleep intervention study: using mHealth technology (eg, a mobile device or app);
Intervention outcome: sleep outcome measurement (eg, Insomnia Severity Index [ISI], Pittsburgh Sleep Quality Index [PSQI], or Epworth Sleepiness Scale [ESS]);
Language: written in English; and
Article type: peer-reviewed publications.
Articles published between January 1983 (the year of the first handheld and commercial cellular phone from Motorola [
After searching the electronic databases, one author (JCS) selected articles that were published in peer-reviewed journals and excluded books, case reports, conference proceedings, product reviews, newspapers, patents, serials, and theses. Secondly, the same author removed duplicate articles from the combined search results and screened articles based on the title and abstract. In addition, JCS hand-searched for relevant articles in the JMIR search engine on February 28, 2017. Once all full-text articles were found, two researchers (JCS and JK) reviewed each article using the eligibility criteria and eliminated unrelated articles. The entire procedure followed the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) [
The following information was extracted from each article included in the systematic review: first author’s name, publication year, the country where the study was conducted, sample size, sample age, sample characteristics, study period, retention rate, study design, the technology used in the intervention, and measurement of sleep outcome. The retention rate was calculated using the number of participants who completed all assessments for the intervention study divided by the number of individuals who were originally recruited. Intervention technology was identified using two approaches: (1) intervention using mobile phone app and (2) supplementary mobile phone usage with traditional intervention. Additional information was extracted by using the empirical approach of statistical analysis such as effect size, standard error, and standard deviation for the difference before and after interventions.
The effect size and standard deviation for each study’s sleep outcome measurement were obtained from data extraction. With each effect size, there was a difference of calculation for the total effect size based on the type of study. For example, the effect size of pre-post test was only considered the difference between pretest scores and posttest scores. On the other hand, the effect size of RCTs was calculated using the difference between the intervention and the control groups’ effect sizes. Based on the total effect size, sample size, and standard error, two-sample
The study quality assessment tool was derived from Zhu and An [
The results of the literature search are summarized in
Study selection procedure according to the PRISMA guidelines.
Basic characteristics of the studies of mobile phone interventions on sleep disorders (N=16).
Author(s), Year | Study |
Sample |
Sample |
Mean |
Study |
Intervention |
Study |
Sleep outcome |
Anttalainen el al, 2014 [ |
Finland | OSA patients | 111 | 55.27 | 3 m | Phone + CPAP | RCT | AHI, ESS |
Babson et al, 2015 [ |
United States | Veterans with CUD | 3 | 47 | 2 w | App: “CBT-I Coach” for iOS | Pre-post | PSQI |
Bauer et al, 2012 [ |
United States | Metropolitan area adults | 12 | 32 | 4 w | App: “ShutEye” for Android | Pre-post | ESS |
Chen et al, 2015 [ |
Taiwan | Elderly female | 1 | 64 | 5 w | App: “Win-Win A Sleep” | Case study | SSR |
Filion et al, 2015 [ |
United States | Young adult smokers | 116 | 18-25 | 6 w | Text message | RCT | SQ_PSQI, TST |
Fox el al, 2012 [ |
Canada | OSA patients | 75 | 53.54 | 3 m | Phone + CPAP | RCT | AHI, ESS |
Freeman et al, 2015 [ |
United States | Breast cancer survivors | 102 | 55.44 | 3 m | Phone-supported teleconference | RCT | PSQI |
Ho et al, 2014 [ |
Hong Kong | Insomnia patients | 149 | 38.5 | 12 w | Phone + CBT-I | RCT | PSQI, ISI, DBAS, SE, SQ, SOL, WASO, TST |
Jernelov et al, 2012 [ |
Sweden | Adults with insomnia | 133 | 47.9 | 6 w | Phone + bibliotherapy | RCT | ISI, DBAS, SE, SQ, SOL, WASO, TST, BTS, SRBQ |
Koffel et al, 2016 [ |
United States | Veterans | 18 | 48.5 | 5 w | Apps: “CBT-I Coach” | RCT | ISI |
Lichstein et al, 2013 [ |
United States | Rural area adults with insomnia | 5 | 65.8 | 5 w | Apps: “Skype” + CBT | Pre-post | ISI, HRDS, NWAK, SOL, SQ, WASO |
Kauffman 2016 [ |
United States | Menopausal status with insomnia | 106 | 54.85 | 24 w | Phone + CBT-I | RCT | ISI, PSQI, SE, SOL, TST, WASO |
Mendelson et al, 2014 [ |
France | OSA patients | 107 | 63 | 4 w | Researcher-built app + CPAP | RCT | ESS |
Stremler et al, 2006 [ |
Canada | First-time mothers | 30 | 31.85 | 6 w | Phone + sleep education | RCT | GSDS |
van Dron-gelen et al, 2014 [ |
Netherlands | Airline pilots | 502 | 40.9 | 6 m | App: “More energy” | RCT | PSQI |
Vuletic el al., 2016 [ |
United States | Soldiers With MTBI | 356 | 29.35 | 6 m | Telephone-based problem-solving treatment | RCT | PSQI, SE, SOL, SQ |
aCUD: cannabis use disorders; MTBI: mild traumatic brain injury; OSA: obstructive sleep apnea.
bCBT-I: Cognitive Behavioral Therapy for Insomnia; CPAP: continuous positive airway pressure.
cRCT: randomized controlled trial.
dAHI: Apnea-Hypopnea Index; BTS: bed time stress; DBAS: Dysfunctional Beliefs and Attitudes about Sleep scale; ESS: Epworth Sleepiness Scale; GSDS: General Sleep Disturbance Scale; HRSD: Hamilton Rating Scale for Depression with sleep; ISI: Insomnia Severity Index; PSQI: Pittsburgh Sleep Quality Index; NWAK: number of awakenings; SE: sleep efficiency; SOL: sleep-onset latency; SQ: sleep quality; SQ_PSQI: extracted sleep quality score based on PSQI; SRBQ: Sleep-Related Behavior Questionnaire; SSR: Sleep Satisfaction Rate; TST: total sleep time; WASO: wakefulness after sleep onset.
Summary of sleep measurement tools and study design for mobile phone sleep intervention included in the systematic review (N=16).
Measurement | Scales | Case study & pre-post test (n=4) | RCT (n=12) | ||
Standard treatment: CBT-I & CPAP (n=5)a | Other recognized treatment (n=7) | Waitlist (n=4)b | |||
Apnea-Hypopnea Index (AHI) | Score (total apneas event/TST) | 0 | 2 [ |
0 | 0 |
Bed time stress (BTS) | Scored on scale 0-5 | 0 | 0 | 1 [ |
1 [ 59] |
Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS) | 30 items with scale 1-10 (total: 300) | 0 | 1 [ |
1 [ |
2 [ |
Epworth Sleepiness Scale (ESS) | 8 items with scale 0-3 (total: 24) | 1 [ |
2 [ |
1 [ |
0 |
General Sleep Disturbance Scale (GSDS) | 21 items with scale 0-7 (total:147) | 0 | 0 | 1 [ |
0 |
Hamilton Rating Scale for Depression with sleep (HRSD) | Total 21 items:c: 10 items with scale 0-4, 2 items with scale 0-3, and 10 items with scale 0-2 (total: 66) | 1 [ |
0 | 0 | 0 |
Insomnia Severity Index (ISI) | 7 items with scale 0-4 (total: 28) | 1 [ |
3 [ |
2 [ |
2 [ |
Number of awakenings (NWAK) | Frequency of awakening | 1 [ |
0 | 0 | 0 |
Pittsburgh Sleep Quality Index (PSQI) | 7 components calculated from 9 questions with scale 0-3 (total: 21) | 1 [ |
2 [ |
2 [ |
3 [ |
Sleep efficiency (SE)c | Percentage (TST/total time in bed) | 0 | 2 [ |
2 [ |
2 [ |
Sleep-onset latency (SOL) | Minutes | 1 [ |
2 [ |
2 [ |
2 [ |
Sleep quality (SQ)c | Scored on scale 1-5 | 1 [ |
1 [ |
2 [ |
2 [ |
Extracted sleep quality score based on PSQI (SQ_PSQI) | 8 items with scale 1-4 (total: 32) | 0 | 0 | 1 [ |
1 [ |
Sleep-Related Behavior Questionnaire (SRBQ) | 32 items with scale 1-5 (total: 160) | 0 | 0 | 1 [ |
1 [ |
Sleep Satisfaction Rate (SSR)c | Scored on scale 0-3 | 1 [ |
0 | 0 | 0 |
Total sleep time (TST) | Hours | 0 | 2 [ |
2 [ |
3 [ |
Wakefulness after initial sleep onset (WASO) | Minutes | 1 [ |
2 [ |
1 [ |
2 [ |
aCBT-I: Cognitive Behavioral Therapy for Insomnia; CPAP: continuous positive airway pressure.
bThree articles have two comparison groups (waitlist vs other).
cHigher scores indicate lesser severity.
Specifically, the Pittsburgh Sleep Quality Index (PSQI) [
Summary statistics: quality assessment of each journal and the effects of intervention through
Author(s), year | Quality |
Study design: intervention typea | Sleep |
Effect size, |
95% CI | |
Anttalainen el al, 2014 [ |
7 | RCT: mobile + CPAP vs standard CPAP | AHI | –1.9 (2.9) | –2.4, –1.4 | <.001 |
ESS | 0.0 (1.7) | –0.3, 0.3 | >.99 | |||
Babson et al, 2015 [ |
3 | Pre-post | PSQI | –1.5 (2.4) | –6.2, 3.2 | NA |
Bauer et al, 2012 [ |
5 | Pre-post | ESS | –1.7 (1.3) | –2.4, –0.9 | .001 |
Chen et al, 2015 [ |
2 | Case study | SSR | NA | ||
Filion et al, 2015 [ |
8 | RCT: smoking prevention text vs sleep or activity | SQ_PSQI | –0.2 (1.7) | –0.5, 0.1 | .14 |
TST (weekday) | 0.6 (0.5) | 0.5, 0.6 | <.001 | |||
Promotion text | TST (weekend) | 0.6 (0.7) | 0.5, 0.7 | <.001 | ||
Fox el al, 2012 [ |
8 | RCT: mobile + CPAP vs standard CPAP | AHI | –1.9 (4.3) | –2.9, –0.9 | <.001 |
ESS | –0.9 (5.1) | –2.1, 0.3 | .13 | |||
Freeman et al, 2015 [ |
9 | RCT: mobile vs waitlist | PSQI | –2.6 (1.5) | –2.9, –2.2 | <.001 |
RCT: mobile vs standard treatment | PSQI | –0.7 (1.4) | –1.0, –0.4 | <.001 | ||
Ho et al, 2014 [ |
8 | RCT: mobile + CBT-I vs waitlist | DBAS | –23.5 (15.1) | –25.5, –21.5 | <.001 |
ISI | –3.1 (1.8) | –3.3, –2.9 | <.001 | |||
PSQI | –2.3 (1.5) | –2.5, –2.1 | <.001 | |||
SE | 4.5 (4.8) | 3.8, 5.2 | <.001 | |||
SOL | –7.4 (12.9) | –9.2, –5.6 | <.001 | |||
SQ | –0.2 (0.2) | –0.2, –0.2 | <.001 | |||
TST | 0.0 (8.8) | –1.2, 1.2 | .99 | |||
WASO | –10.8 (14.8) | –12.8, –8.8 | <.001 | |||
RCT: mobile + CBT-I vs standard CBT-I | DBAS | –0.3 (16.0) | –2.5, 1.9 | <.001 | ||
ISI | –1.1 (1.8) | –1.3, –0.9 | <.001 | |||
PSQI | –0.8 (1.5) | –1.0, –0.6 | <.001 | |||
SE | 1.2 (4.8) | 0.5, 1.9 | <.001 | |||
SOL | –7.1 (12.9) | –8.9, –5.3 | <.001 | |||
SQ | –0.1 (0.2) | –0.1, –0.1 | <.001 | |||
TST | –0.1 (8.8) | –1.3, 1.1 | .74 | |||
WASO | –7.7 (14.7) | –9.7, –5.7 | <.001 | |||
Jernelov et al, 2012 [ |
9 | RCT: mobile + bibliotherapy vs waitlist | BTS | –0.9 (0.4) | –1.0, –0.8 | <.001 |
DBAS | –56.4 (9.8) | –58.4, –54.4 | <.001 | |||
ISI | –8.8 (1.6) | –9.1, –8.5 | <.001 | |||
SE | 15.3 (7.1) | 13.8, 16.8 | <.001 | |||
SOL | –30.7 (20.4) | –35.0, –26.4 | <.001 | |||
SQ | 0.9 (0.2) | 0.9, 0.9 | <.001 | |||
SRBQ | –21.9 (4.8) | –22.9, –20.9 | <.001 | |||
TST | 0.4 (0.5) | 0.3, 0.5 | <.001 | |||
WASO | –30.1 (18.9) | –34.1, –26.1 | <.001 | |||
RCT: mobile+ bibliotherapy vs bibliotherapy only | BTS | –0.9 (0.4) | –1.0, –0.8 | <.001 | ||
DBAS | –37.0 (11.0) | –39.3, –34.7 | <.001 | |||
ISI | –4.5 (1.5) | –4.8, –4.2 | <.001 | |||
SE | 10.2 (7.0) | 8.7, 11.7 | <.001 | |||
SOL | –14.8 (21.8) | –19.3, –10.3 | <.001 | |||
SQ | 0.5 (0.2) | 0.5, 0.5 | <.001 | |||
SRBQ | –15.3 (5.4) | –16.4, –14.2 | <.001 | |||
TST | 0.0 (0.5) | –0.1, 0.1 | .69 | |||
WASO | –21.1 (19.4) | –25.1, –17.1 | <.001 | |||
Koffel et al, 2016 [ |
8 | RCT: app use vs non app | ISI | 3.4 (4.6) | 1.2, 5.5 | .008 |
Lichstein et al, 2013 [ |
6 | Pre-post | HRSD | –7.0 (3.6) | –10.2, –3.8 | .01 |
ISI | –11.3 (3.3) | –14.2, –8.3 | .002 | |||
NWAK | –1.7 (2.1) | –3.5, 0.1 | .14 | |||
SOL | –18.4 (20.9) | –36.7, –0.1 | .12 | |||
SQ | 0.5 (0.1) | 0.4, 0.6 | .001 | |||
WASO | –23.8 (9.2) | –31.9, –15.7 | .004 | |||
Kauffman 2016 [ |
8 | RCT: mobile + CBT-I vs menopause education | ISI | –4.0 (5.6) | –5.1, –2.9 | <.001 |
PSQI | –1.6 (3.0) | –2.2, –1.0 | <.001 | |||
SE | 3.2 (12.3) | 0.8, 5.6 | .009 | |||
SOL | –11.9 (36.3) | –18.8, –5.0 | .001 | |||
TST | 0.2 (1.2) | 0.0, 0.4 | .08 | |||
WASO | –6.8 (47.3) | –15.8, 2.2 | .14 | |||
Mendelson et al, 2014 [ |
8 | RCT: app + CPAP vs standard CPAP | ESS | –0.2 (1.5) | –0.5, 0.1 | .17 |
Stremler et al, 2006 [ |
8 | RCT: mobile + education vs education | GSDS | –13.3 (8.2) | –16.2, –10.4 | <.001 |
van Drongelen et al, 2014 [ |
9 | RCT: app use vs non app | PSQI | –0.6 (1.3) | –0.7, –0.4 | <.001 |
Vuletic el al, 2016 [ |
8 | RCT: mobile + education vs education | PSQI | –1.5 (1.5) | –1.7, –1.4 | <.001 |
aCBT-I: cognitive behavioral therapy for insomnia; CPAP: continuous positive airway pressure.
bAHI: Apnea-Hypopnea Index; BTS: bed time stress; DBAS: Dysfunctional Beliefs and Attitudes about Sleep Scale; ESS: Epworth Sleepiness Scale; GSDS: General Sleep Disturbance Scale; HRSD: Hamilton Rating Scale for Depression with sleep; ISI: Insomnia Severity Index; PSQI: Pittsburgh Sleep Quality Index; NWAK: number of awakenings; SE: sleep efficiency; SOL: sleep-onset latency; SQ: sleep quality; SQ_PSQI: extracted sleep quality score based on PSQI; SRBQ: Sleep-Related Behavior Questionnaire; SSR: Sleep Satisfaction Rate; TST: total sleep time; WASO: wakefulness after initial sleep onset.
Sample size based on the intervention methods (N=16).
Quality assessment of studies to determine the impact of sleep intervention with mobile technology on sleep disorders.
Criteria item | Score, mean (SD) |
1. Research question and objective were stated clearly | 0.94 (0.24) |
2. Definition of telehealth and/or mHealth was stated | 0.18 (0.39) |
3. A control group was included | 0.82 (0.39) |
4. Participants were randomly recruited from well-defined population | 0.82 (0.39) |
5. Sample size was >30 | 0.76 (0.44) |
6. Attrition was analyzed and determined not to significantly differ by respondents’ baseline characteristics between control and experiment groups (<20%) | 0.47 (0.51) |
7. Baseline characteristics between control and intervention groups were similar | 0.76 (0.44) |
8. The intervention period was at least 4 weeks | 0.94 (0.24) |
9. The sleep disorder measurement tools were shown to be reliable and valid in previously published studies | 0.88 (0.33) |
10. Demographic information is available to control potential confounders for future analysis | 0.82 (0.39) |
Total study quality scorea | 7.41 (0.44) |
aBy summing up items 1 to 10 (range 3-10).
The mobile phone can be used as a tool to effectively deliver and enhance traditional behavioral interventions due to its portability. We found eight articles that described the advantages of using mobile phones to improve traditional intervention methods for sleep disorders [
Mobile phones are a tool for intervention studies due to the portability for the participant. Within the results of our review, two studies focused on the effectiveness of mobile phones as intervention tools.
Stremler et al [
Similarly, Vuletic et al [
Both CPAP and CBT-I are considered standard treatments for obstructive sleep apnea (OSA) [
Fox and colleagues [
Anttalainen et al [
Kauffman [
Three studies [
Jernelov et al [
Although Ho et al [
Freedman et al [
Filion et al [
As a measurement tool for sleep disorder intervention, mobile phone apps are able to perform diverse functions (ie, tracking [
Lichstein et al [
Mendelson et al [
Bauer et al [
Van Drongelen et al [
Three studies [
Babson et al [
Chen et al [
Koffel et al [
Although meta-analysis is a great way to examine the effectiveness of interventions with a specific parameter, we did not have enough articles to conduct a meta-analysis. Sleep measurements were not included in all articles selected for the systematic literature review. Because more than four articles were required to perform a meta-analysis, according to
The purpose of our systematic review was to investigate whether mobile phones are a feasible and usable tool to improve sleep disorders and sleep quality in intervention studies. We also assumed that auxiliary mobile phone use helps to enhance the performance of existing intervention methods.
This review presented the articles based on two intervention methods. The first method was utilizing mobile phone apps and the second was conventional mobile phone support through telephone calls or text messages. Although many mobile phone apps were used independently, the conventional mobile phone supporting methods were (1) to use the mobile phone itself as only an intervention tool (eg teleconferencing or telecounseling), or (2) to combine with another treatment (eg, CBT-I, CBT-B, and CPAP) to enhance the effectiveness of sleep interventions. In our review, six studies were related to CBT-I [
Among the sleep outcome measurements, PSQI was the most frequently reported, and PSQI scores of all seven studies [
The ISI was the second most frequently used sleep outcome measurement tool, and all five articles using the measure [
In general, 87.5% (14/16) of the studies [
There are several limitations of the studies included in this review. First, there is no standardized study design, especially for the test period, procedure, and sleep intervention tools. For example, there is a limitation to use all mobile phone apps to compare each study because researchers used their own personally developed or nonpublicly available apps. As such, we were unable to make comparisons between interventions due to differences in app functions and interfaces. Due to the heterogeneity of the study design and sleep measurement tools, it was difficult to compare the effectiveness of the sleep app interventions. This will be an issue for reproducibility in further research. Second, although it is a common limitation for RCT study designs, the uniqueness of each study’s target population limits further analysis and replication. For instance, results from studies with a cannabis disorder use group [
This study has several strengths. To the best of our knowledge, this study was the first to review the effectiveness of mobile phone interventions on sleep quality, quantity, and sleep disorders. By focusing exclusively on the mobile phone itself, it allows us to tailor future mHealth interventions for sleep. Also, our study examined various aspects of sleep measurement tools that account for sleep quality, sleep quantity, and many sleep disorders such as insomnia and sleep latency. Additionally, our study provided evidence of the potential of mobile-based interventions for improving sleep disorders. Along with current research that support the benefits of cost-efficiency of mHealth interventions, these findings provide an impetus for further research examining the empirical evidence of sleep interventions using mobile phones.
In conclusion, our systematic review supports the evidence that mobile technology-based interventions are an effective tool to improve symptoms of sleep disorders and quality of sleep than traditional intervention without mobile phone. Also, we suggest the following research design for future sleep intervention studies: (1) PSQI and ISI as sleep outcome measurements, (2) RCTs, (3) compare with standard treatment (ie, CPAP, CBT-I), and (4) compare to a waitlist control group. In addition to intervention methods, because mobile phone apps vary and many of these apps are not being studied, it is important to perform a content analysis on commercially available apps to determine common functionalities prior to undertaking interventions [
Searching Keywords.
Quality Assessment Score.
Apnea-Hypopnea Index
bed time stress
Cumulative Index to Nursing and Allied Health Literature
continuous positive airway pressure
Dysfunctional Beliefs and Attitudes about Sleep Scale
Epworth Sleepiness Scale
General Sleep Disturbance Scale
Hamilton Rating Scale for Depression with sleep
Insomnia Severity Index
obstructive sleep apnea
Preferred Reporting Items for Systematic Reviews and Meta-Analysis
Pittsburgh Sleep Quality Index
randomized controlled trial
sleep efficiency
sleep-onset latency
sleep quality
Sleep-Related Behavior Questionnaire
Sleep Satisfaction Rate
total sleep time
wakefulness after initial sleep onset
The authors would like to thank Dr Ruopeng An for his advice regarding systematic reviews.
JCS contributed to the concept and design for the current study, performed data analyses, and drafted the manuscript. JK participated in conception and design, data analysis of review procedure, and provided critical feedback on the draft of the manuscript. DGT provided guidance and critical feedback on the concept of the study and drafts of the manuscript. All authors contributed critical revision of the manuscript for important intellectual content and read and approved the final manuscript.
None declared.