Dataset Information

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

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.

SUBMITTER: Choi YK

PROVIDER: S-EPMC6175783 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Choi Yong K YK Demiris George G Lin Shih-Yin SY Iribarren Sarah J SJ Landis Carol A CA Thompson Hilaire J HJ McCurry Susan M SM Heitkemper Margaret M MM Ward Teresa M TM

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 20181015 10

<h4>Study objectives</h4>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.<h4>Methods</h4>We searched four mobile app stores (iTunes Appstore, Android Google Play, Amazon Appstore, and Microsoft Appstore) using the terms "sleep", "sleep management," "sleep m ...[more]

PMID: 30353814

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Project description:BackgroundMobile health interventions (mHealth) based on smartphone applications (apps) are promising tools to help improve diabetes care and self-management; however, more evidence on the efficacy of mHealth in diabetes care is needed. The objective of this study was to conduct a systematic review and meta-analysis of randomized controlled trials (RCTs) assessing the effect of mHealth apps on changes in hemoglobin A1c (HbA1c), blood glucose, blood pressure, serum lipids, and body weight in type 2 diabetes mellitus (T2DM) patients.MethodsTwo independent reviewers searched three online databases (PubMed, the Cochrane Library, and EMBASE) to identify relevant studies published between January 2005 and June 2016. Of the 2,596 articles retrieved, 13 RCTs were included. We used random effects model to estimate the pooled results.ResultsThirteen studies were selected for the systematic review, six of which with data available containing 1,022 patients were included for the meta-analysis. There was a moderate effect on glycemic control after the mHealth app-based interventions. The overall effect on HbA1c shown as mean difference (MD) was -0.40% (-4.37 mmol/mol) (95% confidence interval [CI] -0.69 to -0.11% [-7.54 to -1.20 mmol/mol]; p = 0.007) and standardized mean differences (SMD) was -0.40% (-4.37 mmol/mol) (95% confidence interval [CI] -0.69 to -0.10% [-7.54 to -1.09 mmol/mol]; p = 0.008). A subgroup analysis showed a similar effect with -0.33% (-3.61 mmol/mol) (95% CI -0.59 to -0.06% [-6.45 to -0.66 mmol/mol]; p = 0.02) in MD and -0.38% (-4.15 mmol/mol) (95% CI -0.71 to -0.05% [-7.76 to -0.55 mmol/mol]; p = 0.02) in SMD in studies where patients' baseline HbA1c levels were less than 8.0%. No effects of mHealth app interventions were found on blood pressure, serum lipids, or weight. Assessment of overall study quality and publication bias demonstrated a low risk of bias among the six studies.ConclusionsSmartphone apps offered moderate benefits for T2DM self-management. However, more research with valid study designs and longer follow-up is needed to evaluate the impact of mHealth apps for diabetes care and self-management.

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Dataset Information

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

Study objectives

Methods

Results

Conclusions

Publications

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

Similar Datasets

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