Help for insomnia from the app store? A standardized rating of mobile health applications claiming to target insomnia

Cognitive Behavioral Therapy for Insomnia Self-Management Mobile Apps: A Review of Efficacy and Quality

Cognitive behavioral therapy for insomnia (CBT-I) is the recommended treatment for insomnia, yet multiple barriers limit utilization. Digital CBT-I may present a solution, though related reviews have focused on Internet-based delivery rather than app use. The high utilization of health apps and prevalence of sleep apps indicate the need to equip clinicians with app-specific research. Toward this end, we reviewed efficacy and quality data on self-management CBT-I smartphone apps, revealing efficacy research on eleven apps, five of which were publicly available. While preliminary, these efficacy studies showed consistent positive findings. When examining quantitative quality indicators for the five publicly available apps, two had consistent data. Overall, two apps, CBTi Coach and Insomnia Coach, had positive, empirical findings across all efficacy and quality assessment approaches. We provide recommendations to guide clinician decision making regarding CBT-I self-management apps based on the literature and publicly available methods of app evaluations.

A shot in the dark: the impact of online visibility on the search for an effective sleep app

STUDY OBJECTIVES

Dictated by consumer ratings and concealed algorithms, high levels of online visibility are granted to certain sleep apps on mainstream modes of app selection. Yet, it remains unclear to what extent these highly visible apps are evidence-based. The objectives of this review were to identify and describe the apps with the greatest online visibility when searching for a sleep app and to assess the claimed and actual research associated with them.

METHODS

A keyword search was conducted in Google Play and Google search. Titles of the most visible apps were retrieved. App descriptions were examined to identify research claims made about app effectiveness on sleep and other health-related outcomes. A follow-up search on PubMed and Google Scholar was conducted to verify claims.

RESULTS

The keyword search identified 53 highly visible apps. Examination of app store descriptions found that no reference to research was made for the majority of apps (n = 45, 84.9%). Published research studies were available for just 3 apps, with most studies evaluating app impact on non sleep-related outcomes. There was some evidence to attesting to the effectiveness of 2 apps in improving sleep.

CONCLUSIONS

This review demonstrates how, when carrying out a typical search for a sleep app, information about the evidence base for the majority of highly visible apps is not available. Results highlight the need for the improvement of mainstream modes of app selection in terms of better consumer-app specificity and increased transparency regarding the access to information about the evidence base for apps.

CITATION

Power N, Boivin DB, Perreault M. A shot in the dark: the impact of online visibility on the search for an effective sleep app. J Clin Sleep Med. 2024;20(8):1379-1386.

Effectiveness of mobile applications in improving insomnia symptoms among adults from multi-community: A systematic review and meta-analysis

OBJECTIVE/BACKGROUND

To clarify whether mobile sleep applications alleviate insomnia symptoms among adults from multi-community.

PATIENTS/METHODS

A literature search was conducted using the PubMed, Cochrane, PsycINFO, and Web of Science databases for articles related to mobile technology and sleep interventions published between database inceptions and March 27, 2023. We pooled relevant data using a random-effects model, and a meta-analysis was performed using a web version of the Cochrane Review Manager. The effect size was estimated and reported as the combined overall effect (weighted average). Forest plots were created, and the Cochrane risk-of-bias tool and Newcastle-Ottawa Scale were used to evaluate studies.

RESULTS

After an initial screening and full-text reviews, seven studies were identified with a total of 10,139 participants (females n = 8844, 87.2 %) recruited from multi-community and not diagnosed with sleep disorders or taking medications. These studies included one cross-sectional study investigating mindfulness meditation apps and six randomized-controlled trials (RCTs), including one with sleep-feedback messaging, one comparing sleep applications with or without a wearable device, and four with multicomponent interventions based on cognitive theory and subsequent behavioral change techniques. In a meta-analysis of three cognitive behavior therapy (CBT)-based RCTs, the intervention group showed statistically significant improvements in insomnia symptoms according to the Pittsburgh Sleep Quality Index but with high heterogeneity, while two CBT-based RCTs showed no significant improvements in the Insomnia Severity Index with low heterogeneity.

CONCLUSIONS

A small body of evidence supports the use of CBT-based sleep applications to improve insomnia symptoms among adults from multi-community.

The European Insomnia Guideline: An update on the diagnosis and treatment of insomnia 2023

Progress in the field of insomnia since 2017 necessitated this update of the European Insomnia Guideline. Recommendations for the diagnostic procedure for insomnia and its comorbidities are: clinical interview (encompassing sleep and medical history); the use of sleep questionnaires and diaries (and physical examination and additional measures where indicated) (A). Actigraphy is not recommended for the routine evaluation of insomnia (C), but may be useful for differential-diagnostic purposes (A). Polysomnography should be used to evaluate other sleep disorders if suspected (i.e. periodic limb movement disorder, sleep-related breathing disorders, etc.), treatment-resistant insomnia (A) and for other indications (B). Cognitive-behavioural therapy for insomnia is recommended as the first-line treatment for chronic insomnia in adults of any age (including patients with comorbidities), either applied in-person or digitally (A). When cognitive-behavioural therapy for insomnia is not sufficiently effective, a pharmacological intervention can be offered (A). Benzodiazepines (A), benzodiazepine receptor agonists (A), daridorexant (A) and low-dose sedating antidepressants (B) can be used for the short-term treatment of insomnia (≤ 4 weeks). Longer-term treatment with these substances may be initiated in some cases, considering advantages and disadvantages (B). Orexin receptor antagonists can be used for periods of up to 3 months or longer in some cases (A). Prolonged-release melatonin can be used for up to 3 months in patients ≥ 55 years (B). Antihistaminergic drugs, antipsychotics, fast-release melatonin, ramelteon and phytotherapeutics are not recommended for insomnia treatment (A). Light therapy and exercise interventions may be useful as adjunct therapies to cognitive-behavioural therapy for insomnia (B).

Quality of Digital Health Interventions Across Different Health Care Domains: Secondary Data Analysis Study

BACKGROUND

There are more than 350,000 digital health interventions (DHIs) in the app stores. To ensure that they are effective and safe to use, they should be assessed for compliance with best practice standards.

OBJECTIVE

The objective of this paper was to examine and compare the compliance of DHIs with best practice standards and adherence to user experience (UX), professional and clinical assurance (PCA), and data privacy (DP).

METHODS

We collected assessment data from 1574 DHIs using the Organisation for the Review of Care and Health Apps Baseline Review (OBR) assessment tool. As part of the assessment, each DHI received a score out of 100 for each of the abovementioned areas (ie, UX, PCA, and DP). These 3 OBR scores are combined to make up the overall ORCHA score (a proxy for quality). Inferential statistics, probability distributions, Kruskal-Wallis, Wilcoxon rank sum test, Cliff delta, and Dunn tests were used to conduct the data analysis.

RESULTS

We found that 57.3% (902/1574) of the DHIs had an Organisation for the Review of Care and Health Apps (ORCHA) score below the threshold of 65. The overall median OBR score (ORCHA score) for all DHIs was 61.5 (IQR 51.0-73.0) out of 100. A total of 46.2% (12/26) of DHI's health care domains had a median equal to or above the ORCHA threshold score of 65. For the 3 assessment areas (UX, DP, and PCA), DHIs scored the highest for the UX assessment 75.2 (IQR 70.0-79.6), followed by DP 65.1 (IQR 55.0-73.4) and PCA 49.6 (IQR 31.9-76.1). UX scores had the least variance (SD 13.9), while PCA scores had the most (SD 24.8). Respiratory and urology DHIs were consistently highly ranked in the National Institute for Health and Care Excellence Evidence Standards Framework tiers B and C based on their ORCHA score.

CONCLUSIONS

There is a high level of variability in the ORCHA scores of DHIs across different health care domains. This suggests that there is an urgent need to improve compliance with best practices in some health care areas. Possible explanations for the observed differences might include varied market maturity and commercial interests within the different health care domains. More investment to support the development of higher-quality DHIs in areas such as ophthalmology, allergy, women's health, sexual health, and dental care may be needed.

Association Between the Characteristics of mHealth Apps and User Input During Development and Testing: Secondary Analysis of App Assessment Data

BACKGROUND

User involvement is increasingly acknowledged as a central part of health care innovation. However, meaningful user involvement during the development and testing of mobile health apps is often not fully realized.

OBJECTIVE

This study aims to examine in which areas user input is most prevalent and whether there is an association between user inclusion and compliance with best practices for mobile health apps.

METHODS

A secondary analysis was conducted on an assessment data set of 1595 health apps. The data set contained information on whether the apps had been developed or tested with user input and whether they followed best practices across several domains. Background information was also available regarding the apps' country of origin, targeted condition areas, subjective user ratings, download numbers, and risk (as per the National Institute for Health and Care Excellence Evidence Standards Framework [ESF]). Descriptive statistics, Mann-Whitney U tests, and Pearson chi-square analyses were applied to the data.

RESULTS

User involvement was reported by 8.71% (139/1595) of apps for only the development phase, by 33.67% (537/1595) of apps for only the testing phase, by 21.88% (349/1595) of apps for both phases, and by 35.74% (570/1595) of apps for neither phase. The highest percentage of health apps with reported user input during development was observed in Denmark (19/24, 79%); in the condition areas of diabetes (38/79, 48%), cardiology (15/32, 47%), pain management (20/43, 47%), and oncology (25/54, 46%); and for high app risk (ESF tier 3a; 105/263, 39.9%). The highest percentage of health apps with reported user input during testing was observed in Belgium (10/11, 91%), Sweden (29/34, 85%), and France (13/16, 81%); in the condition areas of neurodiversity (42/52, 81%), respiratory health (58/76, 76%), cardiology (23/32, 72%), and diabetes (56/79, 71%); and for high app risk (ESF tier 3a; 176/263, 66.9%). Notably, apps that reported seeking user input during testing demonstrated significantly more downloads than those that did not (P=.008), and user inclusion was associated with better compliance with best practices in clinical assurance, data privacy, risk management, and user experience.

CONCLUSIONS

The countries and condition areas in which the highest percentage of health apps with user involvement were observed tended to be those with higher digital maturity in health care and more funding availability, respectively. This suggests that there may be a trade-off between developers' willingness or ability to involve users and the need to meet challenges arising from infrastructure limitations and financial constraints. Moreover, the finding of a positive association between user inclusion and compliance with best practices indicates that, where no other guidance is available, users may benefit from prioritizing health apps developed with user input as the latter may be a proxy for broader app quality.