Mobile App Use for Insomnia Self-Management: Pilot Findings on Sleep Outcomes in Veterans

Reilly, Erin D.; Robinson, Stephanie A.; Petrakis, Beth Ann; Kuhn, Eric; Pigeon, Wilfred R.; Wiener, Renda Soylemez; McInnes, D. Keith; Quigley, Karen S.

doi:10.2196/12408

Cited by 30 publications

(29 citation statements)

References 81 publications

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“…Overall, there were 12 published studies: Six of them were feasibility studies [ 22 , 23 , 24 , 25 , 26 , 27 ], and six were clinical effect studies [ 14 , 28 , 29 , 30 , 31 , 32 ]. Figure 3 gives an overview of the systematic search process.…”

Section: Methodsmentioning

confidence: 99%

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A Review of Digital Cognitive Behavioral Therapy for Insomnia (CBT-I Apps): Are They Designed for Engagement?

Uyumaz

Feijs

2021

IJERPH

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There are different ways to deliver Cognitive Behavioral Therapy for Insomnia (CBT-I), of which in-person (face to face) is the traditional delivery method. However, the scalability of in-person therapy is low. Digital Cognitive Behavioral Therapy for Insomnia (dCBT-I) is an alternative and there are tools on the market that are validated in clinical studies. In this paper, we provide a review of the existing evidence-based CBT-I apps and a summary of the published usability-oriented studies of these apps. The goal is to explore the range of interaction methods commonly applied in dCBT-I platforms, the potential impact for the users, and the design elements applied to achieve engagement. Six commercially available CBT-I apps tested by scientifically valid methods were accessed and reviewed. Commonalities were identified and categorized into interactive elements, CBT-I-related components, managerial features, and supportive motivational features. The dCBT-I apps were effectively assisting the users, and the type of interactions promoted engagement. The apps’ features were based on design principles from interactive product design, experience design, online social media, and serious gaming. This study contributes to the field by providing a critical summary of the existing dCBT-I apps that could guide future developers in the field to achieve a high engagement.

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Section: Methodsmentioning

confidence: 99%

“…User Study Report Review: A second review study is carried out based on feasibility and user aspects of each platform with the information gained from the published studies studies [ 22 , 23 , 24 , 25 , 26 , 27 ]. Each article is read by the researcher, notes were taken and then summaries were produced.…”

Section: Methodsmentioning

confidence: 99%

A Review of Digital Cognitive Behavioral Therapy for Insomnia (CBT-I Apps): Are They Designed for Engagement?

Uyumaz

Feijs

2021

IJERPH

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“…Table 1 presents the papers and corresponding studies numbered under the column Number of studies and their objectives. Subjects were veterans or military service members (n=5) [30][31][32][33][34], healthy participants (n=6) [35][36][37][38][39][40], individuals with insomnia or sleep disturbances (n=4) [41][42][43][44], veterans affairs CBT-I clinicians (n=2) [45,46], and nurses with insomnia (n=1) [47]. The sample size varied among studies, ranging from 2 to 176.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Self-report questionnaires [30][31][32][33]41,43,44,47] Insomnia Severity Index [30,32,34,41,43,44] Pittsburgh Sleep Quality Index [30,39,40,43] Epworth Sleepiness Scale [41,44] Dysfunctional Beliefs and Attitudes about Sleep-16 [32] Functional Outcomes of Sleep Questionnaire App sleep diary measures [44] Time in bed [44] Total sleep time [41,43,44] Sleep efficiency (percentage of time spent asleep while in bed) [43] Wake after sleep onset (wake time after initial sleep onset) [41,43] Sleep onset latency (time taken to fall asleep) Actigraphy (objective) [39,41] Sleep efficiency [39,41] Total sleep time [41] Stages of sleep [32] Number of awakenings Fitbit [43] Wake after sleep onset [43] Sleep efficiency…”

Section: Sleepmentioning

confidence: 99%

Framework for the Design Engineering and Clinical Implementation and Evaluation of mHealth Apps for Sleep Disturbance: Systematic Review

Aji¹,

Gordon²,

Stratton³

et al. 2021

J Med Internet Res

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Background Mobile health (mHealth) apps offer a scalable option for treating sleep disturbances at a population level. However, there is a lack of clarity about the development and evaluation of evidence-based mHealth apps. Objective The aim of this systematic review was to provide evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. Methods A systematic search of studies published from the inception of databases through February 2020 was conducted using 5 databases (MEDLINE, Embase, Cochrane Library, PsycINFO, and CINAHL). Results A total of 6015 papers were identified using the search strategy. After screening, 15 papers were identified that examined the design engineering and clinical implementation and evaluation of 8 different mHealth apps for sleep disturbance. Most of these apps delivered cognitive behavioral therapy for insomnia (CBT-I, n=4) or modified CBT-I (n=2). Half of the apps (n=4) identified adopting user-centered design or multidisciplinary teams in their design approach. Only 3 papers described user and data privacy. End-user acceptability and engagement were the most frequently assessed implementation metrics. Only 1 app had available evidence assessing all 4 implementation metrics (ie, acceptability, engagement, usability, and adherence). Most apps were prototype versions (n=5), with few matured apps. A total of 6 apps had supporting papers that provided a quantitative evaluation of clinical outcomes, but only 1 app had a supporting, adequately powered randomized controlled trial. Conclusions This is the first systematic review to synthesize and examine evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. The minimal number of apps with published evidence for design engineering and clinical implementation and evaluation contrasts starkly with the number of commercial sleep apps available. Moreover, there appears to be no standardization and consistency in the use of best practice design approaches and implementation assessments, along with very few rigorous efficacy evaluations. To facilitate the development of successful and evidence-based apps for sleep disturbance, we developed a high-level framework to guide researchers and app developers in the end-to-end process of app development and evaluation.

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“…Previous studies have focused on assessing the efficacy, feasibility, and usability of CBT-I apps to complement standard treatment for insomnia, such as sleep medication use or traditional CTB-I programs [ 29 - 32 ]; they have not focused on how to lower the barriers to the acceptance of a mobile app–based CBT-I by older adults, who often have learning difficulties in the initial phase. In terms of age-related barriers to usability evaluations with older adults, half of the identified high-level usability issues with mHealth app use were related to motivational barriers, such as low computer literacy and low trust in their own ability to use the apps, more than cognitive and perceptional barriers, such as working memory and visual acuity [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Mobile App Use for Insomnia Self-Management in Urban Community-Dwelling Older Korean Adults: Retrospective Intervention Study

Chung¹,

Kim²,

Lee³

et al. 2020

JMIR Mhealth Uhealth

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Background As an evidence-based psychotherapy for treating insomnia, cognitive behavioral therapy for insomnia (CBT-I), which helps people with sleep problems to change their unhelpful sleep-related beliefs and habits, has been well-established in older adults. Recently, the utilization of mobile CBT-I apps has been getting attention from mental health professionals and researchers; however, whether mobile CBT-I apps are usable among older users has yet to be determined. Objective The aims of this study were to explore the relationships between subjective sleep quality and subjective memory complaints and depressive symptoms; to explore the relationship between perceived difficulty in mobile app use and usability of the mobile phone–based self-help CBT-I app, named MIND MORE, in urban community-dwelling Korean older adults; to compare changes in subjective sleep quality from pre-intervention to post-intervention, during which they used the mobile app over a 1-week intervention period; and evaluate adherence to the app. Methods During the 2-hour training program delivered on 1 day titled “Overcoming insomnia without medication: How to use the ‘MIND MORE’ mobile app for systematic self-management of insomnia” (pre-intervention), 41 attendants were asked to gain hands-on experience with the app facilitated by therapists and volunteer workers. They were then asked to complete questionnaires on sociodemographic characteristics, subjective evaluation of mental health status (ie, depression, memory loss and impairment, and sleep problems), and app usability. For the 1-week home-based self-help CBT-I using the app (post-intervention), 9 of the 41 program attendants, who had already signed up for the pre-intervention, were guided to complete the given questionnaires on subjective evaluation of sleep quality after the 1-week intervention, specifically 8 days after the training program ended. Results Due to missing data, 40 of 41 attendants were included in the data analysis. The main findings of this study were as follows. First, poor subjective sleep quality was associated with higher ratings of depressive symptoms (40/40; ρ=.60, P<.001) and memory complaints (40/40; ρ=.46, P=.003) at baseline. Second, significant improvements in subjective sleep quality from pre-intervention to post-intervention were observed in the older adults who used the MIND MORE app only for the 1-week intervention period (9/9; t8=3.74, P=.006). Third, apart from the program attendants who did not have a smartphone (2/40) or withdrew from their MIND MORE membership (3/40), those who attended the 1-day sleep education program adhered to the app from at least 2 weeks (13/35, 37%) to 8 weeks (2/35, 6%) without any further contact. Conclusions This study provides empirical evidence that the newly developed MIND MORE app not only is usable among older users but also could improve subjective sleep quality after a 1-week self-help intervention period.

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Mobile App Use for Insomnia Self-Management: Pilot Findings on Sleep Outcomes in Veterans

Cited by 30 publications

References 81 publications

A Review of Digital Cognitive Behavioral Therapy for Insomnia (CBT-I Apps): Are They Designed for Engagement?

A Review of Digital Cognitive Behavioral Therapy for Insomnia (CBT-I Apps): Are They Designed for Engagement?

Framework for the Design Engineering and Clinical Implementation and Evaluation of mHealth Apps for Sleep Disturbance: Systematic Review

Mobile App Use for Insomnia Self-Management in Urban Community-Dwelling Older Korean Adults: Retrospective Intervention Study

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