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Sleep apps and behavioral constructs: A content analysis
Abstract
Although sleep apps are among the most popular commercially available health apps, little is known about how well these apps are grounded in behavioral theory. Three-hundred and sixty-nine apps were initially identified using the term “sleep” from the Google play store and Apple iTunes in September 2015. The final sample consisted of 35 apps that met the following inclusion criteria: 1) Stand-alone functionality; 2) Sleep tracker or monitor apps ranked by 100 + users; 3) Sleep Alarm apps ranked by 1000 + users; and 4) English language. A coding instrument was developed to assess the presence of 19 theoretical constructs. All 35 apps were downloaded and coded. The inter-rater reliability between coders was 0.996. A “1” was assigned if a construct was present in the app and “0” if it was not. Mean scores were calculated across all apps, and comparisons were made between total scores and app ratings using R. The mean behavior construct scores (BCS) across all apps was 34% (5% - 84%). Behavioral constructs for realistic goal setting (86%), time management (77%), and self-monitoring (66%) were most common. Although a positive association was observed between BCS and user ratings, this was not found to be statistically significant (p > 0.05). The mean persuasive technology score was 42% (20% to 80%), with higher scores for paid compared to free apps (p < 0.05). While the overall behavior construct scores were low, an opportunity exists to develop or modify existing apps to support sustainable sleep hygiene practices.
... There is a growing concern that apps should not be used for sleep because they can cause orthosomnia: an individual's unhealthy obsession with achieving perfect sleep [8]. However, there are other reports that technology has the potential to improve sleep outcomes [9][10][11]. Possibly, the lack of consensus for the viability of mHealth apps with sleep is because of the components making up the intervention. ...
... Number of null outcomes, n (%) Number of positive outcomes, n (%) a 5 (50) 5 (50) 11 Horsch et al [45] 2 (40) 3 (60) 10 Espie et al [48] 0 (0) 1 (100) 10 Luik et al [50] 7 (54) 6 (46) 9 Murawski et al [24] 2 (22) 7 (78) 9 Kang et al [47] 0 (0) 4 (100) 8 Pulantara et al [46] 6 (67) 3 (33) 8 Reilly et al [49] 2 (100) 0 (0) 7 Oftedal et al [51] 0 (0) 5 (100) 7 Chu et al [55] 0 (0) 1 (100) 6 Horsch et al [54] 1 (25) 3 (75) 4 Bostock et al [53] 3 (75) 1 (25) 3 van Drongelen et al [52] a The percentage of positive outcomes for sleep was calculated using the number of outcomes that were positive divided by the total number of outcomes (sum of the number of positive results and number of null results) reported across studies using this number of BCTs. ...
... Number of null outcomes, n (%) Number of positive outcomes, n (%) a 5 (50) 5 (50) 11 Horsch et al [45] 2 (40) 3 (60) 10 Espie et al [48] 0 (0) 1 (100) 10 Luik et al [50] 7 (54) 6 (46) 9 Murawski et al [24] 2 (22) 7 (78) 9 Kang et al [47] 0 (0) 4 (100) 8 Pulantara et al [46] 6 (67) 3 (33) 8 Reilly et al [49] 2 (100) 0 (0) 7 Oftedal et al [51] 0 (0) 5 (100) 7 Chu et al [55] 0 (0) 1 (100) 6 Horsch et al [54] 1 (25) 3 (75) 4 Bostock et al [53] 3 (75) 1 (25) 3 van Drongelen et al [52] a The percentage of positive outcomes for sleep was calculated using the number of outcomes that were positive divided by the total number of outcomes (sum of the number of positive results and number of null results) reported across studies using this number of BCTs. ...
Background:
Mobile health (mHealth) apps targeting health behaviors using behavior change techniques (BCTs) have been successful in promoting healthy behaviors; however, their efficacy with sleep is unclear. Some work has shown success in promoting sleep through mHealth, whereas there have been reports that sleep apps can be adverse and lead to unhealthy obsessions with achieving perfect sleep.
Objective:
This study aims to report and describe the use of BCTs in mHealth apps for sleep with the following research questions: How many BCTs are used on average in sleep apps, and does this relate to their effectiveness on sleep outcomes? Are there specific BCTs used more or less often in sleep apps, and does this relate to their effectiveness on sleep outcomes? Does the effect of mHealth app interventions on sleep change when distinguishing between dimension and measurement of sleep?
Methods:
We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review articles on mHealth app interventions for sleep published between 2010 and 2020.
Results:
A total of 12 studies met the eligibility criteria. Most studies reported positive sleep outcomes, and there were no negative effects reported. Sleep quality was the most common dimension of sleep targeted. Subjective measures of sleep were used across all apps, whereas objective measures were often assessed but rarely reported as part of results. The average number of BCTs used was 7.67 (SD 2.32; range 3-11) of 16. Of the 12 studies, the most commonly used BCTs were feedback and monitoring (n=11, 92%), shaping knowledge (n=11, 92%), goals and planning (n=10, 83%), and antecedents (n=10, 83%), whereas the least common were scheduled consequences (n=0, 0%), self-belief (n=0, 0%), and covert learning (n=0, 0%). Most apps used a similar set of BCTs that unfortunately did not allow us to distinguish which BCTs were present when studies reported more positive outcomes.
Conclusions:
Our study describes the peer-reviewed literature on sleep apps and provides a foundation for further examination and optimization of BCTs used in mHealth apps for sleep. We found strong evidence that mHealth apps are effective in improving sleep, and the potential reasons for the lack of adverse sleep outcome reporting are discussed. We found evidence that the type of BCTs used in mHealth apps for sleep differed from other health outcomes, although more research is needed to understand how BCTs can be implemented effectively to improve sleep using mHealth and the mechanisms of action through which they are effective (eg, self-efficacy, social norms, and attitudes).
... While no studies have previously examined sleep apps for children, 2 studies have examined sleep apps for adults. One study examined behavioral constructs contained within the apps to evaluate how well these apps are grounded in behavioral theory, which has a strong evidence base for sleep interventions [6]. Grigsby-Toussaint et al [6] evaluated 35 sleep apps for adults and found that only 34% incorporated evidence-based behavioral constructs. ...
... One study examined behavioral constructs contained within the apps to evaluate how well these apps are grounded in behavioral theory, which has a strong evidence base for sleep interventions [6]. Grigsby-Toussaint et al [6] evaluated 35 sleep apps for adults and found that only 34% incorporated evidence-based behavioral constructs. The most common behavioral constructs were realistic goal setting, time management, and self-monitoring. ...
... Overall, our findings show that apps targeting sleep in pediatric populations were less likely to incorporate evidence-based behavioral strategies than sleep apps targeted to adult populations (only 21.6% vs 33%-34%) [6,7]. Our findings are consistent with a lack of evidence-based support in apps for other childhood difficulties, such as apps for infant feeding [12,13]. ...
Background
Empirically supported treatments for pediatric sleep problems exist, but many families turn to other sources for help with their children’s sleep, such as smartphone apps. Sleep apps are easy for families to access, but little evidence exists regarding the validity of the services and information provided in the developer descriptions of the apps.
Objective
The goal of this study was to examine the features and claims of developer descriptions of sleep apps for children.
Methods
A search of the Apple iTunes store and Google Play was conducted using the terms “kids sleep,” “child sleep,” and “baby sleep.” Data on the type of app, price, user rating, and number of users were collected. Apps were analyzed in comparison with evidence-based behavioral strategies and were thematically coded on the basis of claims provided in developer descriptions.
Results
A total of 83 app descriptions were examined, of which only 2 (2.4%) offered sleep improvement strategies. The majority were sound and light apps (78%) and 19% were bedtime games or stories. Only 18 of 83 (21.6%) apps were identified as containing empirically supported behavioral sleep strategies. Despite this, many apps asserted claims that they will help children “fall asleep instantly,” “cry less and sleep better,” or improve child development.
Conclusions
A large variety of sleep apps exist for use among children, but few include evidence-based behavioral strategies according to the developer descriptions of the apps. Addressing sleep difficulties in children is important to promote physical, cognitive, and emotional development. Collaboration between sleep researchers and technology developers may be beneficial for creating evidence-supported apps to help with children’s sleep in the future.
... It substantiates a potential appeal to the body of literature that has revealed that the voices of professional caregivers can invasively pervade the sound environment. [11][12][13] Additionally, where acoustic obtrusion compromises sleep health in the most fragile patient environments, such as geriatric wards, 9 where incidents of falls and memory loss are often attributed to sleep impairment, EMT may offer a safe, viable, cost-effective option. ...
... They included realistic goal setting (86%), time management (77%), and self-monitoring (66%) as the most common sought contingencies in developing programs that were most suitable for sleep. 12 The potential for successful use of music in therapeutic and clinical settings may present a viable, low-cost option, particularly appealing in our culture of pharmacologic addiction, as it is free from side effects. 13 Yet, the majority of studies claiming to implement "music therapy" in numerous clinical trials do not involve music therapy, nor do they include consultation with a music therapist. ...
Sleep deficiency is linked to chronic health problems, such as heart disease, kidney disease, high blood pressure, diabetes, stroke, obesity, and depression. Healthcare practitioners are increasingly paying close attention to sleep and its impact on health and wellness as a measure of critical vitality. Sleep’s impact on neurologic function, and cognitive endurance affect capacity throughout the lifespan. This article will address recent findings related to the potential of music to induce sleep in illness and wellness. Music therapy research findings and its efficacy as a potent cost-effective intervention will be highlighted.
... How compatible are apps with psychologically validated models of behavioral change interventions? Grigsby-Toussaint et al. [18] concluded that the scientific grounding of current apps, as regarding behavioral change, is rather modest. They developed research methods for analyzing the existence of behavioral change constructs in different sleeping apps based on 19 theoretical constructs categorized in 5 clusters: knowledge, cognitive constructs, behavior strategies, emotion-focused strategies, and therapeutic interventions. ...
... Realistic goal setting, time management, and self-monitoring were the behavioral constructs found in most of the apps. Grigsby-Toussaint et al. [18] concluded that "phone apps integrating behavioral constructs could positively influence sleep behaviors", but one should take into account the side effects of using digital devices in bed or right before sleeping. ...
... While there is some recent research supporting the presence of empirically supported apps that provide some degree of education, tools, or recommendations/interventions for sleep difficulties in the general population, it is unclear whether any apps provide a comprehensive intervention geared towards AYA populations. A recent review of all available sleep-focused apps (i.e., non-AYA-specific) yielded 35 sleep apps that met basic functionality requirements [26]. Of these apps, the researcher noted that many included various key functionality tools such as goal setting, time management, self-monitoring of symptoms, and generally seemed to encourage healthy sleep hygiene; however, ultimately few apps were found to support behavior changes consistent with improving sleep. ...
... Additionally, the search terms, inclusion/exclusion criteria, and functionality assessment components were developed for the present study based on the authors' knowledge of insomnia and its treatment, clinical experience, and expertise in the field of sleep. Although reference was made to other scoping reviews of smartphone apps [20,26], it is possible that in designing this study, we reviewed apps that we may have otherwise excluded based on more rigorous, empirically supported criteria. ...
BACKGROUND
Sleep difficulties are prevalent and concerning for many North Americans. Despite strong empirical support for insomnia treatment, lack of access presents a significant barrier to treatment dissemination. This is particularly true amongst teens and young adults. Mobile applications (‘apps’) are uniquely suited to address this need.
OBJECTIVE
We conducted a scoping review to identify and appraise commercially available apps for AYAs with sleep difficulties.
METHODS
Proceeding in 3 phases, a comprehensive search of commercially available apps was conducted between August 2016 and January 2017. The initial phase involved a search of app stores using relevant search terms (sleep; sleeping; insomnia; sleep aid; night). In the second phase, apps were assessed for eligibility using the following inclusion criteria: 1) Goal is to provide education, tools, or advice related to management of insomnia symptoms. 2) Primary intended users are AYAs. Exclusion criteria were: 1) App is classified as an ‘e-book.’ 2) Primary utility is meditation, hypnosis, or relaxation for sleep. 3) Primary function is background sleep music or sounds. 4) Primary function is alarm clock. 5) Sole sleep aid function is tracking/monitoring, with no education, tools, or advice for insomnia. In the third phase, apps were culled for functionality information, including: A) Self-monitoring of symptoms; B) Tracking sleep; C) Education related to insomnia; D) Advice or intervention for managing insomnia symptoms. Finally, the primary investigator conducted a final review of phase 3 apps, closely examining the functionality of these apps, based on app descriptions, app content, and developer website (where available).
RESULTS
The initial search yielded 2036 apps; after eligibility criteria were applied, functionality information was extracted for 48 apps. Twenty-three of these were later excluded. Of the final 25 apps, 24% included self-monitoring of symptoms; 28% included a sleep tracking function; 56% provided insomnia education; and 92% provided advice or intervention for managing sleep difficulties. The majority (80%) were free. Several (20%) provided sleep interventions that are not supported by research. In the final evaluation, only 6 apps met all four of the functionality criteria; of these, none were geared towards AYA users specifically. The purported and examined functionality of these six apps are discussed.
CONCLUSIONS
Insomnia is a unique problem among AYAs, as non-insomnia factors must also be considered when designing an appropriate intervention (e.g., AYAs are more delayed in sleep schedule, require more sleep than adults). There are currently 6 apps that are appropriate for self-management of adult insomnia. There are 0 apps designed for AYA users. Development of an evidence-based app for managing insomnia in this population is critical. Once an appropriate app becomes available, future studies should test its usability and efficacy in AYA samples.
... These scales could be further validated among the Chinese working population. Further studies can also consider the use of objective measures (e.g., sleep applications in smartphone devices; see Grigsby-Toussaint et al., 2017) in addition to selfreported data. Third, we collected survey data from the general working population and did not have access to clinical samples with sleep or addictive disorders. ...
The prevalence of workaholism has negative consequences on human health. Lack of sleep, a well-known problem among adults in modern society, is often attributed to overwork as a result of workaholism. Yet there is a lack of empirical research examining how and when workaholism will lead to sleep problems. To answer this question and to examine the longitudinal effect of workaholism on sleep in China, we investigate the mediating role of perceived evening responsibilities of work and the moderating effect of work autonomy. Two hundred and five Chinese working adults (58.0% female) voluntarily completed the online questionnaires at Time 1 (T1) and Time 2 (T2; 1-month later). Results showed that workaholism at T1 had a significant and positive correlation with sleep problem at T2. Further analysis suggested that perceived evening responsibilities of work fully mediated the relationship between workaholism and sleep problem. Work autonomy was shown to buffer the positive effect of workaholism on perceived evening responsibilities of work and attenuate the indirect effect of workaholism on sleep problem. While workers should be made aware of the negative impact of workaholism on sleep, organizations should also consider interventions to enhance employees’ autonomy and control of their work.
... Recently, various consumer-grade wearable devices operating with smartphone-specific applications are available. 2 These applications focus on activity and sleep and enable individuals to monitor their overall health. 3 They can also be used to improve patient empowerment when treating sleep disorders. ...
Objectives: Galaxy Watch 3 (GW3) is a commercially available smartwatch equipped with a sleep-tracking function capable of collecting longitudinal sleep data in a real-world environment. We aimed to investigate the validity of GW3 for estimating sleep stages compared with reference data from polysomnography (PSG).Methods: Thirty-two healthy adults (mean age 37.8, male 87.5%) were recruited to wear a GW3 concurrently with in-laboratory overnight PSG recording. Sleep parameters, including total sleep time (TST) and the duration of each sleep stage (light, deep, and rapid eye movement [REM] sleep), were calculated for both GW3 and PSG. Sleep parameters were compared using intraclass correlation coefficients (ICCs) and Bland–Altman plots. The epoch-by-epoch classification performance was evaluated to determine the sensitivity, specificity, accuracy, kappa values, and confusion matrices.Results: Bland–Altman plots showed moderate agreement between GW3 and PSG for TST (ICC=0.640), light sleep (ICC=0.518), and deep sleep (ICC=0.639), whereas REM sleep duration was not reliably estimated using the GW3. The GW3 overestimated TST by a mean of 9.5 min. The sensitivity of epoch-by-epoch sleep detection was 0.954; however, the specificity was 0.524. The sensitivity of each sleep stage estimation was 0.695 for light sleep, 0.612 for deep sleep, and 0.598 for REM sleep. The overall accuracy of GW3 in distinguishing the four-stage sleep epochs was 0.651.Conclusions: GW3 demonstrated high performance in sleep detection but moderate performance in wake determination and sleep stage estimation compared with PSG results, which were comparable to previously reported results for other consumer wearable devices.
... Mobile apps can be used to support healthy sleep habits. Grigsby-Toussaint et al. (43) examined 35 apps and found that only a few apps included features to change behavior. ...
Sleep disorders are prevalent nowadays, leading to anxiety, depression, high blood pressure, and other health problems. Due to the proliferation of mobile devices and the development of communication technologies, mobile apps have become a popular way to deliver sleep disorder therapy or manage sleep. This scoping review aims to conduct a systematic investigation of mobile apps and technologies supporting sleep, including the essential functions of sleep apps, how they are used to improve sleep and the facilitators of and barriers to using apps among patients and other stakeholders. We searched articles (2010 to 2022) from Scopus, Web of Science, Science Direct, PubMed, and IEEE Xplore using the keyword sleep apps. In total, 1,650 peer-reviewed articles were screened, and 51 were selected for inclusion. The most frequently provided functions by the apps are sleep monitoring, measuring sleep, providing alarms, and recording sleep using a sleep diary. Several wearable devices have been used with mobile apps to record sleep duration and sleep problems. Facilitators and barriers to using apps were identified, along with the evidence-based design guidelines. Existing studies have proved the initial validation and efficiency of delivering sleep treatment by mobile apps; however, more research is needed to improve the performance of sleep apps and devise a way to utilize them as a therapy tool.
... Interestingly, most of the systems reviewed in this article have not formally self-identified as RSs despite offering tailored actionable recommendations for improving sleep. There have been promising reviews on sleep apps, but they are notably limited to the traditional scope of mHealth [44][45][46][47][48][49][50]. To our knowledge, the present study is the first to examine digital sleep health systems through the lens of CARS and to formally introduce the concept of RS to ubiquitous sleep computing research. ...
The practice of quantified-self sleep tracking has become increasingly common among healthy individuals as well as patients with sleep problems. However, existing sleep-tracking technologies only support simple data collection and visualization and are incapable of providing actionable recommendations that are tailored to users’ physical, behavioral, and environmental context. A promising solution to address this gap is the context-aware sleep health recommender system (CASHRS), an emerging research field that bridges ubiquitous sleep computing and context-aware recommender systems. This paper presents a narrative review to analyze the type of contextual information, the recommendation algorithms, the context filtering techniques, the behavior change techniques, the system evaluation, and the challenges identified in peer-reviewed publications that meet the characteristics of CASHRS. The analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS.
... International Journal of Bipolar Disorders (2022) 10:10 Fitbit is one of the most commonly installed health and fitness apps amongst US mobile owners (Krebs and Duncan 2015); similarly, Calm, Headspace, and Daylio rank among the most commonly downloaded mental health apps (Carlo et al. 2019;Woodward et al. 2020). While the apps most frequently nominated in this survey were found to largely meet minimum standards for safety and privacy, previous reviews have noted limitations regarding adherence to clinical guidelines for both mood and sleep apps (Nicholas et al. 2015;Lagan et al. 2020a;Grigsby-Toussaint et al. 2017;Qu et al. 2020), and their efficacy for improving outcomes in a BD population is unknown. Importantly, generic health apps may recommend strategies with the potential for harm in a BD population, or fail to detect early warning signs unique to this group. ...
Background
Individuals with bipolar disorder (BD) are increasingly turning to smartphone applications (apps) for health information and self-management support. While reviews have raised concerns regarding the effectiveness and safety of publicly available apps for BD, apps surveyed may not reflect what individuals with BD are using. The present study had two aims: first, to characterize the use of health apps to support mood and sleep amongst people with BD, and second, to evaluate the quality, safety and functionality of the most commonly used self-management apps.
Methods
A web-based survey was conducted to explore which apps people with BD reported using to support self-management of mood and sleep. The characteristics of the most commonly nominated apps were described using a standardized framework, including their privacy policy, clinical foundations, and functionality.
Results
Respondents ( n = 919) were 77.9% female with a mean age of 36.9 years. 41.6% of participants ( n = 382) reported using a self-management app to support mood or sleep. 110 unique apps were nominated in relation to mood, and 104 unique apps nominated in relation to sleep; however, most apps were only mentioned once. The nine most frequently nominated apps related to mood and sleep were subject to further evaluation. All reviewed apps offered a privacy policy, however user control over data was limited and the complexity of privacy policies was high. Only one app was developed for BD populations. Half of reviewed apps had published peer-reviewed evidence to support their claims of efficacy, but little research was specific to BD.
Conclusion
Findings illustrate the potential of smartphone apps to increase the reach of psychosocial interventions amongst people with BD. Apps were largely created by commercial developers and designed for the general population, highlighting a gap in the development and dissemination of evidence-informed apps for BD. There may be risks in using generic health apps for BD self-management; clinicians should enquire about patients’ app use to foster conversations about their particular benefits and limitations.
... Mobile phone apps are already widely used by consumers in areas such as smoking cessation, irritable bowel syndrome, allergy, sleep management, and weight management. [32][33][34][35] Many currently available apps include the ability for consumers to track symptoms, medication use, and lifestyle-related data, potentially enabling collection of RWD and generation of RWE on effectiveness and related quality of life outcomes. 36 At least one marketed nonprescription medicine uses near field communication technology, which enables semi-automated recording of product use data via a connected behavioral support app. 33 Specifically developed apps have already been used for study subjects to collect data within clinical studies and the wider potential to harness RWD from health apps has been recognized by a European regulatory agencies task force. ...
Real‐world evidence (RWE) is an emerging scientific discipline which is being increasingly utilised for decision making on prescription only medicines. However, there has been little focus to date on the application of RWE within the non‐prescription sector. This paper reviews the existing and potential applications of RWE for non‐prescription medicines, using the non‐prescription medicine life‐cycle as a framework for discussion. Relevant sources of real‐world data (RWD) are reviewed and compared with those available for prescribed medicines. Existing life‐cycle data gaps are identified where RWE is required or where use of RWE can complement data from randomised controlled trials (RCTs). Published RWE examples relating to non‐prescription medicines are summarised, and potential relevant future sources of RWD discussed. Challenges and limitations to the use of RWE on non‐prescription medicines are discussed, and recommendations made to promote optimal and appropriate use of RWE in this sector. Overall, RWE currently plays a key role in specific phases of the non‐prescription medicine life‐cycle, including reclassification and post‐marketing safety surveillance. The increasing availability of patient‐generated health data (PGHD) is likely to further increase the utilisation of RWE to aid decision making on non‐prescription medicines.
... The impact of technology on general health has been widely explored. Evidence suggests that technology and smartphone applications might be an effective tool for improving health when used for interventions such as smoking cessation programs, increasing physical activity, improving sleep control and diet change [23][24][25][26]. Hence, internet-based health interventions have considerable potential in changing people's behaviours towards health management and empowerment [27]. ...
Objectives
To investigate the association of dental caries with use of internet and social media to gain information on oral health and to evaluate the impact of this practice on oral health inequalities among 12 and 15-year-olds.
Methods
Data from the Children’s Dental Health Survey (CDHS) 2013, England Wales and Northern Ireland were used. The survey included sociodemographic, health behaviours, use of internet and social media, and clinical dental data. The association between the prevalence of caries experience (DMFT ≥ 1) and the number of untreated dental decay with use of internet and social media was assessed adjusting for age, gender, country, deprivation, toothbrushing, dental visits and consumption of sugary drinks and food.
Results
A total of 4414 children were included in the analysis. Children who reported using internet and social media to gain information on oral health had significantly higher odds for caries experience (1.29, 95%CI: 1.03–1.62) in the model adjusting for age, gender and country. After adjusting for behavioural and socioeconomic factors the relationship remained positive, but not statistically significant. The use of internet and social media was not significantly associated with the number of untreated caries. Social gradients in oral health remained significant after adjusting for use of internet and social media.
Conclusions
The association between use of internet and social media, and dental caries could be mediated by health behaviours. Younger adults and children may be using the Internet and social media seeking information on dental caries, but longer online hours may also increase the caries risk. Internet-based health interventions should be supported by preventive strategies to promote effective and positive internet use.
... Several reviews of commercial sleep apps have demonstrated a lack of validated sleep measurement algorithms [68], evidence-based principles for insomnia management [69], behavior change constructs [70], and overall low quality of functionality and content based on established app assessment criteria [71,72]. Evidently, commercial development of apps has severely outpaced academic research, putting their trustworthiness in question [73]. ...
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.
... On a population level, there is a wide and growing interest from the general public in wellness mobile and wearable applications, which in many cases are related to sleep and inform people's lifestyle decisions and understanding of their health 143 . Nowadays, there are hundreds of sleep applications and a plethora of wearable devices that claim to track sleep quality 159 . However, most of those devices have little or no information regarding their reliability and validity, the testing they underwent or how the data is acquired (i.e., sampling rates, pre-processing, etc) and processed 160,161 . ...
In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human–computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.
... These apps address everything from physical activity and weight control to mental health support and medication management (Zhao, Freeman, & Li, 2016), to general parenting (Davis et al., 2017) and prenatal care (Mauriello, Van Marter, Umanzor, Castle, & de Aguiar, 2016). Sleep, an important health domain across the lifespan, is also a common area for app development (Grigsby-Toussaint et al., 2017;Lee-Tobin, Ogeil, Savic, & Lubman, 2017). Considering the individual, familial, and societal toll poor sleep can precipitate, there is promising value in disseminating information and evidence-based intervention strategies through a widespread, accessible, mobile outlet. ...
Bedtime problems and night wakings are highly prevalent in infants. This study assessed the real-world effectiveness of an mHealth behavioral sleep intervention (Customized Sleep Profile; CSP). Caregivers (83.9% mothers) of 404 infants (age 6 to 11.9 m, M = 8.32 m, 51.2% male) used the CSP (free and publicly available behavioral sleep intervention delivered via smartphone application, Johnson’s® Bedtime® Baby Sleep App). Caregivers completed the Brief Infant Sleep Questionnaire–Revised (BISQ-R) at baseline and again 4 to 28 days later. Changes in sleep patterns were analyzed, based on sleep problem status (problem versus no problem sleepers; PS; NPS). Sleep onset latency improved in both groups. Earlier bedtimes, longer continuous stretches of sleep, as well as decreased number and duration of night wakings, were evident in the PS group only. The BISQ-R Total score, total nighttime sleep, and total 24-hour sleep time improved for both groups, with a greater change for the PS group. Further, caregivers of infants in the PS group decreased feeding (bedtime and overnight) and picking up overnight, and perceived better sleep. Bedtime routine regularity, bedtime difficulty, sleep onset difficulty, and caregiver confidence improved for both groups, with the PS group showing a greater magnitude of change. Thus, a real-world, publicly available, mHealth behavioral sleep intervention was associated with improved outcomes for older infants. Intervention recommendations resulted in changes in caregivers’ behavior and improvements in caregiver-reported sleep outcomes in infants, in as few as 4 days.
... The use of activity and sleep trackers has become very popular in the general population. Different devices are available that work with dedicated smartphone applications [1]. These applications are focused on activity and sleep and allow individuals to monitor their general health [2]. ...
Objective
The use of activity and sleep trackers that operate through dedicated smartphone applications has become popular in the general population. However, the validity of the data they provide has been disappointing and only Total Sleep Time (TST) is reliably recorded in healthy individuals for any of the devices tested. The purpose of this study was to evaluate the ability of two sleep trackers (Withings pulse 02 (W) and Jawbone Up (U)) to measure sleep parameters in patients suffering from obstructive sleep apnea (OSA).
Methods
All patients evaluated for OSA in our sleep laboratory underwent overnight polysomnography (PSG). PSG was conducted simultaneously with three other devices: two consumer-level sleep monitors (U and W) and one actigraph (Bodymedia SenseWear Pro Armband (SWA)).
Results
Of 36 patients evaluated, 22 (17 men) were diagnosed with OSA (mean apnea-hypopnea index of 37+ 23/h). Single comparisons of sleep trackers (U and W) and actigraph (SWA) were performed. Compared to PSG, SWA correctly assessed TST and Wake After Sleep Onset (WASO), and U and W correctly assessed Time In Bed (TIB) and light sleep. Intraclass correlations (ICC) revealed poor validity for all parameters and devices, except for WASO assessed by SWA.
Conclusions
This is the first study assessing the validity of sleep trackers in OSA patients. In this series, we have confirmed the limited performance of wearable sleep monitors that has been previously observed in healthy subjects. In OSA patients, wearable app-based health technologies provide a good estimation of TIB and light sleep but with very poor ICC.
... To our knowledge, our review is unique as its primary goal is to examine combined interventions targeting PA, diet, and sleep dimensions through smartphone apps and their personalization methods. Other similar reviews explore the effect of interventions for one [32,33] or two dimensions [34,35] only. For example, some reviews examine feedback in diet and PA interventions only without considering sleep [29,36]. ...
Background: The use of smartphone apps to track and manage physical activity (PA), diet, and sleep is growing rapidly. Many apps aim to change individual behavior on these three key health dimensions (PA, sleep, diet) by using various interventions. Earlier reviews have examined interventions using smartphone apps for one or two of these dimensions. However, there is lack of reviews focusing on interventions for all three of these dimensions in combination with each other. This is important since the dimensions are often inter-related, and all are required for a healthy lifestyle. Objective: The objective of this study is to conduct a review to: (1) map out the research done using smartphone app interventions targeting all three or any two of the three dimensions (PA, sleep, and diet), (2) examine if the studies consider the inter-relationships among the dimensions, and (3) identify the personalization methods implemented by the studies. Methods: A literature search was conducted in electronic databases and libraries related to medical and informatics literature – PubMed, ScienceDirect, PsycINFO (ProQuest, Ovid) – using relevant selected keywords. Article selection and inclusion were done by removing duplicates, analyzing titles and abstracts, and then reviewing the full text of the articles. Results: In the final analysis, 14 articles were selected – 2 articles focusing on PA and sleep, 8 on PA and diet, and 4 that examine or (at least) collect data of all three dimensions (PA, sleep, and diet). No research was found that focused on sleep and diet together. Of the 14 articles, only 4 build user profiles. Further, 3 of these 4 studies deliver personalized feedback based on the user's profile, with only 1 study providing automated, personalized recommendations for behavior change. Additionally, 6 of the included studies report all positive outcomes, while for 3 studies the primary outcomes are awaited. The remaining 5 studies do not report significant changes in all outcomes. In all, only 1 study examines the relationship between two (PA and diet) dimensions. No study was found to assess the relationships among the 3 dimensions.
... More studies are needed in this direction. As put forth by Griesby-Toussaint and colleagues [59], sleep apps can serve as tools for behavior change through features specifically designed to encourage healthy sleeping habits. It is also possible that longterm use of smartphone sleep monitoring can promote in the long run sustainable sleep hygiene among healthy users and also assist in the management of sleep-related problems [58]. ...
Smartphone applications are considered as the prime candidate for the purposes of large-scale, low-cost and long-term sleep monitoring. How reliable and scientifically grounded is smartphone-based assessment of healthy and disturbed sleep remains a key issue in this direction. Here we offer a review of validation studies of sleep applications to the aim of providing some guidance in terms of their reliability to assess sleep in healthy and clinical populations, and stimulating further examination of their potential for clinical use and improved sleep hygiene. Electronic literature review was conducted on Pubmed. Eleven validation studies published since 2012 were identified, evaluating smartphone applications’ performance compared to standard methods of sleep assessment in healthy and clinical samples. Studies with healthy populations show that most sleep applications meet or exceed accuracy levels of wrist-based actigraphy in sleep-wake cycle discrimination, whereas performance levels drop in individuals with low sleep efficiency (SE) and in clinical populations, mirroring actigraphy results. Poor correlation with polysomnography (PSG) sleep sub-stages is reported by most accelerometer-based apps. However, multiple parameter-based applications (i.e., EarlySense, SleepAp) showed good capability in detection of sleep-wake stages and sleep-related breathing disorders (SRBD) like obstructive sleep apnea (OSA) respectively with values similar to PSG. While the reviewed evidence suggests a potential role of smartphone sleep applications in pre-screening of SRBD, more experimental studies are warranted to assess their reliability in sleep-wake detection particularly. Apps’ utility in post treatment follow-up at home or as an adjunct to the sleep diary in clinical setting is also stressed.
... Although cell phone use can interfere with sleep, Williams and colleagues propose capitalizing on the popularity of smartphone apps with youth, and testing a sleep promoting mHealth intervention to improve sleep behavior (Williams, Coveney, & Meadows, 2015). The literature reviewing available sleep apps that target sleep behaviors is the subject of several reviews (Behar, Roebuck, Domingos, Gederi, & Clifford, 2013;Grigsby-Toussaint et al., 2017;Ong & Gillespie, 2016). These reviews have found that many apps report on sleep structure, while only one third of the sleep apps had incorporated behavioral constructs such as goal setting, time management, and self-monitoring. ...
Background:
Daily behaviors such as sleep can be targeted by smartphone app-based interventions, with potential utility among young people of minority ethnic backgrounds who commonly access smartphone devices and are short sleepers. There is a need to understand the acceptability and youth's readiness to use apps to improve sleep, and to identify desired app components that would motivate engagement.
Participants and methods:
We conducted three focus group discussions (N = 27 total, age 14-18 years) within low- and middle-income ethnically diverse Boston neighborhoods. We also interviewed 10 participants who provided specific feedback on two commercially available sleep-promoting apps, one of which they had used on their smartphone preceding the interviews. All focus group discussions and interviews were audio-recorded, transcribed, and thematically analyzed.
Results:
We identified several barriers to adoption of sleep hygiene interventions, namely reluctance to follow scheduled sleep routines on weekends and concern about "parting" with electronics at bedtime. Participants were intrigued by the idea of adopting an app-based sleep intervention, but were skeptical that they could successfully adopt sleep hygiene practices, and were more interested in making changes on school days than on weekends. Nonetheless, the overall feedback on two commercial sleep apps, neither targeted at youth, was positive, with a good adherence and engagement rate, and perceived health benefits.
Conclusions:
Our findings highlight the need to adapt sleep hygiene recommendations to targeted populations, considering preferences and social and cultural contextual factors. Our research also underscores the importance of the platform, setting, and messenger when delivering health information to adolescents.
... Web-based cognitive or pain management platforms is a key sub-theme in this area. These may be fully automated educational systems or platforms that mimic counseling based on cognitive behavioral therapy [13,29,35,36,38,42], or may contain partial support from a live therapist [39, 41]. ...
As part of the Personal Connected Health Alliance’s (PCHAlliance) efforts to define personal
connected health, evidence was identified as a key driver for shaping the field. This publication
aims to set an initial baseline for the current body of evidence in personal connected health
in key sub-domains, namely behavior change and self-care, remote patient monitoring, remote
counseling and mental health, as well as more broadly through key condition-specific studies.
The publication incorporates a formal review of randomized controlled trials; a broad
discussion of the role of evidence in adoption of personal connected health; and a discussion
of evidence challenges and proposed areas for further research and collaboration.
... 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 [75]. Our finding was not only applicable to those with sleep disorders who need clinical care, but also to medical professionals who are interested in ways to determine effective sleep interventions. ...
Background:
Although mobile health technologies have been developed for interventions to improve sleep disorders and sleep quality, evidence of their effectiveness remains limited.
Objective:
A systematic literature review was performed to determine the effectiveness of mobile technology interventions for improving sleep disorders and sleep quality.
Methods:
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.
Results:
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).
Conclusions:
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.
... 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 [75]. Our finding was not only applicable to those with sleep disorders who need clinical care, but also to medical professionals who are interested in ways to determine effective sleep interventions. ...
Introduction
Sleep disorders and poor sleep quality and quantity are considerable issues in public health. Although various mobile health technologies have proliferated to improve sleep disorders, the scientific evidence and effectiveness remain unclear. The purpose of our study is to review current research trends in mobile technology interventions for sleep disorders and the scientific evidence of its effectiveness.
Methods
Four electronic databases (CINAHL, PubMed/ Medline, Scopus (EBSCO), and Web of Science) were searched for articles published between January 2001 and December 2015. Studies were eligible for inclusion if they met the following criteria: adequate details on study design, focus on sleep intervention research, sleep index measurement outcome provided, and publication in peer-reviewed journals.
Results
Nine eligible studies were evaluated to examine the impact of mobile phone interventions on sleep disorders. These included one case study, two pre/posttest studies, and six randomized control trials (RCTs). The studies were categorized as mobile ‘traditional intervention augmented with mobile phones’ or ‘smartphone application (Apps) intervention’. For outcome measurement tools, PSQI was most frequently used (n=5). All nine studies concluded that mobile phone interventions have the capability to attenuate sleep disorders.
Conclusion
We found evidence to support the use of mobile phone interventions to address sleep disorders, suggesting support for future intervention efforts.
Support (If Any)
This work was partially supported by funds from the Department of Kinesiology and Community Health at the University of Illinois at Urbana-Champaign.
Bu çalışma, mobil sağlık uygulamalarının kullanım niyetini etkileyen faktörleri belirlemeyi ve kullanıcıların sağlık bilincinin mobil sağlık uygulaması kullanım niyetini ne yönde etkilediğini ortaya koymayı amaçlamaktadır. Çalışma, 18 yaş ve üzeri, fiziksel aktivite (Adımsayar, Evde Egzersiz, Düzenli Uyku vb), sağlıklı beslenme (Kalori Sayacı, Su Tüketimi Takibi vb) gibi uygulamaları kullanan bireylerle yürütülmüştür. Araştırmada veri toplama aracı olarak anket formu kullanılmıştır. Verilerin analizinde SPSS ve “Process Macro” paket programı kullanılmıştır. Derlenen veriler tanımlayıcı istatistikler, geçerlik ve güvenirlik analizi, basit doğrusal regresyon analizi ve “bootstrap model 4” analizi ile değerlendirilmiştir. Araştırma sonucunda algılanan fayda, algılanan kullanım kolaylığı, subjektif norm ve davranış değiştirme tekniklerinin sağlık bilinci ile memnuniyet arasındaki ilişkide aracı etkiye sahip olduğu ve algılanan fayda, algılanan kullanım kolaylığı, subjektif norm ve davranış değiştirme teknikleri ile mobil sağlık uygulamalarını kullanım niyeti arasındaki ilişkide memnuniyet düzeyinin aracı etkiye sahip olduğu sonucuna ulaşılmıştır. Ayrıca sağlık bilincinin kullanım niyetini pozitif yönde etkilediği tespit edilmiştir. Çalışma sonucunda, mobil sağlık uygulamalarının sağlık hizmeti sunumunda önem arz eden bir potansiyeli olduğu vurgulanmış, hastane yöneticilerine, politika yapıcılara, kamu ve özel sektör temsilcilerine, iç ve dış paydaşlara mobil sağlık uygulamalarının gelecekteki yerine yönelik stratejik hamleler geliştirerek adımlar atılması önerilmiştir.
Objectives
Mobile health apps may be an effective way to increase sleep management skills. Although little has been documented about the content and quality of available sleep management apps, providers often make app recommendations to help with sleep self-management. The objective of this study was to systematically evaluate the content and quality of commercially available sleep apps.
Methods
Following a systematic search of the Apple App and Google Play stores, 56 sleep management apps were evaluated. App content was evaluated using the taxonomy of behavior change techniques (BCTs), and app quality was assessed using the Mobile App Rating Scale.
Results
Sleep management apps included 0–15 BCTs (M = 6.89) and 0–9 sleep BCTs (M = 4.87). App quality ranged from 2.51 to 4.80 (M = 3.78) out of 5.00. Sleepiest Sleep Sounds Stories, ShutEye: Sleep Tracker, and Mintal Tracker: Sleep Recorder included the highest number of sleep BCTs and highest quality scores.
Conclusions
While the content and quality of sleep management apps is variable, the findings are promising as many apps included a high number of BCTs and high quality. Although evidence of efficacy through randomized controlled trials is necessary to establish efficacy, this review can aid in app selection in the interim.
Self-tracking technologies are possible approaches to support recovery self-management activities for athletes. These may have become even more appealing due to stay-at-home restrictions as part of the 2020 pandemic regulations. This study examined user behaviour of smartphone and wearable technologies among 217 athletes (29% women, M age = 26.9 ± 7 years). The online survey comprised demographic questions and standardised questionnaires to assess usage of technologies, sleep quality (Pittsburgh Sleep Quality Index), daytime sleepiness (Epworth Sleepiness Scale), attitudes about sleep (Dysfunctional Beliefs and Attitudes about Sleep Scale), bedtime procrastination (Bedtime Procrastination Scale), and self-control (Brief Self-Control Scale). Fitness apps (46.5%) were more popular than sleep apps (15.7%) followed by nutrition apps (12%). The correlation between sleep apps and the other two apps indicate that non-users of sleep apps are probably also non-users of fitness or nutrition apps. Wearables were more frequently used to track fitness activities (36.9%) than sleep (17.5%). Considering sex, type of sport, competition participation, and training volume, no remarkable characteristics among users versus non-users of sleep apps were identified. There were also no significant differences among sleep indices between sleep app users and non-users. However, self-control was highest among sleep app users compared to non-users (d = 0.58). Despite 34.1% being identified as poor sleepers, behavioural sleeping patterns were within normal range. The results imply that athletes are not as attracted to self-tracking technologies as expected, which makes them less vulnerable to unsubstantiated feedback and inappropriate interventions by those tools. This serves as a starting point to explore the potential of self-tracking ambulatory assessment for physical activity and sleep behaviour of athletes in the post-pandemic era.
Sağlık uygulamalarının kolaylaştırılması, ölçülebilir, karşılaştırılabilir ve ulaşılabilir hale gelmesi hedefi, bu sektörde dijital bir dönüşümü gerekli kılmıştır. Randevu sistemlerinden, görüntüleme sistemlerine, ilaç takibinden, hasta bulgularının takibine kadar birçok konuda hem devlet kurumları hem de özel girişimler çeşitli uygulamalar geliştirmektedir. Cep telefonlarıyla uyumlu kullanılabilen bu uygulamalar, insanların teknoloji okur-yazarlığının da artmasıyla gün geçtikçe daha çok kullanılmaya ve benimsenmeye başlamıştır. Sektörün paydaşlarının bu uygulamalardan haberdar olması, amaçlarına uygun kullanması ile daha büyük kitlelere ulaşılacağı düşünülmektedir. Sağlık sektöründe kullanılacak mobil uygulama geliştirilmesinde uygun bir saha araştırması, ihtiyaçların belirlenmesi, profesyonellerin talepleri, kullanışlılık gibi detaylar dikkate alınmalıdır. Daha önce geliştirilen uygulamaların başarısız olanlarının nedenleri incelenmeli, başarılı olanların özellikleri de yeni tasarımlara taşınmalıdır. Bu çalışmada literatürde, internet sitelerinde, Google Play ve IOS uygulama marketlerinde bulunan sağlık uygulamalarının derlenmesi amaçlanmıştır.
The Learning Sciences, and specifically Mind, Brain, (Health), and Education science, contribute to our understanding of the teaching-learning dynamic. This brief (8,000 word) article captures the highlights of human understanding about the brain and its role in learning from ancient civilizations to the present.
Background: The rapid growth of consumer sleep technology demonstrates the population’s interest in measuring sleep. However, the extent to which these devices can be used in the delivery of behavioral sleep interventions is currently unknown. The objectives of this systematic review were to evaluate the use of consumer sleep technology (wearable and mobile) in behavioral sleep medicine interventions, identify gaps in the literature and potential future directions.
Methods: We completed a scoping review of studies conducted in adult populations that used consumer sleep tracking technology to deliver sleep-related interventions.
Results: Our initial search returned 4,538 articles and 14 articles met our inclusion/exclusion criteria. Results demonstrated that wearable devices are being used for two main purposes: 1. To deliver treatment for insomnia and 2. Sleep monitoring as part of overall wellness programs. Half of the articles reviewed (n = 7) used consumer sleep technology in a cognitive behavioral therapy for insomnia. The majority of the studies reviewed (n = 10) were fully digital, without human intervention, and only two small studies evaluated interventions delivered with and without a sleep tracking device.
Conclusions: These studies demonstrate opportunities to utilize consumer sleep trackers in insomnia treatment and wellness programs, but most new and innovative interventions are in the early, feasibility stages. Future research is needed to determine how to leverage wearables to improve existing behavioral sleep treatments and determine how this technology can engage patients and reduce barriers to behavioral sleep medicine interventions.
To promote optimal health and well-being, adults aged 18-60 years are recommended to sleep at least 7 hours each night (1). Sleeping <7 hours per night is associated with increased risk for obesity, diabetes, high blood pressure, coronary heart disease, stroke, frequent mental distress, and all-cause mortality (2-4). Insufficient sleep impairs cognitive performance, which can increase the likelihood of motor vehicle and other transportation accidents, industrial accidents, medical errors, and loss of work productivity that could affect the wider community (5). CDC analyzed data from the 2014 Behavioral Risk Factor Surveillance System (BRFSS) to determine the prevalence of a healthy sleep duration (≥7 hours) among 444,306 adult respondents in all 50 states and the District of Columbia. A total of 65.2% of respondents reported a healthy sleep duration; the age-adjusted prevalence of healthy sleep was lower among non-Hispanic blacks, American Indians/Alaska Natives, Native Hawaiians/Pacific Islanders, and multiracial respondents, compared with non-Hispanic whites, Hispanics, and Asians. State-based estimates of healthy sleep duration prevalence ranged from 56.1% in Hawaii to 71.6% in South Dakota. Geographic clustering of the lowest prevalence of healthy sleep duration was observed in the southeastern United States and in states along the Appalachian Mountains, and the highest prevalence was observed in the Great Plains states. More than one third of U.S. respondents reported typically sleeping <7 hours in a 24-hour period, suggesting an ongoing need for public awareness and public education about sleep health; worksite shift policies that ensure healthy sleep duration for shift workers, particularly medical professionals, emergency response personnel, and transportation industry personnel; and opportunities for health care providers to discuss the importance of healthy sleep duration with patients and address reasons for poor sleep health.
To review sleep related consumer technologies, including mobile electronic device "apps," wearable devices, and other technologies. Validation and methodological transparency, the effect on clinical sleep medicine, and various social, legal, and ethical issues are discussed.
We reviewed publications from the digital libraries of the Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and PubMed; publications from consumer technology websites; and mobile device app marketplaces. Search terms included "sleep technology," "sleep app," and "sleep monitoring."
Consumer sleep technologies are categorized by delivery platform including mobile device apps (integrated with a mobile operating system and utilizing mobile device functions such as the camera or microphone), wearable devices (on the body or attached to clothing), embedded devices (integrated into furniture or other fixtures in the native sleep environment), accessory appliances, and conventional desktop/website resources. Their primary goals include facilitation of sleep induction or wakening, self-guided sleep assessment, entertainment, social connection, information sharing, and sleep education.
Consumer sleep technologies are changing the landscape of sleep health and clinical sleep medicine. These technologies have the potential to both improve and impair collective and individual sleep health depending on method of implementation.
Copyright © 2015 American Academy of Sleep Medicine. All rights reserved.
Mobile phone apps have the potential to reduce excessive alcohol consumption cost-effectively. Although hundreds of alcohol-related apps are available, there is little information about the behavior change techniques (BCTs) they contain, or the extent to which they are based on evidence or theory and how this relates to their popularity and user ratings.
Our aim was to assess the proportion of popular alcohol-related apps available in the United Kingdom that focus on alcohol reduction, identify the BCTs they contain, and explore whether BCTs or the mention of theory or evidence is associated with app popularity and user ratings.
We searched the iTunes and Google Play stores with the terms "alcohol" and "drink", and the first 800 results were classified into alcohol reduction, entertainment, or blood alcohol content measurement. Of those classified as alcohol reduction, all free apps and the top 10 paid apps were coded for BCTs and for reference to evidence or theory. Measures of popularity and user ratings were extracted.
Of the 800 apps identified, 662 were unique. Of these, 13.7% (91/662) were classified as alcohol reduction (95% CI 11.3-16.6), 53.9% (357/662) entertainment (95% CI 50.1-57.7), 18.9% (125/662) blood alcohol content measurement (95% CI 16.1-22.0) and 13.4% (89/662) other (95% CI 11.1-16.3). The 51 free alcohol reduction apps and the top 10 paid apps contained a mean of 3.6 BCTs (SD 3.4), with approximately 12% (7/61) not including any BCTs. The BCTs used most often were "facilitate self-recording" (54%, 33/61), "provide information on consequences of excessive alcohol use and drinking cessation" (43%, 26/61), "provide feedback on performance" (41%, 25/61), "give options for additional and later support" (25%, 15/61) and "offer/direct towards appropriate written materials" (23%, 14/61). These apps also rarely included any of the 22 BCTs frequently used in other health behavior change interventions (mean 2.46, SD 2.06). Evidence was mentioned by 16.4% of apps, and theory was not mentioned by any app. Multivariable regression showed that apps including advice on environmental restructuring were associated with lower user ratings (Β=-46.61, P=.04, 95% CI -91.77 to -1.45) and that both the techniques of "advise on/facilitate the use of social support" (Β=2549.21, P=.04, 95% CI 96.75-5001.67) and the mention of evidence (Β=1376.74, P=.02, 95%, CI 208.62-2544.86) were associated with the popularity of the app.
Only a minority of alcohol-related apps promoted health while the majority implicitly or explicitly promoted the use of alcohol. Alcohol-related apps that promoted health contained few BCTs and none referred to theory. The mention of evidence was associated with more popular apps, but popularity and user ratings were only weakly associated with the BCT content.
Several inexpensive, readily available smartphone apps that claim to monitor sleep are popular among patients. However, their accuracy is unknown, which limits their widespread clinical use. We therefore conducted this study to evaluate the validity of parameters reported by one such app, the Sleep Time app (Azumio, Inc., Palo Alto, CA, USA) for iPhones.
Twenty volunteers with no previously diagnosed sleep disorders underwent in-laboratory polysomnography (PSG) while simultaneously using the app. Parameters reported by the app were then compared to those obtained by PSG. In addition, an epoch-by-epoch analysis was performed by dividing the PSG and app graph into 15-min epochs.
There was no correlation between PSG and app sleep efficiency (r = -0.127, p = 0.592), light sleep percentage (r = 0.024, p = 0.921), deep sleep percentage (r = 0.181, p = 0.444) or sleep latency (rs = 0.384, p = 0.094). The app slightly and nonsignificantly overestimated sleep efficiency by 0.12% (95% confidence interval [CI] -4.9 to 5.1%, p = 0.962), significantly underestimated light sleep by 27.9% (95% CI 19.4-36.4%, p < 0.0001), significantly overestimated deep sleep by 11.1% (CI 4.7-17.4%, p = 0.008) and significantly overestimated sleep latency by 15.6 min (CI 9.7-21.6, p < 0.0001). Epochwise comparison showed low overall accuracy (45.9%) due to poor interstage discrimination, but high accuracy in sleep-wake detection (85.9%). The app had high sensitivity but poor specificity in detecting sleep (89.9% and 50%, respectively).
Our study shows that the absolute parameters and sleep staging reported by the Sleep Time app (Azumio, Inc.) for iPhones correlate poorly with PSG. Further studies comparing app sleep-wake detection to actigraphy may help elucidate its potential clinical utility.
Copyright © 2015 American Academy of Sleep Medicine. All rights reserved.
Childhood obesity continues to be a significant public health issue. mHealth systems offer state-of-the-art approaches to intervention design, delivery, and diffusion of treatment and prevention efforts. Benefits include cost effectiveness, potential for real-time data collection, feedback capability, minimized participant burden, relevance to multiple types of populations, and increased dissemination capability. However, these advantages are coupled with unique challenges. This commentary discusses challenges with using mHealth strategies for child obesity prevention, such as lack of scientific evidence base describing effectiveness of commercially available applications; relatively slower speed of technology development in academic research settings as compared with industry; data security, and patient privacy; potentially adverse consequences of increased sedentary screen time, and decreased focused attention due to technology use. Implications for researchers include development of more nuanced measures of screen time and other technology-related activities, and partnering with industry for developing healthier technologies. Implications for health practitioners include monitoring, assessing, and providing feedback to child obesity program designers about users' data transfer issues, perceived security and privacy, sedentary behavior, focused attention, and maintenance of behavior change. Implications for policy makers include regulation of claims and quality of apps (especially those aimed at children), supporting standardized data encryption and secure open architecture, and resources for research–industry partnerships that improve the look and feel of technology. Partnerships between academia and industry may promote solutions, as discussed in this commentary.
Mobile applications (apps) offer a novel way to engage children in behavior change, but little is known about content of commercially available apps for this population. We analyzed the content of apps for iPhone/iPad for pediatric weight loss, healthy eating (HE), and physical activity (PA). Fifty-seven apps were downloaded and tested by two independent raters. Apps were coded for: inclusion of the Expert Committee for Pediatric Obesity Prevention's (ECPOP) eight recommended strategies (e.g., set goals) and seven behavioral targets (e.g., do ≥1 h of PA per day), utilization of gaming elements, and general characteristics. Most apps lacked any expert recommendations (n = 35, 61.4 %). The mean number of recommendations among apps that used recommendations was 3.6 ± 2.7 out of 15, 56.1 % (n = 32) apps were classified as games, and mean price per app was $1.05 ± 1.66. Most apps reviewed lacked expert recommendations and could be strengthened by addition of comprehensive information about health behavior change and opportunities for goal setting.
The purpose of this study was to operationalize the constructs of the Theory of Planned Behavior (TPB) to predict the sleep intentions and behaviors of undergraduate college students attending a Midwestern University. Data collection spanned three phases. The first phase included a semi-structured qualitative interview (n = 11), readability by Flesch-Kincaid, face and content validity by a panel of six experts. The second phase included stability reliability by test–retest (n = 37). The final phase included construct validation applying confirmatory factor analysis, internal consistency by Cronbach’s alpha, and predictive validity (n = 197) employing multiple regression analysis. The majority of the participants reported receiving insufficient sleep (M = 407.3 min, SD = 100.75). Multiple regression modeled perceived behavioral control, subjective norm, and attitude toward adequate sleep behavior on behavioral intention. Collectively, the significant predictors produced an R
adjusted2 value of .362. Further specification of the model identified behavioral intention as a significant predictor of sleep behavior (R
adjusted2 = .185). As a population, undergraduate college students are not achieving adequate sleep. The TPB was found to be a useful framework for predicting the sleep intentions and behaviors of undergraduate students. Practical implications and recommendations for future research are discussed.
Over the past three decades, there has been a dramatic global increase in childhood obesity. A better understanding of stakeholders' perceptions of intervention requirements could contribute to developing more effective interventions for childhood obesity. This study provides a qualitative, in-depth, analysis of stakeholders' (children, parents and health professionals) perspectives toward the efficacy of childhood obesity treatment interventions. Twenty-six stakeholders were recruited using purposive sampling; semi-structured interviews were adopted to explore stakeholders' perceptions with data analysed using a framework approach. Stakeholders concurred that treatment should be family-based incorporating physical activity, nutrition and psychological components, and be delivered in familiar environments to recipients. However, incongruence existed between stakeholders towards the sustainability of obesity treatment interventions. Parents and children reported needing ongoing support to sustain behavioural changes made during treatment, while health professionals suggested interventions should aim to create autonomous individuals who exit treatment and independently sustain behaviour change. This study provides an insight into issues of stakeholder involvement in the obesity intervention design and delivery process. To promote long-term behaviour change, there needs to be increased congruence between the delivery and receipt of childhood obesity treatment interventions. Interventions need to incorporate strategies that promote autonomous and self-regulated motivation, to enhance families' confidence in sustaining behaviour change independent of health professional support.
Interventions that aim to improve child dietary quality and reduce disease risk often involve parents. The most effective methods to engage parents remain unclear. A systematic review of interventions designed to change child and adolescent dietary behavior was conducted to answer whether parent involvement enhanced intervention effectiveness, and what type of involvement was most effective in achieving desired outcomes.
In 2008, Pub Med, Medline, Psych Info, and Cochrane Library databases were searched to identify programs designed to change child and adolescent dietary intake that also involved parents. Methods of parental involvement were categorized based on the type and intensity of parental involvement. These methods were compared against intervention design, dietary outcomes, and quality of reporting (evaluated using CONSORT checklist) for each study.
The literature search identified 1774 articles and 24 met review criteria. Four studies systematically evaluated parent involvement with inconsistent results. Indirect methods to engage parents were most commonly used, although direct approaches were more likely to result in positive outcomes. Four studies met >70% of CONSORT items.
Limited conclusions may be drawn regarding the best method to involve parents in changing child diet to promote health. However, direct methods show promise and warrant further research.
Clinical practice related to sleep problems and sleep disorders has been expanding rapidly in the last few years, but scientific research is not keeping pace. Sleep apnea, insomnia, and restless legs syndrome are three examples of very common disorders for which we have little biological information. This new book cuts across a variety of medical disciplines such as neurology, pulmonology, pediatrics, internal medicine, psychiatry, psychology, otolaryngology, and nursing, as well as other medical practices with an interest in the management of sleep pathology. This area of research is not limited to very young and old patients-sleep disorders reach across all ages and ethnicities. Sleep Disorders and Sleep Deprivation presents a structured analysis that explores the following: Improving awareness among the general public and health care professionals. Increasing investment in interdisciplinary somnology and sleep medicine research training and mentoring activities. Validating and developing new and existing technologies for diagnosis and treatment. This book will be of interest to those looking to learn more about the enormous public health burden of sleep disorders and sleep deprivation and the strikingly limited capacity of the health care enterprise to identify and treat the majority of individuals suffering from sleep problems. © 2006 by the National Academy of Sciences. All rights reserved.
Healthcare providers are often looking for ways to objectively monitor and improve their patients' health and fitness, especially in between patient visits. Some insurance companies are using app data as incentives to improve health and lower premiums. As more and more people start to use smartphones, they may provide a tool to help improve a patient's health and fitness. Specifically, fitness applications or 'apps' on smartphones are programs that use data collected from a smartphone's inbuilt tools such as Global Positioning System (GPS) tracking, accelerometer, microphone, speaker, and camera to measure health and fitness parameters. The apps then analyze this data and summarize it, as well as devise individualized plans based on users' goals, provide frequent feedback, personalized coaching, and additional motivation by allowing milestones to be shared on social media. This paper introduces evidence that apps can better help patients reach their health and fitness goals. It then discusses what features to look for in an app, followed by an overview of popular health and fitness apps. Lastly patient scenarios with app recommendations, limitations of apps, and future research are discussed.
Copyright © 2015 Elsevier Inc. All rights reserved.
Significance
The use of light-emitting electronic devices for reading, communication, and entertainment has greatly increased recently. We found that the use of these devices before bedtime prolongs the time it takes to fall asleep, delays the circadian clock, suppresses levels of the sleep-promoting hormone melatonin, reduces the amount and delays the timing of REM sleep, and reduces alertness the following morning. Use of light-emitting devices immediately before bedtime also increases alertness at that time, which may lead users to delay bedtime at home. Overall, we found that the use of portable light-emitting devices immediately before bedtime has biological effects that may perpetuate sleep deficiency and disrupt circadian rhythms, both of which can have adverse impacts on performance, health, and safety.
The purpose of this study was to determine if mobile phones interfere with adolescent sleep. We conducted a pilot test in a pediatric primary care practice of 454 patients, half female (51.2%), 12 to 20 years old (mean = 15) attending a well-child visit. Adolescents completed paper-and-pencil surveys in the waiting room. More than half took their mobile phone to bed (62.9%) and kept it turned on while sleeping (56.8%). Almost half used their phone as their alarm (45.7%). More than one-third texted after going to bed (36.7%). Two or more times per week, 7.9% were awakened by a text after going to sleep.
Smartphone applications (apps) are increasingly available for smoking cessation.
This study examined the content of popular apps for smoking cessation for both iPhone and Android operating systems in February 2012.
A total of 252 smoking-cessation apps were identified for the iPhone and 148 for the Android. Across both operating systems, the most popular apps were identified (n=47 for the iPhone and n=51 for the Android) and analyzed for their (1) approach to smoking cessation and (2) adherence to an index based on the U.S. Public Health Service's Clinical Practice Guidelines for Treating Tobacco Use and Dependence. Where available, apps were coded for frequency of downloads. The analysis took place in 2012.
Overall, popular apps have low levels of adherence, with an average score of 12.9 of a possible 42 on the Adherence Index. No apps recommended calling a quitline, and only a handful of apps recommended using approved medications (4.1%). Android apps in the sample were downloaded worldwide between 310,800 and 1,248,000 times per month. For both the iPhone and Android, user ratings were positively associated with scores on the Adherence Index. For the iPhone, display order was also positively associated with scores on the Adherence Index.
Apps could be improved by better integration with the Clinical Practice Guidelines and other evidence-based practices.
The use of smartphone applications (apps) to assist with weight management is increasingly prevalent, but the quality of these apps is not well characterized.
The goal of the study was to evaluate diet/nutrition and anthropometric tracking apps based on incorporation of features consistent with theories of behavior change.
A comparative, descriptive assessment was conducted of the top-rated free apps in the Health and Fitness category available in the iTunes App Store. Health and Fitness apps (N=200) were evaluated using predetermined inclusion/exclusion criteria and categorized based on commonality in functionality, features, and developer description. Four researchers then evaluated the two most popular apps in each category using two instruments: one based on traditional behavioral theory (score range: 0-100) and the other on the Fogg Behavioral Model (score range: 0-6). Data collection and analysis occurred in November 2012.
Eligible apps (n=23) were divided into five categories: (1) diet tracking; (2) healthy cooking; (3) weight/anthropometric tracking; (4) grocery decision making; and (5) restaurant decision making. The mean behavioral theory score was 8.1 (SD=4.2); the mean persuasive technology score was 1.9 (SD=1.7). The top-rated app on both scales was Lose It! by Fitnow Inc.
All apps received low overall scores for inclusion of behavioral theory-based strategies.
Physicians have limited time for weight-loss counseling, and there is a lack of resources to which they can refer patients for assistance with weight loss. Weight-loss mobile applications (apps) have the potential to be a helpful tool, but the extent to which they include the behavioral strategies included in evidence-based interventions is unknown.
The primary aims of the study were to determine the degree to which commercial weight-loss mobile apps include the behavioral strategies included in evidence-based weight-loss interventions, and to identify features that enhance behavioral strategies via technology.
Thirty weight-loss mobile apps, available on iPhone and/or Android platforms, were coded for whether they included any of 20 behavioral strategies derived from an evidence-based weight-loss program (i.e., Diabetes Prevention Program). Data on available apps were collected in January 2012; data were analyzed in June 2012.
The apps included on average 18.83% (SD=13.24; range=0%-65%) of the 20 strategies. Seven of the strategies were not found in any app. The most common technology-enhanced features were barcode scanners (56.7%) and a social network (46.7%).
Weight-loss mobile apps typically included only a minority of the behavioral strategies found in evidence-based weight-loss interventions. Behavioral strategies that help improve motivation, reduce stress, and assist with problem solving were missing across apps. Inclusion of additional strategies could make apps more helpful to users who have motivational challenges.
To examine the effects of obesity and frequent mental distress (FMD) on the relationship of sleep duration with coronary heart disease (CHD), stroke, and diabetes.
Cross-sectional study.
Population-based surveillance.
There were 54,269 adults age 45 y or older who completed the 2010 Behavioral Risk Factor Surveillance System survey in 14 states.
Nearly one third (31.1% or an estimated 11.1 million) of respondents age 45 y and older reported being short sleepers (≤ 6 h), 64.8% being optimal sleepers (7-9 h), and 4.1% being long sleepers (≥ 10 h) in a 24-h period. Compared with the optimal sleep duration, both short and long sleep durations were significantly associated with obesity, FMD (mental health was not good ≥ 14 days during the past 30 days), CHD, stroke, and diabetes after controlling for sex, age, race/ethnicity, and education. The U-shaped relationships of sleep duration with CHD, stroke, and diabetes were moderately attenuated by FMD. The relationship between sleep duration and diabetes was slightly attenuated by obesity.
Sleep duration had U-shaped relationships with leading chronic diseases. Further prospective studies are needed to determine how mental health and maintenance of a normal weight may interact with sleep duration to prevent chronic diseases.
Liu Y; Wheaton AG; Chapman DP; Croft JB. Sleep duration and chronic diseases among US adults age 45 years and older: evidence from the 2010 Behavioral Risk Factor Surveillance System. SLEEP 2013;36(10):1421-1427.
Can computers change what you think and do? Can they motivate you to stop smoking, persuade you to buy insurance, or convince you to join the Army? "Yes, they can," says Dr. B.J. Fogg, director of the Persuasive Technology Lab at Stanford University. Fogg has coined the phrase "Captology"(an acronym for computers as persuasive technologies) to capture the domain of research, design, and applications of persuasive computers.In this thought-provoking book, based on nine years of research in captology, Dr. Fogg reveals how Web sites, software applications, and mobile devices can be used to change peoples attitudes and behavior. Technology designers, marketers, researchers, consumers-anyone who wants to leverage or simply understand the persuasive power of interactive technology-will appreciate the compelling insights and illuminating examples found inside. Persuasive technology can be controversial-and it should be. Who will wield this power of digital influence? And to what end? Now is the time to survey the issues and explore the principles of persuasive technology, and B.J. Fogg has written this book to be your guide.
This paper presents a new model for understanding human behavior. In this model (FBM), behavior is a product of three factors: motivation, ability, and triggers, each of which has subcomponents. The FBM asserts that for a person to perform a target behavior, he or she must (1) be sufficiently motivated, (2) have the ability to perform the behavior, and (3) be triggered to perform the behavior. These three factors must occur at the same moment, else the behavior will not happen. The FBM is useful in analysis and design of persuasive technologies. The FBM also helps teams work together efficiently because this model gives people a shared way of thinking about behavior change.
Cognitive behavioral interventions have formed the cornerstone of obesity treatment for the past two decades. These techniques, often combined with diet and exercise strategies, have been shown to produce weight losses of sufficient magnitude so as to reduce health risks. Though success in producing short-term weight loss is improving, many factors, including a metabolic energy gap, continue to challenge long-term weight maintenance results. This article reviews the unique influence of cognitive, behavioral, and metabolic factors on weight loss and weight-loss maintenance, and how future treatment packages might be modified to improve long-term weight loss outcomes.
Epidemiologic studies have shown that sleep duration is associated with overall mortality. We conducted a systematic review of the associations between sleep duration and all-cause and cause-specific mortality. PubMed was systematically searched up to January, 2008 to identify studies examining the association between sleep duration and mortality (both all-cause and cause-specific) among adults. Data were abstracted serially in a standardized manner by two reviewers and analyzed using random-effects meta-analysis. Twenty-three studies assessing the associations between sleep duration and mortality were identified. All examined sleep duration measured using participant self-report. Among the 16 studies which had similar reference categories and reported sufficient data on short sleep and mortality for meta-analyses, the pooled relative risk (RR) for all-cause mortality for short sleep duration was 1.10 [95% confidence interval (CI): 1.06, 1.15]. For cardiovascular-related and cancer-related mortality, the RRs associated with short sleep were 1.06 (95% CI: 0.94, 1.18) and 0.99 (95% CI: 0.88, 1.13), respectively. Similarly, among the 17 studies reporting data on long sleep duration and mortality, the pooled RRs comparing the long sleepers with medium sleepers were 1.23 (95% CI: 1.17, 1.30) for all-cause mortality, 1.38 (95% CI: 1.13, 1.69) for cardiovascular-related mortality, and 1.21 (95% CI: 1.11, 1.32) for cancer-related mortality. Our findings indicate that both short sleepers and long sleepers are at increased risk of all-cause mortality. Further research using objective measures of sleep duration is needed to fully characterize these associations.
Insomnia is a highly prevalent problem that is associated with increased use of health care services and products, as well as functional impairments. This study estimated from a societal perspective the direct and indirect costs of insomnia.
A randomly selected sample of 948 adults (mean age = 43.7 years old; 60% female) from the province of Quebec, Canada completed questionnaires on sleep, health, use of health-care services and products, accidents, work absences, and reduced productivity. Data were also obtained from the Quebec government administered health insurance board regarding consultations and hospitalizations. Participants were categorized as having insomnia syndrome, insomnia symptoms or as being good sleepers using a standard algorithm. Frequencies of target cost variables were obtained and multiplied by unit costs to generate estimates of total costs for the adult population of the province of Quebec.
The total annual cost of insomnia in the province of Quebec was estimated at ). This includes direct costs associated with insomnia-motivated health-care consultations (36.6 million), prescription medications (1.8 million) and alcohol used as a sleep aid (970.6 million, with insomnia-related productivity losses estimated at 5,010 for individuals with insomnia syndrome, 421 for good sleepers.
This study suggests that the economic burden of insomnia is very high, with the largest proportion of all expenses (76%) attributable to insomnia-related work absences and reduced productivity. As the economic burden of untreated insomnia is much higher than that of treating insomnia, future clinical trials should evaluate the cost-benefits, cost-utility, and cost-effectiveness of insomnia therapies.
Cognitive behavioral interventions have formed the cornerstone of obesity treatment for the past two decades. These techniques, often combined with diet and exercise strategies, have been shown to produce weight losses of sufficient magnitude so as to reduce health risks. Though success in producing short-term weight loss is improving, many factors, including a metabolic energy gap, continue to challenge long-term weight maintenance results. This article reviews the unique influence of cognitive, behavioral, and metabolic factors on weight loss and weight-loss maintenance, and how future treatment packages might be modified to improve long-term weight loss outcomes.
U.S. Smartphone Use in 2015
- S. Aaron
Mobile health 2012: half of smartphone owners use their devices to get health information and one-fifth of smartphone owners have health apps
- S Fox
- M Duggan
Fox, S., Duggan, M., 2012. Mobile health 2012: half of smartphone owners use their devices to get health information and one-fifth of smartphone owners have health
apps. Retrieved from Washington. USA, D.C.
Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies: But Advanced Economies Still Have Higher Rates of Technology Use
- J Poushter
Poushter, J., 2016. Smartphone Ownership and Internet Usage Continues to Climb in
Emerging Economies: But Advanced Economies Still Have Higher Rates of Technology Use. (Retrieved from http://www.pewglobal.org/2016/02/22/smartphoneownership-and-internet-usage-continues-to-climb-in-emerging-economies/).
Prescribing Mobile Apps: What to Consider-lessons That Other Physicians Have Learned About Using Mobile Apps to Help Treat Patients
- K Terry
Terry, K., 2015. Prescribing Mobile Apps: What to Consider-lessons That Other Physicians
Have Learned About Using Mobile Apps to Help Treat Patients. (Retrieved from
http://medicaleconomics.modernmedicine.com/medical-economics/news/
prescribing-mobile-apps-what-consider?page=full).
Is there a clinical role for smartphone sleep apps? Comparison of sleep cycle detection by a smartphone application to polysomnography
- S Bhat
- A Ferraris
- D Gupta
Bhat, S., Ferraris, A., Gupta, D., et al., 2015. Is there a clinical role for smartphone sleep
apps? Comparison of sleep cycle detection by a smartphone application to
polysomnography. J. Clin. Sleep Med. 11 (7):709-715 http://doi.org/10.5664/jcsm.
4840/.