Are Sleep Apps Effective Tools For Behavioral Change?

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Smartphones are technological Swiss Army knives – easy to carry and, thanks to apps, able to do almost anything. All you need is a smartphone and an internet connection to unfold a thousand tools.

Apps make communication, traveling, working, and entertainment easier. And they can also allow us to monitor and manage our health, fitness and lifestyle, or even improve them. There are thousands of health and lifestyle apps – for exercise, for nutrition, for weight loss, for meditation, for overall health, for sleep… Their use is on the rise and they have shown great potential in effectively promoting self-improvement.

Technology may actually revolutionize how we take care of ourselves. It can successfully influence behavior and this is empowering in the sense that it offers an opportunity to self-manage our health routines. Apps can be a great aid for lifestyle interventions – they allow us to monitor our behavior and our progress, they can motivate us, give positive reinforcement, and set goals for continued enhancement. A well-designed app, built on scientific background may offer valuable help to behavioral health and lifestyle interventions.

But among the sea of apps, one wonders how many are really effective and how many present evidence-based content and behavioral theory-based interventions. Although it is not clear whether evidence- and theory-based interventions are indispensable for the efficacy of an app, they are known to be effective in changing behavior, being, most likely, a predictor of efficacy.

Traditional theory-based behavioral modification strategies state that behavioral change can be most successfully achieved when multiple strategic approaches and behavioral constructs are combined; these include informational strategies (creating knowledge); cognitive strategies, such as perceived benefits, barriers and risks; behavioral strategies, such as self-monitoring, realistic goal-setting, self-reward, relapse prevention; emotion-focused strategies, such as stress and negative affect management; and therapeutic interventions such as skill-building, for example. Apps that include such features have been proven more effective.

However, app developers are naturally focused on keeping users engaged. Therefore, many app features may tend to favor usability. Also, it is likely that app development may be preferentially aligned with more contemporary behavioral models. These postulate that technology can be designed to change user attitudes and behaviors through persuasion and social influence (Persuasive Technology Theory), and that when technology increases motivation and capacity to change, triggers to change behavior are more likely to work (Fogg Behavioral Model).

But a reliance on either traditional or contemporary behavioral models does not seem to be the case for most health, fitness and lifestyle apps: a 2011 review revealed that most had insufficient evidence-based content; a 2012 analysis showed a general lack of theory-based strategies; a 2013 study of exercise apps found that, overall, the apps contained few features based on behavioral change theory; another 2013 study reached the same conclusion for weight management apps; a 2015 study reported similar findings for alcohol reduction apps.

And what about sleep apps? They are one of the most popular type of lifestyle and health apps, which comes as no surprise – sleep disorders affect millions of people and this creates a huge demand for interventional strategies. But sleep apps are particular in the sense that interventional constructs need to go way beyond motivation.

Therefore, a new study aimed at determining whether sleep apps follow evidenced-based guidelines or are grounded in behavioral change or persuasive technology theories.

The study included the most downloaded and reviewed sleep apps for both iOS and Android. From the 369 apps found using the term “sleep” (in September 2015), 35 apps met the authors’ inclusion criteria. They scored them based on the presence of behavioral and persuasive technology constructs and correlated these scores with the average user rating for each app.

The average behavioral construct score was 34%, whereas the average persuasive technology score was 42%, which is not impressive. Realistic goal setting (86%), time management (77%), and self-monitoring (66%) were the behavioral constructs most commonly included in sleep apps; factors that contributed most to the apps’ persuasiveness were the user interface (94%), provision of positive feedback (54%), and social praise (40%).

Interestingly, the authors found a positive association between the presence of behavioral constructs and the apps’ popularity and ratings, showing that a good scientific design is an indication of probable success.

This also indicates that, since there is still a relatively poor inclusion of theory-based constructs, there is room to grow. Building strong evidence-based apps is likely to result in a real opportunity for effective behavioral intervention and be beneficial to the management of sleep disorders.

(An important side note: one obvious limitation of using smartphone apps for sleep management is the well-known negative impact of LED devices on the circadian rhythm and, consequently, on sleep. Something that should be kept in mind while designing a sleep app is the possibility on minimizing one’s interaction with our phone before bedtime.)

References

Azar KM, et al (2013). Mobile applications for weight management: theory-based content analysis. Am J Prev Med, 45(5):583-9. doi: 10.1016/j.amepre.2013.07.005

Breton E, et al (2011). Weight loss—there is an app for that! But does it adhere to evidence-informed practices? Transl Behav Med, 1(4):523–9. doi: 10.1007/s13142-011-0076-5

Cowan LT, et al (2013). Apps of steel: are exercise apps providing consumers with realistic expectations?: a content analysis of exercise apps for presence of behavior change theory. Health Educ Behav, 40(2):133–9. doi: 10.1177/1090198112452126

Crane D, et al (2015). Behavior change techniques in popular alcohol reduction apps: content analysis. J Med Internet Res, 17(5):e118. doi: 10.2196/jmir.4060

Fogg BJ (2003). Persuasive technology: using computers to change what we think and do (interactive technologies). Morgan Kaufmann, San Francisco. ISBN: 978-1-55860-643-2

Glanz K, et al (2008). Theory, research, and practice in health behavior and health education. In Glanz K, Rimer B & Viswanath K, Health behavior and health education: Theory, research, and practice (4th ed., pp. 23-40). San Francisco, CA: Jossey-Bass. ISBN: 978-0-470-39629-2

Grigsby-Toussainta DS, et al (2017). Sleep apps and behavioral constructs: A content analysis. Prev Med Rep, 6: 126–129. doi: 10.1016/j.pmedr.2017.02.018

Higgins JP (2016). Smartphone Applications for Patients’ Health and Fitness. Am J Med, 129(1):11-9. doi: 10.1016/j.amjmed.2015.05.038

Image via 1haboeri/Pixabay.

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