View source for A Framework to Assist Health Professionals in Recommending High-Quality Apps for Supporting Chronic Disease Self-Management: Illustrative Assessment of Type 2 Diabetes Apps

This is a review for Kelli Hale, Sandra Capra, and Judith Bauer's recommendations on High-Quality apps for supporting Type 2 Diabetes self-management. <ref name="Hale 2015">Hale, K., Capra, S., & Bauer, J. (2015). A Framework to Assist Health Professionals in Recommending High-Quality Apps for Supporting Chronic Disease Self-Management: Illustrative Assessment of Type 2 Diabetes Apps.JMIR mHealth and uHealth, 3(3), e87. http://www.ncbi.nlm.nih.gov/pubmed/26369346 </ref>

==Review 1==
=== Introduction ===

Mobile Health or [[MHealth]] represents an innovative path to help self-manage patients suffering from chronic diseases. However, although it is a promising market, how can a physician asses and be sure on which of the current applications in the digital market is the most optimal to provide to his/her patient with the tools required to improve his quality of life. This paper considers different factors crucial in the election of an application to help manage patients chronic status. The main objective from the study is to provide those applications which provide their information to patients from evidence-based programs, consistent with behavioral theory, as well as a patient-centered approach for matching apps to patients' individual needs. 

=== Methods ===

A three-step framework was established for the conduction of the study:

The first step was the Selection or Identification which elected the most popular highest quality apps in which their content was based on evidence-based information. Apps were selected based on the following inclusion criteria:(1) consistent with the 29 app topics, (2) less than AUD $5, and (3) written in English. 

Second, the categorization based on topics and core intervention strategies. Core intervention strategies of each app were identified by using the Behaviour Theory Content Survey (BTS). 

Third, a patient centered-approach was taken into consideration to match the patients needs based on problem etiology and patient motivation.

=== Results ===  

App Identification: From the top 200 free and grossing apps only 4 were recovered. Two were exercise apps and two were diet-focused apps; Interestingly, none of the results were specific to diabetes. 

App Categorization: Five categories were yielded from the vast list of apps including Healthy eating, Physical activity, Self-monitoring, Problem solving, and Healthy coping. With Healthy eating and Physical activity been the categories with the most apps. Scores from the BTS were significantly low, since the max possible score was a 100 been 24.4 the mean total score. 

App Patient Needs: The third step consisted on basically allocating the patient into the different categories established by step two; There was no single common patient-need specific to a single category the results indicated that indeed patient needs couldn't be associated to a category. 

=== Conclusion ===

Most of the applications provided in their content general information and assistance to their users, but were limited in assessment, feedback, or tailored assistance. Results also show that apps incorporated some degree of interaction with the users as an intervention strategy, but the emotional and cognitive areas were very underdeveloped or simply not present, an aspect believed to be crucial for the motivation of the use of technology by men. 
Although the study suggest that the chronic treatment of a patient would improve significantly by customizing the application to the patients-needs it wouldn't be feasible for the app developer to materialize a software so exclusive since it would also affect the range in the market for him. Also, it’s incredible the degree of evolution in applications in categories such as social media, news, games, business or education while sadly having no substantial development in the category of Health applications, specifically for the improvement of chronic health illnesses. Perhaps, it is indeed an unexplored new market with many flaws, but it is also a digital realm which potentially can allow us to monitor and improve a patient’s life expectancy. Focusing and shifting the interest of developers into applications truly functional with an emotional component and of friendly-use for users will play a crucial role in the health service delivered to our patients in a not so distant future. 

==Review 2==
=== Introduction ===
This paper presents an approach to assist health professionals in recommending high quality apps for supporting chronic disease self-management. Most app reviews focus on popularity, aesthetics, functionality, [[usability]], and information quality. There is no doubt these factors are important in selecting trustworthy apps which are appealing to users, but behavioral theory may be also be useful in matching the apps to user needs.

===Methods===
A single disease-type 2 diabetes-was selected to illustrate how the framework can be applied as this was deemed to represent the types of strategies used in many chronic diseases. A systematic approach based on behavioral theory and recommendations from best practice guidelines was developed for matching apps to patients' needs. In March 2014, a series of search strategies was used to identify top-rated iPhone and Android health apps, representing 29 topics from five categories of type 2 diabetes self-management strategies. The topics were chosen from published international guidelines for the management of diabetes. The senior author (KH) assessed the most popular apps found that addressed these topics using the Behavioral Theory Content Survey (BTS), which is based on traditional behavioral theory. A tool to assist decision making when using apps was developed and trialed with health professionals for ease of use and understanding.

===Results===
A total of 14 apps were assessed representing all five topic categories of self-management. Total theoretical scores (BTS scores) were less than 50 on a 100-point scale for all apps. Each app scored less than 50% of the total possible BTS score for all four behavioral theories and for most of the 20 behavioral strategies; however, apps scored higher than 50% of the total possible BTS score for specific strategies related to their primary focus. Our findings suggest that the apps studied would be more effective when used in conjunction with therapy than as stand-alone apps. Apps were categorized according to topic and core intervention strategies. A framework for matching apps to identified patient needs was developed based on app categorization and principles of patient-centered care. The approach was well accepted and understood by a convenience sample of health practitioners.

===Conclusion===
The framework presented can be used by health practitioners to better match apps with client needs. Some apps incorporate highly interactive strategies of behavioral theory, and when used as an adjunct may increase patient participation and the effectiveness of therapy. 

===Comments===
With the rising popularity of mobile technology, apps are proving to be useful in conjunction with clinical guidance. However, it should be emphasized that they are not able to modify behaviors as stand alone treatments.

== References ==
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[[Category:Reviews]]
[[Category:MHealth]]
[[Category:Methodologies and Frameworks]]
[[Category:Technologies]]