A multisectoral and multidisciplinary endeavor: a review of diabetes self-management apps in China | BMC Public Health


Basic characteristics

Sixty-six eligible apps released between 2011 and 2022 were identified. The basic information of the reviewed apps is summarized in Table 1. Thirteen of the reviewed apps (20%) were designed for multi-chronic conditions (including diabetes) and their risk management, while the rest 53 reviewed apps were solely developed for diabetes and its risk management. The top three most popular apps (assessed by app downloads) were Huawei Sports Health (华为运动健康) (224 million), Peppermint Nutritionist (薄荷营养师) (12.68 million), and Excellent Health (优健康) (8.69 million). The average usability score among the 66 reviewed apps was 36 (ranging from 26 to 41). The usability scores did not significantly differ by the app release years, with or without within-app-fee-charge services, or download platforms. Apps developed by health/medical technology companies tended to receive higher usability scores than those developed by information technology companies or health management companies or individuals (37.2 vs. 35.5, p = 0.0644). Only one app, MMC Butler, received highest scores (41 out of 41) for its easy-to-use structures, understandable content, clear image and text presentation, and instant and understandable feedback. The average app rating score was 4.41 (on a scale of 0–5), with no significant difference between the average rating score of the Android apps and the IOS apps (4.60 vs. 4.36, p = 0.1285). Among the apps available from both platforms, the average rating scores on the Android platform was significantly higher than that on the IOS platform (4.71 vs. 4.34, p = 0.0270). Twelve apps—including 1 app only available on the Android platform, 6 apps only available on the IOS platform, and 5 app available on both platforms—were rated 5 out of 5 by users for reasons like “user-friendly”, “providing diabetes management-tailored functions and knowledge”, and “providing personalized feedback and advice for patients.” Several findings deserve special mention. First, private firms play a significant role in app development, and all reviewed apps included at least one private firm as the app developer or a partner. Second, many apps included some form of monetization or profit-seeking. Thirty-nine apps (59%) had in-app charges for membership and/or upgrading services (e.g., access to VIP education packages), twenty-seven apps (41%) included in-app markets (e.g., for medicines, books, nutritional supplements, insurance purchasing, etc.) and two apps (3%) cost an average of 12 RMB (1.75 USD at a rate of 6.87 RMB to 1 USD) to download. Third, most apps were focused on diabetes as a general condition without specifying diabetes types. Only three apps (4.5%) specifically targeted one kind of diabetes, with one each targeted for the management of type I, type II, and gestational diabetes. Fourth, explicit reference to scientific evidence or management guidelines was comparatively rare. Only eighteen apps (27%) referred to scientific evidenced and/or national/international guidelines for diabetes management in their app or content development.

Table 1 Characteristics of the reviewed diabetes self-management apps in China (n = 66)

Most apps were not comprehensive, and many domains and functions were not represented. The average comprehensiveness score of the reviewed apps was 16 (ranging from 3 to 44) (Table 2). Domains covered in more than 20% of reviewed apps included: measures monitoring (61.9%), weight control (29.5%), and medication management (26.9%), nutrition (22.9%), and physical activity (22%). Domains covered by less than 5% of apps included: salt restriction (2.8%), alcohol cessation (4.4%), smoking cessation (4.4%). Functions found in more than 20% of apps included: documentation (34.1%), education (32.9%), and analysis (20.5%). Major limitations observed in the reviewed apps included a tendency for measures monitoring which failed to include more than glucose and bodyweight, educational materials that lacked information about medication, complication and other lifestyle risks (e.g., smoking, alcohol, salt intake) beyond nutrition/weight control/measures monitoring management, analysis that focused on ad hoc data visualization and failed to make predictions of risks for future health events (like vision and kidney issues), lack of robust tools for the effective use of shared data, and advising that failed to address lifestyle modification and complication management.

Table 2 Comprehensiveness of the reviewed diabetes self-management apps in China (n = 66)

Multisectoral collaboration endeavor

Forty reviewed apps (61%) involved more than one sector in their development and/or diabetes self-management service provision (Table 3). Among the multisectoral apps, 14 apps involved two sectoral entities, eight apps involved three entities, eight involved four entities, eight involved five entities, and two involved six entities. Public/private (n = 28) and private/private (n = 26) collaborations were the most common collaborative combinations, and eighteen reviewed apps included both public/private and private/private sector cooperations. Hospitals were the most common public sector entity, which appeared in 27 multisectoral apps, followed by community health centers (n = 3). Health/medical technology companies were the most common private sector entities, appeared in 34 multisectoral apps, followed by healthcare management companies (n = 24), and nutrition and food companies (n = 17), pharmaceutical companies (n = 10), and information technology companies (n = 8). There was only one civil society sector entity (Health Times (健康时报), a media agency, in the Renmin Health app) involved in the reviewed apps.

Table 3 The multisectoral and multidisciplinary features of the reviewed diabetes self-management apps (n = 66)

Different sectors tended to provide different diabetes service functions via the reviewed apps. Public sectors like hospitals were mainly involved in advising and education functions in the reviewed apps. The engagement of private sectors seemed to predict the inclusion of interface, data analysis, data sharing, and shopping functions of the reviewed apps. The participation of medical device companies, in particular, tended to result in an interface function, while the involvement of pharmaceutical companies, food producers, and insurance companies were more likely to include shopping functions. Civil society organization involvement contributed to spreading health education information and empowering the app users.

Even while there was significant multisector presence in the reviewed apps, different organizations tended to take charge of specific parts of the app, but rarely collaborate with each other in an integrated, multisectoral approach to any particular function. For instance, several apps contracted with health professionals from different hospitals and medical companies and allowed the health professionals to provide consulting and advising services to the app users; however, there was lack of obvious communication and collaboration between hospitals (public sector) and medical companies (private sector) in terms of providing holistic, comprehensive diabetes management care services to the app users. Similar patterns have been observed among the same type of sector agencies. For instance, Caring Church (关心堂) app partnered with hospitals and local community health centers, both of which were public sector entities. Although both the agencies provided various services via the app, there was no direct collaboration between the hospitals and the community health centers (like referring patients and providing continuous care to the users).

Multidisciplinary collaboration endeavor

Thirty-seven of the reviewed apps (56%) involve more than one discipline. Four apps – People’s Health, Zhiyun Health, Caring Church, and Master Fang – included over 20 disciplines in their apps. The most common disciplines were endocrinology (n = 34) and nutrition (n = 34), followed by cardiovascular medicine (n = 28), ophthalmology (n = 22), gastroenterology (n = 18), general surgery (n = 18), stomatology (n = 18), nephrology (n = 17), psychiatry (n = 11), rheumatology (n = 11), dermatology (n = 10), orthopedics (n = 10), respiratory medicine (n = 10), obstetrics and gynecology (n = 10), traditional Chinese medicine (n = 9), otorhinolaryngology (n = 8), oncology (n = 6), neurology (n = 5), urology (n = 5), infectious diseases (n = 7), pediatrics (n = 5), andrology (n = 4), anorectal (n = 4), neurosurgery (n = 3), internal medicine (n = 3), thoracic surgery (n = 3), emergency medicine (n = 2), hematology (n = 2), plastic surgery (n = 2), rehabilitation (n = 2), sexually transmitted diseases (n = 2), burns (n = 1), tuberculosis (n = 1), and critical care medicine (n = 1), among which some disciplines that seemed not directly related to diabetes were also presented.

Multidisciplinarity was most often reflected in the education and advising functions of the apps. Regarding the education function, 36 multidisciplinary apps (97%) provided various articles and videos generated by different disciplinary professionals to introduce diabetes and its risk factor and complication management knowledge and skills. Fifteen of the multidisciplinary apps (41%) provided advisory/therapy support that included professionals from multiple disciplinary backgrounds, among which 13 apps (87%) required payment for multidisciplinary consulting services. Multidisciplinary apps tended to have a higher comprehensiveness score than others (6.14 vs. 5.18, p = 0.0345).

Even for multidisciplinary apps, only a few apps (n = 4; 11%) involved multiple disciplines in the provision of any specific service, with most interdisciplinary apps having different disciplines work separately – resulting in a failure to integrate interdisciplinary perspectives. In providing educational information to empower the app users, many of the materials were generated by professionals from a single discipline or only contained information relevant to a single discipline. Regarding health data monitoring and analysis, although many apps allowed multidisciplinary data entering and sharing, the analysis and presentation of the analysis results were independent and not integrated. Similarly, when some apps contracted with professionals from different disciplines to provide consultation services to the app users, the professionals were usually working independently and providing recommendations without consulting professionals from other disciplines.


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