The death risk of hypertension ranks first worldwide and is intensified with aging. Unfortunately, the “traditional passive medical mode” has failed to meet the demands for chronic disease health management of the current aging society in China. The
As a subdiscipline of eHealth, mHealth is a newly appearing health mode in recent years [
As a major research target in the field of HCI, usability refers to how useful, usable, and satisfying a system is for the intended users to accomplish goals in the work domain by performing certain sequences of tasks [
Design and implementation of a mHealth APP is not just an IT project but a workflow activity and human-computer interaction engineering project [
Along with the special rectification by the China National Health and Family Planning Commission since May 2017 [
To our knowledge, this is the first time that a TURF usability evaluation tool has been used to quantitatively analyze the usefulness of main functionalities and data items collected from hypertension APPs from the perspective of the designer, user, and activity models.
After market investigation, we determined 2 information sources: the Android platform and the iOS platform, which account for 70% and 21%, respectively, of the Chinese smartphone market. These two platforms correspond to two official software markets: Google Play store and Apple APP store. Targeting the theme of hypertension prevention, we focused on the APP categories of Health & Fitness and Medical. Terms including “高血压” and “Hypertension” were used to search the official search engines in October 2016.
To select appropriate target APPs from all mHealth APPs for hypertension prevention, we used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) [
Inclusion and exclusion criteria of target APPs.
Criterion name | Declaration |
---|---|
Inclusion criterion 1 (IC1) | The search terms were “高血压” or “hypertension.” |
Inclusion criterion 2 (IC2) | Free Android or iOS APPs (paid APPs were excluded). (If one APP had both free and paid versions, the paid version was excluded.) |
Inclusion criterion 3 (IC3) | Belonging to either health or medical APPs. |
Inclusion criterion 4 (IC4) | Must include APPs for the hypertension prevention and treatment business. |
Inclusion criterion 5 (IC5) | The target users were marked as mHealth APPs for non-health care professionals (non-HCP). HCPs referred to those included in the World Health Organization’s health professional categorization [ |
Exclusion criterion1 (EC1) | APPs were only data receiving and transferring ends of external facilities or sensors and did not function for the prevention, treatment, and management of hypertension. |
Exclusion criterion2 (EC2) | APPs did not support Chinese characters (either simple or traditional Chinese). |
PRISMA flow diagram.
The TURF framework was used to evaluate the usefulness of the data element and services for the included APPs. According to the definitions of TURF, the usability of eHealth outputs is divided into degree of inherent complexity (usefulness) and degree of exogenous complexity. The former is quantified by “designer model,” “user model,” and “activity model” [
As for the usefulness evaluation of data elements, we mapped the “designer model” into the data elements associated with hypertension management acquired and stored by each APP, so the “designer models” of APPs have different instances. The user model was mapped as follows: based on the
To evaluate the usefulness of APP services, we mapped the designer model into the function set realized by each APP, so this APP had unique designer model instances. The service factors of mHealth [
Principles for PIPOH-based customization of hypertension management service factors.
Name | Description |
---|---|
Patients | Hypertensive patients |
Major interventions | Including patient’s awareness and recording of illness situation, data display [ |
Target professional users of APPs | Non-HCP [ |
Therapeutic outcome | Hypertension prevention and treatment |
Use environment | Daily life, nonclinic |
Service factor catalog of mHealth APPs.
Previously defined catalogue | Newly adjusted catalogue |
Reminder | Calendar-based reminding |
Telemedicine | Appointed registration and remote video consultation |
Record | Hypertension prevention and control information records |
Treatment | Drug use records |
Patient monitoring | Automatic and/or manual data processing |
Discussion | Communication and social networks |
Medicine propaganda and education and literatures | Popularization, propaganda, and education |
Call center | Localization service |
Others | Others (emergency contact, time axis-based data display, data security, and privacy protection) |
Finally, according to the intersection between the user model and the activity model from the data elements and service set, we defined a usefulness evaluation item template involving 12 data items, 17 service evaluation options, and 9 safety and privacy indices (Table
The smallest usefulness evaluation item template of APPs.
Catalogue | China hypertension prevention and treatment mHealth service factor catalogue | Number | Evaluation items | ID |
---|---|---|---|---|
APP service evaluation | Calendar-based reminder | 1 | Does it have the function of calendar-based hospital or community treatment and management of chronic disease? | C1 |
2 | Does it have the function of calendar-based to-do list (e.g., drug use on that day or reminder, blood pressure measurement at preset time and reminder, and exercise event and reminder)? | C2 | ||
3 | Does it have the function of calendar-based remark? (Record some subjective symptom or remark information.) | C3 | ||
Appointed registration | 4 | Does it have the function of extra bills for direct contact with doctors or online hospital registration? | A3 | |
Automatic and/or manual data processing | 5 | Does it have the function for the patient to manually input the necessary data above? | P1 | |
6 | Does it support the acquisition of blood pressure and heart rate by using externally placed or inner sensors (e.g., acquisition of blood pressure and heart rate by using iHealth band or Xiaomi band; check list results; and medical records, pictures, or symptoms were photographed by cam)? | P2 | ||
Communication and social networks | 7 | Does it integrate common Chinese social software such as WeChat, Weibo, or QQ? | S2 | |
8 | Does it have the function to share information and communicate with other users (e.g., providing a module for patient community discussion, for discussing experiences, or free propaganda and education activities in community medical institutions)? | S3 | ||
Popularization, propaganda, and education | 9 | Does it push or carry propaganda and recommendations on health habits? | A1 | |
10 | Does it push or carry propaganda and recommendations on nutrition meals? | A2 | ||
Localization service | 11 | Does it have the localization function that helps to localize patients or informed the patients about the position of the nearest doctor? | S1 | |
Others (emergency contact) | 12 | Does it have the function for setting emergency contact persons? Does it allow saving telephone and/or WeChat of contact persons? | E1 | |
13 | Does it have the function of emergency contact, allowing to directly call the emergency contact persons via telephone and/or WeChat? | E2 | ||
14 | Does it have the function of urgency display page (Automatically displaying the abstract of patient’s blood pressure and illness situation and emergency contact persons)? Does it allow visiting the doctor upon emergency treatment at convenience? | E3 | ||
Others (time axis-based data display) | 15 | Does it have two basic timestamps of the patient’s medical data (data generation timestamp and data record timestamp)? | T1 | |
16 | Does it have the function of abstraction (automatic frequency reduction for the collected high-frequency data) and visualization (graph-like description of the patient’s data) of data from spatial-temporal perspectives? | T2 | ||
Others (data security and privacy protection) | 17 | Does the software allow offline input, acquisition, and use of data (cached locally and automatic synchronization upon loading)? | F1 | |
18 | Does it have the function of single- or multiple-user authentication and authorization? | U1 | ||
19 | Does it have the function of urgent information acquisition? | U2 | ||
20 | Does it have the function of local storage and caching of data encryption? | U3 | ||
21 | Does it have the function of data signature antitampering? | U4 | ||
22 | Does it have the function of data backup? | U5 | ||
Data element evaluation | Hypertension prevention and control information records | 23 | History of present illness | D1 |
24 | Previous history | D2 | ||
25 | History of surgery | D3 | ||
26 | Social history (including smoking, alcohols, privacy, and occupation) | D4 | ||
27 | Family history | D5 | ||
28 | History of allergy | D6 | ||
29 | Recording of immunity and inoculation | D7 | ||
30 | Follow-up records (including one-to-one paired community doctor) | D8 | ||
31 | Laboratory examination results | D9 | ||
32 | Vital signs (height, weight, BMI, and blood pressure) | D11 | ||
33 | Demographic information of users (name, gender) | D12 | ||
Drug use information records | 34 | Records of drug use situations | D10 |
Mobile-end hypertension management is widely demanded. In this work, we selected 73 APPs (Table
Selected details of APPs.
Number | APP name | Link to APP (accessed by 31 Oct. 2016) | Platform |
---|---|---|---|
1 | xue-ya-guan-jia-gao-xue-ya | iOS | |
2 | gao-xue-ya-zhi-liao-mi-ji | iOS | |
3 | ti-jian-bao-ce-xue-ya-xin | iOS | |
4 | kang-kang-xue-ya-gao-xue-ya | iOS | |
5 | gao-xue-ya-guan-jia | iOS | |
6 | tu-huan-jian-kang-nin-jia | iOS | |
7 | zhang-kong-gao-xue-ya | iOS | |
8 | gao-xue-ya-zhi-liao-guan-jia | iOS | |
9 | xue-ya-xue-zhi-bao-jian-guan | iOS | |
10 | yue-tang-jian-kang-you-hua | iOS | |
11 | xue-ya-zhun-xiao-zhun-xue | iOS | |
12 | gao-xue-ya-kang-fu-bao-dian | iOS | |
13 | xun-yi-wen-yao-mian-fei-yi | iOS | |
14 | gao-xue-ya-zhi-nan-gao-xue | iOS | |
15 | xue-ya-diao-yang-ke | iOS | |
16 | gao-xue-ya-zhi-duo-shao-gao | iOS | |
17 | kang-kang-xue-ya-lian-tong-ban | iOS | |
18 | xue-ya-guan-jia | iOS | |
19 | lao-nian-yang-sheng-man-xing | iOS | |
20 | tian-tian-xue-ya | iOS | |
21 | ban-ge-yi-sheng | iOS | |
22 | jian-kang-yang-sheng-jian | iOS | |
23 | yi-xue-xiao-gong-ju-zui-zhi | iOS | |
24 | yi-fang-jian-kang-ri-ji-zui | iOS | |
25 | ji-shi-xin-lu-xin-zang-jian | iOS | |
26 | tai-guan-jia-jian-kang-zi | iOS | |
27 | xi-meng-jian-kang | iOS | |
28 | zhang-shang-yi-sheng-zhang | iOS | |
29 | jin-dian-xue-ya-guan-li | iOS | |
30 | xue-ya-smart-xue-ya-smartbp | iOS | |
31 | jian-kang998-wen-yi-sheng | iOS | |
32 | wei-xun-yi-dong-yi-liao-gua | iOS | |
33 | yi-sheng-shu-zai-xian-wen | iOS | |
34 | kuai-su-wen-yi-sheng-guo-nei | iOS | |
35 | dong-ri-zhong-yi | iOS | |
36 | runtastic-heart-rate-xin-lu | iOS | |
37 | hao-da-fu-zai-xian-zi-xun | iOS | |
38 | gao-xue-ya | iOS | |
39 | guan-jia-yi-sheng | iOS | |
40 | zhong-yi-zhi-liao-yang-sheng | iOS | |
41 | zhong-yi-jian-kang-xue-wei | iOS | |
42 | mu-biao-jian-kang-wo-hu-lian | iOS | |
43 | sheng-ming-shu-xue-ya-tong | iOS | |
44 | jian-kang-zhong-xin | iOS | |
45 | xi-en-jian-kang-zai-xian-wen | iOS | |
46 | jian-ya-bao | iOS | |
47 | ifora-mp | iOS | |
48 | control-tension | iOS | |
49 | accutension | iOS | |
50 | dooland-health-bpmanager | Android | |
51 | supertw-apppj-bp | Android | |
52 | blt-bp | Android | |
53 | bloodpressurelog | Android | |
54 | iBP Monitor | Android | |
55 | weightcaloriewatch | Android | |
56 | trackermonitor | Android | |
57 | feelymos-bluebp | Android | |
58 | actionbloodpressure | Android | |
59 | hj-healthcare | Android | |
60 | smartbloodpressure | Android | |
61 | kang-hypertension | Android | |
62 | freshware-bloodpressure | Android | |
63 | Bpservier | Android | |
64 | ffree-BloodPressure | Android | |
65 | lite-bptracker | Android | |
66 | cchong-BloodPressure | Android | |
67 | openit-bpdiary | Android | |
68 | bpressure | Android | |
69 | bpbuster | Android | |
70 | jiang-kang-miao-guan-jia | Android | |
71 | HealthCheck | Android | |
72 | mengtaoye-mybloodpressure | Android | |
73 | cardiojournal | Android |
Distributions of the selected APPs by platform, type, developer, regions, subject area, and multilanguage support.
The useful degrees are highly specific among different APPs and generally are not high (mean = 37.4%). None of the APPs could cover 100% of the usefulness evaluation template. Among the Android APPs, the top three rankings by usefulness are “freshware-bloodpressure” (56%), “kang-hypertension” (53%), and “cchong-BloodPressure” and “jiang-kang-miao-guan-jia” (both 53%), and the last one is “bpressure” (15%). Among the iOS APPs, the top three rankings by usefulness are “tu-huan-jian-kang-nin-jia” (74%), “jian-kang998-wen-yi-sheng” (68%), and “zhang-shang-yi-sheng-zhang” and “jin-dian-xue-ya-guan-li” (both 65%), while the lowest are “gao-xue-ya-zhi-nan-gao-xue,” “xue-ya-diao-yang-ke,” and “gao-xue-ya-zhi-duo-shao-gao” (all 6%). In all, the iOS APPs have slightly higher useful degrees than the Android APPs (39% versus 32%), which is consistent with a previous study [
(a) Selected Android APP score classifications (24 APPs). (b) Selected iOS APP score classifications (49 APPs).
The supporting degrees of data elements and services are largely different among the APPs. As for single items, none of the items could be covered by all 73 APPs. In particular, the highest support degrees come from D12 and U1 (approximately 92%), which are both supported by 67 APPs, but the lowest come from E3 and U2 (both 0%). The real distribution of each data element or service is shown in Figure
Item score classification. Notes: APP sample size = 73; score item size = 38. Calendar-based reminder (C1~C3); appointed registration (A3); automatic and/or manual data processing (P1, P2); localization service (S1); communication and social networks (S2, S3); popularization, propaganda, and education (A1, A2); others (emergency contact) (E1~E3); others (time axis-based data display) (T1, T2); others (data security and privacy protection) (F1, U1~U5); hypertension prevention and control information records (D1~D9, D11, D12); drug use information records (D10).
Designers, users, and medical professionals from China and abroad have very different views about what functionalities should be contained and what data items should be collected from hypertension APPs. The two most commonly collected data elements for mainland-developed and non-mainland-developed APPs are “demographic information” (88% versus 100%, resp.) and “vital signs (e.g., height, weight, blood pressure, or heart rate)” (76% versus 100%, resp.), but the most commonly provided service is “promoting or self-carrying popularization and recommendations on health habits” (94%) and “patient data entry” (100%), respectively. Moreover, the mainland-developed APPs have a higher useful degree in data elements (33% versus 21%, resp.) and a lower useful degree in services (42% versus 43%, resp.), especially lower degrees in data display, system framework, security and privacy, and data transmission (31% versus 56%, resp.) (Figure
Comparison of data element and services between mainland-developed APPs and non-mainland-developed APPs. Notes: mainland-developed APP sample size = 49; non-mainland-developed APP sample size = 24; score item size = 38. Calendar-based reminder (C1~C3); appointed registration (A3); automatic and/or manual data processing (P1, P2); localization service (S1); communication and social networks (S2, S3); popularization, propaganda, and education (A1, A2); others (emergency contact) (E1~E3); others (time axis-based data display) (T1, T2); others (data security and privacy protection) (F1, U1~U5); hypertension prevention and control information records (D1~D9, D11, D12); drug use information records (D10).
Hypertension management depends on the mobility, promptness, and barrier-free access of mobile devices and aims to customize professional mHealth APPs according to patient demands. However, the usefulness of such APPs is unsatisfactory and thus can be largely improved in the future (usefulness degree of neither type of APP exceeds 40%). Additionally, the accuracy of functions is controversial, and the functions are exaggerated. For instance, the Android APP “cchong-BloodPressure” states that users can collect body pulse data through the phone cam; its principle is to count pulses by periodically filming fingertip brightness to form RGB images. The heart rates acquired can only be references, but the APP claims to provide both systolic blood pressure and diastolic blood pressure, which is exaggerated and not science-based. We think these APPs do not meet the user demands for hypertension prevention and are unable to cover the majority of functions. In the future, more comprehensive and more professional APPs should be developed.
The existing mHealth APPs targeting hypertension management have not been adjusted or optimized to the optimal use status of mobile devices. At present, the mHealth APP market in China is explosively growing due to the popularization of smartphones [
The philosophies about hypertension and its treatment differ in the medical field and among the public, which probably has led to the differences in data element collection and support services among APPs. Chinese researchers think hypertension is a disease due to living habits [
Non-mainland-developed APPs largely differ from mainland-developed APPs in terms of information security and privacy protection. As for service usefulness, the mainland-developed APPs are slightly lower than the non-mainland-developed APPs (42% versus 43%, resp.) but especially in information security, privacy protection, and data display (31% versus 56%, resp.). At the level of either market self-discipline or governmental regulations, China has no concrete practical supervision and management measure targeting the information security of mHealth APPs. On the one hand, the majority of APPs do not release, on the user protocol or the supportive websites, any declaration about user data security or privacy protection, which is a hidden risk when individuals or institutions, either informed or not informed, utilize user privacy information to acquire economic benefits. On the other hand, the Chinese State Council released 11 official documents between 2013 and 2015 [
Mainland-developed APPs and non-mainland-developed APPs largely differ in profit-making modes. Non-mainland-developed mHealth APPs almost all focus on disease monitoring and recording. The medical systems of Western countries, Hong Kong, Taiwan, and Macau, the global market, and the governmental laws have made drug sales not the key-profiting factor of APP suppliers but improvement of the profitability of monitoring facilities. In the above regions, the hierarchical diagnosis and treatment systems are complete, so the APP-recorded daily sign information helps general practitioners to continuously and consistently treat/manage hypertensive patients. In contrast, the mainland-developed mHealth APPs mostly focus on the provision of information for users. Specifically, the supportive rates of A1, A2, and A3 are up to 94%, 84% and 55%, respectively, and their profit-making modes are more diversified, including ads, service charge for rapidly and efficiently acquiring high-quality medical resources, and sales of monitoring devices. These differences are mainly attributed to the economy, population, and medical systems. Statistics in 2015 show that mainland China was the second largest economy with GDP up to 10,140 billion dollars [
A professional mHealth APP should be decided by user retention and loyalty. If an APP only simply acquires, organizes, and displays data, such as the simple teaching of health-related information as popularization, propaganda, and education (A1, A2), then its attraction to users will gradually decrease. To continually expand the user group, APP need support from relevant data analysis and mining backend platforms that professionally analyze the data uploaded by users and convey it to users in an easy and understandable way. In response to the health problems identified from data analysis, the backend platforms can give reasonable and effective recommendations. When a user experiences an improvement in health status, he/she spontaneously has retention and thereby loyalty.
Owing to the uniqueness of the “Internet + healthcare” mode, user information becomes more concentrated and accessible, so there might be bugs in any link between online and offline, which harbor the risk of leaking user identity information and health data. Thus, it is urgent to build third-party Internet health information management platforms that rely on the industry association and improve user privacy protection mechanisms. Users are suggested to strengthen their consciousness of privacy protection and their sense of data possession and autonomous acquisition rights. Medical staff is recommended to sufficiently respect users’ right of informed consent and not to use or leak user private information. Technically, the construction of third-party health information management platforms should be based on privacy protection systems with controlled data access; through the access-right restriction, it is ensured the accessing subject (medical staff) only reasonably and legally uses the accessed object (data deposited in the mHealth APP). Moreover, relevant functional departments and the industrial association are recommended to enhance supervision and management over the participating subject.
The legal subjects of mHealth include medical staff, users, medical institutions, and APP service providers, but their definitions and specifications in relevant Chinese laws are ambiguous. However, relevant laws and regulations should be improved as APP users are going to more frequently use Internet health and mobile medical intelligent devices.
The mHealth APP and wearable medical equipment are supplementary to each other under the age of big data. The supplement of high-precision wearable equipment to the mHealth APP will largely promote the realization of targeted and individualized medical treatment, especially for chronic diseases such as high blood pressure. Thus, the industrial association is recommended to establish inclusion criteria for mHealth APPs and wearable medical equipment, which should ensure security, practicability, and effectiveness but not restrict the development and innovation in this industry. When using a mHealth APP, users usually first find out the quality defects and risks. Thus, it is recommended to build a supervision feedback channel, so users will become the main force to monitor and supervise the quality of mHealth APPs and wearable medical equipment. In this way, feedback, complaining, information coordination, and data release can be realized freely, and APPs with risks can be identified.
This work has some limitations. Although the current testing flowchart aimed to maximize accuracy and objectivity, the research effectiveness might be limited from the following aspects. This work is based on 73 product samples and adopts usefulness indices for the first time for a systematic quantitative investigation into hypertension management and control mHealth APPs, which are products from a vertical subdivided domain. This investigation reveals the cross-sectional snapshot of the mHealth industry in mainland China in October 2016. First, the acquired APPs are small in number and target at the management of hypertension, so we are unable to completely explain the behaviors of the mHealth APP market. Second, this work was limited to non-HCP users and excluded HCP users. These two types of APP users are completely different in nature. In addition, paid APPs were excluded. The above reasons might have led to deviation in the analytical results.
Future research trends should include medical institutions, HCP, and governmental duty offices. Therefore, service systems, laws and regulations, and business-profiting modes should be comprehensively analyzed from higher levels. More comprehensive sample subdivision should be studied, targeting personalized usability research involving the demands of users with different age groups, cultures, and habits. Moreover, problems regarding the supervision and management, security, privacy, and reliability of mHealth APPs should be solved as soon as possible.
Jun Liang and Xiaojun He shared co-first authorship.
The authors have no competing interests to declare.
This study was supported in part by the National Natural Science Foundation of China (NSFC) (nos. 81471756 and 81771937) and the Medical and Health Planning Project of Zhejiang Province of China (Grant no. 2017KY386).