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Mobile health (mHealth) apps are rapidly emerging technologies in China due to strictly controlled medical needs during the COVID-19 pandemic while continuing essential services for chronic diseases. However, there have been no large-scale, systematic efforts to evaluate relevant apps.
We aim to provide a landscape of mHealth apps in China by describing and comparing digital health concerns before and after the COVID-19 outbreak, including mHealth app data flow and user experience, and analyze the impact of COVID-19 on mHealth apps.
We conducted a semilongitudinal survey of 1593 mHealth apps to study the app data flow and clarify usage changes and influencing factors. We selected mHealth apps in app markets, web pages from the Baidu search engine, the 2018 top 100 hospitals with internet hospitals, and online shopping sites with apps that connect to smart devices. For user experience, we recruited residents from a community in southeastern China from October 2019 to November 2019 (before the outbreak) and from June 2020 to August 2020 (after the outbreak) comparing the attention of the population to apps. We also examined associations between app characteristics, functions, and outcomes at specific quantiles of distribution in download changes using quantile regression models.
Rehabilitation medical support was the top-ranked functionality, with a median 1.44 million downloads per app prepandemic and a median 2.74 million downloads per app postpandemic. Among the top 10 functions postpandemic, 4 were related to maternal and child health: pregnancy preparation (ranked second; fold change 4.13), women's health (ranked fifth; fold change 5.16), pregnancy (ranked sixth; fold change 5.78), and parenting (ranked tenth; fold change 4.03). Quantile regression models showed that rehabilitation (P75, P90), pregnancy preparation (P90), bodybuilding (P50, P90), and vaccination (P75) were positively associated with an increase in downloads after the outbreak. In the user experience survey, the attention given to health information (prepandemic: 249/375, 66.4%; postpandemic: 146/178, 82.0%;
mHealth apps are an effective health care approach gaining in popularity among the Chinese population following the COVID-19 outbreak. This research provides direction for subsequent mHealth app development and promotion in the postepidemic era, supporting medical model reformation in China as a reference, which may provide new avenues for designing and evaluating indirect public health interventions such as health education and health promotion.
In the wake of the COVID-19 outbreak, digital health technologies to assist medical service systems and people [
Before the COVID-19 outbreak, several studies investigated the characteristics of apps in China; these studies focused on only specific health domains, such as disease management, women's health, and sports, instead of elaborating on the overall mHealth app situation [
After the outbreak, apps directly related to COVID-19 that were used to track high-risk groups and assist in diagnosis were the most studied [
At the same time, unlike other countries, China implemented normalized pandemic prevention and control and rarely used contact tracing apps in a personal form; thus, the apps developed directly as a result of the pandemic were not the focus of this research. There was a spike in the volume of phone calls asking medical questions after the outbreak with a great increase in online demand in China [
However, it is presently unclear what mHealth app characteristics, if any, have been influenced and appropriately deployed in the pre and postpandemic periods.
Here, we conducted a nationwide study of mHealth apps in China. The aim of this study was (1) to describe and compare digital health concerns before and after the COVID-19 outbreak, including mHealth app data flow and user experience, and (2) to analyze the impact of COVID-19 on mHealth apps.
Before the COVID-19 outbreak, we conducted a comprehensive electronic search of 4 sources up to October 25, 2019: (1) apps on leaderboards of health-related categories in the 6 largest app markets in China, including the top 50 on the Huawei Android Market (Huawei Technologies Co Ltd; Shenzhen, China), top 50 on the OPPO Android Market (Oppo Electronics Co Ltd; Dongguan, Guangdong, China), top 100 on the Vivo Android Market (Vivo Co Ltd; Dongguan, Guangdong, China), top 50 on the Tencent Android Market (Tencent Holdings Limited; Shenzhen, China), top 100 on the 360 Android Market (Qihoo 360 Technology Co Ltd; Beijing, China), and top 100 on the Apple iTunes store for China (Apple; Cupertino, CA) [
We collected 4 types of data: (1) basic app characteristics from the description interface, including the size of apps, number of app downloads, and target users; (2) app developers’ information from the largest commercial inquiry platform, the Tianyancha website [
Following the outbreak, the same mHealth apps were investigated a second time in April 2021 as semilongitudinal samples with download data, and we determined whether COVID-19 content had been added. A download change was defined as the difference between post and prepandemic downloads.
We recruited residents through a community health checkup program to explore the user experience with mHealth apps among a large community of more than 20,000 people with a balanced age distribution in southeastern China. We used an offline questionnaire to survey 400 participants from October 2019 to November 2019 before the COVID-19 outbreak and 200 participants from June 2020 to August 2020 after the outbreak. A total of 553 (553/600, 92.2%) participants completed the survey: 375 (375/400, 93.8%) before the outbreak and 178 (178/200, 89.0%) after the outbreak. A predesigned, structured questionnaire was provided to potential participants in the waiting areas of the medical examination center in this community. The questionnaire was designed to collect information on participants’ attention to health information, various aspects of mHealth technology usage, willingness to consume mHealth technology, and health status and demographic characteristics. Trained research assistants who were fluent in Chinese administered the questionnaire and provided verbal instructions about how to complete them.
Before taking the survey, informed consent was obtained from each participant. This study was approved by the Biomedical Research Ethics Committee of Fujian Medical University (2018 number 11). All procedures were performed using the relevant guidelines and regulations.
A descriptive analysis was conducted for mHealth app characteristics, developers, permission, functions, and user experiences in China. Data are presented using frequencies and percentages, bar charts, statistical maps, Venn diagrams, and heat maps. Continuous variables are presented as the mean and SD or the median and IQR, while categorical variables are presented as the frequency and percentage. Mann-Whitney
We also compared the post and prepandemic app downloads of each category using paired
Quantile regression (QR) models were used to explore the relationship between modeling covariates and quartiles of the outcome variable of interest [
All analyses were prespecified and performed using SPSS 25.0 (IBM Corp; Armonk, NY) and Stata version 13 (StataCorp; College Station, TX).
A total of 1593 mHealth apps were included in the analysis (see
Flow diagram of app selection.
Characteristics and developers of mobile health (mHealth) apps available on the Chinese market (1593 apps and 1196 developers).
Characteristics | Results | ||
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Size of app (MB), mean (SD) | 37.26 (75.46) | |
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Size of app (MB), median (IQR) | 24.90 (13.50-39.88) | |
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Free download | 1530 (96.0) |
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Paid | 63 (4.0) |
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If download is free, in-app purchase available, n (%) | 1444 (90.6) | |
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In-app advertisement, n (%) | 175 (11.0) | |
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Medical researchers | 72 (4.5) |
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Medical personnel | 100 (6.3) |
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Patients | 513 (32.2) |
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Healthy people | 921 (57.8) |
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Has a membership system, n (%) | 101 (6.3) | |
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Connects to smart devices, n (%) | 292 (18.3) | |
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User rating score, mean (SD) | 6.99 (2.33) | |
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User rating score, median (IQR) | 7.00 (6.00-9.20) | |
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Prepandemic, mean (SD) | 8967.33 (118,905.97) |
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Prepandemic, median (IQR) | 44.09 (1.47-787.00) |
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Postpandemic, mean (SD) | 17,342.13 (275,177.12) |
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Postpandemic, median (IQR) | 223.12 (10.93-2252.00) |
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Number of functions, mean (SD) | 2.72 (2.25) | |
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Number of functions, median (IQR) | 2 (1-4) | |
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Added functions or content for the pandemic, n (%) | 182 (11.4) | |
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Pandemic consultation | 105 (6.6) |
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Pandemic prevention knowledge | 108 (6.8) |
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Pandemic dynamics | 9 (0.6) |
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Transaction amount (¥; x106)a, mean (SD) | 267.66 (812.75) | |
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Transaction amount (¥; x106)a, median (IQR) | 50.00 (20.50-150.00) | |
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Registered capital (¥; x106)a, mean (SD) | 188.10 (204.15) | |
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Registered capital (¥; x106)a, median (IQR) | 937.50 (166.00-210.53) | |
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Number of staff, mean (SD) | 262 (3016) | |
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Number of staff, median (IQR) | 17 (2-62) | |
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In business | 482 (30.3) | |
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Remainder enterprise | 697 (43.8) |
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Closed down | 15 (1.0) |
aA currency exchange rate of ¥1=US $0.15 is applicable.
Sankey diagram of flow direction in apps. The width of the colored boxes and their connecting gray bands are directly proportional to the frequency of apps from every data source (left side) and flow quantities of these apps to the attributable user communities (right side). Hcps: health care professionals.
(A) Geographical distribution and (B) distribution of the time of establishment of mobile health (mHealth) app developers in China. There is another developer in Canada.
Of the 1593 mHealth apps, 1285 (80.7%) apps had full functionality available to conduct the app trial, including apps for health management (1248/1593, 78.3%) and apps for medical support (697/1593, 43.8%). The frequency of each function available in Chinese mHealth apps is shown in
For each classification of mobile health (mHealth) apps in China: (A) frequency of app functions for 5 classifications; (B) ranking of the frequency of app functions is displayed on a color scale ranging from green (lowest charge rates) to orange, yellow, and red (highest charge rates).
Venn diagrams illustrating the associations between app classifications: (A) user communities, (B) mobile health service function, (C) content or services provided by apps, (D) tertiary prevention, and (E) service time provided by apps.
Overall upward trends in app downloads during the pandemic were driven by some key app functions (see
The 6 functions that decreased in ranking the most in the postpandemic period were health education, genetic screening, medical service purchases, drug purchases, inquiries, and physiological testing via mobile phone; 4 additional functions that declined after the COVID-19 outbreak included medical examination, medical community, patient management, and disease management. Bodybuilding, which was closely related to outdoor activities, also declined in ranking, with an increase of only 2.71 times prepandemic rates, but its absolute change in mean was 28,247,800. All these functions with declining rankings were growing, albeit at lower speeds relative to high-ranking functions. For example, the increase in drug purchases was 2.05 times (absolute change in mean: 7,388,400) the prepandemic rate, and patient management downloads were 4.98 times (absolute change in mean: 3,039,900) the prepandemic rate.
The number of occurrences of each function (ie, function frequency) in the 1593 mobile health (mHealth) apps with multiple functions in China.
Leading functions of mobile health (mHealth) apps in the pre and postpandemic periods in China, connected by lines between the periods to show increased (solid line) or decreased (dashed line) ranking, while bold number indicate significant changes between the periods as determined using paired
Based on the QR analysis, a positive effect for adding COVID-19 function and content on apps (P10:
Quantile regression coefficients of app characteristics for changes in downloads.
App characteristics | Quantiles |
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0.10 | 0.25 | 0.50 | 0.75 | 0.90 |
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Coefficient (x104; 95% CI) | 0.000142 (–0.151 to 0.151) | –0.0428 (–0.500 to 0.414) | 0.809 (–2.786 to 4.404) | –4.951 (–28.311 to 18.409) | –20.304 (–120.276 to 79.669) | |||||||
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>.99 | .85 | .66 | .68 | .69 | ||||||||
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Coefficient (x104; 95% CI) | 0.0000367 (–0.005 to 0.005) | 0.0102 (–0.018 to 0.038) | 0.256 (–0.041 to 0.554) | 3.173 (0.239 to 6.108) | 16.728 (6.124 to 27.332) | |||||||
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.99 | .45 | .09 | .03 | .002 | ||||||||
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Coefficient (x104; 95% CI) | –0.0857 (–0.321 to 0.150) | 1.072 (–11.297 to 13.441) | 101.809 (15.949 to 187.668) | 648.489 (–267.967 to 1564.944) | 2840.221 (–213.048 to 5898.490) | |||||||
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.48 | .87 | .02 | .17 | .07 | ||||||||
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Coefficient (x104; 95% CI) | –0.0504 (–0.252 to 0.151) | –0.691 (–1.340 to –0.041) | –3.501 (–9.198 to 2.196) | –31.571 (–69.032 to 5.889) | –143.767 (–321.698 to 34.165) | |||||||
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.62 | .04 | .23 | .098 | .11 | ||||||||
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Coefficient (x104; 95% CI) | 0.0258 (–0.019 to 0.071) | 0.0593 (–0.147 to 0.265) | 1.675 (–0.448 to 3.798) | 7.928 (–10.998 to 26.855) | 66.478 (–22.249 to 155.205) | |||||||
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.26 | .57 | .12 | .41 | .14 | ||||||||
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Coefficient (x104; 95% CI) | 0.337 (0.008 to 0.665) | 0.166 (–0.323 to 0.655) | –1.576 (–6.213 to 3.062) | 5.069 (–51.167 to 61.305) | 34.596 (–256.520 to 325.712) | |||||||
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.045 | .51 | .51 | .86 | .82 | ||||||||
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Coefficient (x104; 95% CI) | 0.450 (0.190 to 0.710) | 1.788 (–3.121 to 6.698) | 46.627 (9.800 to 83.453) | 529.365 (132.535 to 926.194) | 1517.683 (352.991 to 2682.376) | |||||||
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.001 | .48 | .01 | .009 | .01 |
aSignificance was assessed at the 10% level.
Statistically significant differential effects of app characteristics by quantile: (A) app size, (B) in-app advertisement, (C) number of functions, (D) functions or content added for the pandemic, (E) Internet of Things.
We found that 4 of 28 functions, including rehabilitation (P75:
Quantile regression coefficients of app functions for changes in downloads.
App functions | Quantiles | |||||
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0.10 | 0.25 | 0.50 | 0.75 | 0.90 | |
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Coefficient (x104; 95% CI) | 0.0002 (–0.0687 to 0.0691) | 0.0292 (–0.308 to 0.367) | –1.036 (–4.364 to 2.293) | –21.668 (–46.305 to 2.969) | –108.990 (–234.145 to 16.166) |
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>.99 | .87 | .54 | .09 | .09 | |
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Coefficient (x104; 95% CI) | 0.0739 (–0.388 to 0.536) | 0.133 (–0.866 to 1.132) | –3.450 (–11.716 to 4.816) | –26.942 (–103.928 to 50.045) | –207.444 (–436.652 to 21.764) |
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.75 | .79 | .41 | .49 | .08 | |
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Coefficient (x104; 95% CI) | 0.0992 (–0.218 to 0.417) | 1.624 (–0.807 to 4.055) | 26.345 (–10.549 to 63.239) | 470.025 (160.786 to 779.265) | 1132.3840 (172.086 to 2092.682) |
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.54 | .19 | .16 | .003 | .02 | |
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Coefficient (x104; 95% CI) | 0.0004 (–0.0324 to 0.0332) | 0.0219 (–0.375 to 0.419) | 7.194 (–0.472 to 14.861) | 41.692 (–39.050 to 122.433) | 376.090 (–49.408 to 801.587) |
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.98 | .91 | .07 | .31 | .08 | |
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Coefficient (x104; 95% CI) | –0.0047 (–0.0324 to 0.0332) | –1.497 (–5.075 to 2.081) | –11.928 (–45.420 to 21.565) | –65.017 (–242.440 to 112.407) | –415.092 (–869.710 to 39.527) |
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>.99 | .41 | .49 | .47 | .07 | |
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Coefficient (x104; 95% CI) | 0.1684 (–1.938 to 2.275) | 3.037 (–1.692 to 7.765) | 16.135 (–26.886 to 59.157) | 226.703 (–248.826 to 702.233) | 3257.672 (–446.489 to 6961.834) |
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.88 | .21 | .46 | .35 | .09 | |
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Coefficient (x104; 95% CI) | 8.35b (–0.0418 to 0.0418) | –0.445 (–0.803 to –0.0872) | –4.078 (–8.238 to 0.083) | –39.345 (–81.525 to 2.834) | –166.124 (–461.988 to 129.740) |
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>.99 | .02 | .06 | .07 | .27 | |
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Coefficient (x104; 95% CI) | 0.0046 (–5.995 to 6.004) | 7.758 (–18.734 to 34.251) | 44.707 (–139.634 to 229.048) | 410.179 (–18.195 to 838.553) | 566.790 (–5041.161 to 6174.740) |
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>.99 | .57 | .63 | .06 | .84 | |
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Coefficient (x104; 95% CI) | 0.0418 (–0.377 to 0.460) | –0.102 (–0.856 to 0.652) | –4.011 (–9.643 to 1.622) | –37.508 (–71.617 to –3.398) | –95.521 (–237.203 to 46.161) |
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.85 | .79 | .16 | .03 | .19 |
aSignificance was assessed at the 10% level.
b10-17.
Statistically significant differential effects of app functions by quantile: (A) health education, (B) drug use, (C) rehabilitation, (D) bodybuilding, (E) men’s health, (F) pregnancy preparation, (G) cultivation of lifestyle, (H) vaccination, (I) disease management.
No significant difference was found in the sex (
Population use of mobile health (mHealth) apps in the pre and postpandemic periods.
Characteristics | Before the outbreak (n=375) | After the outbreak (n=178) | |||||
Age (years), median (IQR) | 70 (66-74) | 70 (66-75) | .52 | ||||
Age (years), mean (SD) | 70.46 (6.329) | 70.85 (7.680) | —a | ||||
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.41 | ||||||
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Male | 166 (44.3) | 85 (48.0) |
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Female | 209 (55.7) | 92 (52.0) |
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.006 | ||||||
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Never | 125 (33.3) | 32 (18.0) |
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Sometimes | 131 (34.9) | 75 (42.1) |
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Often | 73 (19.5) | 57 (32.0) |
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Always | 45 (12.0) | 14 ( 7.9) |
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The ability to use electronic products (mobile phone, tablet, computer), n (%) | 186 (49.7) | 108 (60.7) | .02 | ||||
Getting health information offline in the past 6 months, n (%) | 108 (28.8) | 104 (58.4) | <.001 | ||||
Getting health information through the internet in the past 6 months, n (%) | 141 (37.6) | 90 (50.6) | .004 | ||||
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Social network (eg, WeChat, QQ) | 129 (92.1) | 89 (98.8) | .008 | |||
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Portal web | 36 (25.9) | 19 (22.6) | .63 | |||
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mHealth apps | 6 ( 4.3) | 2 ( 2.5) | .71 | |||
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Search engine | 36 (25.5) | 22 (26.5) | .87 | |||
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Internet hospitals | 6 ( 1.6) | 23 (12.9) | <.001 | |||
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.70 | ||||||
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0 | 316 (84.3) | 150 (84.3) |
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1-10 | 10 (2.7) | 5 (2.8) |
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11-50 | 19 (5.1) | 9 (5.1) |
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51-100 | 3 (0.8) | 0 (0) |
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101-200 | 2 (0.5) | 1 (0.6) |
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201-500 | 1 (0.3) | 0 (0) |
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>500 | 0 (0) | 0 (0) |
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.45 | ||||||
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0 | 348 (92.8) | 164 (92.1) |
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1-10 | 0 (0) | 2 (1.1) |
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11-50 | 0 (0) | 2 (1.1) |
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51-100 | 1 (0.3) | 0 (0) |
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101-200 | 1 (0.3) | 0 (0) |
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201-500 | 1 (0.3) | 0 (0) |
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>500 | 1 (0.3) | 0 (0) |
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.67 | ||||||
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Never | 15 (4.0) | 0 (0) |
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Sometimes | 12 (3.2) | 16 (9.0) |
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Often | 65 (17.3) | 47 (26.4) |
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Always | 25 (6.7) | 9 (5.0) |
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aAge was not normally distributed, so differences in the median value were assessed using a nonparametric test.
bA currency exchange rate of ¥1=US $0.15 is applicable.
Our study demonstrates that the usage and population utilization of mHealth applications increased after the COVID-19 outbreak. As a powerful tool for providing health care services, functions closely related to the pandemic, including rehabilitation, treatment, drug use, and vaccination, were positively associated with changes in app downloads. The high growth of app use related to maternal and child health, including pregnancy preparation and women’s health, shows the potentially increased desire for family among the Chinese population in the postpandemic era. Moreover, the user experience and high use of health management apps also reflect great attention to self-care. Overall, mHealth apps assist with health improvement against the background of normalized pandemic control and may improve fertility.
The usage of COVID-19 pandemic-related apps, such as vaccination, increased in rank. Furthermore, adding pandemic-related functions positively correlated with increased downloads. The likely reason behind this rise was that apps inherently related to the pandemic can easily capture the attention of the public as a means of obtaining information. Some apps with larger user groups may also add COVID-19 modules to respond to normalized pandemic prevention and control policies [
Unprecedented large-scale quarantine measures and shortages of medical resources have made telemedicine care an important and real demand during the pandemic [
Most apps were designed for health management by all people, mainly focusing on bodybuilding and nutrition. In our research, app flow and user experience surveys both showed that the rankings of most functions related to health management were rising, which reflects great attention to self-care postpandemic.
It is interesting that there was increased use of maternal and child health apps, including pregnancy preparation, women’s health, pregnancy, and parenting, after the outbreak, showing potentially increased desire for fertility among the Chinese population.
One study concluded that the pandemic is affecting people’s desire to become parents, which was consistent with our results [
For bodybuilding apps, this function had positive changes in downloads during the COVID-19 pandemic. One study found that the keyword “mHealth” was closely associated with “physical activity” and “ehealth” in the last 2 decades of research on digital health behavior change technologies [
Many kinds of apps attempted to provide health education, which was the most widely available function in our study. Although this function declined during the pandemic and had negative changes in downloads, the population’s attention to health information increased. The probable cause behind this phenomenon is that new media platforms in China, such as WeChat and Weibo, have been vigorously promoted as important means for pandemic-related health information dissemination, which may decrease interest in acquiring apps when the information is readily available on these platforms. The high levels of knowledge of the Chinese public about COVID-19 prevention mainly comes from WeChat [
The COVID-19 pandemic has caused health anxiety at the population level. Digital intervention by mHealth apps is suitable for alleviating such sociopsychological consequences [
Consistent with the report of the rapid increase in older adult internet users during the COVID-19 pandemic in China, we found that people over 50 years old paid more attention to mHealth apps after the outbreak [
Our study should be considered in the context of important limitations. First, this study excluded apps that were used internally by medical staff because we could not log in as an internal account holder; this dilutes the results of mHealth apps designed for health care professionals. However, our research focused on apps for patients and healthy people rather than internal apps. Future work will be conducted with apps used internally in medical care. Second, the absence of newly emerging apps made it impossible to provide an overview of the mHealth market after the COVID-19 outbreak. Therefore, we compared the changes in downloads during the pre and postpandemic periods to explore the relationship between various types of apps and the pandemic. Third, as a semilongitudinal survey, this study measured exposure and outcome, and it was difficult to derive causal relationships from the analysis; thus, we only made assumptions based on the status quo.
The study has practical implications and applications. This study is the first to investigate the relationship between COVID-19 and population-level utilization of mHealth apps through a semilongitudinal study of app markets’ data and a field questionnaire, combined with the results of both the web-based survey and the population user experience survey. As China is one of the few countries to adopt more active public health prevention and control measures, this study, which involves a multilevel and broad research scope, can provide strong data support for future comparative studies between different countries and regions. In the user experience survey, we explored the changing attitudes of the population toward digital health technology, suggesting that there is a good development environment for mHealth apps in the postpandemic era. This study, with consistent definitions of variables and processes, allowed the investigators to consistently classify mHealth apps and ensure data integrity, underpinning its strength. Our research clarified the relationship between various types of apps and usage changes by conducting investigations in the pre and postpandemic periods. We believe our results provide a good reference for the subsequent development of future mHealth apps. In addition to the increasing number of COVID-19–related apps prompted by pandemic policies, app developers should be aware that maternal, child, and self-care management are app functions about which the population is concerned.
mHealth apps utilize information and telecommunications technology to transfer medical information for diagnosis, therapy, and education and played a significant role following the COVID-19 outbreak. The pandemic made people aware of the value of mHealth in promoting universal health coverage, which promotes stronger management of self-care. Against the backdrop of an increased desire to raise a family among the Chinese population in the postpandemic era, maternal and child health apps, as a health education tool, promote a healthy lifestyle for women’s self-management in the antenatal and postpartum periods. Further research is needed to understand the users’ requirements for these apps, which will influence their adoption. The explicit design of apps is another potential factor that can facilitate or hinder user engagement and requires further investigation.
mHealth apps are an effective approach to providing health care in the context of COVID-19. This study clarifies the increasing usage of different apps during the pre and postpandemic periods, showing greater attention to self-care and the Chinese population’s increasing desire to raise a family. Moreover, our research provides direction for subsequent mHealth app development and promotion in the postepidemic era, supporting medical model reformation in China as a reference. This may provide new avenues for designing and evaluating indirect public health interventions such as health education and health promotion. Further research is needed to investigate the functions in each kind of app, which will contribute to the personalized development and specific improvement measures of mHealth apps as a health promotion strategy.
Supplementary tables.
Differential effect of app functions by quantile (insignificant).
Differential effect of app functions by quantile (insignificant).
cognitive behavioral therapy
mobile health
quantile regression
We thank the study participants. This research was supported by Fujian Medical University Talent Research Funding (XRCZX2019031), National Natural Science Foundation of China Youth Program (82203989), Natural Science Foundation of Fujian (2021J01729), Fujian Province Students’ Innovative Entrepreneurial Training Plan Program (S202010392017; S202010392019), Fujian Medical University College Students’ Innovative Entrepreneurial Training Plan Program (C21118, C22024), and Fujian Medical University Public Health School College Students’ Innovative Entrepreneurial Training Plan Program (xy202010015, xy202010018, xy202110005).
Scientists wishing to use mobile health app study data for noncommercial purposes can obtain the data set by contacting the corresponding author.
None declared.