Introduction
Recruitment and retention in pediatric research have consistently posed challenges [1-3]. In the United States, pediatric randomized clinical trials are often discontinued or unpublished, with patient recruitment difficulties (37%) being the primary reason [4]. Our experiences further corroborate this trend. For instance, in a cross-sectional hospital-based survey, we recruited only 65.8% of the caregivers [5]. Similarly, in a longitudinal study on the growth of critically ill children after liver transplantation, follow-up was completed for only 68.6% of the children one year after the procedure [6]. Recruitment and retention challenges frequently prevent studies from achieving their preset objectives, resulting in inefficiency and wasted personnel and financial resources.
While traditional research has focused on children with evident illnesses, emerging concerns such as overweight, internet addiction, and emotional or behavioral issues [7-10] highlight the importance of studying seemingly healthy children [11-13]. However, recruiting and retaining healthy children for research is more challenging than for those with specific diseases, as shown by pediatric reference interval studies [14]. Additionally, children in remote areas are frequently underrepresented in research [15-17], leading to selection bias and exacerbating health disparities. In China, geographical location has been a primary longstanding contributor to child health inequity [18]. Therefore, researchers need to devise strategies to address these challenges to meet the increasing demands of pediatric research. In this context, obtaining participants’ perspectives by seeking their opinions may provide more practical solutions.
Caregivers, typically parents, are key decision makers regarding younger children’s participation in research [19]. Numerous studies have examined parental decision-making in enrolling children in pediatric research, primarily focusing on traditional research methods [20-27]. In the digital age, medical research has been significantly transformed by digital tools and the process of digitalization [28,29]. Digital medicine has created new opportunities to enhance engagement in clinical research, especially in healthy populations and remote areas, which improves health outcomes for participants [30-32]. This study investigated public perspectives on pediatric research in the digital age and examined potential differences between urban and suburban areas. The goal was to provide insights to advance the digitalization of pediatric research, which may diversify the pool of participants and address health disparities among children.
Methods
Study Design and Participants
This study was a cross-sectional, web-based questionnaire survey. The study targeted caregivers of kindergarten children in Chongqing, a municipality in southwestern China, with a total of 12.58 million households and 995,239 kindergarten children. Chongqing is divided into urban and suburban areas based on geographical location and development level, with 3.07 million (23.9%) households in urban areas and 9.79 million (76.1%) in suburban areas [33]. Kindergarten children are typically between 3 and 6 years old, with exceptions depending on individual circumstances [34]. The inclusion criteria for kindergarten children were (1) residency in Chongqing, (2) kindergarten attendance, (3) aged 2 to 7 years, and (4) voluntary completion of the questionnaire by the caregiver. The exclusion criteria were (1) failure to pass validation questions, (2) logical errors in responses, and (3) self-reported lack of seriousness in answering.
Ethical Considerations
This study was approved by the Ethics Committee of the Children’s Hospital of Chongqing Medical University (Protocol #2023-236), with informed consent signatures waived. The collected data were anonymized, and no compensation was given.
Reporting Guidelines
The CHERRIES (Checklists for Reporting Results of Internet E-Surveys) and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines were followed for reporting the results [35,36].
Research Outcomes and Measurements
The primary outcome of this study was public perspectives on pediatric research, with differences between urban and suburban areas as the secondary outcome. These perspectives were divided into 4 key categories: “Facilitators and Barriers to Participation,” “Perspectives on Digital Medicine,” “Perspectives on Recruitment,” and “Perspectives on the Research Process.” Each category comprised single-choice and multiple-choice questions designed to collect data. In the intergroup comparison, participants were classified as urban or suburban (exposure) based on their responses to a residential address question (Multimedia Appendix 1).
Development of the Questionnaire
The questionnaire was initially developed and refined based on the literature and clinical experience [20-27,37-39], then revised and culturally adapted by 7 pediatric doctors with diverse professional backgrounds (see Acknowledgments). The final version was converted to a web-based format using “Lediaocha,” a web-based survey platform, with 50 mandatory questions spread across 4 pages. Of these, 11 questions focused on general characteristics, while the rest investigated perspectives on pediatric research, particularly digital medicine. To ensure data reliability, one question on caregivers’ roles was repeated at the beginning and end of the questionnaire, along with a self-assessment query on response seriousness before submission. Participation was voluntary, anonymous, and accessible through WeChat (Tencent) on smartphones. Device IDs were recorded to prevent duplicate entries. When caregivers started answering, the system marked them as “in progress.” Although it was technically feasible to save “in progress” answers automatically, ethical concerns led to a decision not to do so, as these were not final submissions. The process of developing questionnaires and the methodological considerations for web-based surveys followed our team’s established practices [40].
Determination of the Sample Size
Due to the lack of a clear sample size calculation method for multioutcome survey studies, we used the estimation method from quality-of-life studies, which suggests 5-10 questionnaires per question [41]. Therefore, with 50 questions, a minimum of 500 questionnaires were required. To mitigate potential selection bias [42], the minimum sample size was doubled to 1000. Further calculations took into account the number of households in Chongqing, with questionnaires planned to be collected at a 1:3 ratio between urban and suburban areas. As a result, a total of 4000 questionnaires were needed, with at least 1000 from urban areas and 3000 from suburban areas. Given the possibility of a high invalid response rate (approximately 50%) in public surveys [43], at least 8000 households were estimated to be needed. This sample size represented 0.4% of the entire population of kindergarten children in Chongqing, placing the sampling ratio between the national population dynamics survey (0.1%) and the sample survey (1%). Finally, the actual proportions of recruited participants from urban and suburban areas were 25.1% and 74.9%, respectively, which is consistent with the planned sample distribution.
Survey Process
The survey was conducted between June and December 2023. Prior to the official launch, a pilot test with 50 participants was conducted to validate the survey process. A flexible survey was then conducted in urban and suburban areas over 20 days to accommodate different kindergarten schedules. The questionnaire was distributed to kindergartens and forwarded to caregivers, ensuring that recruitment focused on the target audience and minimized nontarget responses. Forwarding questionnaires by kindergarten teachers was not mandatory. Caregivers could view a short introduction and an anonymous, voluntary declaration of the study on the questionnaire homepage to avoid any sense of obligation. After the survey, researchers downloaded and saved the data from the cloud servers and then removed them from the cloud.
Data Processing
The collected questionnaires were assessed for adherence to the inclusion and exclusion criteria. Specifically, 8600 online visits were documented during the survey, with 8266 (96.1%) identified as unique after eliminating duplicates. Among the visitors, 6834 started the questionnaire (response rate of 82.7%), and 5530 completed it (completion rate of 66.9%). After unreliable and nontarget questionnaires were excluded, 4231 questionnaires were ultimately analyzed (inclusion rate of 76.5%), representing 0.43% of the entire population of kindergarten children (Figure 1).
Statistical Analysis
Data analysis was performed using SPSS (version 25; IBM Corp). Qualitative data are presented as frequencies (percentages), while quantitative data are presented as medians (IQRs) after tests for normality. Intergroup comparisons were conducted using chi-square and Mann-Whitney tests. Multivariate logistic regression was used to adjust for potential confounders, including child age and sex, caregiver age, role, education level, and family size, when examining the relationship between region (independent variable) and public perspectives (outcome variables). Stepwise methods were not applied (Multimedia Appendix 2). The results of the logistic regression are presented as adjusted odds ratios (aORs) with 95% CIs. A P value less than .05 was considered statistically significant.
Results
General Characteristics
Table 1 presents the general characteristics of the children and caregivers. Among the children, 1064 (25.1%) resided in urban areas, while 3167 (74.9%) children were from suburban areas. Most children (n=3774, 89.2%) had never participated in pediatric research, while only 7.7% (n=326) and 5.4% (n=230) had taken part in offline and online research, respectively. Interestingly, suburban children had a greater proportion of online research participation than urban children (6% vs 3.9%; P=.008). Among the caregivers, 3547 (83.8%) were mothers, 2334 (55.2%) had an education level of senior high school or below, and 2297 (54.3%) lived in households with 4 or fewer members.
Table 1.General characteristics of the children and caregivers.
| General characteristics | Overall | Urban | Suburban | P value | ||||||
| Age of the children (years), median (IQR) | 4.8 (4.1-5.6) | 4.6 (4.0-5.5) | 4.9 (4.2-5.7) | < .001 | ||||||
| Sex of the children, n (%) | .44 | |||||||||
| Male | 2167 (51.2) | 534 (50.2) | 1633 (51.6) | |||||||
| Female | 2064 (48.8) | 530 (49.8) | 1534 (48.4) | |||||||
| Ever participated in offline pediatric research, n (%) | 326 (7.7) | 72 (6.8) | 254 (8) | .19 | ||||||
| Ever participated in online pediatric research, n (%) | 230 (5.4) | 41 (3.9) | 189 (6) | .008 | ||||||
| Age of the caregivers (years), median (IQR) | 33 (29-36) | 33 (30-36) | 32 (29-36) | .81 | ||||||
| Role of the caregivers, n (%) | .005 | |||||||||
| Mother | 3547 (83.8) | 921 (86.6) | 2626 (82.9) | |||||||
| Father or others | 684 (16.2) | 143 (13.4) | 541 (17.1) | |||||||
| Education levels of the caregivers, n (%) | < .001 | |||||||||
| Senior high school or below | 2334 (55.2) | 455 (42.8) | 1879 (59.3) | |||||||
| College or above | 1897 (44.8) | 609 (57.2) | 1288 (40.7) | |||||||
| Number of people in the family, n (%) | < .001 | |||||||||
| 2-4 people | 2297 (54.3) | 707 (66.4) | 1590 (50.2) | |||||||
| ≥5 people | 1934 (45.7) | 357 (33.6) | 1577 (49.8) | |||||||
Facilitators and Barriers to Participation
When questioned about their perspectives on research participation, 2531 (59.8%) caregivers expressed their first impressions, with 1561 (36.9%) caregivers inclined to participate and 970 (22.9%) caregivers inclined to decline. Suburban caregivers had more positive impressions than urban caregivers (38.6% vs 32%; P < .001; aOR 1.27, 95% CI 1.09-1.47). Additionally, caregivers with positive impressions reported higher rates of both offline (14.2% vs 2.9%; P < .001) and online (9.8% vs 2.7%; P < .001) research participation compared to those with negative impressions.
Table 2 presents the facilitators and barriers to participation, with most facilitators reporting rates lower than the barriers. The most significant facilitator was “be beneficial for children’s health” (n=3579, 84.6%), which was more common among urban caregivers (87.5% vs 83.6%; P < .001; aOR 1.45, 95% CI 1.17-1.79). Interestingly, although “be suggested by close people” was deemed less important (n=635, 15%), it had a higher rate among suburban caregivers (16.2% vs 11.5%; P < .001; aOR 1.47, 95% CI 1.18-1.79). On the other hand, the most significant barriers were “worry about being an experimental subject” (Guinea pig concerns: n=3060, 72.3%) and “pose a risk to children’s health” (n=2577, 60.9%). These barriers reflected a general apprehension about potential health risks. Furthermore, suburban caregivers had lower reporting rates for most barriers than urban caregivers, including “worry about being an experimental subject” (70.9% vs 76.6%; P < .001; aOR 0.79, 95% CI 0.67-0.93), “pose a risk to children’s health” (58.6% vs 67.8%; P < .001; aOR 0.71, 95% CI 0.61-0.83), “do not have enough background information” (55.2% vs 61.6%; P < .001; aOR 0.78, 95% CI 0.67-0.89), and “worry about recommending other products” (48.2% vs 55%; P < .001; aOR 0.78, 95% CI 0.67-0.89).
Table 2.Facilitators and barriers to pediatric research participation.
| Questions about facilitators and barriers | Overall, n (%) | Urban, n (%) | Suburban, n (%) | P value | |
| Facilitators | |||||
| Be beneficial for children’s health | 3579 (84.6) | 931 (87.5) | 2648 (83.6) | < .001a | |
| Trust the hospital and researchers | 1454 (34.4) | 371 (34.9) | 1083 (34.2) | .69 | |
| Acknowledge the importance of research | 1409 (33.3) | 358 (33.6) | 1051 (33.2) | .78 | |
| Increase contact with doctors | 1189 (28.1) | 295 (27.7) | 894 (28.1) | .75 | |
| Have small burden on the participants | 1139 (26.9) | 323 (30.4) | 816 (25.8) | .003a | |
| Be driven by altruism | 1044 (24.7) | 261 (24.5) | 783 (24.7) | .90 | |
| Be suggested by close people | 635 (15) | 122 (11.5) | 513 (16.2) | < .001a | |
| Have economic subsidies | 390 (9.2) | 100 (9.4) | 290 (9.2) | .81 | |
| Barriers | |||||
| Worry about being an experimental subject | 3060 (72.3) | 815 (76.6) | 2245 (70.9) | < .001a | |
| Pose a risk to children’s health | 2577 (60.9) | 721 (67.8) | 1856 (58.6) | < .001a | |
| Do not have enough background information | 2404 (56.8) | 655 (61.6) | 1749 (55.2) | < .001a | |
| Worry about personal privacy | 2153 (50.9) | 574 (53.9) | 1579 (49.9) | .02 | |
| Worry about recommending other products | 2112 (49.9) | 585 (55) | 1527 (48.2) | < .001a | |
| Refuse procedures like venipuncture | 1721 (40.7) | 468 (44) | 1253 (39.6) | .01 | |
| Cause disagreements among family members | 1106 (26.1) | 294 (27.6) | 812 (25.6) | .20 | |
| Affect the purchase of medical insurance | 803 (19) | 201 (18.9) | 602 (19) | .93 | |
aConfirmed by multivariate logistic regression that adjusted general characteristics (Multimedia Appendix 2).
Perspectives on Digital Medicine
When questioned about digital medicine (Table 3), 36.4% (n=1542) of caregivers reported following online health-related accounts, while 38.4% (n=1626) had used online information for health-related decisions. Additionally, 38.3% (n=1621) and 36.7% (n=1552) of caregivers recognized the increasing popularity of digital tools in future health care practices and supported their integration into pediatric research. Nearly half of caregivers (n=2073, 49%) were willing to collect data daily using digital tools, and 61.7% (n=2610) supported the use of gamification in pediatric research. However, only 25.2% (n=1068) of caregivers supported online-only research methods, with slightly more support from suburban caregivers (26% vs 22.9%; P=.045; aOR 1.19, 95% CI 1.01-1.41). When asked about concerns regarding smartphone data collection software, privacy was the main issue (n=3273, 77.4%), followed by concerns about potential software addiction (n=2457, 58.1%). Multivariate logistic regression revealed privacy concerns as the barrier to supporting digital tools in pediatric research (aOR 0.61, 95% CI 0.52-0.71).
Table 3.Perspectives on digital medicine.
| Questions about digital medicine | Overall, n (%) | Urban, n (%) | Suburban, n (%) | P value | |||||
| Have followed online health-related accounts | 1542 (36.4) | 410 (38.5) | 1132 (35.7) | .10 | |||||
| Can use online information to make health-related decisions | 1626 (38.4) | 327 (30.7) | 1299 (41) | < .001a | |||||
| Knowing that digital tools will become popular in future health care practices | 1621 (38.3) | 411 (38.6) | 1210 (38.2) | .81 | |||||
| Support digital tools in pediatric research | 1552 (36.7) | 372 (35) | 1180 (37.3) | .18 | |||||
| Support gamification of pediatric research | 2610 (61.7) | 680 (63.9) | 1930 (60.9) | .09 | |||||
| Support online-only research methods | 1068 (25.2) | 244 (22.9) | 824 (26) | .045a | |||||
| Be willing to use digital tools daily to collect data | 2073 (49) | 484 (45.5) | 1589 (50.2) | .008 | |||||
| Concerns about using smartphone software in research | |||||||||
| Personal privacy issues | 3273 (77.4) | 855 (80.4) | 2418 (76.3) | .007a | |||||
| Possible addiction of the tools | 2457 (58.1) | 606 (57) | 1851 (58.4) | .39 | |||||
| Transparency of data | 2159 (51) | 564 (53) | 1595 (50.4) | .14 | |||||
| Health impacts of the tools (such as radiation) | 1830 (43.3) | 439 (41.3) | 1391 (43.9) | .13 | |||||
| Possible expenses | 1465 (34.6) | 367 (34.5) | 1098 (34.7) | .92 | |||||
| Ease of use | 1279 (30.1) | 320 (30.1) | 959 (30.3) | .90 | |||||
aConfirmed by multivariate logistic regression that adjusted general characteristics (Multimedia Appendix 2).
Perspectives on Recruitment
Regarding recruitment (Table 4), 39.4% (n=1666) and 39.5% (n=1670) of caregivers expressed interest in reviewing recruitment information presented offline and online, respectively. Only 930 (22%) caregivers reported they would check both online and offline recruitment information. Notably, a greater proportion of suburban caregivers preferred checking offline recruitment information (41.2% vs 34%; P < .001; aOR 1.28, 95% CI 1.11-1.49). Additionally, 42.9% (n=1813) of caregivers expressed doubts about the reliability of online recruitment, while 58.1% (n=2457) doubted that research participation through online recruitment would lead to different treatments compared to offline methods.
When questioned about preferences, caregivers indicated that doctors with senior titles (n=2157, 51%) and specific researchers (n=2053, 48.5%) were the preferred recruiters, together accounting for 70.8% (n=2996) of caregivers. Research institution public accounts (n=3400, 80.4%) were the most favored source of online recruitment, with higher preferences among urban caregivers (85.9% vs 78.5%; P < .001; aOR 1.62, 95% CI 1.33-1.97). Although social media friends (n=984, 23.3%) and moments (n=608, 14.4%) were less popular sources for online recruitment, they were more frequently reported by suburban caregivers (friends: 24.7% vs 18.9%; P < .001; aOR 1.33, 95% CI 1.11-1.59; moments: 15.5% vs 11.1%; P < .001; aOR 1.35, 95% CI 1.09-1.67). Finally, the majority of caregivers (n=2963, 70%) preferred cartoon-style recruitment advertisements.
Table 4.Perspectives on recruitment.
| Questions about recruitment | Overall, n (%) | Urban, n (%) | Suburban, n (%) | P value | |
| Know that informed consent is required before beginning | 3549 (83.9) | 900 (84.6) | 2649 (83.6) | .47 | |
| Be willing to review offline recruitment information | 1666 (39.4) | 362 (34) | 1304 (41.2) | < .001a | |
| Be willing to review online recruitment information | 1670 (39.5) | 408 (38.3) | 1262 (39.8) | .39 | |
| Doubt the reliability of online recruitment information | 1813 (42.9) | 474 (44.5) | 1339 (42.3) | .20 | |
| Believe that the treatment between online and offline recruitment is different | 2457 (58.1) | 603 (56.7) | 1854 (58.5) | .29 | |
| Preferred role of recruiter | |||||
| Doctors (senior title) | 2157 (51) | 549 (51.6) | 1608 (50.8) | .64 | |
| Specific researchers | 2053 (48.5) | 552 (51.9) | 1501 (47.4) | .01 | |
| Medical students | 786 (18.6) | 188 (17.7) | 598 (18.9) | .38 | |
| Doctors (ordinary title) | 459 (10.8) | 130 (12.2) | 329 (10.4) | .10 | |
| Nurses | 294 (6.9) | 75 (7) | 219 (6.9) | .88 | |
| Preferred source of online recruitment information | |||||
| Public accounts of research institutions | 3400 (80.4) | 914 (85.9) | 2486 (78.5) | < .001a | |
| Friends on social media | 984 (23.3) | 201 (18.9) | 783 (24.7) | < .001a | |
| Moments on social media | 608 (14.4) | 118 (11.1) | 490 (15.5) | < .001a | |
| Prefer cartoon version of the recruitment information | 2963 (70) | 752 (70.7) | 2211 (69.8) | .60 | |
aConfirmed by multivariate logistic regression that adjusted general characteristics (Multimedia Appendix 2).
Perspectives on the Research Process
When questioned about the research process (Table 5), 1445 (34.2%) caregivers reported that they knew they could withdraw from the study, and 79.8% (n=1153) of them would consult with the research team before doing so. Regarding follow-up, 3076 (72.7%) caregivers reported that regular contact was necessary. Telephones (n=1916, 62.3%) were the preferred method, followed by social media apps (n=1801, 58.6%). Social media apps were more common among urban participants (64.7% vs 56.4%; P < .001; aOR 1.39, 95% CI 1.17-1.65). For research feedback, 4017 (94.9%) caregivers expressed willingness to receive it, with social media apps (n=2624, 65.3%) being the preferred method, followed by email (n=2535, 63.1%). Additionally, we questioned at the beginning and end of the questionnaire whether caregivers were willing to provide phone numbers in anonymous surveys; a total of 3065 (72.4%) caregivers expressed willingness in offline surveys, a rate significantly higher than that in online surveys (72.4% vs 43.4%; P < .001).
Table 5.Perspectives on the research process.
| Questions about research process | Overall, n (%) | Urban, n (%) | Suburban, n (%) | P value | |||||
| Know that withdrawing midway is unconditionally allowed | 1445 (34.2) | 366 (34.4) | 1079 (34.1) | .85 | |||||
| Would discuss with the research team before withdrawing | 1153 (79.8) | 300 (82.0) | 853 (79.1) | .23 | |||||
| Believe that regular contact is necessary | 3076 (72.7) | 785 (73.8) | 2291 (72.3) | .36 | |||||
| Preferred method (telephone) | 1916 (62.3) | 514 (65.5) | 1402 (61.2) | .03 | |||||
| Preferred method (social media apps) | 1801 (58.6) | 508 (64.7) | 1293 (56.4) | < .001a | |||||
| Preferred method (video) | 715 (23.2) | 199 (25.4) | 516 (22.5) | .11 | |||||
| Be willing to receive research feedback | 4017 (94.9) | 1024 (96.2) | 2993 (94.5) | .03a | |||||
| Preferred route (social media apps) | 2624 (65.3) | 687 (67.1) | 1937 (64.7) | .17 | |||||
| Preferred route (mail) | 2535 (63.1) | 694 (67.8) | 1841 (61.5) | < .001a | |||||
| Preferred route (telephone) | 2071 (51.6) | 457 (44.6) | 1614 (53.9) | < .001a | |||||
| Be willing to provide phone number in anonymous surveys | |||||||||
| Offline surveys | 3065 (72.4) | 757 (71.1) | 2308 (72.9) | .27 | |||||
| Online surveys | 1835 (43.4) | 452 (42.5) | 1383 (43.7) | .50 | |||||
aConfirmed by multivariate logistic regression that adjusted general characteristics (Multimedia Appendix 2).
Discussion
Principal Findings
This study investigated public perspectives on the digitalization of pediatric research, comparing views between urban and suburban areas. The findings indicate that the current support for digital approaches in pediatric research is suboptimal but still offers valuable strategies for using digital methods to reduce urban-suburban health disparities in children. This study is relatively rare in the literature and explores innovative ways to enhance participation and equity through digital approaches. Previous studies have emphasized the importance of understanding the barriers and facilitators that parents face when considering their children’s participation in research [20-27]. As much of the research has already begun digitalization [28-32], it is beneficial to periodically pause and gather insights from participants by considering their perspectives.
Perspectives on Pediatric Research
A previous study on neonatal clinical trials revealed that one-quarter of parents made immediate enrollment decisions [27]. Our research found that 59.8% (n=2531) of caregivers formed initial impressions of pediatric research, with 36.9% (n=1561) expressing positive inclinations and 22.9% (n=970) expressing negative inclinations. These findings emphasize the importance of recruitment strategies during initial contact. Further analysis identified health benefits as the main facilitators and risks as the primary barriers, which aligns with previous studies from other regions [20]. This suggests the results could be applicable beyond Chongqing. The lower reporting rates of facilitators than barriers may explain challenges in pediatric research. Preferences in recruitment leaned toward doctors with senior titles and specific researchers as recruiters, emphasizing the influence of authority on caregivers’ decision-making. Additionally, previous research has shown that cartoon images influence children’s decision-making [44]. This study found that 70% (n=2963) of caregivers also preferred cartoon-style recruitment materials, possibly because they convey a sense of care for children, which resonates well with caregivers [45].
Perspectives on Digital Medicine
Public perspectives on digital medicine revealed suboptimal acceptance among caregivers. Approximately one-third of caregivers acknowledged the increasing popularity of digital tools and supported their application in pediatric research. This was reflected in a lower willingness to provide phone numbers in online surveys than in offline surveys. The transition from traditional to remote trials, particularly during the COVID-19 pandemic, has been successful [30,46], though some unsatisfactory outcomes have been reported [47,48]. Our results showed that public accounts from research institutions were the most preferred source of online recruitment, aligning with offline sources that emphasize authority. Given that 42.9% (n=1813) and 58.1% (n=2457) of caregivers doubted the reliability and consistency of online recruitment, establishing a credible public account by research institutions could enhance recruitment success on social media, a novel strategy compared to previous studies [49]. On the other hand, the most commonly mentioned methods for regular contact were telephone and social media apps. This may explain the effectiveness of the hybrid follow-up model, which encourages using both phone calls and social media [50]. However, only 25.2% (n=1068) of caregivers supported online-only research methods, highlighting the importance of face-to-face interactions. This raises important questions about the effectiveness of a fully remote model in postpandemic pediatric research. When questioned about gamification in research, 61.7% (n=2610) of caregivers were in favor. Studies on gamified medicine have shown positive outcomes [51,52], but concerns about potential risks remain [53,54]. In this study, 58.1% (n=2457) of caregivers expressed concerns about addiction. Additionally, 77.4% (n=3273) of caregivers expressed concerns about personal privacy when using smartphone software for research, which served as a barrier to supporting digital tools in pediatric research. Another advantage of digitalizing pediatric research is the availability of personalized real-time feedback, with 94.9% (n=4017) of participants willing to receive it. This meets caregivers’ needs and serves as a strategy to increase motivation [55].
Differences Between Urban and Suburban Areas
When comparing urban and suburban areas, demographic differences were not adjusted using methods like propensity score matching, as these differences were inherent to the population. Instead, multivariate logistic regression was used to validate the results. Interestingly, suburban caregivers expressed greater interest in pediatric research and fewer barriers and concerns. These findings contradict common knowledge [15,16,56] but align with a previous study on congenital heart disease [57]. Another recent study also showed that rural caregivers had lower concerns about data privacy and security [58]. A possible explanation for suburban caregivers’ higher enthusiasm is that the suburban areas in this study may not accurately represent resource-poor regions. These caregivers demonstrate sufficient health awareness and capabilities, making them value participation in pediatric research. This is evident in their willingness to access offline recruitment information, use digital tools for data collection, and support online-only research methods. Since suburban areas usually have more kindergarten children than urban areas (eg, Chongqing [33]), they represent a valuable potential pool of potential research participants. Further analysis revealed that suburban caregivers more frequently mentioned suggestions from close people. Similarly, suburban caregivers more often cited social media friends and moments as sources of online recruitment information. While these results raise concerns about caregivers’ ability to assess the quality of online health information [59], they also highlight the potential for incorporating informal channels in recruitment strategies for suburban participants [60].
Strengths and Limitations
This study has several strengths. First, this study examined perspectives on digital medicine, which is distinct from conventional pediatric research topics [16,20-27] and provides essential data for the digital age. Second, this study included a more diverse sample from the general public, offering a broader representation compared to studies focused on specific illnesses. Third, the sample size was larger than that in previous studies, likely providing a more population-based overview. Finally, we performed a comparative analysis between urban and suburban areas, contributing to the understanding and mitigation of health inequalities among children in different geographical locations.
Despite these strengths, the limitations require careful consideration. First, this study was conducted in Chongqing, China. It is important to consider this when generalizing the findings to other regions. Therefore, further studies across different regions or countries are recommended to expand these findings. Second, although comparisons were made between urban and suburban areas, conditions in rural areas remain unknown and require further investigation. Third, perspectives were assessed using a structured questionnaire but lacked qualitative interview data. Additionally, despite cultural adaptations and a preliminary survey, individuals may still interpret certain questions differently based on their personal experiences. Finally, although smartphones are a prominent tool in digital health, the study’s focus on them may introduce bias. However, less common digital tools may not be widely known to the general public, making it difficult to survey participants effectively about unfamiliar technologies.
Conclusions
In the digital age, approximately one-third of caregivers support pediatric research and endorse the use of digital tools in such studies. Caregivers in suburban areas show greater enthusiasm for research and digital medicine. Integrating official and unofficial social media recruitment strategies, implementing a hybrid online-offline follow-up approach, and addressing privacy concerns could help researchers increase participant recruitment and retention rates.
Acknowledgments
YH was affiliated with the Children’s Hospital of Chongqing Medical University at the time of the study and is currently affiliated with Beijing Children’s Hospital. The authors would like to thank Drs Shuyao Zhu, Keran Ling, Lin Chen, Zehao Li, Wei Zhang, Liyu Chen, and Yuxin Lv for their contributions to revising and culturally adapting the questionnaire. Special thanks are also extended to the kindergartens, teachers, and caregivers for their cooperation in distributing and responding to the questionnaire. This study was supported by the Major Program of the National Clinical Research Center for Child Health and Disorders in Children’s Hospital of Chongqing Medical University (NCRCCHD-2023-MP-01) and the Program for Youth Innovation in Future Medicine, Chongqing Medical University, “Clinical Innovation Team for Standardized Management of Food Allergy in Children” (2022-26-W0120). The funder had no role in the preparation, review, or approval of the manuscript or the decision to submit it for publication. No generative artificial intelligence was used to generate the manuscript content.
Data Availability
The datasets generated during or analyzed during this study are available from the corresponding author upon reasonable request.
Authors' Contributions
HF and RX contributed equally to this work and should be considered joint first authors. YZ and YH contributed equally to this work and should be considered joint corresponding authors. HF and RX conceptualized and designed the study, designed the data collection instruments, carried out the initial analyses, and drafted the initial manuscript. JL designed the data collection instruments, collected the data, carried out the initial analyses, and reviewed and revised the manuscript. YL and EL designed the data collection instruments, obtained funding, and reviewed and revised the manuscript. YZ and YH designed the data collection instruments, coordinated and supervised the data collection, obtained funding, and reviewed and revised the manuscript for important intellectual content.
Conflicts of Interest
None declared.
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Abbreviations
| aOR: adjusted odds ratio |
| CHERRIES: Checklists for Reporting Results of Internet E-Surveys |
| STROBE: Strengthening the Reporting of Observational Studies in Epidemiology |
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Edited by T de Azevedo Cardoso; submitted 07.05.24; peer-reviewed by L Giacomelli, SL Coven; comments to author 09.09.24; revised version received 15.09.24; accepted 21.11.24; published 07.01.25.
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; Ruoling Xian
; Li, Juan
; Li, Yingcun
; Liu, Enmei
; Zhao, Yan
; Hu, Yan