This study was a single-center, single-blind, and parallel-group randomized controlled study and complied with the CONSORT (Consolidated Standards of Reporting Trials) guidelines [24] (Checklist 1).

Potential participants were recruited using a convenience sampling method from the West China Hospital of Sichuan University in Chengdu, China, between February 2021 and July 2021. The inclusion criteria for the participants were as follows: (1) met the 2015 gout classification criteria modified by the American College of Rheumatology and European Alliance Against Rheumatology [25,26]; (2) aged ≥18 years; (3) knew about their disease status; (4) used Android smartphones independently; (5) was able to read and understand the questionnaire and the content of this intervention; and (6) agreed to voluntarily participate in the study. Patients with cognitive dysfunction or mental illness and those using other gout management apps or participating in other research programs were excluded.

A total of 3 registered nurses were assigned to implement the program, and they were assisted by 2 rheumatologists. All investigators were fully aware of the study protocol and questionnaires to ensure a complete understanding of the study-related principles and implementation.

Participants were first invited by their attending rheumatologist to join the study. Once verbal consent was obtained, a research nurse approached each participant to provide further study information and obtain written informed consent to complete the baseline data collection. Randomization was then conducted. Another nurse assisted the intervention group participants to scan a special QR code to download and install the Gout Intelligent Management app (Android version 1.0) and complete the registration. The participants used the app for free during the study. In the control group, the participants were given one-to-one health education on the spot and received health education materials and a card marked with a consultation phone number.

We used SPSS software (version 25.0, IBM Corp) to generate a 1:1 randomization sequence. The randomization sequences were placed in opaque sealed envelopes to hide the assignment message and were kept by 1 investigator. After the participant signed the informed consent form and completed the baseline information, another investigator opened the envelope and assigned the participant to the intervention or control group.

The control group received routine continuous care, including face-to-face health education, a health education manual consistent with the intervention group’s web-based health materials, and participant-initiated telephone consultations.

All participants completed the baseline assessment at enrollment. Gout knowledge and compliance were assessed at baseline and the 12th and 24th weeks, and SUA level was measured at baseline and the 24th week. Data from the intervention group were collected through the app, whereas the those from the control group were collected through a web-based platform [30].

The sample size was estimated using a completely random, 2-sample means sample size formula. The test efficacy was set to significance levels of .90 and .05. According to the randomized controlled trial by Doherty et al [15], the mean SUA difference between the intervention and control groups was 104.64, and the SD of the SUA value was 141.64. Thus, each group required 39 patients. Considering a 20% loss to follow-up, at least 98 participants needed to be included in this study.

Data analysis was conducted using SPSS (version 25.0, IBM Corp). Demographic, clinical, and lifestyle data between the groups were presented using appropriate descriptive statistics and evaluated for homogeneity using independent 2-tailed t tests, Mann-Whitney χ² tests, and Fisher exact tests, as appropriate. The changes in GKQ and CCQR scores at the 12th and 24th weeks from baseline between the 2 groups were assessed using generalized estimating equations. The difference in SUA levels between the 2 groups at the 24th week from baseline and comparison between groups at the same time point were assessed using a 2-tailed t test for 2 independent samples. The intention-to-treat principle was adopted in the outcome analysis. All participants were included in the generalized estimating equation analysis since all participants provided baseline data. All statistical tests were 2-sided, with the level of significance set at .05.

This study met the ethical guidelines of the 1975 Declaration of Helsinki. Ethical approval was obtained from the Medical Ethics Committee of West China Hospital in 2020 (ID: 2020898). All participants signed an informed consent form prior to enrollment and were informed that they can withdraw at any time without their medical treatment and rights being affected. Recruited participants will be provided with free disease management services for 6 months and will be assisted by the investigator to seek medical attention or hospitalization in the event of adverse reactions to treatment or other comorbidities during the course of the study. The data collected for this study will be anonymized and used for research purposes only.

Figure 2 shows the recruitment and retention of the participants. Between February and July 2021, a total of 258 potential participants underwent eligibility assessments, and 138 were excluded. We recruited 120 patients with gout and randomized them into the intervention group (n=60, 50%) and the control group (n=60, 50%). Of the 120 participants, 93 (77.5%) completed the 24-week study. Since we used the intention-to-treat principle, we included all 120 patients for statistical analysis.

The baseline characteristics of the participants are shown in Table 2. The mean age was 41.53 (SD 12.58) years. The average BMI was 26.28 (SD 3.27) kg/m², and the average waist circumference was 93.24 (SD 12.04) cm. We collected 115 participants’ SUA level; the average SUA level was 457.67 (SD 141.83) μmol/L. Of the 120 participants, 119 (99.2%) were male, 116 (96.7%) were of Han ethnicity, 97 (80.8%) were married, and 73 (60.8%) had a college degree or above. Among them, 96 (80%) were receiving ULT, 31 (25.8%) had tophi, 50 (41.7%) had hyperlipidemia, 18 (15%) had hypertension, 15 (12.5%) had kidney disease, and 6 (5%) had diabetes. Furthermore, 41 (34.2%) participants smoked, and 58 (48.3%) drank alcohol. The 2 groups had no significant differences in sociodemographic or clinical characteristics (all P>.05).

The mean GKQ scores at baseline were 5.88 (SD 2.41) in the intervention group and 5.80 (SD 2.34) in the control group. No significant intergroup differences were observed (P=.85; Table 3). At the 12th week, the GKQ scores of the intervention and control groups increased, but this change was not statistically significant (P=.06; Table 3). At the 24th week, the GKQ scores of the intervention and control groups continued to increase, and significant differences were observed between the intervention and control groups (P<.001; Table 3). Furthermore, a significant interaction term (group × time) of the model at the 12th week (β=0.617, 95% CI 0.104-1.129; P=.02) and the 24th week (β=1.300, 95% CI 0.669-1.931; P<.001) verified that mHealth-based continuous care effectively increased the GKQ scores over time (Table 4 and Figure 3).

The mean CCQR scores at baseline were 69.80 (SD 14.04) in the intervention group and 70.13 (SD 15.38) in the control group. No significant intergroup differences were observed (P=.80; Table 3). The mean CCQR scores gradually increased from baseline to the 24th week in the intervention group and gradually declined in the control group. However, at the 12th week, there was no significant difference between the 2 groups (P=.67; Table 3). A significant difference was observed between both groups at the 24th week (P=.03; Table 3). Furthermore, no significant interaction term (group × time) of the model was observed at the 12th week (β=1.667, 95% CI −3.283 to 6.617; P=.51), but a significant difference was observed at the 24th week (β=6.287, 95% CI 1.357-11.216; P=.01), verifying that mHealth-based continuous care effectively increased the CCQR scores over time (Table 4 and Figure 4 ).

The mean SUA levels at baseline were 455.13 (SD 140.70) μmol/L in the intervention group and 460.18 (SD 144.11) μmol/L in the control group. No significant intergroup differences were observed (P=.85; Table 3). At the 24th week, the SUA levels in both the intervention and control groups were lower than those at baseline. However, no significant intergroup differences were observed at the 24th week (P=.43; Table 3). Furthermore, the decline in SUA levels were compared between the 2 groups. Compared with the baseline data, SUA levels decreased by 71.24 (95% CI −40.38 to 141.12) μmol/L in the intervention group and by 28.50 (95% CI −53.50 to 160.03) μmol/L in the control group. However, the difference between the 2 groups was still not statistically significant (P=.55).

In this study, the level of gout knowledge refers to patients’ knowledge of gout disease. Similar to the study by Chandratre et al [34], we found that patients with gout had overall low levels of disease knowledge at baseline. In this study, both groups demonstrated an upward trend in GKQ scores over 24 weeks; however, the mHealth app–based continuous care group performed better. Our findings are consistent with those of network-based self-management intervention programs, such as those reported by Oh et al [35], Kang and Lee [36], and Serlachius et al [37]. Additionally, a study in China found that continuous intervention through WeChat can improve gout knowledge [16]. Previous research has shown that patients with gout are more concerned about dietary intervention, gout attack triggers [38], and treatment information [39,40] and enjoy tracking their SUA levels [38]. However, their awareness of epidemiology, comorbidity management, daily lifestyle intervention, and ULT goals is limited [41-43]. Health education is a crucial intervention method for continuous care and a primary means to promote chronic disease self-management. In our previous studies, we identified the health education needs of patients with gout through qualitative interviews [27]. Next, we used this information to develop an mHealth-based gout continuous care program. In addition, compared to traditional continuous care, the app-based intervention provides regular weekly reminders supplemented by a gout encyclopedia, food purine query, web-based consultations, and other auxiliary modules for further patient reinforcement and feedback. This intervention provides patients with knowledge and skills about disease management and may contribute to better treatment adherence and health outcomes. According to Skinner’s [44] reinforcement theory, the impact of reinforcement on individual behavior needs to be achieved through feedback, and individuals adjust, correct, and refine their behavior according to the feedback information. The mHealth app provides multichannel reinforcement and personalized feedback that improve disease-related knowledge in patients with gout. This study shows that continuous care based on a mobile medical app can effectively enhance the knowledge level of patients with gout and prompt continuous gout nursing practices. Medical staff can use the app to provide high-quality health information and improve the knowledge of patients with gout.

In this study, the treatment compliance among patients with gout—the extent to which their medication behavior matched medical orders—had scores of <80 at baseline; this is consistent with the findings of some previous studies [13], meaning that treatment adherence is low in patients with gout. This study showed that continuous care based on an mHealth app could improve treatment adherence in patients with gout. Miao et al [16] reported that a continuous care intervention based on the WeChat public platform could improve treatment adherence in patients with gout, whereas the study by Fields et al [45] failed to improve treatment adherence through a structured gout course and pharmacists’ telephone calls. A qualitative study showed that treatment adherence in patients with gout is influenced by negative experiences and false beliefs [43]. Individuals who obtain information consistent with their health beliefs and behaviors are more likely to adopt recommended health behaviors [23]. The IFSMT proposes that context-specific interventions can reduce risk or promote self-management of conditions through improving knowledge and beliefs [23]. This intervention should address the participants’ needs and strengthen behavior change through various forms of health education, including counseling, communication, peer support, behavioral recording, and reminder services. Therefore, mHealth-based continuous care has advantages over traditional continuous care. Moreover, we also found that the CCQR score was higher in the intervention group at the 12th week than in the control group. However, the difference did not reach statistical significance until the group differences were more pronounced at the 24th week. The transtheoretical model and stages of change propose that human behavior is a gradual and continuous process, requiring creating a supportive environment and self-reinforcement [46]. This study found that mHealth-based continuous care can provide knowledge and skills to support patients with gout, improve treatment compliance, and provide repeated reinforcement, thus playing a more lasting role than traditional continuous care. This suggests that in the practice of continuous gout nursing, medical staff can use mHealth apps to change patients’ incorrect cognition and provide positive incentives and timely communication, enabling patients to master strategies for improved treatment adherence. It also suggests that improving treatment adherence in patients with gout requires a longer intervention period.

This study found that the SUA level for patients with gout at baseline was 457.72 (SD 144.38) µmol/L, which is higher than the treatment target, suggesting that the management of ULT should still be the focus of continuous nursing intervention. Doherty et al [15] reported that patients significantly decreased their SUA levels after a comprehensive intervention, including patient education, patient participation, and ULT. However, the findings of Bulbin et al [20] did not show an improvement in SUA levels through web-based education and reminders. The findings of this study indicate that both intervention strategies contribute to lower SUA levels in patients with gout; however, there were no significant differences between the 2 groups. This study was conducted only for 24 weeks, but the effects of continuous care on SUA levels may be detected in the long term. In addition, another study highlighted that SUA levels were affected by several factors [47]. Therefore, we suggest increasing the follow-up time for further exploration.

This study has several limitations. First, the participants in this study were recruited from 1 tertiary hospital in Sichuan, China, with an inadequate number of female participants, which may limit the generalizability of the study results. Second, the participants in this study need to be able to use smartphones and have certain learning abilities, which may limit the application of this intervention to all patients with gout. Third, the study protocol was planned for 24 weeks, and the sustainability of the intervention’s effects after this period could not be established. Furthermore, this study did not analyze participants’ engagement with the app and its impact on outcome measures.

To address these limitations, the following considerations are essential in future studies: (1) strengthening the links and collaboration between hospitals and communities, in which multicenter studies can further validate the utility of mHealth app–based continuous care; (2) including populations with other rheumatic diseases to expand the scope of mHealth app–based continuous care; (3) communicating with the medical consortium and hospital information systems to solve the difficulty in data collection, which would allow researchers to evaluate the effect of the intervention more comprehensively; and (4) conducting multicenter studies and extending follow-up duration to evaluate the long-term effects of the intervention.

The strengths of this study include the development of a nurse-led protocol for continuous care based on patient needs, refined by guidelines, and revised through expert consultations, as well as its representation of the “4C” characteristics of continuous care: comprehensiveness, coordination, continuity, and collaboration. Furthermore, we demonstrated the benefits of intelligent management of mobile medical apps for continuous care for patients with gout.

We developed an mHealth app for continuous care based on the IFSMT and assessed its effect on disease knowledge, treatment adherence, and SUA levels in Chinese patients with gout. The results show that continuous care based on the mHealth app can improve disease knowledge and treatment adherence in patients with gout. However, the advantages of reducing SUA levels in patients with gout require additional investigation. Our findings suggest that the mHealth app can be used effectively for the continuous care of patients with gout in China.