IntroductionPituitary adenoma (PA) is a prevalent primary brain tumor in adults. Recent epidemiological data indicate an age-standardized incidence of up to 10.1 out of 100,000 and an overall prevalence of approximately 175.1 out of 100,000, highlighting that PA is not uncommon and may be increasing in frequency [1]. Transsphenoidal surgery has been the primary surgical treatment, with most patients undergoing elective procedures. In China, most hospitals require patients to undergo a preoperative at-home waiting period of several days to weeks prior to formal admission. Rather than a passive interval, this stage represents a critical window in which patients must complete key preparatory tasks, including acquiring disease-related knowledge, arranging preoperative examinations, adjusting medications and lifestyle behaviors, and undertaking psychological preparation. However, without timely support from health care professionals, patients frequently experience psychological distress and unmet information needs [2,3]. On the one hand, a tumor diagnosis is frequently accompanied by anxiety, depression, and feelings of uncertainty, and some patients also experience symptoms such as visual impairment, headache, and cognitive dysfunction. On the other hand, preoperative health management relies heavily on patients’ self-management, yet the lack of continuous, authoritative, and easily comprehensible professional support often results in inadequate preparation and reduced adherence. Among patients undergoing elective surgery, adherence to preoperative instructions remains suboptimal. A quality-improvement study in a preanesthesia assessment clinic reported incomplete adherence rates of up to 38.1% for perioperative medication instructions and 8.8% for fasting instructions, suggesting that conventional preoperative management approaches—largely dependent on verbal or paper-based education—may be insufficient to ensure the quality of out-of-hospital preparation [4].
Mobile health (mHealth) apps, owing to their convenience and capacity for functional integration, have been widely adopted in preoperative management [5,6]. Although patients often express initial interest in mHealth, real-world use is frequently characterized by low engagement after download or rapid attrition, commonly attributed to monotonous content, complex interfaces, limited personalized feedback, and concerns regarding trust and privacy [7,8]. In a single-center retrospective cohort of 549 patients undergoing elective surgery within a colorectal surgical service, 473 (86.2%) activated a digital patient-engagement platform. Among those who activated the platform, 382 (80.8%) completed at least 1 patient-reported outcome survey, 259 (54.8%) completed at least 1 in-hospital health check, and 270 (57.1%) completed at least 1 postdischarge health check during the perioperative follow-up period [9]. These differences between platform activation and subsequent task completion indicate that initial adoption alone may not translate into sustained engagement with digital health interventions. Gamified mHealth apps incorporate game elements into mobile platforms to enhance users’ motivation to engage in health-related activities, thereby promoting sustained healthy behaviors [10]. Seaborn and Fels [11] defined gamification as the integration of game elements, mechanics, features, design, and structure within nongame contexts to achieve nongame objectives. By introducing game elements such as points, badges, leaderboards, and virtual rewards, gamified mHealth apps address the drawbacks of traditional mHealth apps, such as monotonous content, complex operations, and lack of personalization, to enhance user engagement and motivation. This approach has shown promising potential in improving technology acceptance [12,13].
However, existing research on the acceptability of gamified mHealth apps has largely focused on contexts such as long-term chronic disease management, health promotion, or symptom follow-up, with the primary goal of enhancing sustained adherence and engagement over time [14,15]. By contrast, evidence remains limited for short-cycle, task-intensive health care transition contexts characterized by substantial uncertainty. For patients with PA, a clearly defined time window requires the completion of multiple key preadmission tasks outside the hospital while simultaneously coping with the psychological burden associated with diagnosis and surgical decision-making. This setting differs from chronic disease follow-up or long-term symptom management in its temporal structure, task density, and modes of professional support.
Moreover, many prior studies have relied on conventional statistical approaches that emphasize the linear effects of independent variables on behavioral intention, while overlooking potential interactions among determinants [16]. Therefore, this study adopts a mixed methods design to systematically examine PA preadmission patients’ intentions to use gamified mHealth apps and the underlying mechanisms. Qualitative interviews were used to generate candidate antecedent factors and clarify key concepts, whereas explanatory pathways were subsequently identified using qualitative comparative analysis (QCA). QCA offers a novel methodological lens for understanding multicondition configurations and pathway diversity in behavioral decision-making [17]. By emphasizing equifinality and causal asymmetry, QCA facilitates the identification of how different combinations of factors jointly constitute configurations associated with high use intention.
The stimulus-organism-response (SOR) model was originally proposed by Mehrabian and Russell [18] to explain how external environmental stimuli influence behavioral responses through individuals’ cognitive and affective states, emphasizing that behavioral outcomes are not directly triggered by a single factor but are mediated by internal psychological processing. In recent years, the SOR model has been widely applied in research on online user behavior [19,20]. In the context of mHealth, an app’s functional design, interaction modalities, and modes of information presentation can be conceptualized as stimuli; patients’ cognitive appraisals, affective responses, and perceived trust constitute the organism state; and intention to use and sustained engagement represent the response [21]. During the preadmission period, patients with PA face multiple concurrent stressors, including disease-related uncertainty, intensive preoperative task demands, and limited real-time professional support. As a result, technology adoption behaviors in this setting are often shaped jointly by emotional experience, trust judgments, and cognitive load. The SOR model is therefore well suited to elucidating an integrated emotion-cognition-behavior decision-making mechanism in high-risk health care contexts. Guided by SOR theory, this study conceptualizes the design features of gamified mHealth apps (eg, reward mechanisms, interactivity, enjoyment, privacy protection, usability, and perceived authority) as external stimuli; patients’ emotional responses and cognitive evaluations as organism states; and use intention as the behavioral response outcome. This framework provides a clear theoretical basis for developing the interview guide, screening antecedent factors, and conducting subsequent QCA configurational analyses.
DiscussionPrincipal FindingsNo individual condition met the prespecified necessity threshold for high or low willingness to use gamified mHealth apps. Instead, multiple configurations of app-related conditions were associated with each outcome. These findings indicate that willingness was associated with different combinations of app-related conditions within the preadmission context and may not be adequately represented by examining individual conditions in isolation. The fsQCA identified several empirically observed configurations associated with high and low willingness. However, these configurations represent cross-sectional set relationships and should not be interpreted as mechanisms that produce willingness. These findings provide an exploratory configurational perspective on willingness to use gamified mHealth apps.
The SOR framework guided the interview questions and the organization of the qualitative findings. Zhan et al [33] similarly used the SOR framework to organize relationships among external stimuli, psychological responses, and behavioral intentions. The qualitative phase identified 6 app-related conditions relevant to willingness: reward mechanisms, interactivity, enjoyment, privacy protection, usability, and authority background. These 6 conditions were subsequently included in the fsQCA to examine their combinations in relation to willingness to use. Previous studies have reported associations between interactive or rewarding features and users’ engagement with digital interventions [34,35]. Privacy protection and usability have also been identified as relevant considerations in patients’ evaluations of mHealth technologies [36,37].
Reward-driven engagement (H1) configuration was characterized by the core presence of reward mechanisms and the core absence of privacy protection. Interactivity and enjoyment were present as peripheral conditions, whereas usability and authority background were absent as peripheral conditions. H1 had the highest raw coverage (0.298) and unique coverage (0.109) among the 4 high-willingness configurations. Thus, within the current sample, high willingness co-occurred with a combination emphasizing reward, interaction, and enjoyment, even when privacy protection, usability, and authority background were absent. Previous studies have also reported associations between rewarding or interactive features and engagement with digital interventions [34,35]. However, the present cross-sectional findings do not demonstrate that these features increased willingness or operated through social identification or positive affect. H2 was characterized by the core presence of interactivity and the core absence of reward mechanisms and usability. Enjoyment and privacy protection were present as peripheral conditions, whereas authority background was absent as a peripheral condition. This configuration indicates that high willingness co-occurred with interactivity, enjoyment, and privacy protection despite the absence of reward mechanisms, usability, and authority background. Previous studies have identified interactivity, enjoyment, and privacy protection as relevant considerations in users’ evaluations of digital health apps [36-38]. However, the present analysis did not test whether positive affect or trust mediated these associations.
H3 was characterized by the core presence of enjoyment, privacy protection, and usability and the core absence of reward mechanisms. Interactivity and authority background were absent as peripheral conditions. This configuration shows that high willingness was observed when enjoyment was combined with privacy protection and usability, even without reward mechanisms, interactivity, or authority background. Previous research has identified usability and privacy protection as relevant to patients’ evaluations of mHealth apps [36,37,39]. The current findings describe an observed combination of conditions and do not demonstrate that these features reduced technological barriers or increased adoption. H4 was characterized by the core presence of usability and authority background and the core absence of enjoyment. Privacy protection was present as a peripheral condition, whereas reward mechanisms and interactivity were absent as peripheral conditions. This configuration indicates that high willingness co-occurred with authority background, usability, and privacy protection in the absence of reward mechanisms, interactivity, and enjoyment. Previous studies have reported that professional endorsement, usability, privacy, and data security are relevant to patients’ evaluations of mHealth technologies [39,40]. Nevertheless, this configuration represents a cross-sectional association and does not demonstrate that authority background or functional assurance caused willingness or adoption.
To examine asymmetry between the 2 outcomes, we analyzed the configurations associated with low willingness to use gamified mHealth apps and identified 5 empirically observed patterns. Broad feature deficit (L1) was characterized by the core absence of enjoyment and privacy protection and the peripheral absence of reward mechanisms, interactivity, and authority background; usability was not decisive in this configuration. This pattern describes the co-occurrence of broad feature absence and low willingness but does not establish that these absences made the app unattractive or untrustworthy. L2 combined the core presence of authority background with the core absence of enjoyment and usability. Reward mechanisms and interactivity were absent as peripheral conditions, whereas privacy protection was not decisive. Thus, authority background co-occurred with low willingness when several engagement- and usability-related conditions were absent. The cross-sectional data do not establish rejection of use or long-term disengagement. L3 was characterized by the core presence of usability and the core absence of enjoyment. Interactivity was present as a peripheral condition, whereas reward mechanisms and privacy protection were absent as peripheral conditions; authority background was not decisive. This pattern indicates that interactivity and usability co-occurred with low willingness when enjoyment, reward mechanisms, and privacy protection were absent. Emotional responses and trust were not directly tested in this configuration [38-41]. L4 combined the core presence of reward mechanisms, interactivity, and authority background with the core absence of enjoyment and privacy protection; usability was not decisive. This configuration indicates that reward mechanisms, interactivity, and authority background did not invariably correspond to high willingness when enjoyment and privacy protection were absent. L5 was characterized by the core presence of reward mechanisms and usability and the core absence of authority background. Interactivity, enjoyment, and privacy protection were present as peripheral conditions. Thus, the absence of authority background co-occurred with low willingness even when the other 5 conditions were present [40]. Collectively, the 5 low-willingness configurations indicate that low willingness was associated with both broad feature absence and more specific combinations of present and absent conditions.
The configurations associated with high and low willingness were asymmetric rather than direct opposites. The same condition could be present in configurations associated with different outcomes, depending on the conditions with which it was combined. For example, reward mechanisms and interactivity were present in H1 but also in L4 and L5. Authority background was present in H4 but also in L2 and L4. Conversely, authority background was absent in H1-H3 and L5. These findings indicate that no single condition had a uniform relationship with willingness across all configurations.
LimitationsThis study has several limitations. First, participants were recruited from a single medical institution, and both the qualitative and quantitative samples were relatively small. Although the final 2 interviews generated no new codes or categories, indicating code saturation for the focused research question, the 10 interviews may not have achieved meaning saturation or captured the full heterogeneity of patients with PA. The quantitative sample of 120 participants may also limit the stability and generalizability of the identified configurations. Larger and more diverse samples from multiple institutions and geographic regions are therefore needed. Second, the eligibility criteria required participants to own and be able to use a smartphone and to have used an mHealth app within the previous month. These criteria may have favored patients with greater digital experience and at least some prior receptivity to mHealth. Therefore, the low-willingness configurations identified in this study represent relatively low willingness among patients with recent mHealth experience and should not be interpreted as representing individuals who categorically reject mHealth technologies. Future studies should include patients with a wider range of digital experience and initial attitudes toward mHealth. Third, the cross-sectional design assessed willingness at a single time point and could not establish causal relationships. Participants evaluated a description and slide presentation of a hypothetical gamified mHealth app rather than using a functioning app. Therefore, stated willingness should not be interpreted as actual adoption, sustained use, adherence, or clinical effectiveness. The identified configurations may inform preliminary app design but require validation through longitudinal studies using deployed apps and objective measures, such as app log data. Finally, although gamified design enhanced intention in some pathways, potential risks and boundaries should be considered. Excessive or poorly designed gamification may increase cognitive load and lead to engagement fatigue, particularly in high-stress contexts [42]. When gamification is perceived as distracting rather than supportive, its effect may weaken or even become counterproductive [43]. Thus, gamification may not be suitable for all patients or clinical settings, and its effectiveness likely depends on the fit among patient characteristics, disease stage, and clinical context.
Innovation and ImplicationsThe innovation of this study lies in examining gamified mHealth acceptance within the specific and time-limited preadmission pathway of patients with PA. Unlike studies that assess isolated predictors of general mHealth acceptance, this study linked patients’ qualitative accounts with SOR-guided instrument development and fsQCA to identify alternative feature configurations associated with both high and low willingness. This approach contributes disease- and stage-specific configurational evidence rather than assuming that findings from general, chronic disease, or postoperative settings are directly applicable to pre-admission care for PA. The configurations may inform the development of modular prototypes aligned with common preadmission tasks. Under H1, points, progress indicators, and interactive checklists could support the completion of admission documents, required examinations, and preoperative preparation tasks. Under H2, interactive disease education and question-and-answer activities could be combined with clear consent procedures and user-controlled data sharing. Under H3, simple navigation, minimal operation steps, and accessible privacy settings could facilitate access to examination schedules, hospitalization instructions, and symptom-reporting functions. Under H4, hospital identification, clinician-reviewed content, update dates, and transparent data-security information could be displayed prominently to clarify the professional source of PA-specific guidance.
The low-willingness configurations indicate that isolated features may not be sufficient: institutional authority without usable and engaging functions, interactivity without privacy protection, or multiple attractive features without clear professional provenance may still coincide with low willingness. Accordingly, developers could provide alternative feature modules rather than a uniform design and evaluate which combinations patients select and use during the preadmission period. These implications remain preliminary and require prospective testing with functioning apps, app-log data, and objective completion rates for preadmission tasks.
ConclusionsGuided by the SOR framework, this exploratory sequential mixed methods study identified 6 app-related conditions relevant to the willingness of preadmission patients with PA to use gamified mHealth apps: reward mechanisms, interactivity, enjoyment, privacy protection, usability, and authority background. The fsQCA identified 4 configurations associated with high willingness—reward-driven engagement, interactive enjoyment with privacy protection, privacy and usability-supported enjoyment, and authority-supported functional assurance—and 5 configurations associated with low willingness. These findings provide a disease- and stage-specific configurational perspective by showing that willingness was associated with alternative feature combinations rather than any single app feature. The configurations may inform the development of modular preadmission app prototypes incorporating task rewards and progress feedback, interactive education with privacy controls, simplified access to examination and hospitalization instructions, and hospital-endorsed, clinician-reviewed information. However, the findings represent exploratory associations with willingness toward a hypothetical app and do not establish causal effects, actual adoption, or sustained use. Prospective evaluation using functioning apps, larger multicenter samples, and objective app-use data is required before clinical implementation.