Latent Profile Analysis of Technophobia and Its Network Associations with Health Self-Care Behaviors among Older Adults in Rural China

Latent Profile Analysis of Technophobia and Its Network Associations with Health Self-Care Behaviors among Older Adults in Rural China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Latent Profile Analysis of Technophobia and Its Network Associations with Health Self-Care Behaviors among Older Adults in Rural China Yashuang Sun, Shasha Li, Xiaofang Song, Yun Bao, Canqi Ren, Mengxue Shi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8067377/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background This study aimed to identify heterogeneous latent profiles of technophobia among rural older adults and to explore the network structure linking technophobia and health self-care behaviors. Methods From August to October 2025, a total of 466 participants were recruited. Latent profile analysis was conducted using the Technophobia Scale, and the optimal model was determined based on fit indices and likelihood ratio tests. Network analysis was then employed to construct both overall and subgroup networks of technophobia and health self-care behaviors, identifying core and bridge symptoms. Network accuracy and stability were assessed through bootstrap procedures. Results Five subgroups were identified: “moderate tension–low fear,” “low tension–moderate fear,” “moderate tension–fear,” “high tension–moderate fear,” and “high tension–low fear.” Core symptoms were primarily distributed across three dimensions: health maintenance behavior, health belief formation behavior, and medical resource utilization behavior. Bridge symptoms varied across subgroups: HBB1 ( media health information ) in the “moderate tension–low fear” group; HBD7 ( acceptance ) in the “low tension–moderate fear” group; and HBB3 ( online health information ) in the “moderate tension– fear” group. Conclusions Technophobia among rural older adults exhibits distinct heterogeneity and forms specific network structures with health self-care behaviors. These findings provide a theoretical foundation for precision, stratified behavioral intervention strategies targeting diverse technophobia profiles in rural elderly populations. Trial registration Registered in the Chinese Clinical Trial Registry on 10/30/2025(ChiCTR2500111399) technophobia health self-care behavior rural older adults latent profile analysis network analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction As the global population ages, China has entered an advanced stage of demographic aging [ 1 ]. In rural areas, older adults comprise 23.81% of the population—7.99 percentage points higher than in urban areas [ 2 ]—and this proportion is projected to increase to 37.70% by 2035 [ 3 ]. Amid this demographic shift, the integration of information and communication technologies (ICTs) across sectors has catalyzed the rise of digital health as a novel model for health maintenance and care delivery. The World Health Organization (WHO) defines digital health as “the field of knowledge and practice associated with the development and use of digital technologies to improve health,” encompassing smart devices, the Internet of Things (IoT), and related services [ 4 ]. Increasingly, digital health technologies are being extended to rural communities [ 5 ], enabling older adults to enhance their self-management of health. Given their threshold characteristics and limited inclusivity, digital health technologies produce not only digital dividends but also new forms of technophobia. Technophobia denotes the irrational fear, anxiety, and deliberate avoidance that individuals experience when confronted with emerging digital health technologies, such as wearable devices [ 6 – 7 ]. Unlike anxiety defined within clinical psychology, technophobia constitutes a form of social competence anxiety embedded within the broader context of population aging [ 8 – 9 ]. It is an inevitable byproduct of digital health development and remains inherently intertwined with it [ 9 ]. Empirical evidence indicates that older adults are particularly susceptible to technophobia, which not only limits their access to the benefits of digital health technologies but also constrains their health-promoting behaviors and capacity to manage health risks [ 10 ]. Addressing technophobia among older adults has therefore become imperative for ensuring the inclusive and equitable development of digital health. From a psychological perspective, behavior and cognition interact dynamically. Behavior not only externalizes psychological states but also shapes cognitive processes and emotional experiences in return [ 11 ]. This principle provides an emphasis framework for alleviating technophobia among rural older adults. Health self-care behaviors—actions undertaken through active participation and self-management to maintain health, treat illness, and promote recovery [ 12 ]—align with the “proactive health” concept emphasized in the Healthy China 2030 Plan [ 13 ]. These behaviors constitute a key form of self-directed health maintenance for older adults, encompassing activities such as health belief formation, health knowledge–seeking, mutual health support, health resource utilization, and health maintenance [ 12 ]. As a cost-effective, efficient, and sustainable strategy for health promotion, health self-care behaviors not only strengthen self-care management among older adults but also support the global objective of healthy aging, representing a crucial preventive capacity for sustainable health development [ 14 – 15 ]. Empirical research by Wan Yuhan et al. [ 16 ] identified a significant negative correlation between health self-care behaviors and technophobia among community-dwelling older adults in China. This finding suggests that insufficient engagement in proactive health management through digital health technologies may exacerbate technophobia in this population. However, existing research has primarily examined the macro-level relationship between health self-care behaviors and technophobia, while neglecting the micro-level connections between specific behavioral components and technophobia dimensions. Furthermore, the heterogeneous patterns of technophobia among rural older adults, shaped by unique sociocultural contexts, remain unclassified. To address these gaps, this study integrates network analysis (variable-centered) and latent profile analysis (person-centered) to elucidate this complex interplay at both structural and individual levels. The specific aims are to: (i) identify technophobia subgroups via latent profile analysis; (ii) examine structural associations using network analysis to uncover psychosocial and behavioral mechanisms; and (iii) explore the differential roles of health self-care behaviors across subgroups, thereby informing precision interventions for digital inclusion and healthy aging. Methods Study Design and Participants A cross-sectional study was conducted between August and October 2025 in Huzhou City, Northern of Zhejiang Province, China. Participants were eligible if they met the following inclusion criteria: (i) aged 60 years or older; (ii) held rural household registration and had resided in rural areas for at least one year; and (iii) possessed clear consciousness and adequate verbal communication ability to engage effectively in the survey. Exclusion criteria included: (i) severe physical illness that significantly limited activities of daily living; (ii) cognitive impairment identified through a brief mental status examination or a documented history of psychiatric disorders; and (iii) severe visual, auditory, or speech impairments that prevented completion of the survey or effective participation in interviews. Sample Size Using the sample size formula for cross-sectional studies, \(\:\text{n=}{\left(\frac{{\text{u}}_{\text{α}/2}\text{σ}}{\text{δ}}\right)}^{\text{2}}\) [ 17 ], the parameters were determined based on data reported in previous studies [ 18 ], with α = 0.05, σ = 18.09, and δ = 1.6. Substituting these values into the formula yielded an estimated sample size of approximately 421 participants. To account for an anticipated 20% rate of invalid or incomplete responses, the final target sample size was set at 466 participants. Sampling and Recruitment Procedures Participants were recruited via convenience sampling from eight rural townships in Huzhou City. Within each township, three to four villages were selected as recruitment sites. In collaboration with community organizations, nursing homes, and village committees, the research team disseminated information about the study through posters and community announcements. Approximately 10 to 15 older adults were enrolled from each village, yielding 466 valid responses. To ensure standardized and ethical data collection, all field staff received training in recruitment procedures and participant communication. Before the survey, researchers explained the study’s purpose and benefits, obtained written informed consent, and confirmed participants’ full understanding. Measurements General information questionnaire A self-designed general information questionnaire was used to assess participants' demographic and socioeconomic characteristics, including gender, age, education, marital status, religious belief, number of children, chronic diseases, and monthly income. (see Supplementary File S1 for the complete version). Technophobia Scale The Chinese version of the Technophobia Scale, translated by Sun Erhong was adopted [ 18 ]. It includes 13 items across three dimensions—technology tension, technology fear, and privacy and security concerns—rated on a 5-point Likert scale from “completely inconsistent” to “completely consistent,” yielding a potential total score range of 13–65. Higher scores reflect more severe technophobia. The scale showed high internal consistency (Cronbach's α = 0.759–0.911) and content validity (all indices > 0.80). Health Self-care Behavior Scale The Health Self-Care Behavior Scale by Chen Lin [ 19 ] was employed. This 26-item tool covers five domains and uses a 5-point Likert response format (ranging from 1 to 5), providing a total score out of 130 where higher values reflect more advanced self-care behavior. It exhibits strong reliability (α = 0.923) and has been extensively applied with good validity in health behavior studies. Statistical analysis Data were analyzed using SPSS 26.0. Continuous variables were summarized as mean ± standard deviation (M ± SD), and categorical variables as frequency (N) and percentage (%). Latent profile analysis (LPA) was conducted in Mplus 8.3 to identify technophobia subgroups among rural older adults. Model fit was evaluated using: (1) Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and sample-size adjusted BIC (SABIC), with lower values indicating better fit[ 20 ]; (2) entropy, representing classification accuracy, where values closer to 1 indicate higher accuracy (≥ 0.8 acceptable; ≥ 0.9 ideal) [ 21 ]; and (3) the Lo–Mendell–Rubin adjusted likelihood ratio test (LMRT) and bootstrap-based likelihood ratio test (BLRT), with p < 0.05 indicating that the K-class model fits significantly better than the (K − 1)-class model [ 22 ]. Network analysis was conducted in R (version 4.4.1) utilizing the qgraph (v1.9.8) and bootnet (v1.6) packages. The analytical procedure comprised the following steps: (1) Network Estimation: The network was estimated with the EBICglasso (Extended Bayesian Information Criterion Graphical LASSO) method. This technique regularizes the partial correlation matrix via the Graphical LASSO algorithm, prunes spurious edges, and yields a sparse, interpretable network structure. (2) Centrality Estimation: Node strength—the sum of the absolute edge weights connecting a node to all others—was calculated as the primary centrality metric. Strength centrality is considered the most stable and was used to identify core symptoms within the network [ 23 ]. (3) Network Accuracy and Stability: Edge-weight accuracy was evaluated via nonparametric bootstrapping with 95% confidence intervals; narrower intervals indicate greater precision [ 24 ]. The stability of node strength was assessed using the Correlation Stability Coefficient (CS-C), where values above 0.25 indicate acceptable robustness [ 25 ]. (4) Bridge Symptoms: Bridge symptoms were defined by a Bridge Expected Influence (BEI) index exceeding 1 [ 26 ]. The stability of these bridge estimates was further verified using case-dropping bootstrap procedures within the bootnet package. Results Participant characteristics A total of 466 participants were included in the study, with detailed characteristics shown in Table 1 . Participants ranged in age from 60 to 89 years, with a mean age of 70.94 years (SD = 7.21). Most participants were female (68.2%) and married (91.0%). The mean total Technophobia Scale score was 39.74 (SD = 5.34), with an average item score of 3.06 (SD = 0.41). Among the three subscales, privacy and security concerns had the highest mean item score (3.80 ± 0.54), followed by technology tension (2.99 ± 0.64) and technology fear (2.68 ± 0.77). Table 1 General characteristics of participants (n = 466) Variable Group N (%) or M ± SD (Average Score per Item) Age Age (60–69) 215 (46.1%) Age (70–79) 200 (42.9%) Age (≥ 80) 51 (10.9%) Gender Male 148 (31.8%) Female 318 (68.2%) Education level Illiterate 61 (13.1%) Primary school 224 (48.1%) Junior high school 112 (24.0%) Senior high school and above 69 (14.8%) Marital status Unmarried 9 (1.9%) Married 424 (91.0%) Divorced 5 (1.1%) Widowed 28 (6.0%) Religious belief None 404 (86.7%) Yes 62 (13.3%) Number of children 0 12 (2.6%) 1 140 (30.0%) 2 208 (44.7%) ≥ 3 106 (22.7%) Chronic diseases 0 175 (37.6%) 1 199 (42.7%) 2 81 (17.4) ≥ 3 11 (2.3%) Monthly income Low (3000) 44 (9.4%) Technophobia Score 39.74 ± 5.34 (3.06 ± 0.41) Privacy and Security Concerns Dimension 11.41 ± 1.63 (3.80 ± 0.54) Technology Tension Dimension 14.94 ± 3.20 (2.99 ± 0.64) Technology Fear Dimension 13.39 ± 3.84 (2.68 ± 0.77) Latent Profile Analysis of Technophobia among Rural Older Adults Using item scores from the Technophobia Scale administered to rural older adults as observed indicators, latent profile models with one to six profiles were estimated. Model fit indices are summarized in Table 2 . As the number of profiles increased, AIC, BIC, and ABIC values declined steadily. In the five-profile solution, both LMRT and BLRT were significant (P < 0.001), whereas the LMRT for the six-profile model was not (P = 0.186), indicating that adding more profiles did not further improve model fit. The entropy of the four-profile model (0.997) was also slightly lower than that of the five-profile model (0.999), further supporting the superiority of the five-profile solution. Overall, these findings indicate that the five-profile model offers the best fit. Table 2 Fitting statistics for a latent profile model of technophobia among rural older adults (n = 466) Model Loglikelihood AIC BIC SABIC Entropy BLRT LMRT Profile probability(%) P P 1-profile -6211.505 12475.011 12582.759 12500.241 N/A N/A N/A N/A 2-profile -5138.339 10356.677 10522.445 10395.494 1.000 <0.01 <0.01 0.4206\0.57940 3-profile -2742.237 5592.475 5816.261 5644.877 1.000 <0.01 0.8972 0.20172\0.20386\ 0.59442 4-profile -2313.611 4763.221 5045.026 4829.209 0.997 <0.01 0.0008 0.20172\0.34335\0.25107\0.20386 5-profile -2025.109 4214.218 4554.042 4293.792 0.999 <0.01 0.0031 0.25107\0.20172\0.34335\0.12232\ 0.08155 6-profile -1659.045 3510.089 3907.931 3603.249 1.000 <0.01 0.3284 0.08798\0.25107\0.34335\0.11373\0.08155\0.12232 Note: N/A: not applicable;AIC: Akaike Information Criterion, BIC: Bayesian Information Criterion, SABIC: Sample-size Adjusted Bayesian Information Criterion, LMRT: Lo-Mendell–Rubin’s adjusted Likelihood Ratio Test, BLRT༚Bootstrap Likelihood Ratio Test. The 5-profile model (bold) was selected as the optimal model. Figure 1 presents the distribution of technophobia identified in the final five-profile model. All subgroups displayed relatively high scores on privacy and security concern items and were therefore collectively categorized as the “high privacy–security” group. Subgroup 1 showed moderate technology tension and low technology fear (“moderate tension–low fear,” n = 117, 25.11%). Subgroup 2 had the lowest tension but moderate fear (“low tension–moderate fear,” n = 94, 20.17%). Subgroup 3 displayed moderate levels on both dimensions (“moderate tension–fear,” n = 160, 34.33%). Subgroup 4 exhibited the highest tension and moderate fear (“high tension–moderate fear,” n = 57, 12.23%). Subgroup 5 showed relatively high tension but the lowest fear (“high tension–low fear,” n = 38, 8.15%). Detailed mean scores for each dimension across subgroups are presented in Table 3 . Table 3 Descriptive analysis of technophobia scale scores for five latent profiles (M ± SD, n = 466) Model Profile1 Profile2 Profile3 Profile4 Profile5 N 117 94 160 57 38 % 25.11 20.17 34.33 12.23 8.15 A 3.78 ± 0.56 3.74 ± 0.52 3.83 ± 0.58 3.82 ± 0.45 3.89 ± 0.50 B 2.98 ± 0.09 2.0 ± 0.11 2.99 ± 0.08 3.98 ± 0.09 3.98 ± 0.11 C 1.91 ± 0.32 2.67 ± 0.75 3.24 ± 0.44 3.24 ± 0.45 1.84 ± 0.40 A1 3.76 ± 0.582 3.71 ± 0.541 3.80 ± 0.581 3.81 ± 0.480 3.84 ± 0.495 A2 3.79 ± 0.570 3.76 ± 0.522 3.84 ± 0.603 3.84 ± 0.455 3.92 ± 0.539 A3 3.79 ± 0.565 3.76 ± 0.522 3.86 ± 0.592 3.82 ± 0.468 3.92 ± 0.539 B1 3.01 ± 0.092 2.02 ± 0.206 3.02 ± 0.157 4.00 ± 0.000 4.03 ± 0.162 B2 3.00 ± 0.000 1.99 ± 0.103 3.00 ± 0.000 4.00 ± 0.000 4.00 ± 0.000 B3 2.97 ± 0.182 1.99 ± 0.103 2.96 ± 0.191 3.96 ± 0.186 3.95 ± 0.226 B4 2.98 ± 0.130 1.99 ± 0.103 2.99 ± 0.079 3.96 ± 0.186 3.97 ± 0.162 B5 2.96 ± 0.203 1.99 ± 0.103 2.95 ± 0.219 3.98 ± 0.132 3.97 ± 0.162 C1 1.97 ± 0.320 2.71 ± 0.785 3.30 ± 0.486 3.30 ± 0.499 1.92 ± 0.428 C2 1.85 ± 0.362 2.63 ± 0.762 3.23 ± 0.423 3.21 ± 0.411 1.76 ± 0.431 C3 1.85 ± 0.362 2.65 ± 0.786 3.26 ± 0.441 3.28 ± 0.453 1.76 ± 0.431 C4 1.85 ± 0.362 2.57 ± 0.740 3.11 ± 0.514 3.09 ± 0.544 1.76 ± 0.431 C5 2.07 ± 0.450 2.77 ± 0.768 3.30 ± 0.486 3.30 ± 0.499 2.00 ± 0.520 Total 35.83 ± 2.394 34.53 ± 3.915 42.63 ± 2.821 47.56 ± 2.841 40.82 ± 2.608 Network Analysis Characteristics of Technophobia and Health Self-Care Behaviors The network structure illustrating the associations between technophobia and health self-care behaviors is presented in Fig. 2 . In the overall network, 27 nodes and 72 non-zero edges (20.51%) were identified, with an average edge weight of 0.128. Among the subgroup networks, the “moderate tension–low fear” group (average edge weight = 0.137) comprised 46 non-zero edges (13.11%); the “low tension–moderate fear” group (average edge weight = 0.186) comprised 17 non-zero edges (4.84%); the “moderate tension–fear” group (average edge weight = 0.161) comprised 51 non-zero edges (14.53%); the “high tension–moderate fear” group (average edge weight = 0.136) comprised 14 non-zero edges (3.99%); the “high tension–low fear” group (average edge weight = 0.160) comprised 15 non-zero edges (4.27%). In the overall network, the strongest edge was between D4 ( scientific exercise ) and TA ( technophobia ), followed by A2 (health risk ) and E1 ( rational medication ) (Fig. 2 a). Connectivity patterns, however, varied across groups. In the “moderate tension–low fear” group, all nodes were weakly linked to TA1 (Fig. 2 b). In contrast, the “low tension–moderate fear” group showed the strongest connections between TA2 and D7 ( acceptance ) as well as A3 ( meaning in life ) (Fig. 2 c), while in the “moderate tension–fear” group, TA3 was most strongly linked to D12 ( interpersonal ties ) and B1 ( media health information ) (Fig. 2 d). Figure 3a1 displays node strength centrality in the overall network, with D7 ( acceptance ), A2 ( health risk ), and E1 ( rational medication ) emerging as the most central nodes. In the “moderate tension–low fear” group (Fig. 3b1), B2 (health engagement ) had the highest strength, followed by D13 ( group activity ) and E2 (timely care ). In the “low tension–moderate fear” group (Fig. 3c1), B2 ( health engagement ), E2 ( timely care ), and A3 ( meaning in life ) ranked highest, whereas in the “moderate tension–fear” group (Fig. 3d1), D13 ( group activity ) showed the greatest strength, followed by E1 ( rational medication ) and D7 ( acceptance ). Both the “high tension–moderate fear” and “high tension–low fear” groups exhibited a global strength of zero, indicating no meaningful associations among variables; hence, centrality or community analyses were not conducted. Edge weights in the overall and three analyzable group networks had narrow 95% confidence intervals (Fig. 3a2–d2), indicating high estimation precision. Correlation stability coefficients (CS-C) for all networks exceeded 0.25 (Fig. 3a3–d3), confirming satisfactory network stability and reliability. (see Supplementary Table S2 ) To further clarify the cross-domain associations between technophobia and health self-care behaviors, a bridge symptom analysis was conducted using item-level networks derived from the Technophobia Scale and the Health Self-Care Behavior Scale, rather than aggregate scores. Significant bridge symptoms, indicated by elevated Bridge Expected Influence (BEI), were identified in three subgroups: “moderate tension–low fear,” “low tension–moderate fear,” and “moderate tension–fear.” In the “moderate tension–low fear” group (Fig. 4 e), TAB2 ( avoiding device change ) and HBB1 ( media health information ) exhibited the highest BEI values, followed by TAB1 ( discomfort with new devices ) and HBA2 ( health risk ), which also showed notable bridging effects. In the “low tension–moderate fear” group (Fig. 4 f), TAC5 ( fear of technology failure ) and HBD7 ( acceptance ) demonstrated the strongest bridge influence. For the “moderate tension–fear” group (Fig. 4 g), the leading bridge symptoms were HBB3 ( online health information ), HBB1 ( media health information ), and HBB2 ( health engagement ), all originating from the Health Self-Care Behavior Scale. The 95% confidence intervals for bridge strength in each subgroup did not include zero (Fig. 4 e1–g1), confirming that these bridging symptoms were statistically robust and represented stable interconnections between constructs. Discussion By combining latent profile and network analyses, this study mapped heterogeneous technophobia patterns and their structural relationships with health self-care behaviors among rural older adults. The results justify a shift toward precision-oriented interventions, paving the way for evidence-based strategies to mitigate technophobia in aging populations. Among rural Chinese older adults, technophobia was moderately high [ 27 ], with privacy concerns being the most prominent dimension and a key barrier to digital inclusion [ 28 ]. Latent profile analysis revealed five distinct subgroups, indicating a multidimensional heterogeneity driven by cognitive, emotional, and behavioral synergy [ 29 – 30 ]. This finding contrasts with the three-category (low technophobia type, technology-fearful type, and technology-anxious type) typology found in urban peers [ 31 ], likely due to rural-specific contextual factors [ 32 ], highlighting the necessity for tailored interventions. Across the five technophobia subgroups identified among rural older adults, privacy and security concerns were consistently high, while technological tension and fear varied substantially. The largest subgroup, characterized by moderate tension and fear, exhibited aligned cognitive, emotional, and behavioral dimensions, representing the primary high-anxiety profile [ 33 ]. Other subgroups displayed distinct patterns: moderate tension with low fear indicated cognitive vigilance but mild emotional apprehension [ 34 ]; low tension with moderate fear reflected emotional detachment and cognitive avoidance [ 35 ]; high tension with moderate fear showed pervasive anxiety across all dimensions; and high tension with low fear suggested a conflict between external pressure and low internal arousal [ 36 ]. Intervention efforts should prioritize the moderate tension–fear and high tension–moderate fear subgroups, as both experience significant cross-domain distress and are particularly susceptible to digital avoidance. For the moderate tension–low fear subgroup, strengthening privacy awareness and perceived security may help consolidate their readiness to use digital health tools. Interestingly, the overall network analysis revealed that technophobia and health self-care behaviors were primarily linked through three dimensions: Health Belief Formation (A), Health Maintenance Behavior (D), and Medical Resource Utilization (E). Among these, A2 ( health risk ) and E1 ( rational medication ) emerged as pivotal nodes with high edge weights and centrality, indicating their core roles in the interaction between technophobia and self-care. This suggests that health risk perception and rational medication management are central mechanisms in this relationship. According to the Health Belief Model [ 37 ], heightened perceptions of health risk may amplify uncertainty regarding digital health technologies, thereby intensifying technophobia. Concurrently, limited self-efficacy and perceived barriers in medication management may further exacerbate this response. Within the health maintenance dimension, the strong edge weight of D4 ( scientific exercise ) reflects technophobia's direct impact on behavioral choices, while the high centrality of D7 ( acceptance ) indicates its potential role in mitigating technophobia through emotion-focused coping, consistent with the stress-coping model. This model posits that positive, emotion-focused strategies can buffer anxiety responses [ 38 ]. Surprisingly, subgroup network comparisons revealed distinct edge-weight patterns. Subgroup 1 (“moderate tension–low fear”) showed no strong edges, indicating that tension alone does not stably couple technophobia with health behaviors [ 9 ]. In Subgroup 2 (“low tension–moderate fear”), strong edges persisted within Health Belief Formation (A), but the dominant node shifted from A2 ( health risk ) to A3 ( meaning in life ), alongside D7 ( acceptance ) in Health Maintenance Behavior (D), forming an “acceptance–meaning” pathway for cognitive reappraisal [ 39 ]. Subgroup 3 (“moderate tension–fear”) diverged further, with strong edges at D12 ( interpersonal ties ) and B1 ( media health information )—the latter being the first appearance of a node from Health Belief Formation Behavior dimension (B) - suggesting compensatory reliance on social and informational support [ 40 ]. Critically, Health Maintenance Behavior showed strong edges in all subgroups except Subgroup 1, underscoring its central role. Interventions should thus prioritize this dimension: for Subgroup 2, through mindfulness and meaning-making to enhance acceptance; for Subgroup 3, by strengthening social support and information literacy to build trust. These pathways collectively promote adaptive behaviors that alleviate technophobia. Beyond subgroup-specific edge structures, core node distributions showed both consistency and divergence. Medical Resource Btilization Behavior (E) remained highly central across all networks, functioning as a pivotal hub that integrates psychological and behavioral processes [ 41 ]. Specifically, individuals with higher technophobia (e.g., Subgroup 3) emphasized E1 ( rational medication ), reflecting a "self-control" orientation, whereas those with lower technophobia (e.g., Subgroups 1–2) focused more on E2 ( timely care ), indicating "external dependence" [ 42 ]. This aligns with evidence that older adults prefer self-directed medication management when using home-based health technologies [ 43 ] but rely more on external systems for care-service technologies [ 44 ]. Notably, Health Maintenance Behavior (D) also showed high centrality in Subgroups 1 and 3, though with distinct patterns. Subgroup 1 centered on D13 ( group activity ), reflecting adherence to routine health practices, while Subgroup 3 involved both D13 ( group activity ) and D7 ( acceptance ), suggesting the use of acceptance-oriented strategies to buffer technophobia [ 38 ]. This aligns with evidence on the role of psychological flexibility in mitigating technology-related stress [ 45 ]. In Subgroup 2, the core node shifted from A2 ( health risk ) to A3 ( meaning in life ), indicating a reliance on existential purpose to maintain psychological balance—a finding consistent with international studies on resilience in digital health adaptation [ 46 – 47 ]. Additionally, both Subgroups 1 and 2 featured B2 ( health engagement ), suggesting compensatory reliance on social networks in the absence of digital skills. This reflects the "acquaintance culture" of rural communities and underscores the importance of social support in bridging the digital health divide [ 48 – 49 ]. Together, these patterns reveal multifaceted interaction pathways between technophobia and health self-care behaviors, supporting the design of tiered, behaviorally-informed interventions. While core symptom analysis identified key influence pathways, it offered limited insight into cross-community connectivity. To elucidate the bridging mechanisms between technophobia and health self-care behaviors, we conducted a bridge symptom analysis [ 50 ]. In the moderate tension–low fear group, TAB2 ( avoiding device change ) and HBB1 ( media health information ) served as the most stable bridge nodes, indicating resistance to device updates and reliance on traditional information channels [ 51 – 52 ]. In the low tension–moderate fear group, TAC5 ( fear of technology failure ) and HBD7 ( acceptance ) were key bridges, highlighting fears of technological dependency and control loss [ 53 – 54 ]. The moderate tension–fear group showed strong bridging effects for HBB nodes ( online and media health information, health engagement ), suggesting central challenges in health information acquisition amid cognitive overload [ 55 – 56 ]. These subgroup-specific bridge symptoms reveal distinct interaction pathways, offering precise targets for behavior-based interventions. Limation This study has several limitations. First, the limited sample size precluded robust estimation of four- and five-profile models, warranting future replication with larger samples. Second, the reliance on self-reported data may introduce recall bias, suggesting a need for qualitative or mixed-method approaches. Third, the cross-sectional design prohibits causal inference; longitudinal studies are needed to clarify dynamic relationships. Finally, the recruitment of participants solely from Huzhou City, Zhejiang Province, may limit generalizability, highlighting the value of future multi-center studies. Conclusion This study identifies five latent technophobia subgroups(moderate tension–low fear, low tension–moderate fear, moderate tension–fear, high tension–moderate fear, and high tension–low fear ) among rural Chinese older adults, pinpointing distinct network linkages with health self-care behaviors. These findings advance understanding of the psychological–behavioral mechanisms underlying technophobia and provide a theoretical basis for developing precise, tiered interventions. Enhancing health self-care behaviors may serve as an effective pathway to mitigate technophobia in rural older adults. Declarations Acknowledgements We thank the authors for their contributions to the content and data processing of this article. Authors ’ contributions Study conception and design: YS, XS and SL. Data collection and evaluation:YB, XS, and SL. Data extraction and analysis: YS, and SL. Manuscript draft: CR, MS, XY, GG, and SB. Critical revision of important intellectual content: SL, XS and YS. All authors contributed to the article and approved the submitted version. Fuding This work was supported by the National Natural Science Foundation of China (No. 72204084), the Key Research And Development Program Of Zhejiang Province (2025C02106) Data availability The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Committee of Huzhou University ( NO. 202505-6). The trial was registered with the Chinese Clinical Trial Registry (ChiCTR2500111399). Written informed consent was obtained from all participants prior to their enrollment in the study. The consent process involved detailing the study's purpose, procedures, potential risks and benefits, and the participants' rights, including the right to withdraw at any time without penalty. All participant data were handled with strict confidentiality, used solely for research purposes, and securely disposed of upon study completion. Consent for publication Not applicable Competing interests The authors report no declarations of interest. References National Bureau of Statistics of China . (2024). Statistical Communique on National Economic and Social Development of the People's Republic of China in 2023. https://www.stats.gov.cn/sj/zxfb/202402/t20240228_1947915.html National Bureau of Statistics of China. (2021). Communiqué of the Seventh National Population Census (No. 1) – Basic Information on the Census Work[EB/OL]. Beijing: National Bureau of Statistics of China. https://www.stats.gov.cn/sj/pcsj/rkpc/d7c/ Zhao W, Cui R. (2024). 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1","display":"","copyAsset":false,"role":"figure","size":20293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFive latent Profiles of technophobia among rural older adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Technophobia Scale consists of 13 entrys. Entry A1:fear of being monitored; Entry A2:fear of online traces; Entry A3:fear of search tracking; Entry B1:discomfort with new devices; Entry B2:avoiding device change; Entry B3:avoid use new technologies; Entry B4:uneasy learning new systems; Entry B5:afraid of search engines; Entry C1:fear of job replacement; Entry C2:fear of human obsolescence; Entry C3:fear of life impact; Entry C4:fear of life changes; Entry C5:fear of technology failure.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8067377/v1/c8d2f72f812c626c9873eb54.png"},{"id":97192625,"identity":"31b8c71b-6bea-443a-8596-d965aefdebe7","added_by":"auto","created_at":"2025-12-01 20:19:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":750539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork structure of technophobia and health self-care behavior in rural older adults.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote:\" overall network （a）\" \"moderate tension-low fear （b）\" \"low tension-moderate fear （c）\" \"moderate tension-fear （d）\". Blue nodes represent the total score of the technophobia scale, and orange nodes indicate health self-care behavior items.The lines between nodes show the correlation: green edges indicate positive correlation, red edges indicate negative correlation, and the thickness of the edges reflects the strength of the correlation. TA:technophobia; A1:health response; A2:health risk; A3:meaning in life; B1:media health information; B2:health engagement; B3:online health information; C1:monitoring models; C2:role models; C3:health discussion; D1:hydration; D2:regular meals; D3:fiber intake; D4:scientific exercise; D5:exercise limits; D6:adequate sleep; D7:acceptance; D8:stress management; D9:hygiene; D10:substance avoidance; D11:health monitoring; D12:interperson ties; D13:groupactivity; D14:problem sharing; E1:rational medication; E2:timely care; E3:symptom communication.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8067377/v1/063bd9decae57be98edbdf87.png"},{"id":97192628,"identity":"08d52848-9c5f-4586-ac12-10aca060d9c0","added_by":"auto","created_at":"2025-12-01 20:19:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":328377,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea1-d1node strength centrality; a2-d2 bootstrap edge stability; a3-d3 node strength stability\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8067377/v1/3ce77a9ddc73537da0af6f6f.png"},{"id":97250592,"identity":"db17d9e8-99b5-4fec-949b-d1f8314e4394","added_by":"auto","created_at":"2025-12-02 13:14:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":983931,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\"moderate tension-low fear（e）\" \"low tension-moderate fear（f）\" \"moderate tension-fear（g）\". Bridge stability of various networks e1-g1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote:Blue nodes represent the items of the technophobia Scale, and green nodes represent the items of health self-care behaviors and orange node is a bridge symptom. The lines connecting the nodes indicate correlations: green lines represent positive correlations, red lines represent negative correlations, and the thickness of the lines reflects the strength of the correlation.TA:Technophobia; HB:Health self-care behaviors; TAA1:fear of being monitored ; TAA2:fear of online traces; TAA3:fear of search tracking ; TAB1:discomfort with new devices ; TAB2:avoiding device change ; TAB3:avoid use new technologies; TAB4:uneasy learning new systems; TAB5:afraid of search engines; TAC1:fear of job replacement; TAC2:fear of human obsolescence; TAC3:fear of life impact; TAC4:fear of life changes; TAC5:fear of technology failure. HBTA:technophobia; HBA1:health response; HBA2:health risk; HBA3:meaning in life; HBB1:media health information; HBB2:health engagement; HBB3:online health information; HBC1:monitoring models; HBC2:role models; HBC3:health discussion; HBD1:hydration; HBD2:regular meals; HBD3:fiber intake; HBD4:scientific exercise; HBD5:exercise limits; HBD6:adequate sleep; HBD7:acceptance; HBD8:stress management; HBD9:hygiene; HBD10:substance avoidance; HBD11:health monitoring; HBD12:interperson ties; HBD13:groupactivity; HBD14:problem sharing; HBE1:rational medication; HBE2:timely care; HBE3:symptom communication.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8067377/v1/cd9d25ef3a96a86dca39850d.png"},{"id":97252531,"identity":"37d27c06-a001-4f85-834a-e305a795fea2","added_by":"auto","created_at":"2025-12-02 13:22:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3020284,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8067377/v1/4bf7bb92-f601-4f87-9344-3c5f2b25ada4.pdf"},{"id":97250902,"identity":"e2c5016c-b381-42da-b7f4-f44cb3b4b0ac","added_by":"auto","created_at":"2025-12-02 13:15:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13652,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8067377/v1/9983cb95d5a4f63e360be786.docx"},{"id":97192627,"identity":"be283c65-461f-4ad3-98ca-e3e129b4132f","added_by":"auto","created_at":"2025-12-01 20:19:27","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":898866,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2.doc","url":"https://assets-eu.researchsquare.com/files/rs-8067377/v1/c7b531614fb3f30d36746765.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Latent Profile Analysis of Technophobia and Its Network Associations with Health Self-Care Behaviors among Older Adults in Rural China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs the global population ages, China has entered an advanced stage of demographic aging [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In rural areas, older adults comprise 23.81% of the population\u0026mdash;7.99 percentage points higher than in urban areas [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u0026mdash;and this proportion is projected to increase to 37.70% by 2035 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Amid this demographic shift, the integration of information and communication technologies (ICTs) across sectors has catalyzed the rise of digital health as a novel model for health maintenance and care delivery. The World Health Organization (WHO) defines digital health as \u0026ldquo;the field of knowledge and practice associated with the development and use of digital technologies to improve health,\u0026rdquo; encompassing smart devices, the Internet of Things (IoT), and related services [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Increasingly, digital health technologies are being extended to rural communities [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], enabling older adults to enhance their self-management of health.\u003c/p\u003e\u003cp\u003eGiven their threshold characteristics and limited inclusivity, digital health technologies produce not only digital dividends but also new forms of technophobia. Technophobia denotes the irrational fear, anxiety, and deliberate avoidance that individuals experience when confronted with emerging digital health technologies, such as wearable devices [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Unlike anxiety defined within clinical psychology, technophobia constitutes a form of social competence anxiety embedded within the broader context of population aging [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It is an inevitable byproduct of digital health development and remains inherently intertwined with it [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Empirical evidence indicates that older adults are particularly susceptible to technophobia, which not only limits their access to the benefits of digital health technologies but also constrains their health-promoting behaviors and capacity to manage health risks [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Addressing technophobia among older adults has therefore become imperative for ensuring the inclusive and equitable development of digital health.\u003c/p\u003e\u003cp\u003eFrom a psychological perspective, behavior and cognition interact dynamically. Behavior not only externalizes psychological states but also shapes cognitive processes and emotional experiences in return [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This principle provides an emphasis framework for alleviating technophobia among rural older adults. Health self-care behaviors\u0026mdash;actions undertaken through active participation and self-management to maintain health, treat illness, and promote recovery [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u0026mdash;align with the \u0026ldquo;proactive health\u0026rdquo; concept emphasized in the Healthy China 2030 Plan [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These behaviors constitute a key form of self-directed health maintenance for older adults, encompassing activities such as health belief formation, health knowledge\u0026ndash;seeking, mutual health support, health resource utilization, and health maintenance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As a cost-effective, efficient, and sustainable strategy for health promotion, health self-care behaviors not only strengthen self-care management among older adults but also support the global objective of healthy aging, representing a crucial preventive capacity for sustainable health development [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Empirical research by Wan Yuhan et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] identified a significant negative correlation between health self-care behaviors and technophobia among community-dwelling older adults in China. This finding suggests that insufficient engagement in proactive health management through digital health technologies may exacerbate technophobia in this population.\u003c/p\u003e\u003cp\u003eHowever, existing research has primarily examined the macro-level relationship between health self-care behaviors and technophobia, while neglecting the micro-level connections between specific behavioral components and technophobia dimensions. Furthermore, the heterogeneous patterns of technophobia among rural older adults, shaped by unique sociocultural contexts, remain unclassified. To address these gaps, this study integrates network analysis (variable-centered) and latent profile analysis (person-centered) to elucidate this complex interplay at both structural and individual levels. The specific aims are to: (i) identify technophobia subgroups via latent profile analysis; (ii) examine structural associations using network analysis to uncover psychosocial and behavioral mechanisms; and (iii) explore the differential roles of health self-care behaviors across subgroups, thereby informing precision interventions for digital inclusion and healthy aging.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted between August and October 2025 in Huzhou City, Northern of Zhejiang Province, China. Participants were eligible if they met the following inclusion criteria: (i) aged 60 years or older; (ii) held rural household registration and had resided in rural areas for at least one year; and (iii) possessed clear consciousness and adequate verbal communication ability to engage effectively in the survey. Exclusion criteria included: (i) severe physical illness that significantly limited activities of daily living; (ii) cognitive impairment identified through a brief mental status examination or a documented history of psychiatric disorders; and (iii) severe visual, auditory, or speech impairments that prevented completion of the survey or effective participation in interviews.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample Size\u003c/h3\u003e\n\u003cp\u003eUsing the sample size formula for cross-sectional studies, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{n=}{\\left(\\frac{{\\text{u}}_{\\text{\u0026alpha;}/2}\\text{\u0026sigma;}}{\\text{\u0026delta;}}\\right)}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the parameters were determined based on data reported in previous studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], with α\u0026thinsp;=\u0026thinsp;0.05, σ\u0026thinsp;=\u0026thinsp;18.09, and δ\u0026thinsp;=\u0026thinsp;1.6. Substituting these values into the formula yielded an estimated sample size of approximately 421 participants. To account for an anticipated 20% rate of invalid or incomplete responses, the final target sample size was set at 466 participants.\u003c/p\u003e\n\u003ch3\u003eSampling and Recruitment Procedures\u003c/h3\u003e\n\u003cp\u003eParticipants were recruited via convenience sampling from eight rural townships in Huzhou City. Within each township, three to four villages were selected as recruitment sites. In collaboration with community organizations, nursing homes, and village committees, the research team disseminated information about the study through posters and community announcements. Approximately 10 to 15 older adults were enrolled from each village, yielding 466 valid responses. To ensure standardized and ethical data collection, all field staff received training in recruitment procedures and participant communication. Before the survey, researchers explained the study\u0026rsquo;s purpose and benefits, obtained written informed consent, and confirmed participants\u0026rsquo; full understanding.\u003c/p\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eGeneral information questionnaire\u003c/h2\u003e\u003cp\u003eA self-designed general information questionnaire was used to assess participants' demographic and socioeconomic characteristics, including gender, age, education, marital status, religious belief, number of children, chronic diseases, and monthly income. (see Supplementary File S1 for the complete version).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eTechnophobia Scale\u003c/h2\u003e\u003cp\u003eThe Chinese version of the Technophobia Scale, translated by Sun Erhong was adopted [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It includes 13 items across three dimensions\u0026mdash;technology tension, technology fear, and privacy and security concerns\u0026mdash;rated on a 5-point Likert scale from \u0026ldquo;completely inconsistent\u0026rdquo; to \u0026ldquo;completely consistent,\u0026rdquo; yielding a potential total score range of 13\u0026ndash;65. Higher scores reflect more severe technophobia. The scale showed high internal consistency (Cronbach's α\u0026thinsp;=\u0026thinsp;0.759\u0026ndash;0.911) and content validity (all indices\u0026thinsp;\u0026gt;\u0026thinsp;0.80).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHealth Self-care Behavior Scale\u003c/h3\u003e\n\u003cp\u003eThe Health Self-Care Behavior Scale by Chen Lin [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was employed. This 26-item tool covers five domains and uses a 5-point Likert response format (ranging from 1 to 5), providing a total score out of 130 where higher values reflect more advanced self-care behavior. It exhibits strong reliability (α\u0026thinsp;=\u0026thinsp;0.923) and has been extensively applied with good validity in health behavior studies.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using SPSS 26.0. Continuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), and categorical variables as frequency (N) and percentage (%). Latent profile analysis (LPA) was conducted in Mplus 8.3 to identify technophobia subgroups among rural older adults. Model fit was evaluated using: (1) Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and sample-size adjusted BIC (SABIC), with lower values indicating better fit[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; (2) entropy, representing classification accuracy, where values closer to 1 indicate higher accuracy (\u0026ge;\u0026thinsp;0.8 acceptable; \u0026ge; 0.9 ideal) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; and (3) the Lo\u0026ndash;Mendell\u0026ndash;Rubin adjusted likelihood ratio test (LMRT) and bootstrap-based likelihood ratio test (BLRT), with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating that the K-class model fits significantly better than the (K\u0026thinsp;\u0026minus;\u0026thinsp;1)-class model [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNetwork analysis was conducted in R (version 4.4.1) utilizing the qgraph (v1.9.8) and bootnet (v1.6) packages. The analytical procedure comprised the following steps: (1) Network Estimation: The network was estimated with the EBICglasso (Extended Bayesian Information Criterion Graphical LASSO) method. This technique regularizes the partial correlation matrix via the Graphical LASSO algorithm, prunes spurious edges, and yields a sparse, interpretable network structure. (2) Centrality Estimation: Node strength\u0026mdash;the sum of the absolute edge weights connecting a node to all others\u0026mdash;was calculated as the primary centrality metric. Strength centrality is considered the most stable and was used to identify core symptoms within the network [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. (3) Network Accuracy and Stability: Edge-weight accuracy was evaluated via nonparametric bootstrapping with 95% confidence intervals; narrower intervals indicate greater precision [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The stability of node strength was assessed using the Correlation Stability Coefficient (CS-C), where values above 0.25 indicate acceptable robustness [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. (4) Bridge Symptoms: Bridge symptoms were defined by a Bridge Expected Influence (BEI) index exceeding 1 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The stability of these bridge estimates was further verified using case-dropping bootstrap procedures within the bootnet package.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eParticipant characteristics\u003c/h2\u003e\u003cp\u003eA total of 466 participants were included in the study, with detailed characteristics shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants ranged in age from 60 to 89 years, with a mean age of 70.94 years (SD\u0026thinsp;=\u0026thinsp;7.21). Most participants were female (68.2%) and married (91.0%). The mean total Technophobia Scale score was 39.74 (SD\u0026thinsp;=\u0026thinsp;5.34), with an average item score of 3.06 (SD\u0026thinsp;=\u0026thinsp;0.41). Among the three subscales, privacy and security concerns had the highest mean item score (3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54), followed by technology tension (2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64) and technology fear (2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeneral characteristics of participants (n\u0026thinsp;=\u0026thinsp;466)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN (%) or M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003cp\u003e(Average Score per Item)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (60\u0026ndash;69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e215 (46.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (70\u0026ndash;79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e200 (42.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (\u0026ge;\u0026thinsp;80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51 (10.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e148 (31.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e318 (68.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61 (13.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e224 (48.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJunior high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e112 (24.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSenior high school and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (1.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e424 (91.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReligious belief\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e404 (86.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of children\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e140 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e208 (44.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic diseases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175 (37.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e199 (42.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81 (17.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonthly income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow (\u0026lt;1000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e203 (43.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle (1000\u0026ndash;3000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e219 (47.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (\u0026gt;3000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTechnophobia Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.74\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34 (3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrivacy and Security Concerns Dimension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63 (3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTechnology Tension Dimension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20 (2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTechnology Fear Dimension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84 (2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLatent Profile Analysis of Technophobia among Rural Older Adults\u003c/h2\u003e\u003cp\u003eUsing item scores from the Technophobia Scale administered to rural older adults as observed indicators, latent profile models with one to six profiles were estimated. Model fit indices are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As the number of profiles increased, AIC, BIC, and ABIC values declined steadily. In the five-profile solution, both LMRT and BLRT were significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the LMRT for the six-profile model was not (P\u0026thinsp;=\u0026thinsp;0.186), indicating that adding more profiles did not further improve model fit. The entropy of the four-profile model (0.997) was also slightly lower than that of the five-profile model (0.999), further supporting the superiority of the five-profile solution. Overall, these findings indicate that the five-profile model offers the best fit.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFitting statistics for a latent profile model of technophobia among rural older adults (n\u0026thinsp;=\u0026thinsp;466)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLoglikelihood\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSABIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEntropy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBLRT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLMRT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eProfile probability(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1-profile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6211.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12475.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12582.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12500.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-profile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5138.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10356.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10522.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10395.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.4206\\0.57940\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3-profile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2742.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5592.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5816.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5644.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.8972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.20172\\0.20386\\ 0.59442\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4-profile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2313.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4763.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5045.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4829.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.20172\\0.34335\\0.25107\\0.20386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5-profile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-2025.109\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4214.218\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4554.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e4293.792\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.999\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.0031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.25107\\0.20172\\0.34335\\0.12232\\ 0.08155\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6-profile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1659.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3510.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3907.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3603.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.3284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.08798\\0.25107\\0.34335\\0.11373\\0.08155\\0.12232\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: N/A: not applicable;AIC: Akaike Information Criterion, BIC: Bayesian Information Criterion, SABIC: Sample-size Adjusted Bayesian Information Criterion, LMRT: Lo-Mendell\u0026ndash;Rubin\u0026rsquo;s adjusted Likelihood Ratio Test, BLRT༚Bootstrap Likelihood Ratio Test. The 5-profile model (bold) was selected as the optimal model.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the distribution of technophobia identified in the final five-profile model. All subgroups displayed relatively high scores on privacy and security concern items and were therefore collectively categorized as the \u0026ldquo;high privacy\u0026ndash;security\u0026rdquo; group. Subgroup 1 showed moderate technology tension and low technology fear (\u0026ldquo;moderate tension\u0026ndash;low fear,\u0026rdquo; n\u0026thinsp;=\u0026thinsp;117, 25.11%). Subgroup 2 had the lowest tension but moderate fear (\u0026ldquo;low tension\u0026ndash;moderate fear,\u0026rdquo; n\u0026thinsp;=\u0026thinsp;94, 20.17%). Subgroup 3 displayed moderate levels on both dimensions (\u0026ldquo;moderate tension\u0026ndash;fear,\u0026rdquo; n\u0026thinsp;=\u0026thinsp;160, 34.33%). Subgroup 4 exhibited the highest tension and moderate fear (\u0026ldquo;high tension\u0026ndash;moderate fear,\u0026rdquo; n\u0026thinsp;=\u0026thinsp;57, 12.23%). Subgroup 5 showed relatively high tension but the lowest fear (\u0026ldquo;high tension\u0026ndash;low fear,\u0026rdquo; n\u0026thinsp;=\u0026thinsp;38, 8.15%). Detailed mean scores for each dimension across subgroups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive analysis of technophobia scale scores for five latent profiles (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, n\u0026thinsp;=\u0026thinsp;466)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfile1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProfile2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProfile3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProfile4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProfile5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eA1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.495\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eA2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c4\"\u003e\u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.539\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eB1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c6\"\u003e\u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eB3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.226\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eB4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eB5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.431\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.431\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.431\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.520\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.53\u0026thinsp;\u0026plusmn;\u0026thinsp;3.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.608\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eNetwork Analysis Characteristics of Technophobia and Health Self-Care Behaviors\u003c/h2\u003e\u003cp\u003eThe network structure illustrating the associations between technophobia and health self-care behaviors is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the overall network, 27 nodes and 72 non-zero edges (20.51%) were identified, with an average edge weight of 0.128. Among the subgroup networks, the \u0026ldquo;moderate tension\u0026ndash;low fear\u0026rdquo; group (average edge weight\u0026thinsp;=\u0026thinsp;0.137) comprised 46 non-zero edges (13.11%); the \u0026ldquo;low tension\u0026ndash;moderate fear\u0026rdquo; group (average edge weight\u0026thinsp;=\u0026thinsp;0.186) comprised 17 non-zero edges (4.84%); the \u0026ldquo;moderate tension\u0026ndash;fear\u0026rdquo; group (average edge weight\u0026thinsp;=\u0026thinsp;0.161) comprised 51 non-zero edges (14.53%); the \u0026ldquo;high tension\u0026ndash;moderate fear\u0026rdquo; group (average edge weight\u0026thinsp;=\u0026thinsp;0.136) comprised 14 non-zero edges (3.99%); the \u0026ldquo;high tension\u0026ndash;low fear\u0026rdquo; group (average edge weight\u0026thinsp;=\u0026thinsp;0.160) comprised 15 non-zero edges (4.27%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the overall network, the strongest edge was between D4 (\u003cem\u003escientific exercise\u003c/em\u003e) and TA (\u003cem\u003etechnophobia\u003c/em\u003e), followed by A2 \u003cem\u003e(health risk\u003c/em\u003e) and E1 (\u003cem\u003erational medication\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Connectivity patterns, however, varied across groups. In the \u0026ldquo;moderate tension\u0026ndash;low fear\u0026rdquo; group, all nodes were weakly linked to TA1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In contrast, the \u0026ldquo;low tension\u0026ndash;moderate fear\u0026rdquo; group showed the strongest connections between TA2 and D7 (\u003cem\u003eacceptance\u003c/em\u003e) as well as A3 (\u003cem\u003emeaning in life\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), while in the \u0026ldquo;moderate tension\u0026ndash;fear\u0026rdquo; group, TA3 was most strongly linked to D12 (\u003cem\u003einterpersonal ties\u003c/em\u003e) and B1 (\u003cem\u003emedia health information\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Figure\u0026nbsp;3a1 displays node strength centrality in the overall network, with D7 (\u003cem\u003eacceptance\u003c/em\u003e), A2 (\u003cem\u003ehealth risk\u003c/em\u003e), and E1 (\u003cem\u003erational medication\u003c/em\u003e) emerging as the most central nodes. In the \u0026ldquo;moderate tension\u0026ndash;low fear\u0026rdquo; group (Fig.\u0026nbsp;3b1), B2 \u003cem\u003e(health engagement\u003c/em\u003e) had the highest strength, followed by D13 (\u003cem\u003egroup activity\u003c/em\u003e) and E2 \u003cem\u003e(timely care\u003c/em\u003e). In the \u0026ldquo;low tension\u0026ndash;moderate fear\u0026rdquo; group (Fig.\u0026nbsp;3c1), B2 (\u003cem\u003ehealth engagement\u003c/em\u003e), E2 (\u003cem\u003etimely care\u003c/em\u003e), and A3 (\u003cem\u003emeaning in life\u003c/em\u003e) ranked highest, whereas in the \u0026ldquo;moderate tension\u0026ndash;fear\u0026rdquo; group (Fig.\u0026nbsp;3d1), D13 (\u003cem\u003egroup activity\u003c/em\u003e) showed the greatest strength, followed by E1 (\u003cem\u003erational medication\u003c/em\u003e) and D7 (\u003cem\u003eacceptance\u003c/em\u003e). Both the \u0026ldquo;high tension\u0026ndash;moderate fear\u0026rdquo; and \u0026ldquo;high tension\u0026ndash;low fear\u0026rdquo; groups exhibited a global strength of zero, indicating no meaningful associations among variables; hence, centrality or community analyses were not conducted. Edge weights in the overall and three analyzable group networks had narrow 95% confidence intervals (Fig.\u0026nbsp;3a2\u0026ndash;d2), indicating high estimation precision. Correlation stability coefficients (CS-C) for all networks exceeded 0.25 (Fig.\u0026nbsp;3a3\u0026ndash;d3), confirming satisfactory network stability and reliability. (see Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eTo further clarify the cross-domain associations between technophobia and health self-care behaviors, a bridge symptom analysis was conducted using item-level networks derived from the Technophobia Scale and the Health Self-Care Behavior Scale, rather than aggregate scores. Significant bridge symptoms, indicated by elevated Bridge Expected Influence (BEI), were identified in three subgroups: \u0026ldquo;moderate tension\u0026ndash;low fear,\u0026rdquo; \u0026ldquo;low tension\u0026ndash;moderate fear,\u0026rdquo; and \u0026ldquo;moderate tension\u0026ndash;fear.\u0026rdquo; In the \u0026ldquo;moderate tension\u0026ndash;low fear\u0026rdquo; group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee), TAB2 (\u003cem\u003eavoiding device change\u003c/em\u003e) and HBB1 (\u003cem\u003emedia health information\u003c/em\u003e) exhibited the highest BEI values, followed by TAB1 (\u003cem\u003ediscomfort with new devices\u003c/em\u003e) and HBA2 (\u003cem\u003ehealth risk\u003c/em\u003e), which also showed notable bridging effects. In the \u0026ldquo;low tension\u0026ndash;moderate fear\u0026rdquo; group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), TAC5 (\u003cem\u003efear of technology failure\u003c/em\u003e) and HBD7 (\u003cem\u003eacceptance\u003c/em\u003e) demonstrated the strongest bridge influence. For the \u0026ldquo;moderate tension\u0026ndash;fear\u0026rdquo; group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg), the leading bridge symptoms were HBB3 (\u003cem\u003eonline health information\u003c/em\u003e), HBB1 (\u003cem\u003emedia health information\u003c/em\u003e), and HBB2 (\u003cem\u003ehealth engagement\u003c/em\u003e), all originating from the Health Self-Care Behavior Scale. The 95% confidence intervals for bridge strength in each subgroup did not include zero (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee1\u0026ndash;g1), confirming that these bridging symptoms were statistically robust and represented stable interconnections between constructs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy combining latent profile and network analyses, this study mapped heterogeneous technophobia patterns and their structural relationships with health self-care behaviors among rural older adults. The results justify a shift toward precision-oriented interventions, paving the way for evidence-based strategies to mitigate technophobia in aging populations.\u003c/p\u003e\u003cp\u003eAmong rural Chinese older adults, technophobia was moderately high [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], with privacy concerns being the most prominent dimension and a key barrier to digital inclusion [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Latent profile analysis revealed five distinct subgroups, indicating a multidimensional heterogeneity driven by cognitive, emotional, and behavioral synergy [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This finding contrasts with the three-category (low technophobia type, technology-fearful type, and technology-anxious type) typology found in urban peers [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], likely due to rural-specific contextual factors [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], highlighting the necessity for tailored interventions.\u003c/p\u003e\u003cp\u003eAcross the five technophobia subgroups identified among rural older adults, privacy and security concerns were consistently high, while technological tension and fear varied substantially. The largest subgroup, characterized by moderate tension and fear, exhibited aligned cognitive, emotional, and behavioral dimensions, representing the primary high-anxiety profile [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Other subgroups displayed distinct patterns: moderate tension with low fear indicated cognitive vigilance but mild emotional apprehension [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; low tension with moderate fear reflected emotional detachment and cognitive avoidance [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]; high tension with moderate fear showed pervasive anxiety across all dimensions; and high tension with low fear suggested a conflict between external pressure and low internal arousal [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Intervention efforts should prioritize the moderate tension\u0026ndash;fear and high tension\u0026ndash;moderate fear subgroups, as both experience significant cross-domain distress and are particularly susceptible to digital avoidance. For the moderate tension\u0026ndash;low fear subgroup, strengthening privacy awareness and perceived security may help consolidate their readiness to use digital health tools.\u003c/p\u003e\u003cp\u003eInterestingly, the overall network analysis revealed that technophobia and health self-care behaviors were primarily linked through three dimensions: Health Belief Formation (A), Health Maintenance Behavior (D), and Medical Resource Utilization (E). Among these, A2 (\u003cem\u003ehealth risk\u003c/em\u003e) and E1 (\u003cem\u003erational medication\u003c/em\u003e) emerged as pivotal nodes with high edge weights and centrality, indicating their core roles in the interaction between technophobia and self-care. This suggests that health risk perception and rational medication management are central mechanisms in this relationship. According to the Health Belief Model [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], heightened perceptions of health risk may amplify uncertainty regarding digital health technologies, thereby intensifying technophobia. Concurrently, limited self-efficacy and perceived barriers in medication management may further exacerbate this response. Within the health maintenance dimension, the strong edge weight of D4 (\u003cem\u003escientific exercise\u003c/em\u003e) reflects technophobia's direct impact on behavioral choices, while the high centrality of D7 (\u003cem\u003eacceptance\u003c/em\u003e) indicates its potential role in mitigating technophobia through emotion-focused coping, consistent with the stress-coping model. This model posits that positive, emotion-focused strategies can buffer anxiety responses [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSurprisingly, subgroup network comparisons revealed distinct edge-weight patterns. Subgroup 1 (\u0026ldquo;moderate tension\u0026ndash;low fear\u0026rdquo;) showed no strong edges, indicating that tension alone does not stably couple technophobia with health behaviors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In Subgroup 2 (\u0026ldquo;low tension\u0026ndash;moderate fear\u0026rdquo;), strong edges persisted within Health Belief Formation (A), but the dominant node shifted from A2 (\u003cem\u003ehealth risk\u003c/em\u003e) to A3 (\u003cem\u003emeaning in life\u003c/em\u003e), alongside D7 (\u003cem\u003eacceptance\u003c/em\u003e) in Health Maintenance Behavior (D), forming an \u0026ldquo;acceptance\u0026ndash;meaning\u0026rdquo; pathway for cognitive reappraisal [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Subgroup 3 (\u0026ldquo;moderate tension\u0026ndash;fear\u0026rdquo;) diverged further, with strong edges at D12 (\u003cem\u003einterpersonal ties\u003c/em\u003e) and B1 (\u003cem\u003emedia health information\u003c/em\u003e)\u0026mdash;the latter being the first appearance of a node from Health Belief Formation Behavior dimension (B) - suggesting compensatory reliance on social and informational support [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Critically, Health Maintenance Behavior showed strong edges in all subgroups except Subgroup 1, underscoring its central role. Interventions should thus prioritize this dimension: for Subgroup 2, through mindfulness and meaning-making to enhance acceptance; for Subgroup 3, by strengthening social support and information literacy to build trust. These pathways collectively promote adaptive behaviors that alleviate technophobia.\u003c/p\u003e\u003cp\u003eBeyond subgroup-specific edge structures, core node distributions showed both consistency and divergence. Medical Resource Btilization Behavior (E) remained highly central across all networks, functioning as a pivotal hub that integrates psychological and behavioral processes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Specifically, individuals with higher technophobia (e.g., Subgroup 3) emphasized E1 (\u003cem\u003erational medication\u003c/em\u003e), reflecting a \"self-control\" orientation, whereas those with lower technophobia (e.g., Subgroups 1\u0026ndash;2) focused more on E2 (\u003cem\u003etimely care\u003c/em\u003e), indicating \"external dependence\" [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This aligns with evidence that older adults prefer self-directed medication management when using home-based health technologies [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] but rely more on external systems for care-service technologies [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, Health Maintenance Behavior (D) also showed high centrality in Subgroups 1 and 3, though with distinct patterns. Subgroup 1 centered on D13 (\u003cem\u003egroup activity\u003c/em\u003e), reflecting adherence to routine health practices, while Subgroup 3 involved both D13 (\u003cem\u003egroup activity\u003c/em\u003e) and D7 (\u003cem\u003eacceptance\u003c/em\u003e), suggesting the use of acceptance-oriented strategies to buffer technophobia [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This aligns with evidence on the role of psychological flexibility in mitigating technology-related stress [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In Subgroup 2, the core node shifted from A2 (\u003cem\u003ehealth risk\u003c/em\u003e) to A3 (\u003cem\u003emeaning in life\u003c/em\u003e), indicating a reliance on existential purpose to maintain psychological balance\u0026mdash;a finding consistent with international studies on resilience in digital health adaptation [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Additionally, both Subgroups 1 and 2 featured B2 (\u003cem\u003ehealth engagement\u003c/em\u003e), suggesting compensatory reliance on social networks in the absence of digital skills. This reflects the \"acquaintance culture\" of rural communities and underscores the importance of social support in bridging the digital health divide [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Together, these patterns reveal multifaceted interaction pathways between technophobia and health self-care behaviors, supporting the design of tiered, behaviorally-informed interventions.\u003c/p\u003e\u003cp\u003eWhile core symptom analysis identified key influence pathways, it offered limited insight into cross-community connectivity. To elucidate the bridging mechanisms between technophobia and health self-care behaviors, we conducted a bridge symptom analysis [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In the moderate tension\u0026ndash;low fear group, TAB2 (\u003cem\u003eavoiding device change\u003c/em\u003e) and HBB1 (\u003cem\u003emedia health information\u003c/em\u003e) served as the most stable bridge nodes, indicating resistance to device updates and reliance on traditional information channels [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In the low tension\u0026ndash;moderate fear group, TAC5 (\u003cem\u003efear of technology failure\u003c/em\u003e) and HBD7 (\u003cem\u003eacceptance\u003c/em\u003e) were key bridges, highlighting fears of technological dependency and control loss [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The moderate tension\u0026ndash;fear group showed strong bridging effects for HBB nodes (\u003cem\u003eonline and media health information, health engagement\u003c/em\u003e), suggesting central challenges in health information acquisition amid cognitive overload [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. These subgroup-specific bridge symptoms reveal distinct interaction pathways, offering precise targets for behavior-based interventions.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLimation\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, the limited sample size precluded robust estimation of four- and five-profile models, warranting future replication with larger samples. Second, the reliance on self-reported data may introduce recall bias, suggesting a need for qualitative or mixed-method approaches. Third, the cross-sectional design prohibits causal inference; longitudinal studies are needed to clarify dynamic relationships. Finally, the recruitment of participants solely from Huzhou City, Zhejiang Province, may limit generalizability, highlighting the value of future multi-center studies.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies five latent technophobia subgroups(moderate tension\u0026ndash;low fear, low tension\u0026ndash;moderate fear, moderate tension\u0026ndash;fear, high tension\u0026ndash;moderate fear, and high tension\u0026ndash;low fear ) among rural Chinese older adults, pinpointing distinct network linkages with health self-care behaviors. These findings advance understanding of the psychological\u0026ndash;behavioral mechanisms underlying technophobia and provide a theoretical basis for developing precise, tiered interventions. Enhancing health self-care behaviors may serve as an effective pathway to mitigate technophobia in rural older adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the authors for their contributions to the content and data processing of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003econtributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy conception and design: YS, XS and SL. Data collection and evaluation:YB, XS, and SL. Data extraction and analysis: YS, and SL. Manuscript draft: CR, MS, XY, GG, and SB. Critical revision of important intellectual content: SL, XS and YS. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 72204084), the Key Research And Development Program Of Zhejiang Province (2025C02106)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Committee of Huzhou University ( NO. 202505-6). The trial was registered with the Chinese Clinical Trial Registry (ChiCTR2500111399). Written informed consent was obtained from all participants prior to their enrollment in the study. The consent process involved detailing the study\u0026apos;s purpose, procedures, potential risks and benefits, and the participants\u0026apos; rights, including the right to withdraw at any time without penalty. All participant data were handled with strict confidentiality, used solely for research purposes, and securely disposed of upon study completion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no declarations of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNational Bureau of Statistics of China . (2024). 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How older adults manage misinformation and information overload - A qualitative study. BMC Public Health, 24(1), 871. https://doi.org/10.1186/s12889-024-18335-x\u003c/li\u003e\n\u003cli\u003eShao, Y., Yang, X., Chen, Q., et al. (2025). Determinants of digital health literacy among older adult patients with chronic diseases: a qualitative study. Frontiers in Public Health, 13, 1568043. https://doi.org/10.3389/fpubh.2025.1568043.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"technophobia, health self-care behavior, rural older adults, latent profile analysis, network analysis","lastPublishedDoi":"10.21203/rs.3.rs-8067377/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8067377/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThis study aimed to identify heterogeneous latent profiles of technophobia among rural older adults and to explore the network structure linking technophobia and health self-care behaviors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eFrom August to October 2025, a total of 466 participants were recruited. Latent profile analysis was conducted using the Technophobia Scale, and the optimal model was determined based on fit indices and likelihood ratio tests. Network analysis was then employed to construct both overall and subgroup networks of technophobia and health self-care behaviors, identifying core and bridge symptoms. Network accuracy and stability were assessed through bootstrap procedures.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFive subgroups were identified: \u0026ldquo;moderate tension\u0026ndash;low fear,\u0026rdquo; \u0026ldquo;low tension\u0026ndash;moderate fear,\u0026rdquo; \u0026ldquo;moderate tension\u0026ndash;fear,\u0026rdquo; \u0026ldquo;high tension\u0026ndash;moderate fear,\u0026rdquo; and \u0026ldquo;high tension\u0026ndash;low fear.\u0026rdquo; Core symptoms were primarily distributed across three dimensions: health maintenance behavior, health belief formation behavior, and medical resource utilization behavior. Bridge symptoms varied across subgroups: HBB1 (\u003cem\u003emedia health information\u003c/em\u003e) in the \u0026ldquo;moderate tension\u0026ndash;low fear\u0026rdquo; group; HBD7 (\u003cem\u003eacceptance\u003c/em\u003e) in the \u0026ldquo;low tension\u0026ndash;moderate fear\u0026rdquo; group; and HBB3 (\u003cem\u003eonline health information\u003c/em\u003e) in the \u0026ldquo;moderate tension\u0026ndash; fear\u0026rdquo; group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eTechnophobia among rural older adults exhibits distinct heterogeneity and forms specific network structures with health self-care behaviors. These findings provide a theoretical foundation for precision, stratified behavioral intervention strategies targeting diverse technophobia profiles in rural elderly populations.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003eRegistered in the Chinese Clinical Trial Registry on 10/30/2025(ChiCTR2500111399)\u003c/p\u003e","manuscriptTitle":"Latent Profile Analysis of Technophobia and Its Network Associations with Health Self-Care Behaviors among Older Adults in Rural China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 20:19:22","doi":"10.21203/rs.3.rs-8067377/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-02T07:37:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-08T10:04:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-27T07:47:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318200116472543580719902051897061979016","date":"2025-12-25T15:01:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96262961008297527649796826067158511254","date":"2025-12-18T05:20:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-24T01:14:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T01:12:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-19T15:49:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-19T10:39:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-11-19T10:34:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a07ec31e-51d2-4885-86f8-1bdd17d2a093","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-21T06:41:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 20:19:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8067377","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8067377","identity":"rs-8067377","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}