Abstract
Background
Technological anxiety has a multidimensional negative effect on the medical process of elderly patients with chronic co-morbidities, which is mainly reflected in the increase of cognitive load and the decrease of doctor-patient interaction efficiency. The challenge of how elderly patients can more effectively use digital health technologies is a critical issue that both healthcare providers and patients must address. We aimed to explore technology anxiety in elderly patients with chronic co-morbidities by identifying and characterising distinct technology anxiety profiles using latent profile analysis.
Methods
This study explored the relationship between the Technology Anxiety Scale and the Self-perceived Burden Scale, as well as the different subgroups of medication literacy, using latent profile analysis. The study involved 611 elderly patients with chronic diseases. The research tools used were the Medication Literacy Scale for Elderly Patients with Chronic Diseases, the Self-perceived Burden Scale, and the Technology Anxiety Scale.
Results
Latent profile analysis revealed four distinct technology anxiety groups of elderly patients with comorbid chronic diseases: low technology anxiety type with 212 cases (34.70%), high technology anxiety type with 81 cases (13.26%), medium technology anxiety low privacy security type with 148 cases (24.22%), and medium technology anxiety high privacy security type with 170 cases (27.82%).The results of multivariate logistic regression analyses indicated that self-Perceived burden, medication literacy, personal monthly income, medical insurance type, daily exercise duration, and times of hospitalizations are significant factors influencing technology anxiety among elderly patients with comorbid chronic diseases.
Conclusion
The study showed that higher scores in self-perceived burden and medication literacy, lower daily exercise duration, higher hospitalization frequency, lower personal monthly income, and self-funded medical insurance types are associated with more severe technical anxiety.It is recommended that healthcare professionals tailor interventions to address the specific vulnerabilities of each patient type, aiming to reduce technological anxiety and enhance their ability to utilize health information effectively.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-23616-0.
Keywords: Echnology anxiety, Medication literacy, Self-perceived burden, Elderly patients with chronic co-morbidities, Latent profile analysis, Public health
Introduction
In recent years, the rapid aging and changes in lifestyle have made chronic non-communicable diseases (NCDs) one of the most challenging public health issues globally [1]. While life expectancy has generally increased worldwide, the incidence of chronic diseases has also risen, leading to a continuous increase in the number of NCD patients and a growing trend of multiple diseases coexisting [2, 3]. China has now entered an era of deep aging, with the proportion of people aged 65 and over reaching 13.5% according to the seventh national population census data. As life expectancy increases and various risk factors become more prevalent, the number of chronic disease patients in China continues to rise. Moreover, the coexistence of multiple chronic diseases is becoming increasingly severe [4, 5], China’s elderly population accounts for one fifth of the world’s elderly population [6]. Multiple studies indicate that chronic diseases are a major source of global disease burden [7, 8]. Compared to single chronic diseases, the coexistence of multiple chronic diseases not only causes greater health damage but also significantly increases the complexity of disease treatment and management. Different chronic diseases are interconnected, forming complex and diverse comorbid conditions [9].
In addition, the rapid development of global medical information technology on the one hand reduces the cost of patients ‘medical treatment, on the other hand, it also increases patients’ anxiety and tension about information technology [10]. This is especially true for the elderly population, as many of them are not proficient in using smart information technology, which prevents them from enjoying the convenience brought by intelligent services. This leads to anxiety and tension during the medical treatment process and health information acquisition, a phenomenon that has attracted the attention of many scholars [11, 12]. Technology anxiety refers to individuals’ irrational emotional responses such as anxiety and fear towards digital technology, which can lead to individuality avoidance of using digital technology [13]. This type of anxiety significantly reduces patients ‘use of digital health technologies, negatively impacting the health outcomes of elderly individuals with chronic diseases [14].
Research on assessment tools for technical anxiety, Matthew L. Wilson developed the Abbreviated Technology Anxiety Scale (ATAS), which has undergone multistage validation and demonstrates robust internal consistency, reliability, and validit [15], American technology management scholar Khasawneh designed another technology anxiety scale [16], initially validated in the United States and Germany with confirmed reliability and validity [17, 18]. Sun Erhong ubsequently adapted this scale for the Chinese population [19]. The localized version was found appropriate for evaluating anxiety related to digital health technology use among older adults, with a Cronbach’s α coefficient of 0.911 for the full scale. The three subscales exhibited Cronbach’s α coefficients ranging from 0.759 to 0.885, alongside a split-half reliability coefficient of 0.85. Consequently, The scale was used to investigate 257 elderly people who visited outpatient clinics in China. The results showed that age, educational level, occupation, general self-efficacy scale score and digital health literacy were statistically significant differences, which were the influencing factors of technical anxiety [20].
Furthermore, the scale was used in a study involving 253 patients with type 2 diabetes [21], revealing that older patients with type 2 diabetes mellitus exhibit a negative correlation between their e-health literacy, self-management skills, and technology anxiety. Overall, the scale demonstrates good application effectiveness; however, Existing research have limitations, including a small sample size and a limited range of disease types among the subjects. Therefore, this study employed this scale to assess technology anxiety in elderly patients with chronic diseases.
The sociodemographic factors that affect technical anxiety include age, educational attainment [22], living arrangements, health status, frequency of electronic device use [20], self-perceived technological competence, social utilization of technology [23], and usage frequency [12]. Psychological/attitudinal factors: Self-perception of aging [24], prior technology experience, specific personality traits [25, 26], cultural context [27], general self-efficacy, and information literacy skills [28]. However, most existing studies have employed conventional variable-centered approaches (e.g., regression analysis), relying exclusively on total scale scores or subscale cutoff values to quantify technological anxiety. This methodology inadequately addresses population heterogeneity, which may be critical for accurate identification, prediction, and tailored interventions. Notably, elderly patients with chronic comorbidities exhibit varying degrees of technological anxiety.
Latent Profile Analysis (LPA) is a statistical method primarily used to explore underlying population structures.It can identify latent subpopulations with similar characteristics and needs, thus more accurately and objectively revealing partial independencies among variables [29]. Currently, there is limited research on the subtypes of technology anxiety among elderly patients with Chronic co-morbidities. The aim of this study is to analyze the level of technology anxiety based on scores from the technology anxiety scale and explore potential profile types through LPA.This provides a basis for studying subtypes of technology anxiety, enabling precise interventions for different categories of elderly patients with Chronic co-morbidities and technology anxiety, especially those with higher levels of anxiety, to reduce their technology anxiety levels.
Methods
Study design
This study used a cross-sectional survey, This cross-sectional study followed the STROBE reporting guideline [30].
Sample size
To determine the suitable sample size for this study, established guidelines for conducting sample size calculations in the context of LPA were followed [31]. The literature recommends a minimum sample size of 500 participants to ensure sufficient statistical power in LPA, add 10% of invalid samples, the minimum requirement is 550 cases. In this study, a total of 632 questionnaires were distributed, and 21 invalid questionnaires were excluded (17 with samples with exactly the same answer and 4 with contradictory responses). Consequently, a total of 611 valid questionnaires were recovered, yielding an effective recovery rate of 96.68%.
Participants
This study used a convenient sampling method, the research focused on elderly patients with Chronic co-morbidities who were hospitalized in a tertiary hospital in Shizuishan City, Ningxia Hui Autonomous Region, between January 2024 and April 2024. The inclusion criteria were as follows: patients aged 60 years or older, inpatients, and patients diagnosed with two or more diseases by the attending physician. The diseases considered for inclusion encompassed, but were not limited to, tumors, hypertension, diabetes, coronary heart disease, cerebrovascular diseases, hyperlipidemia, hyperuricemia, asthma, chronic obstructive pulmonary disease, gastroesophageal reflux, atrial fibrillation, thyroid diseases, and arthritis. Exclusion criteria comprised patients participating in other clinical trials or investigations, as well as those who temporarily withdrew due to sudden changes in their condition. Those who lacked the capability to independently complete the online questionnaire were excluded from the study. All subjects in this study agreed to participate and signed the informed consent form voluntarily.
Research tools
General information questionnaire
Through reading relevant literature and consulting experts, this study designed a general information questionnaire. It included 17 items including gender, BMI, smoking history, drinking history, education level, marital status, occupational status, personal monthly income, family location, caregiver, living style, type of medical insurance, daily exercise duration, duration of illness, Times of hospitalizations, self-rated sleep status, and age.
Medication literacy scale for elderly patients with chronic diseases
The Medication Literacy Scale for Elderly Patients with Chronic Diseases was utilized in this study [32]. This scale comprises 23 items across four dimensions: information acquisition ability, drug knowledge reserve, communication and interaction ability, and critical ability. Each item is rated using a 5-point Likert scale ((1 = strongly disagree, 5 = strongly agree). A higher score indicates better medication literacy among elderly patients with chronic diseases. The Cronbach α coefficient is reported to be 0.958, indicating good reliability. In this study, the alpha coefficient of the scale was found to be 0.956,indicating a high level of internal consistency and reliability.
Self-perceived burden scale
This study employs the Self-perceived Burden Scale [33], which is designed to assess the self-perceived burden experienced by patients with chronic diseases and consists of a total of 10 items. Each item is rated using a 5-point Likert scale (1 = never, 5 = always).The total score for the scale ranges from 0 to 50, with scores categorized as follows: 0–25 indicating a low level of burden, 26–33 indicating a medium level, and 34–50 indicating a high level. A higher score reflects a greater burden perceived by the patients. The scale demonstrates reliability with a Cronbach’s alpha coefficient of 0.85, while the Cronbach’s alpha coefficient for this study was found to be 0.897,indicating a high level of internal consistency and reliability.
Technology anxiety scale
This study employs the technology anxiety Scale [19] to assess technology-related anxiety among the elderly. The scale comprises 13 items categorized into three dimensions: technology stress, technology fear, and privacy security concerns. Each item is rated using a 5-point Likert scale (1 = completely inconsistent, 5 = completely consistent), with a higher total score reflecting a greater level of technological anxiety. The Cronbach’s alpha coefficient for the overall scale is 0.867, while in this study, the Cronbach’s alpha coefficient for this scale was found to be 0.932,indicating a high level of internal consistency and reliability.
Data collection
After obtaining consent from the head nurses of each department, the researcher articulated the purpose and significance of the study, along with key points for the respondents to consider. The questionnaire was distributed via an online platform (https://www.wjx.cn). The head nurses shared the questionnaire link and informed consent with the nurses through their WeChat communication groups, allowing the study participants to complete the questionnaire independently online using electronic devices. In the questionnaire design, each question was marked as required, and reminders were implemented for any missing answers. Following the collection of the questionnaires, two members of the research team meticulously reviewed the gathered information and utilized statistical software for data entry and analysis.
Statistical analysis
This study utilized MPLUS 8.3 for latent profile analysis (LPA) and SPSS 27.0 for demographic variables, analysis of variance, and logistic regression analysis. First, SPSS software was used to conduct correlation analyses to examine the mean, standard deviation, and Pearson correlation coefficient of the involved variables. Subsequently, through latent profile analysis (LPA), the factors influencing technical anxiety in elderly patients with chronic diseases were further explored. Eight factors influencing technical anxiety—perceived burden, medication literacy, personal monthly income, type of medical insurance, daily exercise duration, disease course, hospitalization frequency, and self-rated sleep status—were used as explicit indicators. The LPA model was fitted by gradually increasing the number of potential categories affecting technical anxiety, and participant categories were determined according to the analysis data. Finally, based on the results of LPA, the differences of main variables between different types were analyzed.
The evaluation metrics primarily included the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Adjusted BIC (aBIC), and Entropy, which determine the accuracy of classification. Lower values of AIC, BIC, and aBIC indicate better model fit, while the Entropy value ranges from 0 to 1; a value closer to 1 signifies more accurate model classification. The Lo-Mendell-Rubin Likelihood Ratio Test (LMRT) and Bootstrapped Likelihood Ratio Test (BLRT) were employed to compare the fit of two adjacent models, with a P-value less than 0.05 indicating that the k-class model is superior to the (k-1)-class model [34]. After determining the optimal profile model, statistical analysis was conducted using SPSS 27.0 software. The data in this study followed a normal distribution, with measurement data represented by mean ± standard deviation and count data represented by frequency. The chi-square test and multivariate logistic regression analysis were used to identify risk factors for potential technology anxiety among elderly patients with Chronic co-morbidities.
Results
Sample characteristics
In the final analysis, A total of 611 valid questionnaires were collected. Among the respondents, there were 339 males and 272 females, 51 with a bachelor’s degree or higher, 510 married, 31 unmarried, 16 divorced, and 54 widowed of the respondents, 157 were currently employed, 384 were retired, and 70 were in the public sector 219 had a history of smoking, and 232 had a history of drinking. The monthly income of 84 people is over 5000 CNY, with 497 from urban areas and 114 from rural areas 100 respondents had a disease duration of more than five years, and 60 had been hospitalized more than five times within the past year. Self-perceived burden score was 28.11 ± 8.81, medication Literacy score was 77.29 ± 22.78, and technical anxiety score was 28.85 ± 12.50, as shown in Table 1.
Table 1.
The means, standard deviations, and correlation coefcients of the main variables (n=611)
| Score range | Item parity | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 Technology anxiety | 13–52 | 2.21 ± 0.96 | 28.85 | 12.503 | 1 | |||||
| 2 Technology stress | 5–20 | 2.81 ± 1.23 | 11.23 | 4.944 | 0.953** | 1 | ||||
| 3 Technology fear | 5–20 | 1.84 ± 0.84 | 11.05 | 5.045 | 0.974** | 0.878** | 1 | |||
| 4 Privacy security concerns | 3–12 | 2.19 ± 1.01 | 6.58 | 3.052 | 0.943** | 0.831** | 0.917** | 1 | ||
| 5 Self-Perceived Burden | 14–46 | 2.81 ± 0.88 | 28.11 | 8.818 | 0.550** | 0.560** | 0.512** | 0.501** | 1 | |
| 6 Medication Literacy | 23–115 | 3.36 ± 0.99 | 77.29 | 22.787 | 0.251** | 0.222** | 0.239** | 0.273** | 0.184** | 1 |
M Mean, SD Standard Deviation
*P<0.05 **P<0.01 ***P<0.001
Correlation analysis of the variables
The score range, item parity and correlation coefficients of the main variables in this study are presented in Table 1. Among these, technology anxiety, technology stress, technology fear, privacy security concerns, self-Perceived Burden, and medication Literacy are correlated with each other in a pairwise manner.
Latent profile analysis
Using the three dimensions of technology anxiety: technology stress, technology fear, and privacy and security concerns as explicit variables, we gradually increased the number of latent models from 1 to 5.When retaining four categories, the values of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted Bayesian Information Criterion (aBIC) were all relatively low, The entropy value of high. This indicates that the model exhibits a high classification accuracy. Furthermore, with both the Lo-Mendell-Rubin Likelihood Ratio Test (LMR) and the Bootstrap Likelihood Ratio Test (BLRT) yielding significant results (P < 0.001).Consequently, we retained four categories as shown in Table 2. The data in Table 2 indicates that, during the testing process from 2 to 5 categories, the 5-category model outperforms the 4-category model compared to the information criterion. However, in the comparison test between the 5-category and 4-category models, from the perspective of class probability, the lowest proportion for the 5-category model is 0.019 (611 * 0.019 ≈ 12), which does not meet the minimum sample size requirement of 50 for the profile analysis grouping. From the perspective of the entropy index, the 3-category model is lower than the 4-category model, Considering all factors, the 4-category model was ultimately selected as the optimal model. The probabilities of correct classification for each category are 99.2%, 98.5%, 95.8%, and 95.5%, The results validate the reliability of the classification and latent profile analysis, as shown in Table 3.The response characteristics of different latent categories in each item, as shown in Fig. 1.
Table 2.
Fit indices for five models using latent profile analysis (n = 611)
| Profile | AIC | BIC | aBIC | Entropy | P | Class probability | |
|---|---|---|---|---|---|---|---|
| LMR | BLRT | ||||||
| 1 | 10504.820 | 10531.310 | 10512.262 | —— | —— | —— | 1<0.001 |
| 2 | 9233.908 | 9278.059 | 9246.311 | 0.917 | <0.001 | <0.001 | 0.52700/0.47300 |
| 3 | 8692.390 | 8754.201 | 8709.754 | 0.931 | <0.001 | <0.001 | 0.37480/0.15385/0.47136 |
| 4 | 8140.707 | 8220.179 | 8163.033 | 0.953 | 0.0452 | <0.001 | 0.34697/0.13257/0.24223/0.27823 |
| 5 | 8044.410 | 8141.542 | 8071.697 | 0.956 | 0.0048 | <0.001 | 0.01964/0.23241/0.32897/0.12602/0.29296 |
The selected models AIC, BIC and ABIC are the information evaluation indexes; p−LMR and p−BLRT are the model fit test indexes
Table 3.
Probability of correct classification for each category (n=611)
| Profile | C1 | C2 | C3 | C4 |
|---|---|---|---|---|
| C1 | 0.992 | <0.001 | <0.001 | <0.001 |
| C2 | <0.001 | 0.985 | <0.001 | 0.015 |
| C3 | 0.018 | <0.001 | 0.958 | 0.024 |
| C4 | <0.001 | 0.010 | 0.035 | 0.955 |
C1 low technology anxiety type, C2 high technology anxiety type, C3 medium technology anxiety low privacy security type, and C4 medium technology anxiety high privacy security type
Fig. 1.
The response characteristics of diferent latent categories in each item
Univariate analysis of potential categories of technical anxiety characteristics
The four potential categories of technical anxiety characteristics among elderly patients with chronic diseases were compared in terms of personal monthly income, medical insurance type, daily exercise duration, disease course, number of hospitalizations, self-rated sleep status, self-perceived burden, and medication literacy scores, showing statistically significant differences (P < 0.05), as shown in Table 4.
Table 4.
Comparison of baseline data among the four profiles of elderly patients with chronic co-morbidities (n = 611)
| Variable | C1 (n = 212) | C2 (n = 81) | C3 (n = 148) | C4 (n = 170) |
 /F |
p |
|---|---|---|---|---|---|---|
| Age | 64.53 ± 15.28 | 61.51 ± 14.90 | 65.51 ± 13.54 | 61.82 ± 16.05 | F = 2.379 | 0.069 |
| Gender |
 = 4.513 |
0.211 | ||||
| Male | 118 (54.08) | 43 (44.58) | 92 (62.5) | 86 (57.81) | ||
| Female | 94( 54.92) | 38 (55.42) | 56 (37.5) | 84 (41.88) | ||
| BMI |
 = 4.020 |
0.674 | ||||
| <18.5 | 25 (10.72) | 8 (15.67) | 12 (3.84) | 17 (10.47) | ||
| 18.5–24 | 90 (44.21) | 42 (38.55) | 64 (53.84) | 80 (44.50) | ||
| ≥ 24 | 97 (45.06) | 31 (45.78) | 72 (42.30) | 73 (45.02) | ||
| Smoking history |
 = 1.060 |
0.787 | ||||
| Yes | 79 (36.48) | 27 (22.89) | 56 (41.34) | 57 (37.69) | ||
| No | 133 (63.51) | 54 (77.11) | 92 (58.66) | 113 (62.30) | ||
| Drinking history |
 = 1.486 |
0.686 | ||||
| Yes | 77 (33.47) | 33 (21.69) | 61 (50.96) | 61 (43.45) | ||
| No | 135 (66.53) | 48 (78.31) | 87 (49.04) | 109 (56.54) | ||
| Degree of education |
 =11.773 |
0.464 | ||||
| Primary school | 58 (30.04) | 27 (44.58) | 43 (15.38) | 44 (25.65) | ||
| Junior high school | 63 (31.33) | 20 (20.48) | 42 (25.00) | 61 (36.64) | ||
| Vocational or junior College | 46 (20.17) | 24 (8.43) | 38 (39.42) | 37 (26.17) | ||
| Bachelor’s degree or above | 21 (5.58) | 7 (4.82) | 9 (19.23) | 14 (7.33) | ||
| Illiterate | 24 (12.87) | 3 (21.69) | 16 (0.96) | 14 (4.19) | ||
| Marital status |
 = 111.495 |
0.243 | ||||
| Married | 184 (81.97) | 66 (71.08) | 126 (91.34) | 134 (86.38) | ||
| Unmarried | 8 (3.00) | 5 (9.64) | 3 (5.77) | 15 (5.24) | ||
| Widowed or widowed | 17 (11.16) | 8 (18.07) | 14 (1.92) | 15 (5.76) | ||
| Divorce | 3 (3.87) | 2 (0.12) | 5 (0.96) | 6 (2.62) | ||
| Status of Occupational |
 = 11.743 |
0.068 | ||||
| Employees | 59 (18.03) | 28 (15.67) | 25 (54.80) | 45 (23.56) | ||
| Retired | 128 (71.24) | 42 (62.65) | 107 (39.42) | 107 (65.45) | ||
| Unemployment | 25 (10.73) | 11 (21.69) | 16 (5.76) | 18 (10.99) | ||
| Personal monthly income |
 = 12.908 |
0.005 | ||||
| Below 5000 CNY | 177 (83.49) | 62 (76.54) | 133 (89.86) | 155 (91.17) | ||
| Above 5000 CNY | 35 (16.51) | 19 (23.46) | 15 (10.14) | 15 (8.83) | ||
| Family location |
 = 5.755 |
0.124 | ||||
| City | 183 (81.97) | 63 (69.88) | 119 (92.30) | 132 (79.58) | ||
| Rural areas | 29 (18.03) | 18 (30.12) | 29 (7.69) | 38 (20.42) | ||
| Caregivers |
 = 6.238 |
0.099 | ||||
| Spouse | 198 (93.39) | 68 (83.95) | 132 (89.18) | 151 (88.82) | ||
| Others | 14 (6.61) | 13 (16.05) | 16 (10.82) | 19 (11.18) | ||
| Residential mode |
 = 12.766 |
0.173 | ||||
| Living with husband and wife | 138 (63.09) | 52 (38.55) | 102 (78.84) | 104 (70.68) | ||
| Living alone | 20 (9.44) | 12 (18.07) | 16 (9.61) | 14 (7.85) | ||
| Children living together | 51 (24.46) | 15 (39.76) | 27 (10.57) | 42 (17.80) | ||
| Other | 3 (3.00) | 2 (3.61) | 3 (0.96) | 10 (3.66) | ||
| Medical insurance type | ||||||
| Employee medical insurance | 133 (53.65) | 32 (37.35) | 88 (59.61) | 81 (60.73) |
 = 22.863 |
<0.001 |
| Resident medical insurance | 77 (42.92) | 44 (57.83) | 56 (40.38) | 79 (34.55) | ||
| Self-financing | 2 (3.43) | 5 (4.82) | 4 (0) | 10 (4.71) | ||
| Daily exercise duration |
 = 10.184 |
0.017 | ||||
| Less than 30 min | 171 (80.66) | 77 (95.06) | 127 (85.81) | 139 (81.76) | ||
| More than 30 min | 41 (19,34) | 4 (4.94) | 21 (14.19) | 31 (18.24) | ||
| Course of disease |
 = 23.825 |
0.021 | ||||
| Less than 6 months | 61 (24.46) | 37 (22.89) | 38 (50.00) | 55 (32.98) | ||
| 6 months − 1 year | 36 (21.03) | 14 (12.05) | 30 (16.34) | 28 (16.75) | ||
| 1–3 years | 43 (23.18) | 17 (15.67) | 27 (21.15) | 39 (19.37) | ||
| 3–5 years | 31 (18.03) | 7 (13.25) | 19 (11.53) | 29 (10.99) | ||
| 5 years and above | 41 (13.30) | 6 (36.14) | 34 (0.96) | 19 (19.89) | ||
| Times of hospitalizations |
 = 17.357 |
0.043 | ||||
| 0 time | 42 (12.45) | 21 (15.67) | 21 (36.53) | 36 (20.94) | ||
| 1–2 times | 110 (52.79) | 45 (49.40) | 74 (52.88) | 78 (46.07) | ||
| 3–5 times | 34 (27.46) | 9 (24.10) | 38 (4.80) | 43 (18.32) | ||
| 5 times or more | 26 (7.29) | 6 (10.83) | 15 (5.76) | 13 (14.66) | ||
| Self-rated sleep status |
 = 24.925 |
<0.001 | ||||
| Very good | 54 (17.59) | 36 (13.25) | 27 (52.88) | 38 (25.13) | ||
| General | 127 (62.66) | 33 (57.83) | 95 (41.34) | 93 (58.12) | ||
| Relatively poor | 31 (19.74) | 12 (28.92) | 26 (5.76) | 39 (16.75) | ||
| Self-Perceived Burden | 23.14 ± 8.51 | 36.57 ± 9.29 | 27.25 ± 6.80 | 31.02 ± 5.86 | F = 73.121 | <0.001 |
| Medication Literacy | 73.17 ± 27.23 | 92.79 ± 23.61 | 73.34 ± 17.37 | 78.51 ± 16.42 | F = 17.814 | <0.001 |
C1 low technology anxiety type, C2 high technology anxiety type, C3 medium technology anxiety low privacy security type, and C4 medium technology anxiety high privacy security type
Multivariate logistic regression analysis of potential categories of technical anxiety characteristics
The statistically significant variables were used as independent variables, and the categories of technical anxiety among elderly patients with chronic diseases were used as dependent variables. Using the low technical anxiety type as the reference group, a multivariate logistic regression analysis was conducted. The results showed that self-perceived burden, medication literacy, daily exercise duration, and hospitalization frequency are protective factors for technical anxiety in elderly patients with chronic diseases, while personal monthly income and medical insurance type are risk factors. As shown in Table 5.
Table 5.
Results of multinomial logistic regression analysis (n=611)
| Characteristics | C2 VS C1 | C3 VS C1 | C4 VS V1 | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| β | P | OR | 95%CI | β | P | OR | 95%CI | β | P | OR | 95%CI | |||||
| Intercept | −9.080 | <0.001 | −2.328 | 0.050 | −4.208 | <0.001 | ||||||||||
| Self-Perceived Burden | 0.212 | <0.001 | 1.236 | 1.182 | 1.292 | 0.079 | <0.001 | 1.082 | 1.048 | 1.118 | 0.136 | <0.001 | 1.146 | 1.108 | 1.184 | |
| Medication Literacy | 0.041 | <0.001 | 1.042 | 1.024 | 1.061 | 0.006 | 0.281 | 1.006 | 0.995 | 1.017 | 0.017 | 0.004 | 1.017 | 1.005 | 1.029 | |
| Personal monthly income | Below 5 000 CNY | −1.045 | 0.025 | 0.352 | 0.141 | 0.877 | 0.321 | 0.369 | 1.379 | 0.684 | 2.782 | 0.402 | 0.293 | 1.495 | 0.706 | 3.166 |
| Above 50,000 CNY | Ref | . | . | . | . | Ref | . | . | . | . | Ref | . | . | . | . | |
| Medical insurance type | Employee medical insurance | −3.442 | 0.001 | 0.032 | 0.004 | 0.232 | −1.195 | 0.193 | 0.303 | 0.050 | 1.829 | −2.394 | 0.005 | 0.091 | 0.017 | 0.494 |
| Resident medical insurance | −2.464 | 0.014 | 0.085 | 0.012 | 0.602 | −1.237 | 0.179 | 0.290 | 0.048 | 1.765 | −2.010 | 0.020 | 0.134 | 0.025 | 0.727 | |
| Self-financing | Ref | . | . | . | . | Ref | . | . | . | . | Ref | . | . | . | . | |
| Daily exercise duration | Less than 30 min | 1.766 | 0.006 | 5.849 | 1.672 | 20.461 | 0.322 | 0.302 | 1.380 | 0.748 | 2.545 | 0.040 | 0.896 | 1.041 | 0.569 | 1.904 |
| More than30 minutes | Ref | . | . | . | . | Ref | . | . | . | . | Ref | . | . | . | . | |
| Course of disease | Less than 6 months | 0.021 | 0.973 | 1.022 | 0.297 | 3.512 | −0.101 | 0.788 | 0.904 | 0.435 | 1.880 | 0.617 | 0.143 | 1.854 | 0.812 | 4.233 |
| 6 months − 1 year | 0.248 | 0.700 | 1.282 | 0.363 | 4.531 | 0.036 | 0.922 | 1.036 | 0.505 | 2.129 | 0.467 | 0.274 | 1.595 | 0.692 | 3.679 | |
| 1–3 years | 0.221 | 0.717 | 1.247 | 0.378 | 4.116 | −0.515 | 0.162 | 0.597 | 0.290 | 1.229 | 0.259 | 0.524 | 1.296 | 0.584 | 2.872 | |
| 3–5 years | −0.421 | 0.550 | 0.656 | 0.165 | 2.614 | −0.470 | 0.232 | 0.625 | 0.289 | 1.351 | 0.390 | 0.364 | 1.477 | 0.637 | 3.426 | |
| 5 years and above | Ref | . | . | . | . | Ref | . | . | . | . | Ref | . | . | . | . | |
| Times of hospitalizations | 0 time | 0.362 | 0.616 | 1.436 | 0.349 | 5.914 | 0.137 | 0.782 | 1.146 | 0.436 | 3.015 | 0.771 | 0.137 | 2.162 | 0.784 | 5.963 |
| 1–2 times | 1.089 | 0.080 | 2.971 | 0.880 | 10.039 | 0.495 | 0.213 | 1.640 | 0.753 | 3.572 | 0.823 | 0.064 | 2.277 | 0.955 | 5.433 | |
| 3–5 times | 0.713 | 0.311 | 2.040 | 0.514 | 8.102 | 0.871 | 0.041 | 2.390 | 1.034 | 5.522 | 1.267 | 0.007 | 3.550 | 1.413 | 8.916 | |
| 5 times or more | Ref | . | . | . | . | Ref | . | . | . | . | Ref | . | . | . | . | |
| Self-rated sleep status | Very good | 0.092 | 0.861 | 1.096 | 0.392 | 3.061 | −0.299 | 0.448 | 0.741 | 0.342 | 1.606 | −0.576 | 0.137 | 0.562 | 0.263 | 1.201 |
| General | −0.329 | 0.489 | 0.720 | 0.283 | 1.828 | −0.049 | 0.878 | 0.952 | 0.507 | 1.786 | −0.446 | 0.160 | 0.640 | 0.343 | 1.193 | |
| Relatively poor | Ref | . | . | . | . | Ref | . | . | . | . | Ref | . | . | . | . | |
C1 low technology anxiety type, C2 high technology anxiety type, C3 medium technology anxiety low privacy security type, and C4 medium technology anxiety high privacy security type
Discussion
This study used the LPA method to identify four distinct potential characteristics of technical anxiety among elderly patients with chronic diseases and determined that technical anxiety should be classified into four categories. When considering multiple factors such as AIC, BIC, aBIC, Entropy, Class Probability, and P-value, the model achieved optimal fit when classified into four categories. This study provides an important public health strategy framework for reducing Risk factors of technology anxiety in the elderly population with chronic diseases. The findings revealed a significant positive correlation between self-perceived burden and medication literacy among elderly patients with chronic diseases. The study also revealed that for patients with high technical anxiety, Employee Medical Insurance, Resident Medical Insurance, and personal monthly income below 5,000 CNY are risk factors for technical anxiety, while daily exercise time less than 30 min, self-perceived burden, and medication literacy are protective factors. This suggests that health care practitioners can reduce their level of technical anxiety by optimizing variables such as patients’ exercise time and their knowledge of medications. Policymakers need to address the impact of economic barriers such as medical burden and personal income on individual technical anxiety.
The findings of this study indicate that the technology anxiety score among 611 elderly patients with chronic comorbidities was 28.85 ± 12.50,The technology anxiety score among elderly patients with chronic comorbidities in this study was lower than the results reported by Tang et al. [10, 19]. This may be due to regional factors, the participants in this study were all from remote cities in western China, where patients have less frequent access to medical smart resources and equipment compared to those in other developed cities in China. As a result, the subjects experienced less exposure to advanced health information devices during their hospital stay and thus had lower levels of technical anxiety.
Latent Profile Analysis categorized the characteristics of technology anxiety among these patients into four distinct types: low technology anxiety type, high technology anxiety type, medium technology anxiety with low privacy security type and medium technology anxiety with high privacy security type. It is worth noting that, A higher proportion of C4 type patients experience technical anxiety, indicating that most elderly patients with chronic diseases are not genuinely afraid of health information technology. However, they have significant concerns about privacy issues, a finding also supported by Sun’s research [35]. Therefore, when developing health information platforms, it is essential to consider the psychological characteristics of elderly patients and enhance the design of privacy protection features. Besides, C2 type elderly patients with chronic diseases have a high level of technical anxiety. These patients usually have lower income and medical security, less exercise time in personal life, generally self-assessed sleep status, and long disease course, This is consistent with the results of Di [23]. Compared with other traditional methods of influencing factors analysis [20, 22–24], this study provides an important basis for understanding the influencing factors of different types of technical anxiety groups in Chinese elderly patients with chronic diseases. The potential characteristics of technical anxiety in elderly patients with chronic diseases identified in this study highlight the level differences of different types of technology anxiety.
The findings of this study indicate that the influencing factors of technology anxiety can be categorized into three main groups. First, among socioeconomic and healthcare resource-related factors, monthly personal income and type of health insurance significantly influenced technology anxiety. This may be attributed to financial pressures experienced by low-income individuals who struggle to afford technological devices or associated services (e.g., remote monitoring), as well as concerns stemming from inadequate insurance coverage regarding the risks of technology use. Second, health behaviors and disease status factors, including daily exercise duration, disease duration, frequency of hospitalizations, and self-reported sleep quality, were found to significantly affect technology anxiety. This may be explained by the psychosomatic interactions between health behaviors/disease states and technology anxiety. Individuals with longer exercise durations tend to have better health awareness, yet paradoxically exhibit higher levels of anxiety toward health information technologies. Additionally, those with more frequent hospitalizations may experience heightened technology anxiety due to repeated exposure to complex and unfamiliar medical devices. Third, in terms of subjective perception-related factors, statistically significant differences (P < 0.05) were observed in self-perceived burden (SPB) and medication literacy (ML) scores among different latent classes of elderly individuals. Multivariate logistic regression analysis revealed that SPB and ML scores served as protective factors against technology anxiety in healthcare settings among the elderly. Specifically, higher SPB and ML scores were associated with increased technology anxiety in elderly patients with chronic multimorbidity. Self-perceived burden refers to the subjective sense of burden an individual experiences due to illness or caregiving responsibilities, encompassing financial strain (e.g., medical expenses), emotional burden (e.g., guilt or dependency), and physical fatigue (e.g., treatment exhaustion).Its core lies in the patient’s perception of being a burden to others, often accompanied by psychological distress and reduced quality of life [36]. Consequently, individuals with high emotional burden are more prone to excessive worry about technology failure (e.g., remote monitoring errors) and fear losing autonomy in technology use Technology anxiety also adversely affects their mental health [37]. Medication literacy denotes an individual’s ability to obtain, comprehend, and apply medication-related information for safe and effective drug use. It includes understanding prescription labels, dosage instructions, adverse effects, and decision-making capacity for rational medication use. Individuals with high ML are typically more attentive to medication details and risks [38]. Analysis suggests that high-ML individuals demand greater accuracy in technology-provided medication advice (e.g., app reminders). Discrepancies between technology and professional knowledge (e.g., dosage recommendation errors) may trigger distrust and anxiety in this population. This finding aligns with Peng’s previous study [14].
Future directions to mitigate technology anxiety in elderly patients with chronic multimorbidity, Clinical practitioners, educational and Research and development institutions, and community-based service providers should adopt multifaceted interventions tailored to these influencing factors. Community health service providers should promptly assess patients’ technostress, analyze the underlying causes of their anxiety and concerns, and facilitate their integration into smart healthcare systems [24]. Specifically, community institutions can: Organize regular smart healthcare education sessions or training workshops. Mobilize volunteers to conduct simplified tutorials on registration, payment, and report retrieval procedures, with live demonstrations of mobile apps and self-service kiosks. Establish a “one-to-one” support mechanism, pairing elderly patients with younger community members or students for door-to-door guidance and simulated medical scenarios to enhance technological familiarity. Healthcare professionals should Designate dedicated service counters for elderly patients in prominent hospital locations, staffed by patient and detail-oriented personnel to assist with technical operations. Proactively inquire about elderly patients’ difficulties with medical technology during consultations, offering clear, jargon-free instructions to avoid cognitive overload. Provide written or video guides for post-consultation use of smart medical devices [22]. Educational and research institutions, should: Optimize the design of medical software and hardware, enlarging interface fonts/icons, streamlining user interfaces, and incorporating voice prompts and step-by-step guidance. Develop one-click services (e.g., one-click appointment booking or physician contact).Collaborate with medical institutions to iteratively refine products based on elderly patient feedback, enhancing usability and user-friendliness [39] to improve healthcare accessibility for this population.
Limitations
There are several limitations in this study. Firstly, the convenience sampling was conducted exclusively in the urban area of Shizuishan, resulting in a relatively limited sample size, which may introduce reporting bias. In the future, employing multi-center survey methods for multi-site sample collection could enhance the generalizability of the research findings and complement self-report data. Secondly, during the research design phase, only patients with chronic diseases were considered, without analyzing the impact of specific disease types on technical anxiety. Future research could incorporate multiple variables such as disease type, severity, medical costs, and recurrence to provide guidance for intervention studies. Finally, this study only surveyed elderly patients with chronic comorbidities aged 60 and above. Future research could conduct stratified and diverse studies across different age groups, analyzing the critical turning points for the occurrence of technology anxiety among these patients. Targeted interventions and preventions can be implemented at different stages to alleviate negative emotions in the elderly population.
Conclusion
This study employed the LPA method to explore the levels of technical anxiety among elderly patients with chronic diseases, using self-perceived burden, medication literacy, and demographic sociological data. The study revealed significant differences among four types of technical anxiety: low technical anxiety, high technical anxiety, medium technical anxiety with low privacy security, and medium technical anxiety with high privacy security. It identifies four potential subgroups, each with distinct indicator combinations, and analyzes the influencing factors for these subgroups. Clinical workers, educational and research institutions, and community service institutions at the grass-roots level should understand the individual characteristics of the elderly population, focus on those with medium and high technical anxiety characteristics, and carry out targeted intervention to reduce their technical anxiety level.
Supplementary Information
Acknowledgements
Not applicable.
Authors’ contributions
QQC applied for approval from the ethics committee and designed this study. QQC and ZHL collected clinical data. XYH and MHZ performed data analysis. QQC and XYH revised and edited the manuscript. All authors read and approved the final manuscript.
Funding
The study was supported by Medical Scientific Research Foundation of Zhejiang Province, China(Grant No.2025KY160).
Data availability
All data generated or analysed during this study are included in this article and its supplementary information files.
Declarations
Ethics approval and consent to participate
This study strictly adhered the World Medical Association’s Declaration of Helsinki.This study was conducted with the approval from the Ethics Committee of the Fifth People’s Hospital of Ningxia, (NXWYYLL-2023-0024). Before the issuance of the questionnaire in the study, the study participants were explained their purpose, explained that the collected data were only used for the survey and will not be expose, and the study participants were informed to sign the informed consent form.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Supplementary Materials
Data Availability Statement
All data generated or analysed during this study are included in this article and its supplementary information files.