An integrated method to evaluate the design of elderly-friendly home nursing beds

An integrated method to evaluate the design of elderly-friendly home nursing beds | 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 Article An integrated method to evaluate the design of elderly-friendly home nursing beds Dong Liu, Hui Li, Yu Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5165517/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research introduces an integrated evaluation model: TM-SEM-TOPSIS, to address the subjectivity and one-sidedness in indicator derivation, weight calculation, and scheme ranking in the design evaluation process. First, text mining (TM) and interviews were employed to select the evaluation indicators for elderly-friendly home nursing beds. Second, Structural Equation Modeling (SEM) was adopted to establish a model to gauge user satisfaction, and significant evaluation indicators were extracted using principal component analysis. AMOS was applied to analyze the model’s goodness of fit and how it works, elucidating the coefficients of evaluation indicators. Lastly, our research adopted the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to compute the Euclidean distances and the relative fit of the three nursing beds and make a ranked evaluation. The product evaluation system, design strategies, and comparative method discussed in our research can offer a vital reference for selecting or developing elderly-friendly products. Biological sciences/Psychology/Human behaviour Physical sciences/Engineering/Mechanical engineering Biological sciences/Psychology Physical sciences/Engineering Physical sciences/Mathematics and computing/Scientific data Physical sciences/Mathematics and computing/Statistics TM-SM-TOPSIS elderly-friendly products home nursing bed design evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction As of 2024, the global population of older adults over age 60 is approximately 1.4 billion and is estimated to increase to 2.1 billion by 2050, accounting for 22% of the global population, indicating that the world is about to enter a severely aging society 1 . As the aging trend intensifies, concerns about elderly care emerge, and the demand for elderly care grows. Moreover, the aging population also poses health challenges such as spinal issues, sleeping disorders, frequent urination, constipation, and mobility issues, significantly hampering the quality of life for older adults. Nowadays, home care stands as the predominant method of elderly care. As the core products in home care, nursing beds’ design and functions directly impact the elderly care effects 2 . By offering humanized position options such as backrest adjustment, turning in bed, and leg raising and lowering, elderly-friendly nursing beds are dedicated to improving the quality of life, contributing to daily care and convalescence, and securing comfortable later lives 3 . Against this backdrop, researchers have conducted multi-faceted studies regarding nursing beds. Geng et al. (2024) probed into the potential needs of older adults through a novel methodology that integrates a scenario-driven dual-layer requirement network with a modified Quality Function Deployment (QFD) model, highlighting engineering characteristics essential for the functional enhancement of nursing beds based on user satisfaction 2 . Su and Fu (2022) utilized the Jack simulation experiment technology and the Kano model to design the mattress size, functions, and structure of nursing beds and make evaluations 4 . Zhu et al. (2021) proposed an innovative central embedded wheelchair nursing-bed automatic docking method. This advancement improves the operational precision and response time of auto-docking 5 . Li et al. (2021) explored the potential of a brain-computer interface (BCI) in developing an intention-controlled nursing bed robot to help the body make actions and verify its intention recognition accuracy 6 . Yuan et al. (2023) applied Kansei Engineering to optimize the aesthetic design of nursing beds by establishing a mapping relationship between users’ perpetual needs and the design characteristics of nursing beds 7 . Zhou et al. (2021) made an evaluation of the appearance of the handrail of nursing beds through the physiological indicators of subjective feelings, eye movement, and electroencephalography, and the study culminates in a recommendation for a rounded rectangular combined with a linear surface design 8 . While the extant literature has laid a solid foundation for future research, there is still a research gap. Existing research primarily focuses on the optimization of functionality, design, and structure, and there is an absence of comprehensive and objective evaluation systems that could mitigate subjectivity, ill conception, and a lack of scientific evidence, thus undermining the practicability of products. Meanwhile, previous studies inadequately address the concerns of older adults, an oversight that is particularly critical given the demographic shift towards an aging global population. Therefore, it is imperative to not only champion elderly-friendly designs but also establish scientific and complete evaluation systems for products like nursing beds, thus bridging research gaps. In recent years, many scholars have adopted integrated methods in studying design evaluation. Tiwari, Jain, and Tandon (2016) introduced the MR-VIKOR model, which reduces uncertainties in the evaluation process and bolsters the objectivity and effectiveness of the design evaluation 9 . Tian et al. (2018) devised a framework combining AHP, GC, and TOPSIS to evaluate the performance of design proposals and verified the effectiveness of the method via a case study involving three refrigerators 10 . Zhao, Wang, and Liu (2021) adopted the methods of CRITIC and GI, combined with the objective and subjective weights of indicators of smart city construction 11 . This model distinguishes itself by thoroughly examining the contrast intensity and conflicts among indicators. Based on IIVAHP and CRITIC, Lu, Li, and Xu (2022) established an evaluation index system that has comprehensively considered the influences of subjective and objective weights 12 . Employing the Game Theory Empowerment, Hu et al. (2022) optimized the linear combination of subjective and objective weights, determined the weights of evaluation indicators, and promoted the preferential selection of product design schemes by combining TOPSIS 13 . Tang, Luo, and Wu (2023) adopted AHP and EMW to calculate the weight value and importance ranking of each indicator in the evaluation system of agricultural robots and rank the design schemes accordingly 14 . Despite the valuable insights offered by the above research for studying evaluation systems, conventional methods like AHP, EMW, GC, and CRITIC are confined to the calculation of the weight value of indicators and are incapable of verifying and filtering evaluation indicators scientifically, thus leading to high subjectivity, a lack of quantifiable data support and aid of statistical tools. In comparison, as a multivariate statistical analysis method, the Structural Equation Modeling (SEM) can not only verify and modify evaluation systems but also can be used to analyze the impact level of indicators and the relationships between multiple indicators 15 . SEM is also capable of evaluating the goodness of fit of a model, tolerating measurement error, and conducting scientific and accurate analysis, thus ensuring a solid ground for the evaluation system and the weight values. However, SEM still needs improvement in the preferential selection of design schemes 16 . Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) stands out in the multi-target and multi-factor evaluation, especially in conducting comprehensive evaluation and finding the optimal design schemes, thereby contributing to the decision-making process. Nevertheless, TOPSIS is subject to subjectivity in the weight calculation 17 , 18 . Therefore, it is complementary to integrate SEM with TOPSIS into a new framework, making the evaluation selection system more accurate, objective, and reasonable. Besides, most of the extant literature adopted qualitative methods such as interviews, focus groups, questionnaires, and literature research, thereby leading to the limitations of limited data samples, high time costs, notable subjectivity, and failure to capture invisible user demands. Text Mining (TM), a method using automation and algorithm processing, can address these limitations, thus providing accurate and objective information for product research and development as well as optimization 19 . Based on the above analysis, our research proposes the TM-SEM-TOPSIS method to evaluate and select elderly-friendly nursing beds, thereby boosting the fit between elderly-friendly products and their target users. Methods Overview Lauded for rich information, short feedback time, and easy access, TM of reviews can be employed to develop and improve products by providing more factual and objective data support. This methodological approach extracts user needs in a comprehensive and reliable manner 20 . Based on statistical analysis, SEM is an empirical tool comprising model measurement and structuring. It is used to study the relationships among variables in a model and to evaluate established models. Widely applied in fields such as computer science, psychology, and management science 21 , 22 , SEM is usually employed to dissect the structure of product demands in the study of evaluation systems. It can adeptly explain the quantitative relationships between multiple variables, thereby effectively mitigating the vagueness of quantitative methods 23 . Moreover, SEM can address the collinearity issue among latent variables, isolate error-index, provide reliable path coefficients and factor loadings, and yield objective weighting for evaluation indicators, thus offering a sound design basis for product development 24 . TOPSIS is an evaluation method targeting multi-attribute issues, which can effectively address issues like vagueness, multi-factors, and sentiment judgment, thus providing efficient decision-making analysis for multiple targets. It helps to select the optimal scheme by ranking positive and negative ideal distances 18 . Commended for its precise results and systematic precision, TOPSIS is a well-established method applied in design-related studies and can help to complete product positioning and planning. Evaluation process First, the evaluation indicators of elderly-friendly nursing beds were derived through an approach integrating TM and interviews. Second, SEM was employed to verify and modify the evaluation system and discern the path coefficients and factor loadings among variables. Lastly, these coefficients were combined with TOPSIS to comprehensively consider the impact of subjective and objective weighting on the evaluation. TOPSIS was then adopted to make a comprehensive evaluation of nursing beds to determine the optimal design scheme and provide more tailored design strategies based on the analysis results, improving the compatibility of nursing beds with the needs of elderly users. Furthermore, this analysis offers scientific evidence for establishing a reasonable and efficient selection method. An evaluation and selection model was eventually established based on the framework of TM-SEM-TOPSIS, as shown in Fig. 1 . Ethics approval The research study was approved by the Commission for Ethics in Research at Hunan University. We confirm that the study adhered to ethical standards and legal regulations, and that it was conducted in accordance with the applicable guidelines. Participation was voluntary and all respondents provided their informed consent to participate in the survey. Research procedures and results Evaluation indicator derivation and model establishment The design evaluation of elderly-friendly home nursing beds necessitates the adoption of a multidimensional and multi-indicator model, and the indicator selection should be comprehensive, redundancy-free, and representative 25 . Mining the text of reviews can reflect users’ focus on the product and product improvement priorities, thus contributing to the quantification of evaluation indicators 20 . In the context of global e-commerce, China emerges as an industrial leader with its advantages in market scale, payment system, logistic efficiency, and innovation model. China is also deemed one of the best soils for e-commerce development 26 . Given the above context, our research employed Gooseeker, a web crawler software, to collect customer reviews of the top twenty best-selling elderly nursing beds on Alibaba and JD.com, two Chinese e-commerce platforms. 3569 reviews were collected. Subsequently, we extracted keywords from the reviews and invited ten design experts to make a selection. The final result is displayed in a word cloud, as shown in Fig. 2 . To further refine the demand collection, user interviews were conducted in the faculty residential area of a university, involving eighteen older adults with nursing bed user experience and twelve of their family members as the interviewees. The interviews investigated various aspects of user demands and experience and encountered challenges, resulting in a collection of demands. Next, to ensure a good fit between the evaluation indicators and the product, we analyzed and summarized nursing bed cases to enrich the collection further. Ultimately, five primary indicators were determined: safety, comfort, ease of use, aesthetics, and functionality, along with 36 secondary indicators. According to the studies by Boland et al. (2014) 27 , Han and Hong (2003) 28 and Zhang et al. (2019) 29 , user satisfaction is an essential criterion for product evaluation, and the collection of demands is a crucial index for measuring user satisfaction. SEM can effectively determine the priorities of the collection and construct a high-quality evaluation system 24 . Therefore, user satisfaction was taken as the dependent variable in the SEM, with the five evaluation dimensions as latent variables and the 36 evaluation indicators as observed variables, to construct an evaluation model for elderly-friendly home nursing beds, as shown in Fig. 3 . The following research hypotheses were also proposed: Satisfaction is positively influenced by safety, functionality, aesthetics, comfort, and ease of use. Empirical analysis and model testing Three experts were invited to conduct a pretest and make revisions to address the potential limitations of the questionnaire. Subsequently, ten elderly users were invited to fill out the questionnaire, and the issues they encountered were recorded. Based on feedback from experts and users, minor adjustments were made to the questionnaire. The final questionnaire consists of two sections: the first section is designed to collect basic information about the respondents, including gender, age, and user experience, and the second section is the scale questionnaire, designed to evaluate each measurement indicator using a 7-point Likert scale, where 1 indicates complete disagreement, and 7 indicates complete agreement 30 . Both online and field surveys were conducted to collect the questionnaires from March to May 2024. First, the questionnaires were distributed via various social platforms, with elderly individuals experienced in using nursing beds and their family members as target research subjects. The professions of family members include industrial designers, relevant experts, and design management personnel. 182 valid online responses were received. Additionally, given the challenges faced by elderly users, such as difficulties in comprehending the questionnaire content and expressing themselves clearly, our research conducted field surveys in residential areas of Nanjing, Changzhou, and Zhenjiang, yielding 198 valid responses. Cumulatively, 380 valid questionnaires were collected, fulfilling the validity benchmarks proposed by scholars such as Bentler. The demographic profile of the survey participants revealed that the majority had over two years of experience with nursing beds. These participants are primarily semi-independent or fully dependent elderly individuals. To this end, the selected research subjects are representative and universally applicable, boosting the study’s validity. SPSS was employed to ensure the reliability and validity of the questionnaire. The test results showed that Cronbach’s alpha coefficients for the five evaluation dimensions were 0.921, 0.954, 0.810, 0.880, and 0.918, respectively. The result of Bartlett’s test of sphericity was significant (p < 0.05), and the KMO value was 0.934, exceeding the standard value of 0.5, indicating that the questionnaire has good reliability and validity and is suitable for factor analysis 31 . Exploratory factor analysis (EFA) was adopted to study the relationships between observed variables and latent variables, the result of which determined whether the initially set index system needed modification and the primary evaluation indicators were extracted 32 . Principal component analysis of the scale that was tested valid and reliable produced six components with eigenvalues greater than 1, and the percentage of total variance explained is 67.254%. To further ensure the accuracy of the study, an absolute factor loading value greater than 0.7 was used as the criterion for indicator selection, ensuring that the selected indicators accurately reflect the evaluation results 33 . Consequently, five evaluation indicators—ease of storage, lighting, turning reminder, reasonable structure, and foldable handle—were removed. Subsequently, a principal component analysis was performed and the total variance explained and the factor rotation component matrix, as shown in Table 1 and Table 2 , were yielded. The results demonstrated five components with eigenvalues greater than 1, and the percentage of total variance explained is 70.241%, meeting statistical research standards and proving the rationality of the initially set evaluation dimensions 34 . After indicator screening and incorporating expert suggestions, five evaluation dimensions and their corresponding 31 evaluation indicators were finalized, as shown in Table 3 . Table 1 Total variance explained. Component Total % of Variance Cumulative % 1 10.919 21.623 21.623 2 4.199 16.744 38.367 3 2.629 12.376 50.742 4 2.391 11.187 61.929 5 1.636 8.311 70.241 Table 2 Rotated component matrix. Evaluation Indicators Component 1 2 3 4 5 Bed rail (A 1 ) 0.161 0.253 0.769 0.085 0.173 Emergency stop button (A 2 ) 0.170 0.174 0.810 0.126 0.127 Alarm system (A 3 ) 0.237 0.173 0.816 0.071 0.105 … … … … … … Bed and wheelchair separation (E 8 ) 0.135 0.783 0.176 0.132 0.112 Table 3 Components and corresponding observed variables. Latent variables Observed variables Safety (A) Bed rail (A 1 ) Emergency stop button (A 2 ) Alarm system (A 3 ) Non-slip fixation (A 4 ) Stability and durability (A 5 ) Functionality (B) Adaptable table board (B 1 ) Auxiliary training (B 2 ) Intelligent control (B 3 ) Defecation system (B 4 ) Body cleaning (B 5 ) Infusion function (B 6 ) Back lifting and lowering (B 7 ) Left and right turning (B 8 ) Leg lifting and lowering (B 9 ) Aesthetics (C) Roundness and softness (C 1 ) Elegance (C 2 ) Color coordination (C 3 ) Appropriate materials (C 4 ) Comfort (D) Ergonomic rationality (D 1 ) Massage function (D 2 ) Anti- decubitus (D 3 ) Air permeability (D 4 ) Waterproof and dustproof (D 5 ) Ease of use (E) Ease of assembly and disassembly (E 1 ) Smooth operation (E 2 ) Manual and electric operation (E 3 ) Height adjustment (E 4 ) Ease of gripping (E 5 ) Ease of cleaning (E 6 ) Ease of mobility (E 7 ) Bed-wheelchair separation (E 8 ) As a confirmatory model analysis method, SEM requires a test of the goodness of fit of the measurement model. If the test does not meet predefined criteria, the model needs to be adjusted by deleting unreasonable paths and releasing the original paths. Subsequently, the collected data were imported into AMOS to establish the structural equation model, as shown in Fig. 4 . This study applied MLE to analyze the model, and the results are shown in Table 4 . According to the studies by Rönkkö and Cho (2022) 35 and Wang et al. (2020) 36 , all indicators in the table met the research standards, indicating that the model has a high degree of fit and is suitable for further analysis. Table 4. Goodness of fit results. Index Criteria Model results Criteria met or not CMIN/DF 1.0–3.0 1.439 Yes GFI > 0.9 0.896 Yes NFI > 0.9 0.924 Yes TLI > 0.9 0.973 Yes CFI > 0.9 0.976 Yes IFI > 0.9 0.976 Yes RMSEA < 0.08 0.034 Yes After the model passed the testing, AMOS was further used to analyze the interaction mechanisms between the variables, resulting in standardized regression coefficients and hypotheses for each path, as shown in Table 5. All p-values in the table were less than 0.05, indicating that all five path coefficients had a significant positive impact, thus supporting the previous research hypotheses. The impact of the five latent variables on the dependent variable, in descending order, is Safety (A), Functionality (B), Ease of Use (E), Comfort (D), and Aesthetics (C). The factor loadings for each observed variable are shown in Table 6. From the table, it can be seen that most standardized factor loadings are greater than 0.7, indicating that the observed variables reliably reflect the latent variables, demonstrating the high reliability of the evaluation indicators 33 . Table 5 Path coefficients of structural model and hypothesis testing. Latent variables Path coefficient Loadings Safety (A) 0.264 A 1 : 0.800 A 2 : 0.828 A 3 : 0.847 A 4 : 0.806 A 5 : 0.908 Functionality (B) 0.199 B 1 : 0.778 B 2 : 0.865 B 3 : 0.904 B 4 : 0.847 B 5 : 0898 B 6 : 0.825 B 7 : 0.824 B 8 : 0.814 B 9 : 0.801 Aesthetics (C) 0.140 C 1 : 0.746 C 2 : 0.721 C 3 : 0.702 C 4 : 0.705 Comfort (D) 0.160 D 1 : 0.938 D 2 : 0.633 D 3 : 0.773 D 4 : 0.671 D 5 : 0.875 Ease of use (E) 0.172 E 1 : 0.626 E 2 : 0.849 E 3 : 0.717 E 4 : 0.776 E 5 : 0.797 E 6 : 0.676 E 7 : 0.774 E 8 : 0.836 Table 6 Standardized factor loadings of indicators. Structural path Path coefficient S.E. T values P values Hypothesis result A→S 0.264 0.050 4.624 *** Supported B→S 0.199 0.044 3.846 *** Supported C→S 0.140 0.073 2.269 0.023 Supported D→S 0.160 0.048 2.848 0.004 Supported E→S 0.172 0.041 3.126 0.002 Supported Evaluation and preferential selection of design schemes Many scholars have demonstrated that the path coefficients and factor loadings in SEM can provide a robust foundation for determining indicator weights 37 , 38 . Therefore, a more scientific and objective examination can be guaranteed by integrating the insights garnered from SEM analyses in the previous section into the evaluation of elderly-friendly home nursing bed designs. Following the SEM analysis, it is necessary to select specific design schemes for elderly-friendly home nursing beds. TOPSIS should be adopted to normalize the original data matrix and calculate the positive and negative ideal solutions for each scheme based on the evaluation indicators. The proximity of each scheme to the positive ideal solution is assessed, and the design schemes are ranked 39 . Based on the “China Nursing Bed Industry Research Report 2023” and nursing bed brand rankings in Maigoo, our research selected three brands with high market share: Paramount Bed from Japan, Mateside from China, and Sidhil from the UK. Three similarly priced elderly-friendly home nursing beds were selected, as shown in Table 7 . The SEM-TOPSIS method was then applied to evaluate and optimize the selection among these three beds. Table 7. Case study of home nursing beds. To ensure the accuracy of the evaluation results, we invited six experts in age-friendly design, four industrial designers, six elderly users, and four family members of the elderly participants to give scores to the three elderly-friendly home nursing beds after fully learning about them. Based on the 31 evaluation indicators obtained earlier, the three design schemes were scored using a 1-10 scale method 17 . All evaluation indicators are benefit (positive) indicators, so a score of 0-3 indicates very poor performance, 3-5 indicates poor, 5-6 indicates average, 6-8 indicates good, and 8-10 indicates excellent 40 . The calculation of the arithmetic mean of the scoring results yielded the initial evaluation matrix A, as shown in Table 8. Table 8 Initial evaluation matrix. Product schemes Evaluation indicators A 1 A 2 A 3 … E 7 E 8 Scheme 1 9.1 2.8 2.1 … 2.1 4.1 Scheme 2 8.8 3.7 5.6 … 9.2 5.7 Scheme 3 9.3 2.7 2.3 … 9.4 4.4 The initial evaluation matrix A was standardized using Eq. (1) to avoid the influence of different dimensions of the indicators, resulting in the standardized matrix B . By combining the path coefficients and factor loadings from Table 6 , the relative factor loadings, i.e., the relative weights of the evaluation indicators, W , can be calculated. The weighted standardized matrix C can be computed using Eq. (2), as shown in Table 9 . \({{\varvec{b}}_{ij}}=\frac{{{{\varvec{a}}_{ij}}}}{{\sqrt {\sum\limits_{{i=1}}^{m} {{\varvec{a}}_{{ij}}^{2}} } }}\) ( i =1, 2… m , j =1, 2… n )(1) \({{\varvec{c}}_{ij}}={{\varvec{w}}_j}{{\varvec{b}}_{ij}}\) ( i =1, 2… m , j =1, 2… n )(2) In the equation, a ij is the indicator in the i row and j column; w j is the weight of the j -th indicator. Table 9 The weighted standardized matrix. Product schemes Evaluation indicators A 1 A 2 A 3 … E 7 E 8 Scheme 1 0.1309 0.1219 0.0784 … 0.0224 0.0761 Scheme 2 0.1265 0.1611 0.2090 … 0.0983 0.1058 Scheme 3 0.1337 0.1176 0.0858 … 0.1005 0.0817 Combining Table 9 with equations ( 3 ) and ( 4 ), the positive ideal solution ( C + ) and negative ideal solution ( C − ) for the evaluation objects in the text are as follows: Positive Ideal Solution ( C + ) = (0.1337, 0.1611, 0.2090, 0.1466, 0.1599, 0.1114, 0.1137, 0.1146, 0.1651, 0.1532, 0.1433, 0.1026, 0.1665, 0.1064, 0.0671, 0.0652, 0.0631, 0.0678, 0.0947, 0.0750, 0.0891, 0.0696, 0.0917, 0.0779, 0.0939, 0.0787, 0.1035, 0.0962, 0.0767, 0.1005, 0.1058); Negative Ideal Solution ( C − ) = (0.1265, 0.1176, 0.0784, 0.1169, 0.1398, 0.0580, 0.1003, 0.1087, 0.0452, 0.0787, 0.0689, 0.0995, 0.0329, 0.0817, 0.0607, 0.0588, 0.0584, 0.0558, 0.0896, 0.0507, 0.0441, 0.0639, 0.0828, 0.0549, 0.0848, 0.0732, 0.0216, 0.0772, 0.0670, 0.0224, 0.0761). $${C^+}=(C_{1}^{+},C_{2}^{+}, \cdots ,C_{m}^{+})$$ 3 $${C^ - }=(C_{1}^{ - },C_{2}^{ - }, \cdots ,C_{m}^{ - })$$ 4 After constructing the positive and negative ideal solutions, the Euclidean distances of the three schemes are calculated using equations (5) and (6). The fit degree, S i , of each design scheme to the positive ideal solution is determined using Eq. (7), and the schemes are normalized and ranked accordingly. The final scores for each scheme are shown in Table 10 . A higher S i value indicates a higher degree of preferential selection, which means the scheme can cater to user needs better, while a lower S i value indicates a lower degree of preferential selection 41 . As shown in Table 10 , the final ranking of the three product schemes is Scheme 2 > Scheme 3 > Scheme 1, with Scheme 2 being the optimal solution. This indicates that the TM-SEM-TOPSIS evaluation method for elderly-friendly home nursing beds is feasible. In the above equations, c j + , c j − are the values of C + and C − in the weighted standardized matrix respectively. Table 10 Overall scores of schemes. Product scheme d i + d i − S i Normalization Ranking Scheme 1 0.2703 0.0982 0.2665 0.1975 3 Scheme 2 0.1104 0.2678 0.7081 0.5247 1 Scheme 3 0.2393 0.1436 0.3750 0.2779 2 Design strategies for elderly-friendly home nursing beds In safety evaluation, the explanatory level of the observed variables for the latent variables is ranked in descending order as follows: A 5 , A 3 , A 2 , A 4 , and A 1 . This ranking underscores the foundational role of stability and durability (A 5 ) in ensuring the safety of the bed. To this end, high-strength materials such as quality steel or reinforced alloys should be used in the design. Additionally, the center of gravity of the bed rails should be close to the mattress or bed frame center to minimize wobbling and triangular support structures should be used to enhance the bed’s stability in case of long-term use and the unstable movements of elderly users. Additionally, it is crucial to ensure that elderly users can trigger alarms promptly. Instead of terminal touch alarms, button-type alarms should be installed on both sides of the bed, and body condition monitoring alarms should be introduced 42 . For nursing beds with functions such as turning over, bed lifting, or auxiliary training, a one-touch emergency stop design should be adopted, and the stop should be placed in the middle of the bed to enable quick interruption to all operations in case of emergency to ensure the elderly user’s safety 43 . Lastly, non-slip mattresses and well-designed fixation devices should be used to reduce the risk of displacement. Regarding humanized design, the bed rails should be designed as foldable grids, with height and angle flexibly adjustable, and equipped with a safety locking system 44 . In functionality evaluation, scheme 2 emerges as markedly superior to its counterparts due to its comprehensive functions. According to Table 6 , the explanatory level of the observed variables for the latent variables is ranked in descending order as follows: B 3 , B 5 , B 2 , B 4 , B 6 , B 7 , B 8 , B 9 , and B 1 . Intelligent control is considered the most crucial function. With ongoing breakthroughs in IoT computing, cloud storage, and intelligent technology, artificial intelligence has been widely applied in various aspects of life 45 . Among these applications, intelligent control nursing beds can counterbalance the physiological deficiencies of elderly users and should be equipped with multiple automated functions such as back lifting, lowering, turning, and leg lifting to alleviate pressure on the lumbar, thoracic, and spinal regions of elderly users. Remote control is also necessary to enhance the autonomy of elderly users in controlling the bed 2 . Moreover, the design of voice control functions can enable intelligent adjustments, with which elderly users can adjust the nursing bed as per their preferences, thus achieving human-computer interaction at a deeper level 46 . Overall, intelligent nursing beds epitomize a significant leap forward in enhancing the life quality and health levels of elderly users by providing comfortable, safe, and personalized care and management services, making them an essential tool in modern elderly care. In terms of design details, easy-to-clean bed surfaces and accessories should be prioritized, as well as bathing and hair-washing functions. Drawing on principles of rehabilitation medicine, adjustable support poles and exercise aids should be designed to support elderly users’ daily and rehabilitation training. In evaluating ease of use, the explanatory level of the observed variables for the latent variables is ranked in descending order as follows: E 2 , E 8 , E 5 , E 4 , E 7 , E 3 , E 6 , and E 1 . Drawing from this analysis, the design of nursing beds should focus on operability, compatibility, and mobility. Given the routine post-bed mobility needs of elderly individuals, such as the use of wheelchairs, the transition from bed to wheelchair emerges as a critical care challenge. Therefore, a modular design concept should be adopted, whereby the bed frame is capable of folding and contracting to a reduced horizontal length, and the bed board and mattress can be transformed into a movable, adjustable-angle wheelchair. This design would meet elderly users’ needs for mobility and entertainment while reducing manufacturing costs 5 . Additionally, it is imperative to integrate ergonomic principles to enhance the gripping designs in key areas, such as expanding the gripping surface area and using non-slip materials to enhance safety 47 . Furthermore, a stable and reliable height adjustment function is necessary for multi-level adjustments, accommodating patients of different heights and care needs. Besides, the height adjustment function should allow both manual and electric operations, ensuring that the bed can still be operated manually in case of electric system failure, thus guaranteeing the nursing bed’s functionality under any circumstances 48 . In comfort evaluation, scheme 3 received the highest overall scores. The explanatory level of the observed variables for the latent variables is ranked in descending order as follows: D 1 , D 5 , D 3 , D 4 , and D 2 . Therefore, nursing bed designs should primarily focus on a deep integration of ergonomic principles in design to realize ergonomic rationality. By measuring the body data of different elderly groups, the structure, dimensions, and angle adjustments of the nursing bed can be designed to perfectly fit the natural curves and movement needs of the human body, reducing discomfort from prolonged bed rest 49 . For instance, the headboard, footboard, and leg support panels should be adjustable to accommodate various lying positions and intelligent control of these functions should be enabled. Additionally, easy-to-clean and stain-resistant materials are essential to ensure a smooth bed surface. Components need to be easily detachable and washable, and a waterproof layer should be added to the bed base to prevent liquid infiltration 50 . Ventilation holes or breathable layer structures should be incorporated into the mattress, or sufficient space should be left at the bed base to facilitate air circulation. Finally, anti-decubitus designs are essential for combating bed sores caused by long-term pressure. These designs include dynamic air mattresses that periodically change pressure distribution and timed automatic position changes 51 . In summary, the design of nursing beds should focus on enhancing ergonomic rationality while also considering basic requirements such as waterproofing, dustproofing, anti-decubitus features, and air permeability, thereby comprehensively improving user comfort and experience. In aesthetics evaluation, the explanatory level of the observed variables for the latent variables in this dimension is ranked in descending order as follows: C 1 , C 2 , C 4 , C 3 . Due to the physiological decline of elderly individuals, transitions must be made at the corners and edges of the bed to enhance safety. The headboard, footboard, and bed rails should be subject to rounded, full curves and surfaces, and the overall design should be in symmetrical, balanced forms, thus creating a simple, balanced appearance that imparts a sense of stability and safety 7 , 52 . In material selection, while ensuring product comfort, the insensitivity brought by the degeneration of tactile functions among elderly users should be considered. The headboard and footboard should use wood materials that fit in with a home environment. Sound-absorbing cotton in a semi-enclosed structure would be suitable materials for cushions to create a safe and serene sleeping environment 2 . The bed frame and main structure should be made of powder-coated stainless steel, the mattress should use waterproof natural latex and soft gel, and the bed cover and sheets should use warm fleece fabric. Other parts should use ABS plastic and medical TPR soft materials 53 . Three aspects should be considered in color design: the subjective factors of elderly users, the furniture function, and the indoor environment. The subjective factors refer to the special physiological and psychological needs of elderly users, who generally prefer warm colors, beige, and warm gray tones, which are low in chroma and brightness and are conducive to a peaceful mood 54 . Discussion Regarding the theoretical implications, this study innovatively integrated TM, SEM, and TOPSIS to establish a comprehensive framework that addresses the limitations of each method, resolves the interference of interactions among indicators and reduces the impact of subjectivity. A case study was conducted, and this integrated method demonstrated its scientific validity and superiority. This interdisciplinary approach not only enriches theoretical research on user experience in the field of design evaluation but also provides new ideas and a methodological framework for the evaluation of similar products in the future. Moreover, this study provides a systematic and complete set of evaluation dimensions and indicators for elderly nursing beds, filling a gap in extant literature. It offers a new perspective on research and provides reliable evidence for the design and development of nursing beds. Lastly, the constructed design hierarchy strategy provides effective guidance for the design and improvement of schemes, enhancing the adaptability and scientific rigor of products. In terms of practical implications, the evaluation system for nursing beds established in this study equips designers with a more profound comprehension of user requirements, thus facilitating the design of products catering to the usage habits and safety requirements of elderly users. This, in turn, can improve user satisfaction 55 . Additionally, the design strategies and comparative approaches introduced in this study offer systematic guidance for enterprises, which can promote the development and innovation of elderly-friendly products. Government authorities can leverage the evaluation indicators and system referenced in this study in formulating industrial standards and policies to advance the standardization and normalization of elderly-friendly products, thus enhancing the service level and product quality within the industry 56 . Despite its significant value in innovating research methods and perspectives and valuable insights for the design and evaluation of elderly-friendly nursing beds, it still has certain limitations. (1) This study combined qualitative and quantitative research methods to enhance the scientific and systematic nature of the design evaluation and decision-making for elderly-friendly home nursing beds. However, there is inevitably some subjectivity in the scoring process. Future research can measure the physiological data via relevant experimental equipment and further expand the sample size to obtain more objective results 57 . (2) The design demands for nursing beds will continue to evolve with technological advancements and social development. Therefore, future research needs to engage in continuous modification and enhancement of the evaluation indicators and model to ensure their adaptability and foresightedness. Conclusion Elderly-friendly home nursing beds are pivotal in building an active aging society. As societal structures evolve, design influencing factors become increasingly complex. This research demonstrates that the innovative integration of TM, SEM, and TOPSIS can ensure the rationality and scientific nature of indicator quantification and better evaluate the pertinence and effectiveness of designs. The case study in this research manifests that this integrated method not only guarantees the comprehensiveness and objectivity of the selection of evaluation indicators but also provides a quantitative approach to address ambiguity and uncertainties in the design evaluation. It also offers compelling evidence and guidance for design and improvement schemes. Specifically, based on text mining and user interviews, this study extracted indicators in multiple dimensions for evaluating the design of elderly-friendly home nursing beds, ensuring a high relevance between design and user needs. 31 evaluation indicators were identified across five dimensions using SEM. Safety, functionality, aesthetics, comfort, and ease of use, and their priorities were determined. In the case study, Scheme 2 was selected as the optimal solution via TOPSIS. The selection process comprehensively considered the impact of subjective and objective factors on the evaluation. Ultimately, this research established a comprehensive and systematic evaluation system for elderly-friendly home nursing beds and proposed design strategies across five dimensions, offering significant insights for related research and design. Declarations Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to D.L. or H.L. Reprints and permissions information is available at www.nature.com/reprints . Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afliations. Author Contribution Conceptualization: D.L., H.L. Data curation: D.L., Y.S. Formal analysis: D.L. Investigation: D.L. Methodology: D.L., Y.S. Project administration: H.L. Writing-original draft: D.L. Writing-review & editing: D.L., H.L. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References van der Weijden, D. A. Y., Koerts, N. D. K., van Munster, B. C., van der Zee, H. H. & Horváth, B. Hidradenitis suppurativa tarda: defining an understudied elderly population. Br. J. Dermatol. 190 , 105-113 (2023). Geng, X., Li, Y., Wang, D. & Zhou, Q. A scenario-driven sustainable product and service system design for elderly nursing based on QFD. Adv. Eng. Inform. 60 , 102368 (2024). Miskelly, F. G. Assistive technology in elderly care. Age Ageing 30 , 455-458 (2001). Su, X. & Fu, R. in Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Anthropometry, Human Behavior, and Communication. (ed Vincent G. Duffy) 135-147 (Springer International Publishing). Zhu, Y. et al. Wheelchair automatic docking method for body-separated nursing bed based on grid map. IEEE Access 9 , 79549-79561 (2021). Li, Y. et al. A coloring and timing brain-computer interface for the nursing bed robot. Comput. Electr. Eng. 95 , 107415 (2021). Yuan, B., Ye, J., Wu, X. & Yang, C. Applying latent dirichlet allocation and support vector regression to the aesthetic design of medical nursing beds. J. Comput. Inf. Sci. Eng. 23 , 051014 (2023). Zhou, Z., Cheng, J., Wei, W. & Lee, L. Validation of evaluation model and evaluation indicators comprised Kansei Engineering and eye movement with EEG: an example of medical nursing bed. Microsyst. Technol. 27 , 1317-1333 (2021). Tiwari, V., Jain, P. K. & Tandon, P. Product design concept evaluation using rough sets and VIKOR method. Adv. Eng. Inform. 30 , 16-25 (2016). Tian, G., Zhang, H., Zhou, M. & Li, Z. AHP, gray correlation, and TOPSIS combined approach to green performance evaluation of design alternatives. IEEE T. Syst. Man. Cy-S. 48 , 1093-1105 (2018). Zhao, H., Wang, Y. & Liu, X. The evaluation of smart city construction readiness in China using CRITIC-G1 method and the bonferroni operator. IEEE Access 9 , 70024-70038 (2021). Lu, N., Li, Y. & Xu, B. Evaluation of the suitability of smart health products for aging based on the IIVAHP-CRITIC model: A case study of smart health kiosk. Sustainability 14 , 9212 (2022). Hu, S. et al. An evaluation method for product design solutions for healthy aging companionship. Front. Public Health 10 , 919300 (2022). Tang, Q., Luo, Y. W. & Wu, X. D. Research on the evaluation method of agricultural intelligent robot design solutions. PLoS One 18 , e0281554 (2023). Muthén, B. & Asparouhov, T. Causal effects in mediation modeling: An introduction with applications to latent variables. Struct. Equ. Modeling 22 , 12-23 (2015). Al-Emran, M., AlQudah, A. A., Abbasi, G. A., Al-Sharafi, M. A. & Iranmanesh, M. Determinants of using AI-Based chatbots for knowledge sharing: evidence from PLS-SEM and fuzzy sets (fsQCA). IEEE T. Eng. Manage. 71 , 4985-4999 (2024). Lin, M.-C., Wang, C.-C., Chen, M.-S. & Chang, C. A. Using AHP and TOPSIS approaches in customer-driven product design process. Comput. Ind. 59 , 17-31 (2008). Yazdani, H., Baneshi, M. & Yaghoubi, M. Techno-economic and environmental design of hybrid energy systems using multi-objective optimization and multi-criteria decision making methods. Energ. Convers. Manage. 282 , 116873 (2023). Ren, G. & Hong, T. Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews. Inform. Process. Manag. 56 , 1425-1438 (2019). Ahani, A. et al. Evaluating medical travelers’ satisfaction through online review analysis. J. Hosp. Tour. Manag. 48 , 519-537 (2021). Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M. & Thiele, K. O. Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. J. of the Acad. Mark. Sci. 45 , 616-632 (2017). Tan, G. W.-H., Ooi, K.-B., Leong, L.-Y. & Lin, B. Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Comput. Hum. Behav. 36 , 198-213 (2014). Astrachan, C. B., Patel, V. K. & Wanzenried, G. A comparative study of CB-SEM and PLS-SEM for theory development in family firm research. J. Fam. Bus. Strateg. 5 , 116-128 (2014). Seva, R. R., Gosiaco, K. G., Santos, M. C. & Pangilinan, D. M. Product design enhancement using apparent usability and affective quality. Appl. Ergon. 42 , 511-517 (2011). Shore, L. et al. Exoscore: A design tool to evaluate factors associated with technology acceptance of soft lower limb exosuits by older adults. Hum. Factors 62 , 391-410 (2020). Cao, X., Deng, M. & Li, H. How does e-commerce city pilot improve green total factor productivity?Evidence from 230 cities in China. J. Environ. Manage. 289 , 112520 (2021). Boland, M. R. et al. From expert-derived user needs to user-perceived ease of use and usefulness: a two-phase mixed-methods evaluation framework. J. Biomed. Inform. 52 , 141-150 (2014). Han, S. H. & Hong, S. W. A systematic approach for coupling user satisfaction with product design. Ergonomics 46 , 1441-1461 (2003). Zhang, C., Liu, Y., Lu, W. & Xiao, G. Evaluating passenger satisfaction index based on PLS-SEM model: Evidence from Chinese public transport service. Transport. Res. A-pol. 120 , 149-164 (2019). Jo, H. I. & Jeon, J. Y. Influence of indoor soundscape perception based on audiovisual contents on work-related quality with preference and perceived productivity in open-plan offices. Build. Environ. 208 , 108598 (2022). Hair, J. F., Sarstedt, M., Ringle, C. M. & Mena, J. A. An assessment of the use of partial least squares structural equation modeling in marketing research. J. of the Acad. Mark. Sci. 40 , 414-433 (2012). Wang, B., Shao, C., Li, J., Weng, J. & Ji, X. Holiday travel behavior analysis and empirical study under integrated multimodal travel information service. Transp. Policy 39 , 21-36 (2015). Bagozzi, R. P. & Yi, Y. On the evaluation of structural equation models. JAMS 16 , 74-94 (1988). Leite, W. L., Shen, Z., Marcoulides, K., Fisk, C. L. & Harring, J. Using ant colony optimization for sensitivity analysis in structural equation modeling. Struct. Equ. Modeling 29 , 47-56 (2022). Rönkkö, M. & Cho, E. An updated guideline for assessing discriminant validity. Organ. Res. Methods 25 , 6-14 (2020). Wang, K., Xu, Y., Wang, C., Tan, M. & Chen, P. A Corrected Goodness-of-Fit Index (CGFI) for model evaluation in structural equation modeling. Struct. Equ. Modeling 27 , 735-749 (2020). Danks, N. P., Sharma, P. N. & Sarstedt, M. Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM). J. Bus. Res. 113 , 13-24 (2020). Hair, J. F., Sharma, P. N., Sarstedt, M., Ringle, C. M. & Liengaard, B. D. The shortcomings of equal weights estimation and the composite equivalence index in PLS-SEM. Eur. J. Mark. 58 , 30-55 (2024). Liu, H. C., Wang, L. E., Li, Z. & Hu, Y. P. Improving risk evaluation in FMEA with cloud model and hierarchical TOPSIS method. IEEE Trans. Fuzzy Syst. 27 , 84-95 (2019). Hu, Y., Wu, L., Shi, C., Wang, Y. & Zhu, F. Research on optimal decision-making of cloud manufacturing service provider based on grey correlation analysis and TOPSIS. Int. J. Prod. Res. 58 , 748-757 (2020). Zhang, Z. & Li, Z. Consensus-based TOPSIS-Sort-B for multi-criteria sorting in the context of group decision-making. Ann. Oper. Res. 325 , 911-938 (2023). Murphy, M. M. Telehealth alerts and nurse response. Telemed. J. E. Health 24 , 517-526 (2018). Li, Y., Pan, J., Wang, F. & Yu, Z. A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control. IEEE T. Biomed. Eng. 60 , 3156-3166 (2013). Boocock, M. G., Weyman, A. K. & McIlroy, R. Bedside safety rails: assessment of strength requirements and the appropriateness of current designs. Ergonomics 49 , 631-650 (2006). Ni, T. F., Wang, J. L., Chen, C. K., Shih, F. & Wang, J. Can a prolonged healing pressure injury be benefited by using an AI mattress? A case study. BMC Geriatr. 24 , 307 (2024). Rogowski, A. Web-based remote voice control of robotized cells. Robot. Cim-Int. Manuf. 29 , 77-89 (2013). Miyazaki, Y., Hirano, K., Kitamura, K. & Nishida, Y. Analysis of relationship between natural standing behavior of elderly people and a class of standing aids in a living space. Sensors 22 , 1178 (2022). Miller, K. E. M., Chatterjee, P. & Werner, R. M. Trends in supply of nursing Home beds, 2011-2019. JAMA Netw. Open 6 , e230640 (2023). Walker, D. S., Lee, W. Y., Skov, N. M., Berger, C. F. & Athley, B. D. Investigating users' requirements: computer-based anatomy learning modules for multiple user test beds. J. Am. Med. Inform. Assoc. 9 , 311-319 (2002). Hung, L. C. et al. Design and evaluation of the bed-cleaning mobile application. J. Nurs. Manag. 28 , 771-776 (2020). Barsocchi, P. Position recognition to support bedsores prevention. IEEE J. Biomed. Health Inform. 17 , 53-59 (2013). Zhou, Z., Wang, L., Ye, R. & Yue, H. A humanistic-care factors application hierarchical design-model for intelligent elderly products. Heliyon 9 , e13734 (2023). van Kesteren, I., de Bruijn, S. & Stappers, P. J. Evaluation of materials selection activities in user-centred design projects. J. Eng. Design 19 , 417-429 (2008). Mao, Y., Li, P. & Hao, P. The effects of wooden furniture color, floor material, and age on design evaluation, visual attention, and emotions in office environments. Buildings 14 , 1498 (2024). Kujala, S. Effective user involvement in product development by improving the analysis of user needs. Behav. Inform. Technol. 27 , 457-473 (2008). Wang, H., Liu, J. M. & Chen, L. A comparative analysis of government intervention under the EPR system: Eco-design and authorized remanufacturing. Expert Syst. Appl. 249 , 123680 (2024). Kim, W., Kim, N., Lyons, J. B. & Nam, C. S. Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach. Appl. Ergon. 85 , 103056 (2020). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5165517","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":365102172,"identity":"2b55cb64-977e-4ded-a4cf-c420353b330a","order_by":0,"name":"Dong Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACPmYGBgkgDaSYDxz48IMILWwILWyJB2f2EKOFAaIFCHiMD3OwEaOFncfwxscdtezyM3I+HGbgYZDnFztAyGE8xpYzzxxnNriRu+FwgQWD4czZCQS1mEnzth1jNpAAapnBw5BgcJtYLUCHPTjMw0a8lhpmhhs5DMRqYSu2nNl2gNngzDMDYCBLEPYLP//hjTc+ttUly7cnP/7w4YeNPL80AS1QcDgZypAgSjkI1NkRrXQUjIJRMApGHgAAFnk9YihbuXwAAAAASUVORK5CYII=","orcid":"","institution":"Hunan University","correspondingAuthor":true,"prefix":"","firstName":"Dong","middleName":"","lastName":"Liu","suffix":""},{"id":365102173,"identity":"12af6120-e631-463d-ba32-eddfc05ccfdb","order_by":1,"name":"Hui Li","email":"","orcid":"","institution":"Hunan University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""},{"id":365102174,"identity":"c4ef6e6e-f853-4862-96ef-c9cd52294615","order_by":2,"name":"Yu Shi","email":"","orcid":"","institution":"Hunan University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2024-09-27 13:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5165517/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5165517/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66654649,"identity":"db6caefc-527f-4b2c-9a6b-68e8b286304f","added_by":"auto","created_at":"2024-10-15 08:06:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87827,"visible":true,"origin":"","legend":"\u003cp\u003eThe evaluation process of elderly-friendly home nursing beds.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5165517/v1/ab6d3ff09fbab71a9f4c373c.png"},{"id":66654652,"identity":"32bfb603-005f-46cb-8c6b-958bc4147ca1","added_by":"auto","created_at":"2024-10-15 08:06:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":279435,"visible":true,"origin":"","legend":"\u003cp\u003eReview keyword word cloud.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5165517/v1/953e5e2abd0fc3feaf4147b8.png"},{"id":66654651,"identity":"03c838de-5d4e-4077-997c-459e991242dd","added_by":"auto","created_at":"2024-10-15 08:06:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83156,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation model of elderly-friendly home nursing beds.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5165517/v1/e29ff11ab6ec2ebb79d7cb8d.png"},{"id":66654650,"identity":"54f923fc-4318-46ec-933f-b701ed12abce","added_by":"auto","created_at":"2024-10-15 08:06:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":163899,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model of nursing beds.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5165517/v1/14d331cff710a103ab2ba1b2.png"},{"id":70558361,"identity":"ae0be6c8-e74e-4e66-9495-71793f55f231","added_by":"auto","created_at":"2024-12-04 11:31:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1478367,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5165517/v1/47cbb476-1aa9-40c9-8afa-50cb065184a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An integrated method to evaluate the design of elderly-friendly home nursing beds","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs of 2024, the global population of older adults over age 60 is approximately 1.4\u0026nbsp;billion and is estimated to increase to 2.1\u0026nbsp;billion by 2050, accounting for 22% of the global population, indicating that the world is about to enter a severely aging society \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. As the aging trend intensifies, concerns about elderly care emerge, and the demand for elderly care grows. Moreover, the aging population also poses health challenges such as spinal issues, sleeping disorders, frequent urination, constipation, and mobility issues, significantly hampering the quality of life for older adults. Nowadays, home care stands as the predominant method of elderly care. As the core products in home care, nursing beds\u0026rsquo; design and functions directly impact the elderly care effects\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. By offering humanized position options such as backrest adjustment, turning in bed, and leg raising and lowering, elderly-friendly nursing beds are dedicated to improving the quality of life, contributing to daily care and convalescence, and securing comfortable later lives\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, researchers have conducted multi-faceted studies regarding nursing beds. Geng et al. (2024) probed into the potential needs of older adults through a novel methodology that integrates a scenario-driven dual-layer requirement network with a modified Quality Function Deployment (QFD) model, highlighting engineering characteristics essential for the functional enhancement of nursing beds based on user satisfaction\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Su and Fu (2022) utilized the Jack simulation experiment technology and the Kano model to design the mattress size, functions, and structure of nursing beds and make evaluations\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Zhu et al. (2021) proposed an innovative central embedded wheelchair nursing-bed automatic docking method. This advancement improves the operational precision and response time of auto-docking\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Li et al. (2021) explored the potential of a brain-computer interface (BCI) in developing an intention-controlled nursing bed robot to help the body make actions and verify its intention recognition accuracy\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Yuan et al. (2023) applied Kansei Engineering to optimize the aesthetic design of nursing beds by establishing a mapping relationship between users\u0026rsquo; perpetual needs and the design characteristics of nursing beds\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Zhou et al. (2021) made an evaluation of the appearance of the handrail of nursing beds through the physiological indicators of subjective feelings, eye movement, and electroencephalography, and the study culminates in a recommendation for a rounded rectangular combined with a linear surface design\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. While the extant literature has laid a solid foundation for future research, there is still a research gap. Existing research primarily focuses on the optimization of functionality, design, and structure, and there is an absence of comprehensive and objective evaluation systems that could mitigate subjectivity, ill conception, and a lack of scientific evidence, thus undermining the practicability of products. Meanwhile, previous studies inadequately address the concerns of older adults, an oversight that is particularly critical given the demographic shift towards an aging global population. Therefore, it is imperative to not only champion elderly-friendly designs but also establish scientific and complete evaluation systems for products like nursing beds, thus bridging research gaps.\u003c/p\u003e \u003cp\u003eIn recent years, many scholars have adopted integrated methods in studying design evaluation. Tiwari, Jain, and Tandon (2016) introduced the MR-VIKOR model, which reduces uncertainties in the evaluation process and bolsters the objectivity and effectiveness of the design evaluation\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Tian et al. (2018) devised a framework combining AHP, GC, and TOPSIS to evaluate the performance of design proposals and verified the effectiveness of the method via a case study involving three refrigerators\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Zhao, Wang, and Liu (2021) adopted the methods of CRITIC and GI, combined with the objective and subjective weights of indicators of smart city construction\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This model distinguishes itself by thoroughly examining the contrast intensity and conflicts among indicators. Based on IIVAHP and CRITIC, Lu, Li, and Xu (2022) established an evaluation index system that has comprehensively considered the influences of subjective and objective weights\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Employing the Game Theory Empowerment, Hu et al. (2022) optimized the linear combination of subjective and objective weights, determined the weights of evaluation indicators, and promoted the preferential selection of product design schemes by combining TOPSIS\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Tang, Luo, and Wu (2023) adopted AHP and EMW to calculate the weight value and importance ranking of each indicator in the evaluation system of agricultural robots and rank the design schemes accordingly\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the valuable insights offered by the above research for studying evaluation systems, conventional methods like AHP, EMW, GC, and CRITIC are confined to the calculation of the weight value of indicators and are incapable of verifying and filtering evaluation indicators scientifically, thus leading to high subjectivity, a lack of quantifiable data support and aid of statistical tools. In comparison, as a multivariate statistical analysis method, the Structural Equation Modeling (SEM) can not only verify and modify evaluation systems but also can be used to analyze the impact level of indicators and the relationships between multiple indicators\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. SEM is also capable of evaluating the goodness of fit of a model, tolerating measurement error, and conducting scientific and accurate analysis, thus ensuring a solid ground for the evaluation system and the weight values. However, SEM still needs improvement in the preferential selection of design schemes\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) stands out in the multi-target and multi-factor evaluation, especially in conducting comprehensive evaluation and finding the optimal design schemes, thereby contributing to the decision-making process. Nevertheless, TOPSIS is subject to subjectivity in the weight calculation\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Therefore, it is complementary to integrate SEM with TOPSIS into a new framework, making the evaluation selection system more accurate, objective, and reasonable. Besides, most of the extant literature adopted qualitative methods such as interviews, focus groups, questionnaires, and literature research, thereby leading to the limitations of limited data samples, high time costs, notable subjectivity, and failure to capture invisible user demands. Text Mining (TM), a method using automation and algorithm processing, can address these limitations, thus providing accurate and objective information for product research and development as well as optimization\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Based on the above analysis, our research proposes the TM-SEM-TOPSIS method to evaluate and select elderly-friendly nursing beds, thereby boosting the fit between elderly-friendly products and their target users.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eOverview\u003c/h2\u003e\n \u003cp\u003eLauded for rich information, short feedback time, and easy access, TM of reviews can be employed to develop and improve products by providing more factual and objective data support. This methodological approach extracts user needs in a comprehensive and reliable manner\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Based on statistical analysis, SEM is an empirical tool comprising model measurement and structuring. It is used to study the relationships among variables in a model and to evaluate established models. Widely applied in fields such as computer science, psychology, and management science\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, SEM is usually employed to dissect the structure of product demands in the study of evaluation systems. It can adeptly explain the quantitative relationships between multiple variables, thereby effectively mitigating the vagueness of quantitative methods\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Moreover, SEM can address the collinearity issue among latent variables, isolate error-index, provide reliable path coefficients and factor loadings, and yield objective weighting for evaluation indicators, thus offering a sound design basis for product development\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. TOPSIS is an evaluation method targeting multi-attribute issues, which can effectively address issues like vagueness, multi-factors, and sentiment judgment, thus providing efficient decision-making analysis for multiple targets. It helps to select the optimal scheme by ranking positive and negative ideal distances\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Commended for its precise results and systematic precision, TOPSIS is a well-established method applied in design-related studies and can help to complete product positioning and planning.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEvaluation process\u003c/h3\u003e\n\u003cp\u003eFirst, the evaluation indicators of elderly-friendly nursing beds were derived through an approach integrating TM and interviews. Second, SEM was employed to verify and modify the evaluation system and discern the path coefficients and factor loadings among variables. Lastly, these coefficients were combined with TOPSIS to comprehensively consider the impact of subjective and objective weighting on the evaluation. TOPSIS was then adopted to make a comprehensive evaluation of nursing beds to determine the optimal design scheme and provide more tailored design strategies based on the analysis results, improving the compatibility of nursing beds with the needs of elderly users. Furthermore, this analysis offers scientific evidence for establishing a reasonable and efficient selection method. An evaluation and selection model was eventually established based on the framework of TM-SEM-TOPSIS, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eEthics approval\u003c/h3\u003e\n\u003cp\u003eThe research study was approved by the Commission for Ethics in Research at Hunan University. We confirm that the study adhered to ethical standards and legal regulations, and that it was conducted in accordance with the applicable guidelines. Participation was voluntary and all respondents provided their informed consent to participate in the survey.\u003c/p\u003e"},{"header":"Research procedures and results","content":"\u003ch2\u003eEvaluation indicator derivation and model establishment\u003c/h2\u003e\n \u003cp\u003eThe design evaluation of elderly-friendly home nursing beds necessitates the adoption of a multidimensional and multi-indicator model, and the indicator selection should be comprehensive, redundancy-free, and representative\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Mining the text of reviews can reflect users\u0026rsquo; focus on the product and product improvement priorities, thus contributing to the quantification of evaluation indicators\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In the context of global e-commerce, China emerges as an industrial leader with its advantages in market scale, payment system, logistic efficiency, and innovation model. China is also deemed one of the best soils for e-commerce development\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Given the above context, our research employed Gooseeker, a web crawler software, to collect customer reviews of the top twenty best-selling elderly nursing beds on Alibaba and JD.com, two Chinese e-commerce platforms. 3569 reviews were collected. Subsequently, we extracted keywords from the reviews and invited ten design experts to make a selection. The final result is displayed in a word cloud, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eTo further refine the demand collection, user interviews were conducted in the faculty residential area of a university, involving eighteen older adults with nursing bed user experience and twelve of their family members as the interviewees. The interviews investigated various aspects of user demands and experience and encountered challenges, resulting in a collection of demands. Next, to ensure a good fit between the evaluation indicators and the product, we analyzed and summarized nursing bed cases to enrich the collection further. Ultimately, five primary indicators were determined: safety, comfort, ease of use, aesthetics, and functionality, along with 36 secondary indicators.\u003c/p\u003e\n \u003cp\u003eAccording to the studies by Boland et al. (2014)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, Han and Hong (2003)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and Zhang et al. (2019)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, user satisfaction is an essential criterion for product evaluation, and the collection of demands is a crucial index for measuring user satisfaction. SEM can effectively determine the priorities of the collection and construct a high-quality evaluation system\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Therefore, user satisfaction was taken as the dependent variable in the SEM, with the five evaluation dimensions as latent variables and the 36 evaluation indicators as observed variables, to construct an evaluation model for elderly-friendly home nursing beds, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The following research hypotheses were also proposed: Satisfaction is positively influenced by safety, functionality, aesthetics, comfort, and ease of use.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eEmpirical analysis and model testing\u003c/h2\u003e\n \u003cp\u003eThree experts were invited to conduct a pretest and make revisions to address the potential limitations of the questionnaire. Subsequently, ten elderly users were invited to fill out the questionnaire, and the issues they encountered were recorded. Based on feedback from experts and users, minor adjustments were made to the questionnaire. The final questionnaire consists of two sections: the first section is designed to collect basic information about the respondents, including gender, age, and user experience, and the second section is the scale questionnaire, designed to evaluate each measurement indicator using a 7-point Likert scale, where 1 indicates complete disagreement, and 7 indicates complete agreement\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Both online and field surveys were conducted to collect the questionnaires from March to May 2024. First, the questionnaires were distributed via various social platforms, with elderly individuals experienced in using nursing beds and their family members as target research subjects. The professions of family members include industrial designers, relevant experts, and design management personnel. 182 valid online responses were received. Additionally, given the challenges faced by elderly users, such as difficulties in comprehending the questionnaire content and expressing themselves clearly, our research conducted field surveys in residential areas of Nanjing, Changzhou, and Zhenjiang, yielding 198 valid responses. Cumulatively, 380 valid questionnaires were collected, fulfilling the validity benchmarks proposed by scholars such as Bentler. The demographic profile of the survey participants revealed that the majority had over two years of experience with nursing beds. These participants are primarily semi-independent or fully dependent elderly individuals. To this end, the selected research subjects are representative and universally applicable, boosting the study\u0026rsquo;s validity.\u003c/p\u003e\n \u003cp\u003eSPSS was employed to ensure the reliability and validity of the questionnaire. The test results showed that Cronbach\u0026rsquo;s alpha coefficients for the five evaluation dimensions were 0.921, 0.954, 0.810, 0.880, and 0.918, respectively. The result of Bartlett\u0026rsquo;s test of sphericity was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the KMO value was 0.934, exceeding the standard value of 0.5, indicating that the questionnaire has good reliability and validity and is suitable for factor analysis\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Exploratory factor analysis (EFA) was adopted to study the relationships between observed variables and latent variables, the result of which determined whether the initially set index system needed modification and the primary evaluation indicators were extracted\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Principal component analysis of the scale that was tested valid and reliable produced six components with eigenvalues greater than 1, and the percentage of total variance explained is 67.254%. To further ensure the accuracy of the study, an absolute factor loading value greater than 0.7 was used as the criterion for indicator selection, ensuring that the selected indicators accurately reflect the evaluation results\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Consequently, five evaluation indicators\u0026mdash;ease of storage, lighting, turning reminder, reasonable structure, and foldable handle\u0026mdash;were removed. Subsequently, a principal component analysis was performed and the total variance explained and the factor rotation component matrix, as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, were yielded. The results demonstrated five components with eigenvalues greater than 1, and the percentage of total variance explained is 70.241%, meeting statistical research standards and proving the rationality of the initially set evaluation dimensions\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. After indicator screening and incorporating expert suggestions, five evaluation dimensions and their corresponding 31 evaluation indicators were finalized, as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003cbr\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTotal variance explained.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% of Variance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCumulative %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRotated component matrix.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEvaluation Indicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBed rail (A\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmergency stop button (A\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlarm system (A\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBed and wheelchair separation (E\u003csub\u003e8\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComponents and corresponding observed variables.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLatent variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eObserved variables\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSafety (A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBed rail (A\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmergency stop button (A\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlarm system (A\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-slip fixation (A\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStability and durability (A\u003csub\u003e5\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFunctionality (B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdaptable table board (B\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAuxiliary training (B\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntelligent control (B\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDefecation system (B\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody cleaning (B\u003csub\u003e5\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfusion function (B\u003csub\u003e6\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBack lifting and lowering (B\u003csub\u003e7\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right turning (B\u003csub\u003e8\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeg lifting and lowering (B\u003csub\u003e9\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAesthetics (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoundness and softness (C\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElegance (C\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColor coordination (C\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAppropriate materials (C\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComfort (D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErgonomic rationality (D\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMassage function (D\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti- decubitus (D\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAir permeability (D\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaterproof and dustproof (D\u003csub\u003e5\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEase of use (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEase of assembly and disassembly (E\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmooth operation (E\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManual and electric operation (E\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight adjustment (E\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEase of gripping (E\u003csub\u003e5\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEase of cleaning (E\u003csub\u003e6\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEase of mobility (E\u003csub\u003e7\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBed-wheelchair separation (E\u003csub\u003e8\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs a confirmatory model analysis method, SEM requires a test of the goodness of fit of the measurement model. If the test does not meet predefined criteria, the model needs to be adjusted by deleting unreasonable paths and releasing the original paths. Subsequently, the collected data were imported into AMOS to establish the structural equation model, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. This study applied MLE to analyze the model, and the results are shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. According to the studies by R\u0026ouml;nkk\u0026ouml; and Cho (2022)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and Wang et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, all indicators in the table met the research standards, indicating that the model has a high degree of fit and is suitable for further analysis.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eGoodness of fit results.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCriteria\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel results\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCriteria met or not\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCMIN/DF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u0026ndash;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cp\u003eAfter the model passed the testing, AMOS was further used to analyze the interaction mechanisms between the variables, resulting in standardized regression coefficients and hypotheses for each path, as shown in Table 5. All p-values in the table were less than 0.05, indicating that all five path coefficients had a significant positive impact, thus supporting the previous research hypotheses. The impact of the five latent variables on the dependent variable, in descending order, is Safety (A), Functionality (B), Ease of Use (E), Comfort (D), and Aesthetics (C). The factor loadings for each observed variable are shown in Table 6. From the table, it can be seen that most standardized factor loadings are greater than 0.7, indicating that the observed variables reliably reflect the latent variables, demonstrating the high reliability of the evaluation indicators\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePath coefficients of structural model and hypothesis testing.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLatent variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eLoadings\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSafety (A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003csub\u003e1\u003c/sub\u003e: 0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003csub\u003e2\u003c/sub\u003e: 0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003csub\u003e3\u003c/sub\u003e: 0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003csub\u003e4\u003c/sub\u003e: 0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003csub\u003e5\u003c/sub\u003e: 0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFunctionality (B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003csub\u003e1\u003c/sub\u003e: 0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003csub\u003e2\u003c/sub\u003e: 0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003csub\u003e3\u003c/sub\u003e: 0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003csub\u003e4\u003c/sub\u003e: 0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003csub\u003e5\u003c/sub\u003e: 0898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003csub\u003e6\u003c/sub\u003e: 0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003csub\u003e7\u003c/sub\u003e: 0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003csub\u003e8\u003c/sub\u003e: 0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003csub\u003e9\u003c/sub\u003e: 0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAesthetics (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e1\u003c/sub\u003e: 0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e2\u003c/sub\u003e: 0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e3\u003c/sub\u003e: 0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e4\u003c/sub\u003e: 0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComfort (D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u003csub\u003e1\u003c/sub\u003e: 0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u003csub\u003e2\u003c/sub\u003e: 0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u003csub\u003e3\u003c/sub\u003e: 0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u003csub\u003e4\u003c/sub\u003e: 0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u003csub\u003e5\u003c/sub\u003e: 0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEase of use (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e1\u003c/sub\u003e: 0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e2\u003c/sub\u003e: 0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e3\u003c/sub\u003e: 0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e4\u003c/sub\u003e: 0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e5\u003c/sub\u003e: 0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e6\u003c/sub\u003e: 0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e7\u003c/sub\u003e: 0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e8\u003c/sub\u003e: 0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStandardized factor loadings of indicators.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStructural path\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT values\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP values\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypothesis result\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u0026rarr;S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u0026rarr;S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u0026rarr;S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u0026rarr;S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u0026rarr;S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEvaluation and preferential selection of design schemes\u003c/h3\u003e\n\u003cp\u003eMany scholars have demonstrated that the path coefficients and factor loadings in SEM can provide a robust foundation for determining indicator weights\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Therefore, a more scientific and objective examination can be guaranteed by integrating the insights garnered from SEM analyses in the previous section into the evaluation of elderly-friendly home nursing bed designs. Following the SEM analysis, it is necessary to select specific design schemes for elderly-friendly home nursing beds. TOPSIS should be adopted to normalize the original data matrix and calculate the positive and negative ideal solutions for each scheme based on the evaluation indicators. The proximity of each scheme to the positive ideal solution is assessed, and the design schemes are ranked\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Based on the \u0026ldquo;China Nursing Bed Industry Research Report 2023\u0026rdquo; and nursing bed brand rankings in Maigoo, our research selected three brands with high market share: Paramount Bed from Japan, Mateside from China, and Sidhil from the UK. Three similarly priced elderly-friendly home nursing beds were selected, as shown in Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. The SEM-TOPSIS method was then applied to evaluate and optimize the selection among these three beds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7.\u0026nbsp;\u003c/strong\u003eCase study of home nursing beds.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"575\" height=\"419\"\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003eTo ensure the accuracy of the evaluation results, we invited six experts in age-friendly design, four industrial designers, six elderly users, and four family members of the elderly participants to give scores to the three elderly-friendly home nursing beds after fully learning about them. Based on the 31 evaluation indicators obtained earlier, the three design schemes were scored using a 1-10 scale method\u003csup\u003e17\u003c/sup\u003e. All evaluation indicators are benefit (positive) indicators, so a score of 0-3 indicates very poor performance, 3-5 indicates poor, 5-6 indicates average, 6-8 indicates good, and 8-10 indicates excellent\u003csup\u003e40\u003c/sup\u003e. The calculation of the arithmetic mean of the scoring results yielded the initial evaluation matrix A, as shown in Table 8.\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInitial evaluation matrix.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eProduct schemes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eEvaluation indicators\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScheme 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScheme 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScheme 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe initial evaluation matrix \u003cstrong\u003eA\u003c/strong\u003e was standardized using Eq. (1) to avoid the influence of different dimensions of the indicators, resulting in the standardized matrix \u003cstrong\u003eB\u003c/strong\u003e. By combining the path coefficients and factor loadings from Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the relative factor loadings, i.e., the relative weights of the evaluation indicators, \u003cstrong\u003eW\u003c/strong\u003e, can be calculated. The weighted standardized matrix \u003cstrong\u003eC\u003c/strong\u003e can be computed using Eq. (2), as shown in Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({{\\varvec{b}}_{ij}}=\\frac{{{{\\varvec{a}}_{ij}}}}{{\\sqrt {\\sum\\limits_{{i=1}}^{m} {{\\varvec{a}}_{{ij}}^{2}} } }}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e (\u003cem\u003ei\u003c/em\u003e=1, 2\u0026hellip;\u003cem\u003em\u003c/em\u003e, \u003cem\u003ej\u003c/em\u003e=1, 2\u0026hellip;\u003cem\u003en\u003c/em\u003e)(1)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({{\\varvec{c}}_{ij}}={{\\varvec{w}}_j}{{\\varvec{b}}_{ij}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e (\u003cem\u003ei\u003c/em\u003e=1, 2\u0026hellip;\u003cem\u003em\u003c/em\u003e, \u003cem\u003ej\u003c/em\u003e=1, 2\u0026hellip;\u003cem\u003en\u003c/em\u003e)(2)\u003c/p\u003e\n\u003cp\u003eIn the equation, \u003cstrong\u003ea\u003c/strong\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e is the indicator in the \u003cem\u003ei\u003c/em\u003e row and \u003cem\u003ej\u003c/em\u003e column; \u003cstrong\u003ew\u003c/strong\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e is the weight of the \u003cem\u003ej\u003c/em\u003e-th indicator.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe weighted standardized matrix.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eProduct schemes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eEvaluation indicators\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScheme 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScheme 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScheme 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eCombining Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e with equations (\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) and (\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), the positive ideal solution (\u003cstrong\u003eC\u003c/strong\u003e\u003csup\u003e+\u003c/sup\u003e) and negative ideal solution (\u003cstrong\u003eC\u003c/strong\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) for the evaluation objects in the text are as follows: Positive Ideal Solution (\u003cstrong\u003eC\u003c/strong\u003e\u003csup\u003e+\u003c/sup\u003e) = (0.1337, 0.1611, 0.2090, 0.1466, 0.1599, 0.1114, 0.1137, 0.1146, 0.1651, 0.1532, 0.1433, 0.1026, 0.1665, 0.1064, 0.0671, 0.0652, 0.0631, 0.0678, 0.0947, 0.0750, 0.0891, 0.0696, 0.0917, 0.0779, 0.0939, 0.0787, 0.1035, 0.0962, 0.0767, 0.1005, 0.1058); Negative Ideal Solution (\u003cstrong\u003eC\u003c/strong\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) = (0.1265, 0.1176, 0.0784, 0.1169, 0.1398, 0.0580, 0.1003, 0.1087, 0.0452, 0.0787, 0.0689, 0.0995, 0.0329, 0.0817, 0.0607, 0.0588, 0.0584, 0.0558, 0.0896, 0.0507, 0.0441, 0.0639, 0.0828, 0.0549, 0.0848, 0.0732, 0.0216, 0.0772, 0.0670, 0.0224, 0.0761).\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$${C^+}=(C_{1}^{+},C_{2}^{+}, \\cdots ,C_{m}^{+})$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$${C^ - }=(C_{1}^{ - },C_{2}^{ - }, \\cdots ,C_{m}^{ - })$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eAfter constructing the positive and negative ideal solutions, the Euclidean distances of the three schemes are calculated using equations (5) and (6). The fit degree, \u003cstrong\u003eS\u003c/strong\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, of each design scheme to the positive ideal solution is determined using Eq. (7), and the schemes are normalized and ranked accordingly. The final scores for each scheme are shown in Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e. A higher \u003cstrong\u003eS\u003c/strong\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e value indicates a higher degree of preferential selection, which means the scheme can cater to user needs better, while a lower \u003cstrong\u003eS\u003c/strong\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e value indicates a lower degree of preferential selection\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. As shown in Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, the final ranking of the three product schemes is Scheme 2\u0026thinsp;\u0026gt;\u0026thinsp;Scheme 3\u0026thinsp;\u0026gt;\u0026thinsp;Scheme 1, with Scheme 2 being the optimal solution. This indicates that the TM-SEM-TOPSIS evaluation method for elderly-friendly home nursing beds is feasible.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"367\" height=\"175\"\u003e\u003c/p\u003e\n\u003cp\u003eIn the above equations, \u003cstrong\u003ec\u003c/strong\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, \u003cstrong\u003ec\u003c/strong\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e are the values of \u003cstrong\u003eC\u003c/strong\u003e\u003csup\u003e+\u003c/sup\u003e and \u003cstrong\u003eC\u003c/strong\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e in the weighted standardized matrix respectively.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOverall scores of schemes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProduct scheme\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormalization\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScheme 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScheme 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScheme 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eDesign strategies for elderly-friendly home nursing beds\u003c/h3\u003e\n\u003cp\u003eIn safety evaluation, the explanatory level of the observed variables for the latent variables is ranked in descending order as follows: A\u003csub\u003e5\u003c/sub\u003e, A\u003csub\u003e3\u003c/sub\u003e, A\u003csub\u003e2\u003c/sub\u003e, A\u003csub\u003e4\u003c/sub\u003e, and A\u003csub\u003e1\u003c/sub\u003e. This ranking underscores the foundational role of stability and durability (A\u003csub\u003e5\u003c/sub\u003e) in ensuring the safety of the bed. To this end, high-strength materials such as quality steel or reinforced alloys should be used in the design. Additionally, the center of gravity of the bed rails should be close to the mattress or bed frame center to minimize wobbling and triangular support structures should be used to enhance the bed\u0026rsquo;s stability in case of long-term use and the unstable movements of elderly users. Additionally, it is crucial to ensure that elderly users can trigger alarms promptly. Instead of terminal touch alarms, button-type alarms should be installed on both sides of the bed, and body condition monitoring alarms should be introduced\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. For nursing beds with functions such as turning over, bed lifting, or auxiliary training, a one-touch emergency stop design should be adopted, and the stop should be placed in the middle of the bed to enable quick interruption to all operations in case of emergency to ensure the elderly user\u0026rsquo;s safety\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Lastly, non-slip mattresses and well-designed fixation devices should be used to reduce the risk of displacement. Regarding humanized design, the bed rails should be designed as foldable grids, with height and angle flexibly adjustable, and equipped with a safety locking system\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn functionality evaluation, scheme 2 emerges as markedly superior to its counterparts due to its comprehensive functions. According to Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the explanatory level of the observed variables for the latent variables is ranked in descending order as follows: B\u003csub\u003e3\u003c/sub\u003e, B\u003csub\u003e5\u003c/sub\u003e, B\u003csub\u003e2\u003c/sub\u003e, B\u003csub\u003e4\u003c/sub\u003e, B\u003csub\u003e6\u003c/sub\u003e, B\u003csub\u003e7\u003c/sub\u003e, B\u003csub\u003e8\u003c/sub\u003e, B\u003csub\u003e9\u003c/sub\u003e, and B\u003csub\u003e1\u003c/sub\u003e. Intelligent control is considered the most crucial function. With ongoing breakthroughs in IoT computing, cloud storage, and intelligent technology, artificial intelligence has been widely applied in various aspects of life\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Among these applications, intelligent control nursing beds can counterbalance the physiological deficiencies of elderly users and should be equipped with multiple automated functions such as back lifting, lowering, turning, and leg lifting to alleviate pressure on the lumbar, thoracic, and spinal regions of elderly users. Remote control is also necessary to enhance the autonomy of elderly users in controlling the bed\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Moreover, the design of voice control functions can enable intelligent adjustments, with which elderly users can adjust the nursing bed as per their preferences, thus achieving human-computer interaction at a deeper level\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Overall, intelligent nursing beds epitomize a significant leap forward in enhancing the life quality and health levels of elderly users by providing comfortable, safe, and personalized care and management services, making them an essential tool in modern elderly care. In terms of design details, easy-to-clean bed surfaces and accessories should be prioritized, as well as bathing and hair-washing functions. Drawing on principles of rehabilitation medicine, adjustable support poles and exercise aids should be designed to support elderly users\u0026rsquo; daily and rehabilitation training.\u003c/p\u003e\n\u003cp\u003eIn evaluating ease of use, the explanatory level of the observed variables for the latent variables is ranked in descending order as follows: E\u003csub\u003e2\u003c/sub\u003e, E\u003csub\u003e8\u003c/sub\u003e, E\u003csub\u003e5\u003c/sub\u003e, E\u003csub\u003e4\u003c/sub\u003e, E\u003csub\u003e7\u003c/sub\u003e, E\u003csub\u003e3\u003c/sub\u003e, E\u003csub\u003e6\u003c/sub\u003e, and E\u003csub\u003e1\u003c/sub\u003e. Drawing from this analysis, the design of nursing beds should focus on operability, compatibility, and mobility. Given the routine post-bed mobility needs of elderly individuals, such as the use of wheelchairs, the transition from bed to wheelchair emerges as a critical care challenge. Therefore, a modular design concept should be adopted, whereby the bed frame is capable of folding and contracting to a reduced horizontal length, and the bed board and mattress can be transformed into a movable, adjustable-angle wheelchair. This design would meet elderly users\u0026rsquo; needs for mobility and entertainment while reducing manufacturing costs\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Additionally, it is imperative to integrate ergonomic principles to enhance the gripping designs in key areas, such as expanding the gripping surface area and using non-slip materials to enhance safety\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Furthermore, a stable and reliable height adjustment function is necessary for multi-level adjustments, accommodating patients of different heights and care needs. Besides, the height adjustment function should allow both manual and electric operations, ensuring that the bed can still be operated manually in case of electric system failure, thus guaranteeing the nursing bed\u0026rsquo;s functionality under any circumstances\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn comfort evaluation, scheme 3 received the highest overall scores. The explanatory level of the observed variables for the latent variables is ranked in descending order as follows: D\u003csub\u003e1\u003c/sub\u003e, D\u003csub\u003e5\u003c/sub\u003e, D\u003csub\u003e3\u003c/sub\u003e, D\u003csub\u003e4\u003c/sub\u003e, and D\u003csub\u003e2\u003c/sub\u003e. Therefore, nursing bed designs should primarily focus on a deep integration of ergonomic principles in design to realize ergonomic rationality. By measuring the body data of different elderly groups, the structure, dimensions, and angle adjustments of the nursing bed can be designed to perfectly fit the natural curves and movement needs of the human body, reducing discomfort from prolonged bed rest\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. For instance, the headboard, footboard, and leg support panels should be adjustable to accommodate various lying positions and intelligent control of these functions should be enabled. Additionally, easy-to-clean and stain-resistant materials are essential to ensure a smooth bed surface. Components need to be easily detachable and washable, and a waterproof layer should be added to the bed base to prevent liquid infiltration\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Ventilation holes or breathable layer structures should be incorporated into the mattress, or sufficient space should be left at the bed base to facilitate air circulation. Finally, anti-decubitus designs are essential for combating bed sores caused by long-term pressure. These designs include dynamic air mattresses that periodically change pressure distribution and timed automatic position changes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. In summary, the design of nursing beds should focus on enhancing ergonomic rationality while also considering basic requirements such as waterproofing, dustproofing, anti-decubitus features, and air permeability, thereby comprehensively improving user comfort and experience.\u003c/p\u003e\n\u003cp\u003eIn aesthetics evaluation, the explanatory level of the observed variables for the latent variables in this dimension is ranked in descending order as follows: C\u003csub\u003e1\u003c/sub\u003e, C\u003csub\u003e2\u003c/sub\u003e, C\u003csub\u003e4\u003c/sub\u003e, C\u003csub\u003e3\u003c/sub\u003e. Due to the physiological decline of elderly individuals, transitions must be made at the corners and edges of the bed to enhance safety. The headboard, footboard, and bed rails should be subject to rounded, full curves and surfaces, and the overall design should be in symmetrical, balanced forms, thus creating a simple, balanced appearance that imparts a sense of stability and safety\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In material selection, while ensuring product comfort, the insensitivity brought by the degeneration of tactile functions among elderly users should be considered. The headboard and footboard should use wood materials that fit in with a home environment. Sound-absorbing cotton in a semi-enclosed structure would be suitable materials for cushions to create a safe and serene sleeping environment\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The bed frame and main structure should be made of powder-coated stainless steel, the mattress should use waterproof natural latex and soft gel, and the bed cover and sheets should use warm fleece fabric. Other parts should use ABS plastic and medical TPR soft materials\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Three aspects should be considered in color design: the subjective factors of elderly users, the furniture function, and the indoor environment. The subjective factors refer to the special physiological and psychological needs of elderly users, who generally prefer warm colors, beige, and warm gray tones, which are low in chroma and brightness and are conducive to a peaceful mood\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRegarding the theoretical implications, this study innovatively integrated TM, SEM, and TOPSIS to establish a comprehensive framework that addresses the limitations of each method, resolves the interference of interactions among indicators and reduces the impact of subjectivity. A case study was conducted, and this integrated method demonstrated its scientific validity and superiority. This interdisciplinary approach not only enriches theoretical research on user experience in the field of design evaluation but also provides new ideas and a methodological framework for the evaluation of similar products in the future. Moreover, this study provides a systematic and complete set of evaluation dimensions and indicators for elderly nursing beds, filling a gap in extant literature. It offers a new perspective on research and provides reliable evidence for the design and development of nursing beds. Lastly, the constructed design hierarchy strategy provides effective guidance for the design and improvement of schemes, enhancing the adaptability and scientific rigor of products. In terms of practical implications, the evaluation system for nursing beds established in this study equips designers with a more profound comprehension of user requirements, thus facilitating the design of products catering to the usage habits and safety requirements of elderly users. This, in turn, can improve user satisfaction\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Additionally, the design strategies and comparative approaches introduced in this study offer systematic guidance for enterprises, which can promote the development and innovation of elderly-friendly products. Government authorities can leverage the evaluation indicators and system referenced in this study in formulating industrial standards and policies to advance the standardization and normalization of elderly-friendly products, thus enhancing the service level and product quality within the industry\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite its significant value in innovating research methods and perspectives and valuable insights for the design and evaluation of elderly-friendly nursing beds, it still has certain limitations. (1) This study combined qualitative and quantitative research methods to enhance the scientific and systematic nature of the design evaluation and decision-making for elderly-friendly home nursing beds. However, there is inevitably some subjectivity in the scoring process. Future research can measure the physiological data via relevant experimental equipment and further expand the sample size to obtain more objective results\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. (2) The design demands for nursing beds will continue to evolve with technological advancements and social development. Therefore, future research needs to engage in continuous modification and enhancement of the evaluation indicators and model to ensure their adaptability and foresightedness.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eElderly-friendly home nursing beds are pivotal in building an active aging society. As societal structures evolve, design influencing factors become increasingly complex. This research demonstrates that the innovative integration of TM, SEM, and TOPSIS can ensure the rationality and scientific nature of indicator quantification and better evaluate the pertinence and effectiveness of designs. The case study in this research manifests that this integrated method not only guarantees the comprehensiveness and objectivity of the selection of evaluation indicators but also provides a quantitative approach to address ambiguity and uncertainties in the design evaluation. It also offers compelling evidence and guidance for design and improvement schemes. Specifically, based on text mining and user interviews, this study extracted indicators in multiple dimensions for evaluating the design of elderly-friendly home nursing beds, ensuring a high relevance between design and user needs. 31 evaluation indicators were identified across five dimensions using SEM. Safety, functionality, aesthetics, comfort, and ease of use, and their priorities were determined. In the case study, Scheme 2 was selected as the optimal solution via TOPSIS. The selection process comprehensively considered the impact of subjective and objective factors on the evaluation. Ultimately, this research established a comprehensive and systematic evaluation system for elderly-friendly home nursing beds and proposed design strategies across five dimensions, offering significant insights for related research and design.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAdditional information\u003c/h2\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to D.L. or H.L.\u003c/p\u003e\n\u003cp\u003eReprints and permissions information is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.nature.com/reprints\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003ePublisher\u0026rsquo;s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afliations.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization: D.L., H.L. Data curation: D.L., Y.S. Formal analysis: D.L. Investigation: D.L. Methodology: D.L., Y.S. Project administration: H.L. Writing-original draft: D.L. Writing-review \u0026amp; editing: D.L., H.L.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003evan der Weijden, D. A. Y., Koerts, N. D. K., van Munster, B. C., van der Zee, H. H. \u0026amp; Horv\u0026aacute;th, B. Hidradenitis suppurativa tarda: defining an understudied elderly population. \u003cem\u003eBr. J. Dermatol.\u003c/em\u003e \u003cstrong\u003e190\u003c/strong\u003e, 105-113 (2023).\u003c/li\u003e\n\u003cli\u003eGeng, X., Li, Y., Wang, D. \u0026amp; Zhou, Q. A scenario-driven sustainable product and service system design for elderly nursing based on QFD. \u003cem\u003eAdv. Eng. Inform.\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 102368 (2024).\u003c/li\u003e\n\u003cli\u003eMiskelly, F. G. Assistive technology in elderly care. \u003cem\u003eAge Ageing\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 455-458 (2001).\u003c/li\u003e\n\u003cli\u003eSu, X. \u0026amp; Fu, R. in \u003cem\u003eDigital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Anthropometry, Human Behavior, and Communication.\u003c/em\u003e (ed Vincent G. Duffy) 135-147 (Springer International Publishing).\u003c/li\u003e\n\u003cli\u003eZhu, Y.\u003cem\u003e et al.\u003c/em\u003e Wheelchair automatic docking method for body-separated nursing bed based on grid map. \u003cem\u003eIEEE Access\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 79549-79561 (2021).\u003c/li\u003e\n\u003cli\u003eLi, Y.\u003cem\u003e et al.\u003c/em\u003e A coloring and timing brain-computer interface for the nursing bed robot. \u003cem\u003eComput. Electr. Eng.\u003c/em\u003e \u003cstrong\u003e95\u003c/strong\u003e, 107415 (2021).\u003c/li\u003e\n\u003cli\u003eYuan, B., Ye, J., Wu, X. \u0026amp; Yang, C. Applying latent dirichlet allocation and support vector regression to the aesthetic design of medical nursing beds. \u003cem\u003eJ. Comput. Inf. Sci. Eng.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 051014 (2023).\u003c/li\u003e\n\u003cli\u003eZhou, Z., Cheng, J., Wei, W. \u0026amp; Lee, L. Validation of evaluation model and evaluation indicators comprised Kansei Engineering and eye movement with EEG: an example of medical nursing bed. \u003cem\u003eMicrosyst. Technol.\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 1317-1333 (2021).\u003c/li\u003e\n\u003cli\u003eTiwari, V., Jain, P. K. \u0026amp; Tandon, P. Product design concept evaluation using rough sets and VIKOR method. \u003cem\u003eAdv. Eng. Inform.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 16-25 (2016).\u003c/li\u003e\n\u003cli\u003eTian, G., Zhang, H., Zhou, M. \u0026amp; Li, Z. AHP, gray correlation, and TOPSIS combined approach to green performance evaluation of design alternatives. \u003cem\u003eIEEE T. Syst. Man. Cy-S.\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 1093-1105 (2018).\u003c/li\u003e\n\u003cli\u003eZhao, H., Wang, Y. \u0026amp; Liu, X. The evaluation of smart city construction readiness in China using CRITIC-G1 method and the bonferroni operator. \u003cem\u003eIEEE Access\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 70024-70038 (2021).\u003c/li\u003e\n\u003cli\u003eLu, N., Li, Y. \u0026amp; Xu, B. Evaluation of the suitability of smart health products for aging based on the IIVAHP-CRITIC model: A case study of smart health kiosk. \u003cem\u003eSustainability\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 9212 (2022).\u003c/li\u003e\n\u003cli\u003eHu, S.\u003cem\u003e et al.\u003c/em\u003e An evaluation method for product design solutions for healthy aging companionship. \u003cem\u003eFront. Public Health\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 919300 (2022).\u003c/li\u003e\n\u003cli\u003eTang, Q., Luo, Y. W. \u0026amp; Wu, X. D. Research on the evaluation method of agricultural intelligent robot design solutions. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, e0281554 (2023).\u003c/li\u003e\n\u003cli\u003eMuth\u0026eacute;n, B. \u0026amp; Asparouhov, T. Causal effects in mediation modeling: An introduction with applications to latent variables. \u003cem\u003eStruct. Equ. Modeling\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 12-23 (2015).\u003c/li\u003e\n\u003cli\u003eAl-Emran, M., AlQudah, A. A., Abbasi, G. A., Al-Sharafi, M. A. \u0026amp; Iranmanesh, M. Determinants of using AI-Based chatbots for knowledge sharing: evidence from PLS-SEM and fuzzy sets (fsQCA). \u003cem\u003eIEEE T. Eng. Manage.\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 4985-4999 (2024).\u003c/li\u003e\n\u003cli\u003eLin, M.-C., Wang, C.-C., Chen, M.-S. \u0026amp; Chang, C. A. Using AHP and TOPSIS approaches in customer-driven product design process. \u003cem\u003eComput. Ind.\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 17-31 (2008).\u003c/li\u003e\n\u003cli\u003eYazdani, H., Baneshi, M. \u0026amp; Yaghoubi, M. Techno-economic and environmental design of hybrid energy systems using multi-objective optimization and multi-criteria decision making methods. \u003cem\u003eEnerg. Convers. Manage.\u003c/em\u003e \u003cstrong\u003e282\u003c/strong\u003e, 116873 (2023).\u003c/li\u003e\n\u003cli\u003eRen, G. \u0026amp; Hong, T. Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews. \u003cem\u003eInform. Process. Manag.\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 1425-1438 (2019).\u003c/li\u003e\n\u003cli\u003eAhani, A.\u003cem\u003e et al.\u003c/em\u003e Evaluating medical travelers\u0026rsquo; satisfaction through online review analysis. \u003cem\u003eJ. Hosp. Tour. Manag.\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 519-537 (2021).\u003c/li\u003e\n\u003cli\u003eHair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M. \u0026amp; Thiele, K. O. Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. \u003cem\u003eJ. of the Acad. Mark. Sci. \u003c/em\u003e\u003cstrong\u003e45\u003c/strong\u003e, 616-632 (2017).\u003c/li\u003e\n\u003cli\u003eTan, G. W.-H., Ooi, K.-B., Leong, L.-Y. \u0026amp; Lin, B. Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. \u003cem\u003eComput. Hum. Behav.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 198-213 (2014).\u003c/li\u003e\n\u003cli\u003eAstrachan, C. B., Patel, V. K. \u0026amp; Wanzenried, G. A comparative study of CB-SEM and PLS-SEM for theory development in family firm research. \u003cem\u003eJ. Fam. Bus. Strateg.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 116-128 (2014).\u003c/li\u003e\n\u003cli\u003eSeva, R. R., Gosiaco, K. G., Santos, M. C. \u0026amp; Pangilinan, D. M. Product design enhancement using apparent usability and affective quality. \u003cem\u003eAppl. Ergon.\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 511-517 (2011).\u003c/li\u003e\n\u003cli\u003eShore, L.\u003cem\u003e et al.\u003c/em\u003e Exoscore: A design tool to evaluate factors associated with technology acceptance of soft lower limb exosuits by older adults. \u003cem\u003eHum. Factors\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 391-410 (2020).\u003c/li\u003e\n\u003cli\u003eCao, X., Deng, M. \u0026amp; Li, H. How does e-commerce city pilot improve green total factor productivity?Evidence from 230 cities in China. \u003cem\u003eJ. Environ. Manage.\u003c/em\u003e \u003cstrong\u003e289\u003c/strong\u003e, 112520 (2021).\u003c/li\u003e\n\u003cli\u003eBoland, M. R.\u003cem\u003e et al.\u003c/em\u003e From expert-derived user needs to user-perceived ease of use and usefulness: a two-phase mixed-methods evaluation framework. \u003cem\u003eJ. Biomed. Inform.\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 141-150 (2014).\u003c/li\u003e\n\u003cli\u003eHan, S. H. \u0026amp; Hong, S. W. A systematic approach for coupling user satisfaction with product design. \u003cem\u003eErgonomics\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 1441-1461 (2003).\u003c/li\u003e\n\u003cli\u003eZhang, C., Liu, Y., Lu, W. \u0026amp; Xiao, G. Evaluating passenger satisfaction index based on PLS-SEM model: Evidence from Chinese public transport service. \u003cem\u003eTransport. Res. A-pol.\u003c/em\u003e \u003cstrong\u003e120\u003c/strong\u003e, 149-164 (2019).\u003c/li\u003e\n\u003cli\u003eJo, H. I. \u0026amp; Jeon, J. Y. Influence of indoor soundscape perception based on audiovisual contents on work-related quality with preference and perceived productivity in open-plan offices. \u003cem\u003eBuild. Environ.\u003c/em\u003e \u003cstrong\u003e208\u003c/strong\u003e, 108598 (2022).\u003c/li\u003e\n\u003cli\u003eHair, J. F., Sarstedt, M., Ringle, C. M. \u0026amp; Mena, J. A. An assessment of the use of partial least squares structural equation modeling in marketing research. \u003cem\u003eJ. of the Acad. Mark. Sci.\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 414-433 (2012).\u003c/li\u003e\n\u003cli\u003eWang, B., Shao, C., Li, J., Weng, J. \u0026amp; Ji, X. Holiday travel behavior analysis and empirical study under integrated multimodal travel information service. \u003cem\u003eTransp. Policy\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 21-36 (2015).\u003c/li\u003e\n\u003cli\u003eBagozzi, R. P. \u0026amp; Yi, Y. On the evaluation of structural equation models. \u003cem\u003eJAMS\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 74-94 (1988).\u003c/li\u003e\n\u003cli\u003eLeite, W. L., Shen, Z., Marcoulides, K., Fisk, C. L. \u0026amp; Harring, J. Using ant colony optimization for sensitivity analysis in structural equation modeling. \u003cem\u003eStruct. Equ. Modeling\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 47-56 (2022).\u003c/li\u003e\n\u003cli\u003eR\u0026ouml;nkk\u0026ouml;, M. \u0026amp; Cho, E. An updated guideline for assessing discriminant validity. \u003cem\u003eOrgan. Res. Methods\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 6-14 (2020).\u003c/li\u003e\n\u003cli\u003eWang, K., Xu, Y., Wang, C., Tan, M. \u0026amp; Chen, P. A Corrected Goodness-of-Fit Index (CGFI) for model evaluation in structural equation modeling. \u003cem\u003eStruct. Equ. Modeling\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 735-749 (2020).\u003c/li\u003e\n\u003cli\u003eDanks, N. P., Sharma, P. N. \u0026amp; Sarstedt, M. Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM). \u003cem\u003eJ. Bus. Res.\u003c/em\u003e \u003cstrong\u003e113\u003c/strong\u003e, 13-24 (2020).\u003c/li\u003e\n\u003cli\u003eHair, J. F., Sharma, P. N., Sarstedt, M., Ringle, C. M. \u0026amp; Liengaard, B. D. The shortcomings of equal weights estimation and the composite equivalence index in PLS-SEM. \u003cem\u003eEur. J. Mark.\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 30-55 (2024).\u003c/li\u003e\n\u003cli\u003eLiu, H. C., Wang, L. E., Li, Z. \u0026amp; Hu, Y. P. Improving risk evaluation in FMEA with cloud model and hierarchical TOPSIS method. \u003cem\u003eIEEE Trans. Fuzzy Syst.\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 84-95 (2019).\u003c/li\u003e\n\u003cli\u003eHu, Y., Wu, L., Shi, C., Wang, Y. \u0026amp; Zhu, F. Research on optimal decision-making of cloud manufacturing service provider based on grey correlation analysis and TOPSIS. \u003cem\u003eInt. J. Prod. Res.\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 748-757 (2020).\u003c/li\u003e\n\u003cli\u003eZhang, Z. \u0026amp; Li, Z. Consensus-based TOPSIS-Sort-B for multi-criteria sorting in the context of group decision-making. \u003cem\u003eAnn. Oper. Res.\u003c/em\u003e \u003cstrong\u003e325\u003c/strong\u003e, 911-938 (2023).\u003c/li\u003e\n\u003cli\u003eMurphy, M. M. Telehealth alerts and nurse response. \u003cem\u003eTelemed. J. E. Health\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 517-526 (2018).\u003c/li\u003e\n\u003cli\u003eLi, Y., Pan, J., Wang, F. \u0026amp; Yu, Z. A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control. \u003cem\u003eIEEE T. Biomed. Eng.\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 3156-3166 (2013).\u003c/li\u003e\n\u003cli\u003eBoocock, M. G., Weyman, A. K. \u0026amp; McIlroy, R. Bedside safety rails: assessment of strength requirements and the appropriateness of current designs. \u003cem\u003eErgonomics\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 631-650 (2006).\u003c/li\u003e\n\u003cli\u003eNi, T. F., Wang, J. L., Chen, C. K., Shih, F. \u0026amp; Wang, J. Can a prolonged healing pressure injury be benefited by using an AI mattress? A case study. \u003cem\u003eBMC Geriatr.\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 307 (2024).\u003c/li\u003e\n\u003cli\u003eRogowski, A. Web-based remote voice control of robotized cells. \u003cem\u003eRobot. Cim-Int. Manuf.\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 77-89 (2013).\u003c/li\u003e\n\u003cli\u003eMiyazaki, Y., Hirano, K., Kitamura, K. \u0026amp; Nishida, Y. Analysis of relationship between natural standing behavior of elderly people and a class of standing aids in a living space. \u003cem\u003eSensors\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 1178 (2022).\u003c/li\u003e\n\u003cli\u003eMiller, K. E. M., Chatterjee, P. \u0026amp; Werner, R. M. Trends in supply of nursing Home beds, 2011-2019. \u003cem\u003eJAMA Netw. Open\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e230640 (2023).\u003c/li\u003e\n\u003cli\u003eWalker, D. S., Lee, W. Y., Skov, N. M., Berger, C. F. \u0026amp; Athley, B. D. Investigating users\u0026apos; requirements: computer-based anatomy learning modules for multiple user test beds. \u003cem\u003eJ. Am. Med. Inform. Assoc.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 311-319 (2002).\u003c/li\u003e\n\u003cli\u003eHung, L. C.\u003cem\u003e et al.\u003c/em\u003e Design and evaluation of the bed-cleaning mobile application. \u003cem\u003eJ. Nurs. Manag.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 771-776 (2020).\u003c/li\u003e\n\u003cli\u003eBarsocchi, P. Position recognition to support bedsores prevention. \u003cem\u003eIEEE J. Biomed. Health Inform.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 53-59 (2013).\u003c/li\u003e\n\u003cli\u003eZhou, Z., Wang, L., Ye, R. \u0026amp; Yue, H. A humanistic-care factors application hierarchical design-model for intelligent elderly products. \u003cem\u003eHeliyon\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e13734 (2023).\u003c/li\u003e\n\u003cli\u003evan Kesteren, I., de Bruijn, S. \u0026amp; Stappers, P. J. Evaluation of materials selection activities in user-centred design projects. \u003cem\u003eJ. Eng. Design\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 417-429 (2008).\u003c/li\u003e\n\u003cli\u003eMao, Y., Li, P. \u0026amp; Hao, P. The effects of wooden furniture color, floor material, and age on design evaluation, visual attention, and emotions in office environments. \u003cem\u003eBuildings\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1498 (2024).\u003c/li\u003e\n\u003cli\u003eKujala, S. Effective user involvement in product development by improving the analysis of user needs. \u003cem\u003eBehav. Inform. Technol.\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 457-473 (2008).\u003c/li\u003e\n\u003cli\u003eWang, H., Liu, J. M. \u0026amp; Chen, L. A comparative analysis of government intervention under the EPR system: Eco-design and authorized remanufacturing. \u003cem\u003eExpert Syst. Appl.\u003c/em\u003e \u003cstrong\u003e249\u003c/strong\u003e, 123680 (2024).\u003c/li\u003e\n\u003cli\u003eKim, W., Kim, N., Lyons, J. B. \u0026amp; Nam, C. S. Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach. \u003cem\u003eAppl. Ergon.\u003c/em\u003e \u003cstrong\u003e85\u003c/strong\u003e, 103056 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TM-SM-TOPSIS, elderly-friendly products, home nursing bed, design evaluation","lastPublishedDoi":"10.21203/rs.3.rs-5165517/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5165517/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research introduces an integrated evaluation model: TM-SEM-TOPSIS, to address the subjectivity and one-sidedness in indicator derivation, weight calculation, and scheme ranking in the design evaluation process. First, text mining (TM) and interviews were employed to select the evaluation indicators for elderly-friendly home nursing beds. Second, Structural Equation Modeling (SEM) was adopted to establish a model to gauge user satisfaction, and significant evaluation indicators were extracted using principal component analysis. AMOS was applied to analyze the model\u0026rsquo;s goodness of fit and how it works, elucidating the coefficients of evaluation indicators. Lastly, our research adopted the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to compute the Euclidean distances and the relative fit of the three nursing beds and make a ranked evaluation. The product evaluation system, design strategies, and comparative method discussed in our research can offer a vital reference for selecting or developing elderly-friendly products.\u003c/p\u003e","manuscriptTitle":"An integrated method to evaluate the design of elderly-friendly home nursing beds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 08:06:31","doi":"10.21203/rs.3.rs-5165517/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dff5d3f2-d939-4707-ba43-4894538e3712","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38852365,"name":"Biological sciences/Psychology/Human behaviour"},{"id":38852366,"name":"Physical sciences/Engineering/Mechanical engineering"},{"id":38852367,"name":"Biological sciences/Psychology"},{"id":38852368,"name":"Physical sciences/Engineering"},{"id":38852369,"name":"Physical sciences/Mathematics and computing/Scientific data"},{"id":38852370,"name":"Physical sciences/Mathematics and computing/Statistics"}],"tags":[],"updatedAt":"2024-12-04T11:23:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-15 08:06:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5165517","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5165517","identity":"rs-5165517","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}