Mobility Aid Design for the Elderly (MADE): a design thinking approach using a smart walker as a case study | Humanities and Social Sciences Communications

Nov 05, 2024

Humanities and Social Sciences Communications volume 11, Article number: 1469 (2024) Cite this article

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Assistive technologies (ATs) are essential for promoting social equity, particularly as the global population ages. This study addresses the persistent issue of high abandonment rates in mobility aids, specifically smart walkers (SWs), by developing a systematic design thinking methodology called Mobility Aid Design for the Elderly (MADE). The MADE approach integrates the Stanford Design Thinking Model (SDTM) with specific design methods tailored for SWs. It includes stages of empathy, define, ideate, prototype, and test, each providing actionable steps to ensure user-centered design. The methodology was applied in a case study involving 20 participants from urban settings, including elderly users and designers, using a combination of random, snowball, and purposive sampling. Interviews utilized the Evaluation Grid Method (EGM) and were analyzed using Quantification Theory Type I (QTT1) and TRIZ. The study found significant cognitive differences in preferences between elderly users and designers, particularly in esthetics and digital features. These findings were used to generate innovative design guidelines to reduce these cognitive gaps and improve user acceptance of SWs. The paper discusses the need for continuous innovation in assistive technology design to enhance social inclusion and reduce inequality. The study’s limitations, such as the use of images instead of physical products, and its urban focus, are acknowledged, suggesting areas for future research.

Assistive technologies (ATs) play a crucial role in promoting social equity and achieving the United Nations Sustainable Development Goals (SDGs). (Tebbutt et al., 2016; World Health Organization, 2022). By maintaining or improving functional activities and independence, these technologies are vital in reducing social inequality and enhancing the inclusion of vulnerable populations. As the global population ages, the importance of ATs in active aging strategies becomes increasingly apparent, facilitating social participation and reducing social inequality (Leite et al., 2018; Zannella et al., 2021). However, there is a substantial gap between the demand for assistive products and their availability. Globally, over one billion people require assistive devices, a number expected to exceed two billion by 2050, yet only 5–15% of this demand is currently me (Rohwerder, 2018). This significant shortfall severely hinders social equity and the inclusion of vulnerable populations by limiting the mobility and independence of individuals with disabilities (Zhang, 2024).

Mobility aids are particularly important for enhancing personal control and freedom of movement, emphasizing independence as a key factor in achieving these goals (Resnik et al., 2009). However, despite their importance, these products often face high abandonment rates. Research indicates that the use of assistive devices is essential for both indoor and outdoor mobility of the elderly, significantly enhancing their quality of life (Tarsuslu Şimşek et al., 2012). Despite this, the abandonment rates for mobility aids remain high, underscoring the need for a better understanding of user needs and preferences. The advent of digital technologies, such as smart walkers, represents a significant development in mobility aid (Shin et al., 2016). Yet, their complexity poses challenges in terms of acceptance and user experience. To improve the accessibility and effectiveness of smart walkers, ongoing research includes standard development, database creation, and methods to enhance sensory experiences and communication (Saracchini et al., 2015; Stavropoulos et al., 2020; Chatterjee and Roy, 2021).

Design thinking (DT) has emerged as a vital approach for providing innovative solutions to the challenges faced by mobility aids. Acceptance of these devices is influenced by a variety of factors, emphasizing the need to focus on user experience. Users’ perspectives and needs often encompass multiple dimensions, making it essential to address cognitive differences among different populations, such as designers and users (Guo et al., 2017). In the digital age, digital assistive technologies (DATs) are crucial, but the digital divide often leaves the elderly overwhelmed by complex digital procedures (Liu et al., 2022). This not only hinders their ability to benefit from digital technologies but also exacerbates social inequality. Therefore, the user experience, value, and acceptance of digital assistive technologies still require further exploration (Bäccman et al., 2020). Addressing the diversity in user needs and cognitive differences is crucial for improving product acceptance (Gong et al., 2022). Additionally, transforming cognitive differences into innovative strategies remains a key challenge.

The development of design thinking methods specifically for mobility aids, rather than general assistive products, is essential due to the unique challenges and requirements these devices present (Hunter-Zaworski and Zaworski, 2001). Mobility aids, such as wheelchairs, walkers, and canes, are critical for enhancing the mobility and independence of individuals with disabilities or mobility impairments, directly impacting their safety and quality of life (Aminzadeh and Edwards, 2000). These devices play a vital role in promoting social equality by enabling individuals to participate fully in society (Boerema et al., 2017). Despite the advanced functionalities of modern mobility aids, powered by assistive and digital technologies, and their high standards of quality, safety, reliability, and durability (van Velsen et al., 2019), there remains a high rate of abandonment or non-compliance among users. This issue is not due to inadequate technology or insufficient policy support but rather a lack of understanding of the diverse needs of mobility aid users. Non-use can result from various factors, including age, gender, identity, experience, social influences, cognitive load from smart technologies, or even esthetic preference (Dos Santos et al., 2022). Design thinking, with its emphasis on empathy, user-centered design, and iterative processes, is well-suited to address these challenges by deeply understanding and responding to user needs (Bazzano et al., 2023). However, existing design thinking models are often abstract and lack practical implementability and transparency (Zhang et al., 2023). They do not integrate sufficiently specific interdisciplinary methods required to effectively design mobility aids that truly meet user needs. Kleinsmann et al. confirmed this view through their study, after proving the innovative value of DT through their study, they suggested that design thinking is not the only reason for innovation success, but also requires the implementation of specific design methods in conjunction (Kleinsmann et al., 2017). By developing a more concrete and actionable design thinking framework, and incorporating interdisciplinary approaches, designers can create more effective, safe, and user-friendly mobility aids, significantly improving the daily lives of users and enhancing their social participation (Boerema et al., 2017; Susmartini et al., 2017; Hunter et al., 2021).

In conclusion, despite significant advancements in the development of assistive technologies, there remains a substantial gap in systematically applying design thinking to address social inequalities inherent in mobility aids. This study aims to bridge this gap by proposing a systematic design thinking methodology for the design of smart walkers, emphasizing the potential for innovation through the lens of social inequality. By focusing on the unique needs of vulnerable populations, this approach seeks to enhance user preference and acceptance of smart walkers, reduce cognitive differences between designers and users, and contribute to building a more inclusive society. The entire research process, as outlined in Fig. 1, illustrates the study’s framework. The remainder of this paper is structured as follows: section “Literature review” reviews the literature to justify the chosen design methods at each stage of design thinking. Section “The proposed design approach (MADE)” introduces the framework for the proposed Mobility Aid Design for the Elderly (MADE) methodology. Section “Case study” details the implementation process of MADE using the design of a smart walker as an example. Section “Discussion” discusses the results in relation to the literature. Finally, the section “Conclusion” concludes the paper and outlines potential directions for future research.

Research flowchart.

This section aims to briefly elucidate the theoretical foundations of the design thinking model and the specific design methods within its five stages. These methods include the Evaluation Grid Method (EGM), Quantification Theory Type 1 (QTT1), Teoriya Resheniya Izobretatelskikh Zadatch (TRIZ), Finite Structure Method (FSM), Morphological Charts (MC), and Fuzzy Comprehensive Evaluation (FCE), to assess their suitability in the design phase of mobile assistive devices. These methods exhibit unique advantages at different stages and form a complementary framework. Their comprehensive analysis will provide theoretical support and practical guidance for the methodology chosen in this study.

Design thinking improves the cognitive decision-making ability of decision-makers and reduces cognitive differences between individuals (Liedtka, 2015). Design thinking (DT) is often defined as an analytical and creative process that allows designers to experiment, create models and prototypes, gather feedback, and redesign (Razzouk and Shute, 2012). The Stanford Design Thinking Model (SDTM) is widely used in various applications, including innovation design (Martins et al., 2020), business modeling (He and Ortiz, 2021), education (Ní Shé et al., 2021), and healthcare (Hsieh et al., 2023). While many studies have demonstrated the innovative value of DT in numerous fields, a complementary combination of other relevant innovation methods is necessary to ensure the reliability of innovation activities. Design thinking encompasses multiple models (Vagal et al., 2020).

Among these, the design thinking model proposed by IDEO, introduced by Tim Brown and David Kelley, is regarded as one of the most influential. This model emphasizes a human-centered approach through its five phases: empathizing, defining, ideating, prototyping, and testing. Research has shown that the IDEO model is widely applicable and effective in various fields such as business, education, and healthcare (Brown, 2008). Similarly, the design thinking model developed by Stanford University’s Hasso-Plattner Institute of Design (d.school) also includes these five stages, highlighting the value of interdisciplinary collaboration and rapid prototyping. This method has proven particularly effective in educational and innovative projects (Meinel et al., 2011).

Additionally, the Double Diamond model proposed by the Design Council describes the design process through four phases: discover, define, develop, and deliver. This model emphasizes the importance of divergent and convergent thinking, helping designers balance broad exploration with precise definition, ensuring the design process is both creative and goal-oriented (Design Council, 2024). The 3I model, developed from the French C-K theory, includes the stages of inspiration, ideation, and implementation, particularly stressing the role of user observation and market research in the inspiration phase (Hatchuel and Weil, 2009).

Empathy in design helps uncover and fulfill users’ latent needs, leading to better solutions (Li and Hölttä-Otto, 2022). The EGM is particularly useful in the empathy stage to effectively capture these needs. EGM captures people’s cognitive concepts of a product through in-depth interviews (Lu et al., 2021). Derived from Kelly’s repertory grid technique (Kelly, 2003), EGM uses a structured process:

Respondents compare samples two by two and select their favorite, providing reasons as the original evaluation items (OEI).

Using a laddering technique, respondents articulate higher-level abstract evaluation items (AEI) and lower-level concrete evaluation items (CEI) based on the initial causes.

The results are organized into a three-tier hierarchical chart, forming a personal evaluation hierarchical map for each respondent.

Individual evaluation hierarchical maps are combined and counted to summarize the overall evaluation hierarchical map.

EGM has been validated in various fields, particularly in product design. For instance, a preference-based assisted product design model (PAPDM) was constructed and validated through a case study (Zhang et al., 2023). Diverse user requirements were successfully integrated into a quantitative solution for universal design (Inoue et al., 2021). Additionally, EGM was combined with fuzzy modeling for new product development, enhancing design efficiency and meeting consumer expectations (Xi et al., 2022). These studies highlight EGM’s effectiveness in translating user preferences into actionable design insights, providing clear guidelines for designers. Despite its proven advantages, EGM is underutilized in the empathy stage of design thinking, particularly for understanding elderly users’ needs. Integrating EGM into this stage could significantly enhance the depth of user insights.

However, EGM also has its limitations. One significant drawback is its subjective nature, relying heavily on personal interviews and interpretations, which can introduce bias (Shen et al., 2012). This subjectivity can affect the reliability of the results. Furthermore, EGM lacks a quantifiable, data-driven approach, making it challenging to generalize findings across broader populations. This limitation suggests a need for combining EGM with more quantitative methods to improve its reliability and applicability in diverse contexts.

In the define stage of design thinking, it’s crucial to analyze user data to clarify and quantify preferences, aiding in precisely defining design problems. QTT1 is a method that achieves this by explaining the relationship between dependent and independent variables through quantitative analysis of qualitative data. QTT1 is relatively simple and transparent, validated in fields such as product design and marketing (Ho and Hou, 2015; Wu and Chen, 2022). It captures the weighting relationship between respondents’ subjective preferences and product characteristics, translating qualitative insights into quantifiable data. However, its numerical weights can reflect contradictions due to differences in respondents’ subjective preferences (Ko et al., 2015). While QTT1 is effective in identifying cognitive differences among users, it has been less explored in the context of design thinking’s define stage. Additionally, QTT1’s reliance on subjective data introduces potential biases and lacks a robust, data-driven approach, suggesting the need for integration with other quantitative methods to enhance reliability and applicability across diverse contexts.

In the ideate stage of design thinking, generating creative solutions is essential. TRIZ, or the Theory of Inventive Problem Solving, effectively aids this process by addressing and resolving contradictions (Hernandez et al., 2013). Originally proposed by Altshuller, TRIZ analyzes approximately 2.5 million invention patents to solve technical problems and provide innovative product structures (Zhang et al., 2014). The strength of TRIZ lies in its ability to eliminate contradictions rather than seeking compromises (Di Gironimo et al., 2013). TRIZ identifies three types of contradictions: administrative, technical, and physical, with technical (TC) and physical contradictions (PC) being more specific and interrelated (Rousselot et al., 2012).

The specific processes of TRIZ are as follows:

Identify the specific contradictions present in the elements and transform them into TRIZ contradictions (PC and TC).

Use the Contradiction Matrix and Inventive Principles to resolve the TCs.

Resolve the PC using the Inventive Principles and the Separation Principle (including Spatial Separation, Conditional Separation, Temporal Separation, and Whole-Part Separation).

Generate innovation strategies based on innovations proposed by inventive principles.

TRIZ has been successfully applied in various fields, such as the design of bicycle handles (Yao et al., 2023), robots in pipes (Xie and Liu, 2023), and portable fire extinguishers (Asyraf et al., 2020). Its ability to solve complex problems is further enhanced when combined with other methods like ECQFD and AHP (Vinodh et al., 2014), Pahl and Beitz’s systematic approach (Mayda and Börklü, 2014), and TOPSIS (Hameed et al., 2022). These applications highlight TRIZ’s versatility and effectiveness in innovative design.

TRIZ is particularly suitable for the ideate stage due to its structured approach to resolving contradictions, and fostering creativity and innovation. Its advantages include providing clear methodologies for problem-solving and enhancing design efficiency. However, TRIZ’s complexity and the need for specialized knowledge can be a barrier, and its integration with other methods is often necessary to address broader design challenges. This integration needs to represent a significant research gap. Additionally, TRIZ’s reliance on patent analysis may limit its applicability in non-technical fields, and its effectiveness can be constrained by the quality of available data.

In the prototype stage of design thinking, visualizing and testing innovation strategies through product appearance is crucial. The FSM helps achieve a rational layout of product functions, supporting form design (Lu and Hsiao, 2022). FSM involves:

Determining main product functions.

Identifying a limited number of functional modules.

Layout of icons for various functional modules.

Filtering and determining the optimal layout.

MCs complement FSM by offering detailed solutions for each functional module, presenting more options (Asyraf et al., 2020). MC allows selecting different options and combining them into a comprehensive solution (Lu et al., 2021). Detailed procedures are available in a previous study (Hsiao and Ko, 2013).

FSM and MC have been validated in various fields for optimizing product design. FSM provides a structured method for arranging functional components, enhancing practical design. MC broadens design possibilities by offering multiple configurations. Combining FSM and MC ensures a comprehensive approach to product development, enabling effective and innovative solutions. However, FSM’s structure may limit creativity, and MC’s extensive options can complicate decision-making, necessitating a balanced application of both methods.

In the test stage of design thinking, evaluating whether the generated alternatives achieve the goal is crucial. FCE effectively addresses the uncertainty and fuzziness in product evaluation (Zhou and Chan, 2017). FCE has wide applications in product evaluation (Wu et al., 2021; Ji and Yu, 2022). The specific process of FCE is as follows:

Determine the evaluation indexes.

Calculate the weights of the evaluation indicators.

Determine the selection method for each evaluation index.

Calculate the comprehensive evaluation score.

Given that innovative design programs may be conceptualized based on interview results, the factors obtained will serve as evaluation indicators. Interviewees will participate in the program evaluation to assess the degree of compliance with the design goals. This approach quantifies the factors mentioned by interviewees, integrating previous steps with the evaluation results.

The above literature review shows that while the SDTM effectively addresses cognitive differences and alleviates social inequalities, it lacks detailed guidance on specific methods for each stage of the design process. Therefore, based on the review of the advantages and limitations of each specific design method, this study proposes a systematic design thinking methodology for mobility aid design, named Mobility Aid Design for the Elderly (MADE) (see Fig. 2). This methodology builds on the five-stage framework of SDTM, focusing on providing specific, step-by-step design methods for each stage. These methods complement each other, maximizing their strengths and mitigating their limitations to ensure a robust and comprehensive design process. The specific implementation steps of MADE are as follows:

The proposed design approach (MADE).

Stage 1: Empathy

Preparation: Extract relevant product terms for sample search and ensure sample photos are clear and complete. Select sample images for interviews after comparison and screening.

Recruitment of interviewees: Announce recruitment around the target product and identify interviewees based on headcount requirements.

Obtaining cognitive structure: Conduct in-depth interviews using the EGM to gather individual cognitive preferences and create a cognitive structure map.

Stage 2: Define

Quantitative weighting: Design a questionnaire based on cognitive structure maps and perform quantitative analysis using QTT1.

Difference-in-difference test: Conduct variance tests on the QTT1 data to identify significant cognitive differences by combining different population variables.

Stage 3: Ideate

Identification of contradictions: Select typical cognitive differences based on analyses, transforming them into technical (TC) or physical contradictions (PC).

Generation of innovation guidelines: Generate innovation guidelines by eliminating contradictions using Altshuller’s Contradiction Matrix and the Principle of Invention.

Stage 4: Prototype

Determine product functional unit layout: Use the FSM to disperse the product functional unit layout and determine the optimal layout form.

Develop functional units: Decentralize each functional unit in different shapes and ways of use.

Identify alternatives: Combine different forms of functional units to generate multiple alternatives and visualize them using computer-aided 3D modeling and rendering.

Phase 5: Test

Determine the evaluation model: Recruit evaluation experts from initial interviewees and use cognitive structure maps as evaluation criteria.

Calculation of weights: Calculate weights for each evaluation indicator and synthesize values of alternatives based on evaluation indicators.

With the development of mobility assistive technology (MAT), significant advancements have been made in smart walkers (SWs) within the field of mobility aids (Mostofa et al., 2021). However, issues related to esthetics, usability, and user experience persist, leading to high abandonment rates (Martins et al., 2012). Therefore, the next logical step in research and development is to create devices suitable for everyday use that balance functionality, esthetics, and usability (Nof, 2023). Based on these considerations, we use the smart walker (SW) as an example to illustrate the implementation of the proposed systematic design thinking approach, MADE. The specific steps are as follows:

Firstly, keyword analysis is carried out according to the research object. Accurate keywords, including “Smart walker”, “smart walking aids”, “ power mobility aid”, and “Rollators” were identified by referencing the World Health Organization’s Priority Assistive Products List (World Health Organization, 2022), National standards for assistive products in China (GBT16432-2016) (National Rehabilitation Aids Research Center, 2016), and EASTIN (Gnocchi, 2024). Secondly, a comprehensive online search using these keywords was conducted across various platforms, such as general search engines, patent databases, design competition websites (e.g., Red Dot, IF, ADesign, DYSO), design community platforms, online shopping platforms, and assistive devices databases. Through these channels, 34 SWs with high clarity and thematic relevance to the research were obtained. To refine the selection, a comparative screening method was employed, eliminating samples with overlapping functionalities, similar appearances, and incomplete feature descriptions. Finally, 12 representative samples were chosen, evenly distributed between traditional and SWs. Distinctive features of each product were extracted and presented in a detailed product profile, encompassing appearance, functionality, and usage methods. These profiles served as visual aids for interviewees, enabling an intuitive understanding of the products. Additionally, they can serve as divergent references for subsequent research endeavors.

In this study, 12 images of typical SWs samples were used as stimuli instead of actual devices for several reasons. Primarily, due to the specific nature of our respondents—mostly elderly individuals with mobility issues—using images minimized potential safety concerns. Additionally, since one focus of our study was to explore esthetic preferences, which are linked to perceived stigma and device abandonment, visual representations effectively conveyed key features and design elements without the logistical challenges of transporting multiple SWs. Images also ensured a standardized presentation, providing all respondents with the same visual reference, crucial for consistent qualitative assessment (Shen, 2013; Yamagishi et al., 2018). Moreover, the use of images rather than physical products is a widely accepted practice in the design field, as supported by numerous studies (Ko et al., 2018; Zhang and Li, 2019; Liu et al., 2022; Pratiwi et al., 2023).

During the sampling phase, three distinct methods were employed: random sampling, snowball sampling, and purposive sampling, aimed at identifying key stakeholders of smart walkers (SWs). The recruitment took place between November and December 2023, resulting in a total of 20 stakeholders, divided into user and designer groups. The study was approved by the Academic Ethics Committee of the author’s institution, and all participants provided informed consent, knowing they would receive 50 RMB or an equivalent gift as compensation upon completing the interviews. In the park environment, random sampling was conducted in a pocket park in a coastal city in China. This method successfully recruited five elderly individuals over 65 with more than three years of SW experience. Three of these participants agreed to immediate on-site interviews, while two, who were not ready at the time, were invited to a subsequent laboratory session. Secondly, snowball sampling was utilized in a highly aged-community in China, with the assistance of social workers. Initial participants were identified through community records as potential SW users. Social workers then visited these elderly individuals to collect further information and encourage participation. Subsequent respondents, including other SW users and related stakeholders, were recruited through referrals from the initial participants. Notably, the experiences from the park and laboratory interviews led to the introduction of an adapted EGM (expert group meeting) interview format. This involved a community technical tea party providing educational sessions on “how to prevent falls and properly use walkers,” which attracted additional interested participants. Snowball sampling resulted in the recruitment of 8 highly engaged SW stakeholders, including one retired surgeon, five SW users, and three caregivers. Furthermore, purposive sampling was employed among design professionals, selecting two teachers and five graduate students with experience in SW design from a pool of product design faculty and students. Detailed information on all respondents is provided in Table 1.

During the EGM interviews, different forms of samples were used in the three environments (Fig. 3) to facilitate the discussion and understanding of smart walkers (SWs). In the park environment, an iPad was used to display images, which helped in conveniently showcasing the samples. The EGM process in this setting involved structured interview questions, laddering probes, and detailed recording of responses. In the laboratory setting, paper-based illustrations were utilized, which allowed participants to focus more closely on categorizing the samples. This environment, being more controlled and formal, was most aligned with the traditional EGM process, offering a structured and distraction-free setting. In contrast, the technical tea party environment featured a modified EGM approach, where the process was conducted in a group setting. This session involved displaying the samples on a large screen while each participant also had a booklet containing paper images of the samples. This setup encouraged the exchange of opinions among participants and facilitated more objective expression, particularly among older participants, by creating a familiar and informal atmosphere. This approach helped ease any discomfort and allowed participants to share their genuine thoughts more freely, as noted by (Coventry and Jones, 2012). The research team was divided into pairs, with one member conducting the interviews and the other observing and recording. The process began with asking respondents to explain their reasons for favoring certain samples (OEIs). The researchers then explored the underlying psychological emotions (CEIs) that influenced these initial reasons. Finally, respondents were questioned about the specific product features (CEIs) that elicited these reasons and emotions. This comprehensive method aimed to uncover each participant’s cognitive preferences regarding mobility aids, as expressed through specific product features. The interviews, totaling 20 sessions, were recorded and transcribed verbatim. The research team, consisting of three authors, independently analyzed the transcripts to identify recurring preference factors for SWs. The combined cognitive preferences were organized into a cognitive structure map (CSM) (see Fig. 4), which visually represents 7 OEIs, 4 CEIs, and 30 CEIs. The thickness of the lines in the diagram indicates the frequency of mentions, highlighting the prominence of certain features and preferences. The team identified four groups of contradictions through internal discussions based on the specific features highlighted in the diagram.

Three different environments for EGM interviews.

Cognitive structure map.

Based on the cognitive structure map, a questionnaire was designed, and the obtained data were weighted by QTTI analysis to determine the weights of the preference factors, which provided a basis for further analysis of cognitive differences. Based on the cognitive structure map, a questionnaire was designed, and weights were calculated using QTT1 analysis to determine the weights of the preference factors. This provided a basis for further analysis of cognitive differences. This questionnaire was divided into two parts. The first part collected demographic information about the participants, including age, gender, education, and walker exposure. The second section consisted of seven questions, beginning with a scoring question in which participants were asked to rate 6 OEIs based on their importance to their walker. They used a 5-point scale, with one indicating that it was not essential and five indicating that it was essential. This was followed by six single-choice questions asking participants to select the most important CEI for each OEI. The questionnaire was distributed through an online survey platform (https://www.wjx.cn/). One hundred thirty valid questionnaires were eventually collected. Based on the data derived from the questionnaire results, multiple regression analysis was conducted using QTTI to determine the weights of each OEI and each CEI included (as shown in Table 2). The positive and negative values of the CEIs in the table represent the specific features to be reinforced and eliminated, respectively; the coefficient of determination (R²) reflects the reliability of the results, the partial correlation coefficient indicates the importance of the OEIs, and the category scores describe the contribution of the CEIs. The positive and negative values in the category scores can be used as a reference for contradictions in the conceptualization phase.

The research team conducted two independent sample T-tests to assess whether there are significant differences in preferences for SWs. The first T-test compared the differences between the elderly user (n = 50) and designer groups (n = 80). The second T-test examined the differences within the elderly group. In Table 3, it is shown that in terms of “novel shape” and “intelligent function”, there is a significant difference in the preference of designers and elderly people for SWs (p < 0.05). The standard deviation and T-value are both positive, indicating that the evaluation of the elderly population is higher than that of the designer population. The analysis results show that elderly people pay more attention to the appearance, design, and digital function of the intelligent walking aid. Based on the demographic information variables set in the questionnaire, the differences were examined for user groups with different identity backgrounds, 25 for the higher education user group and 25 for the user group with less than higher education. It facilitates an in-depth understanding of the important factors affecting the differences in user preferences. After the difference test, it is found that user groups with different educational backgrounds have significant preference differences. Table 4 shows the results of the independent sample T-test conducted in the elderly population at different educational levels (above and below higher education). There were significant differences in preferences for SWs in terms of “multiple functions” and “flexible construction” (p < 0.05). A negative standard deviation means that the elderly with higher education pay more attention to the diversity and flexibility of SWs than the elderly with lower education.

During the Ideate phase, the research team used TRIZ to analyze the specific contradictions identified in the results of the empathy phase. First, four pairs of specific contradictions were subjectively identified based on the cognitive structure map through team deliberation and voting. Based on the partial correlation coefficients in the QTT1 results, the OEIs with the highest values were selected, and the positive and negative values of the CEIs among them were used as the basis for the analysis of the contradiction issues. The OEIs with significant differences in the independent samples T-test were used as the basis for contradiction problem analysis. After comparison and screening, 6 pairs of specific contradictions are finally identified and transformed into TRIZ contradictions, including 2 pairs of physical contradictions (PCs) and 4 pairs of technical contradictions (TCs).

Obtain an inventive principle that eliminates these contradictions based on the inventive principle and the principle of separation. Finally, the research team transformed the inventive principles into innovation guidelines by conceptualizing them based on case studies and personal experiences, including “Restrictive structure”, “ Control systems combined with handles “, “two operation modes “, “Screen interface can be flexibly moved”, “Integration of intelligent control modules into the product structure “ and “ Modular components “. The detailed process is shown in Table 5.

Innovation guidelines need to be presented through external specific product functional units and different layout methods. Using FSM to analyze SW’s product functional unit layout can provide more possibilities for exterior design. First, the research team identified the functional units of the product based on the sample photos and the CEIs in the CSM, which included the wheels, frame, digital screen, operating handle, seat, and backrest. Then, in conjunction with the innovation guidelines, different shapes and colors were used to represent different functional units, and the same product functional units were dispersed in different sizes and positions of the layout. Finally, the research team identified four layouts and represented them in a two-dimensional way (see Fig. 5). In Layout 1, the seat and backrest are connected, which increases the stability of the seat and reduces the operation steps, but the connection of the frame is located at the front of the seat, which may affect the balance when sitting down to rest. In Layout 2, the rear wheels are large, and the front wheels are small, which ensures the stability of the center of gravity in different states of use, but the backrest does not have wrapping on both sides, which reduces the safety when using it. In Layout 3, connecting the seat and backrest to the frame provides overall stability, but the larger front wheel affects the unstable center of gravity when standing and walking. In Layout 4, folding the seat and backrest sides overlap, reducing the complexity of the structure, but the digital screen is in the front of the handle, which is easy to use in the electric wheelchair mode, but it will affect the use of standing walking state.

Several functional unit layouts of SWs.

In this process, the form of each functional component is dispersed through morphological diagrams. By referring to the interview samples and relevant case studies, the research team constructed a morphological diagram (e.g., Table 6) by dispersing each of the six functional components in Fig. 5 in terms of their shape and mode of use. In this chart, the permutations of the different types of functional units can generate up to 5 × 4 × 2 × 5 × 3 × 3 = 2000 design solutions.

Determine alternatives. After combining Fig. 3 and Table 4 for portfolio screening and brainstorming sketching, the research team identified three alternative scenarios, which were rendered and presented using the computer software Rhino7 and KeyShot2023 for 3D modeling and simulation of materials and lights (Fig. 6). Option 1 includes functional units F1-2, F2-2, F3-1, F4-1, F5-1 and F6-2, option 2 includes functional units F1-5, F2-3, F3-3, F4-5, F5-3 and F6-3, and option 3 includes F1-1, F2-4, F3-2, F4-2, F5-2 and F6-1.

Three alternatives for the SWs.

To determine the evaluation model, we selected a group of 5 evaluation experts from the participants who had previously taken part in the study. This group included 2 experienced SW users, 1 surgeon, and 2 associate professors with experience in SW design. These experts were chosen based on their extensive knowledge and experience with smart walkers (SWs), as well as their willingness to provide further feedback. The purpose of this selection was to verify that the program met the expectations of the target users.

Determine the evaluation matrix. The goal of this phase was to determine the optimal program by calculating the priority of the alternatives. The research team used the six OEIs in the Cognitive Architecture Diagram (Fig. 4) as evaluation metrics to prioritize the options as “Very Conforming”, “Conforming”, “Fair”, “Not Conforming”, “Not Conforming”, “Not Conforming”, and “Not Conforming”. “Does not conform” and “Very conform” as evaluation sets to fully assess the three alternatives.

Calculation of weights. Firstly, experts were asked to select the degree of conformity of each alternative on the six evaluation indicators. Secondly, the research team organized the results and calculated them using the SPSSPRO platform (https://www.spsspro.com). As can be seen in Table 7, the largest share of weight for each alternative on the six evaluation indicators is X6, and the same is true for the results of quantifying the cognitive structure. It can be concluded that all three alternatives are valid. The results in Table 8 show that Alternative B is the best performer on the “Very Compliant” evaluation set and has the highest score of the three alternatives. Therefore, option B is recognized as the optimal option.

The cognitive structure among participants is reflected in Fig. 4 for overall preferences. The cognitive structure map presents a complex interleaving of lines, indicating that there are significant differences in preferences between individuals, a phenomenon that is precisely due to inter-individual cognitive differences (Tu et al., 2019). The resulting preference factors have represented different product-specific features, while some product features cannot be integrated with each other, in that the preference factors caused by cognitive differences represent different product-specific features, while some features cannot be integrated with each other. The right side of Fig. 4 shows four pairs of distinct cognitive contradictions. Some of the larger values in the quantitative results represent common preferences for the vast majority of people. However, even in the case of larger weight values for the preference factors, there are still positive and negative values in the sub-factors. Previous studies usually only describe what the positive and negative values represent (Chang and Chen, 2017) without delving into the reasons behind them. This may also be an indication of cognitive differences. If one focuses only on the overall larger values and ignores the existence of negative values, it may satisfy the preference needs of the majority of people, but ignore the preferences of a small portion of them, thus negatively affecting social equity. Therefore, it is necessary to pay more careful attention to the overall and local weighting values at the design stage to ensure that the product can more comprehensively meet the needs of various types of users and promote the realization of social equity.

There were significant cognitive differences between the designer and older adult populations, particularly in terms of their preference for SWs (Table 3). First, in terms of stylistic novelty, there was a cognitive difference between the two, with older adults preferring novel styling compared to designers (p < 0.05, t is positive). This may be because designers pay more attention to the functionality and technological innovation of SWs (Cheng and Shen, 2023), and treat the exterior design as a secondary factor. On the contrary, older people pay more attention to the appearance innovation of SWs, probably because they want to change their image through unique design (Huang and Chang, 2020) and enhance their self-esteem so that they can participate in social activities as equals and be respected as others. Secondly, there is also a cognitive difference between the two in terms of smart features, with older people paying more attention to smart features (p < 0.05, t is positive). This suggests that designers may have underestimated users’ acceptance of smart technologies in product development, and thus may have sacrificed some smart features to improve product ease of use. On the other hand, older adults expect digital technology to enhance product functionality and mobility of mobility aids, a view that has been validated in past studies (Santis et al., 2023). As mentioned above, there are cognitive differences between designers and older adults that result in older adults failing to select SWs that match their personal preferences at the time of purchase, which in turn increases the abandonment rate of SWs, preventing users from participating in normal social activities on an equal footing and generating social inequality.

The idea that differences in educational level often lead to cognitive differences has been well-established in past research. In the present study, we found cognitive differences between individuals with higher education and those with less than higher education in the multiple functions and structural flexibility of SWs in the older adult population (Table 4). In terms of multifunctionality, individuals with less higher education focused more on multifunctionality compared to the higher education group (p < 0.05, t is negative). This result may be attributed to the difference in perceived product functionality due to the difference in education level, where individuals with higher education have stronger learning and acceptance abilities and thus can perceive the diverse effects of multiple functions. In contrast, less educated individuals may focus more on the practical functions of the product and may find the integration of other functions cumbersome and require more learning costs. Interestingly, a similar observation was made in Holt’s study, whereby less educated individuals preferred “practical” and “comfortable” design styles, whereas more educated individuals preferred visually appealing variety and personalization. Higher-educated individuals also showed higher concern for structural flexibility (p < 0.05, t negative). This result may be attributed to the concern of the low-educated older adult population that flexible structures may trigger a fear of falling, as they are concerned that flexible structures may reduce the stability of the SWs and fail to provide adequate support. In contrast, the highly educated older adult population typically has higher technology acceptance because they are concerned that the flexible structure may reduce the stability of the SWs and fail to provide adequate support. On the contrary, the highly educated older adult population usually has higher technology acceptance and is more willing to try flexible SWs that offer more ways of use through polygonal structures. Overall, different levels of education caused differences in SWs perceptions. With the same need for use, low-education groups may not be able to effectively utilize SWs for daily activities, which negatively impacts the promotion of social equality.

The existence of cognitive differences provides useful ideas for innovation design. Specifically, cognitive differences result in different groups not being able to enjoy the value experience brought by SWs at the same time. Such differences not only give rise to differences in preferences between groups (Gong et al., 2022), but also lead to conflicts between preference factors. Attempting to satisfy the preferences of different groups by fusing products will lead to a great deal of conflict (Ko et al., 2015). To achieve equal use by different groups, considering cognitive differences as an ambivalent conflict, integrating the needs of different groups can result in innovative designs that simultaneously satisfy the preferences of different groups so that they are all able to accept and use them. Thus, to a certain extent, this contributes to the promotion of equality in society. In conclusion, despite the existence of cognitive differences, properly analyzing and dealing with these differences will provide more ideas for innovative designs to meet the needs of different groups.

In materializing the innovation guidance into design solutions, through the effective use of the FSM, the innovation guidance became more specific and diffuse, providing the research team with richer creative inspiration. This idea is validated by the study of Lu et al (Lu et al., 2021). Table 5 demonstrates the performance of the pragmatic morphology diagraming method in terms of the dispersion of functional components, which provides more possibilities for the presentation of functions in the design solution. In terms of program evaluation, the previous practice has been to combine the Analytic Hierarchy Process (AHP) with FCE by using the AHP for comprehensive ranking of evaluation metrics and then using FCE for program evaluation (Yang et al., 2023). Interestingly, this study used the OEIs in Fig. 2 as evaluation indicators and found that the comprehensive ranking of evaluation indicators by AHP was the same as the algorithm of QTT1, a result that was verified in Zhang et al.‘s study (Zhang et al., 2023). Therefore, this study successfully evaluates the optimal solution based on FCE, which also indicates that compared to the traditional SDTM method, the current SDTM, which carries out optimization by combining multiple methods, is more scientific and standardized, and can effectively identify the cognitive differences and convert them into design guidance, to eliminate the conflict of preferences between different groups of people through the innovation to make the SWs meet the needs of more people, and to contribute to the promotion of social equality have a positive impact, which is rare in previous studies.

This study introduces MADE, a systematic design thinking methodology for creating smart walkers (SWs) and other mobility aids. MADE addresses challenges in esthetics, usability, and user experience that contribute to high abandonment rates of mobility aids, enhancing the operability of traditional design thinking models. The core philosophy of MADE is respecting the diversity of user preferences and systematically converting these preferences and cognitive differences into actionable design strategies. This approach directly tackles the multifaceted nature of mobility aid abandonment. The EGM captures nuanced user preferences, addressing esthetic concerns that often lead to abandonment due to perceived stigma. QTT1 quantifies these preferences, enabling designers to prioritize features that enhance user acceptance and long-term use. TRIZ methodology helps resolve contradictions between different user needs, creating innovative solutions that balance various requirements, thus reducing the likelihood of abandonment due to dissatisfaction with specific features. In the prototype stage, the FSM and MC generate diverse design solutions, allowing for greater personalization. This flexibility addresses another key factor in abandonment—the lack of adaptability to individual needs. By offering a range of options, users are more likely to find a solution that fits their specific requirements, increasing long-term adoption. The FCE in the testing phase ensures that the final designs are evaluated holistically, considering all factors that influence user acceptance and continued use. This comprehensive assessment helps predict and mitigate potential reasons for abandonment before the product reaches the market. While this study focused on smart walkers, MADE’s potential applications extend to a broader range of mobility aids and assistive technologies. This wider applicability underscores MADE’s potential impact on promoting social equality and inclusion by reducing abandonment rates across various assistive devices. The methodology shows promise for application to assistive technologies for people with various disabilities, not limited to age-related mobility issues. By systematically addressing cognitive differences and translating them into innovative design solutions, MADE contributes to the development of more inclusive and widely acceptable assistive technologies. This approach not only has the potential to reduce abandonment rates of mobility aids but also to promote greater social equality and inclusion for elderly and disabled individuals by ensuring that mobility aids meet their diverse needs and preferences more effectively. Future research should address limitations by exploring regional differences, incorporating physical product evaluations, and combining subjective interviews with objective experiments to provide more comprehensive insights into user preferences and potential abandonment factors.

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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This research was funded by the Humanities and Social Sciences Youth Foundation, Ministry of Education of the People’s Republic of China (21YJC760101).

Xiamen University of Technology, Xiamen, China

Baoyi Zhang & Zongsheng Wang

Osaka University, Osaka, Japan

Zhe Li

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BZ conceptualized the study, secured funding, and was responsible for the overall design and methodology, as well as providing the experimental conditions. ZW conducted the data collection, analysis, and reporting, and made significant contributions to the interpretation of the results. ZL participated in the literature review and manuscript preparation. All authors contributed to the writing and critical revision of the manuscript and approved the final version for submission.

Correspondence to Baoyi Zhang, Zongsheng Wang or Zhe Li.

One of the authors of this article, ZL, is a guest editor for a special issue of Humanities and Social Sciences Communications. They were not involved in the editorial review or the decision to publish this article.

This study did not involve medical research or experiments on humans. Data were collected through interviews and an online survey, with all information kept strictly confidential, anonymous, and used solely for research purposes. The study adhered to the principles of the Declaration of Helsinki and was approved by the School of Design Art at Xiamen University of Technology (Approval date: 13th Dec. 2023).

The study obtained written informed consent from all participants before the interviews began. Special consideration was given to the needs of elderly participants, and we arranged more convenient interview options, such as a community-based technology tea party and large-print reading materials, to ensure everyone could easily participate. As a token of appreciation, participants received a small physical gift valued at CNY 50 (approximately 7 USD) upon completing the interview. Additionally, the online questionnaire included an electronic version of the informed consent form, and participants who completed the questionnaire had a chance to receive a small cash reward distributed randomly.

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Zhang, B., Wang, Z. & Li, Z. Mobility Aid Design for the Elderly (MADE): a design thinking approach using a smart walker as a case study. Humanit Soc Sci Commun 11, 1469 (2024). https://doi.org/10.1057/s41599-024-04007-z

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Received: 30 December 2023

Accepted: 28 October 2024

Published: 05 November 2024

DOI: https://doi.org/10.1057/s41599-024-04007-z

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