A KANO-AHP Integrated Model Based on Behavioral Design: A Study on the Design of Nursing Beds for People with Disabilities

Abstract

In home-based elderly care, nursing beds play a crucial role in the daily lives of older adults. However, most existing nursing beds are designed for general patients, neglecting the specific needs of people with disabilities and their caregivers. To enhance user satisfaction with nursing beds, this study proposes a conceptual design approach based on a KANO-AHP integrated model based on behavioral design. First, the needs of caregivers and people with disabilities are identified through behavioral observations and in-depth interviews. The Fogg Behavior Model is then applied to translate these behavioral insights into extractable design elements, which are subsequently classified and prioritized systematically using the Kano model. Subsequently, the Analytic Hierarchy Process is employed to screen the most critical needs from the extracted ones and transform them into key design elements, thereby defining the structural components of the product. This integrated approach enables an accurate mapping from user requirements to design elements, thereby facilitating the development of nursing beds for people with disabilities. This study demonstrates the feasibility and effectiveness of the KANO-AHP model in design research for the aging population, offering valuable guidance and an innovative perspective for nursing bed design.

1. Introduction

With the changes in the current family structure, a large proportion of people lack both sufficient time to accompany their family members with disabilities and the necessary skills and energy to provide them with quality care services [1]. But with the improvement of living standards, caregivers have gradually been widely accepted by the general public, and people have begun to purchase their services. However, current nursing beds remain unsatisfactory in terms of supporting caregivers in their caregiving work. Caregivers primarily provide care for people with disabilities, who are defined as individuals who have lost or partially lost the ability to care for themselves and have a certain degree of motor impairment, most of whom are elderly [2]. These individuals rely on assistive devices and the assistance of caregivers to perform activities of daily living in medical settings. While in bed, they are often unable to independently carry out basic physiological activities such as turning over, eating, excretion, and personal hygiene. The severity of care issues for elderly individuals with disabilities has received high attention at the national level, and a series of policies have been intensively introduced to provide clear guidance for the research, development and optimization of care equipment. The 14th Five-Year Plan for the Development of the National Aging Cause and the Elderly Care Service System explicitly proposes to “vigorously develop age-friendly products and smart elderly care equipment, and promote rehabilitation and nursing devices equipped with functions such as turning over” [3]. The Opinions on Developing the Silver Economy and Enhancing the Well-being of the Elderly also emphasizes the need to focus on the core needs of elderly daily care and develop specialized nursing equipment tailored to the elderly with diverse physical conditions [4].

As a core piece of equipment for the care of elderly individuals with disabilities, the iterative upgrading of nursing beds not only aligns with the national strategy for the development of the aging cause but also directly responds to the policy advocacy for high-quality elderly care services, thereby enjoying solid policy support. Against the backdrop of the contemporary elderly care model featuring the deep integration of home-based care and institutional care, the indispensability of nursing beds has become increasingly prominent. From the perspective of people with disabilities, their limited physical functions prevent them from independently performing basic daily activities. High-quality nursing beds can assist them in maintaining basic life dignity and enhancing their autonomy in daily living through auxiliary functions. From the perspective of caregivers—whether family caregivers, hospital nurses, or professional attendants—all currently face the challenges of heavy workloads and high labor intensity in caregiving. Scientifically adapted nursing beds can significantly streamline caregiving procedures and reduce operational complexity. Furthermore, with the improvement in medical care standards, the rehabilitation period for elderly individuals with disabilities has been prolonged, and caregiving scenarios have extended from hospitals to home settings. This has further escalated the demand for the multifunctionality and adaptability of nursing beds, creating an urgent need for high-quality nursing bed products suitable for multi-scenario applications. Currently, a variety of targeted nursing bed designs and relevant research findings have emerged both domestically and internationally, providing valuable references for the development of the industry. Internationally, Basmajian et al. from the Massachusetts Institute of Technology (MIT), USA, proposed a rolling sheet repositioning design. This design supports patients via a hammock-type movable sheet equipped with brake rollers, which can reduce shear forces during patient repositioning and sheet changing, and can also be applied to bed-to-chair and bed-to-bed transfers [5]. Seo et al. from the Korea Advanced Institute of Science and Technology (KAIST), South Korea, developed a medical bed integrated with a pressure sensor array, which can accurately determine patient posture and enable manipulator-assisted adjustment [6]. Manohar et al. from the University of Texas at Austin, USA, installed pressure sensors under the mattress to realize real-time posture monitoring through data analysis, with automatic bed adjustment after a set timeout to prevent pressure ulcers [7]. Gaddam et al. from Massey University, New Zealand, designed a nursing bed with wireless monitoring sensors, where monitoring devices installed at the bed legs can send abnormality alerts to caregivers [8]. Jaichandar et al. from Singapore Polytechnic, Singapore, developed a pressure ulcer-preventing medical bed that regulates bed surface temperature via temperature sensors and alerts caregivers accordingly [9]. Vázquez-Santacruz et al. from Mexico optimized bed posture adjustment through sensor technology and programming, enabling the bed to assist users in adjusting their posture adaptively according to individual changes [10].

Domestically, although the nursing bed industry started later than those in Europe, the USA, and Japan, substantial human and material resources have been invested in recent years, leading to certain developmental achievements [11,12]. In terms of application scenarios, public hospitals represented by Wuhan No.2 Hospital have fully outsourced caregiver services to professional caregiving companies, and nursing beds, as core care equipment, are widely used in various departments of the inpatient ward.

Despite numerous attempts both domestically and internationally, existing nursing beds and related designs still exhibit significant shortcomings. Domestically, research and development of nursing beds have largely focused on therapeutic care and integrated medical and elderly care, with a core emphasis on functionality and structural design, resulting in a low degree of customization. In public hospitals, traditional manual hospital beds account for a high proportion; small health centers even still use flat-panel hospital beds, while intelligent electric beds are limited to specialized settings such as intensive care units (ICUs). Products generally fail to fully consider human body differences and user experience, leaving significant room for improvement in functionality, structure, appearance, and interaction design. There is a clear lack of design tailored to the precise needs of people with disabilities. Internationally, while designs place greater emphasis on user needs, they often focus solely on the individuals with disabilities themselves, neglecting the actual operational requirements of the caregiver group [13].

On the other hand, under the current care model, home, hospital, and community settings represent the three core care scenarios. These scenarios differ substantially in spatial characteristics, primary caregivers, and care needs, thereby imposing distinct requirements on the functionality, structure, and adaptability of nursing beds. In home care, family members act as the main caregivers under relatively limited spatial conditions. Nursing beds for home use must balance functional performance and domestic compatibility, with operational complexity suitable for non-professional users. In hospital care, professional nurses and attendants bear heavy workloads and often care for multiple patients simultaneously. Accordingly, hospital-grade nursing beds should support efficient operation, vital sign monitoring, and convenient patient transfer, while complying with standardized hospital care procedures. As a transitional setting between home and hospital, community care emphasizes the portability and versatility of nursing beds to meet demands for short-term rehabilitation and home-visit care. However, most existing nursing beds are designed with a single-scenario orientation. Hospital-oriented nursing beds are overly mechanized and complicated to operate, making them unsuitable for home environments and non-professional caregivers. In contrast, home-use nursing beds offer limited functionality and fail to satisfy the professional care requirements of hospital and community settings. Insufficient multi-scenario adaptability has become one of the major limitations of current nursing bed designs. These observations indicate that the nursing bed design in this study must break away from single-scenario constraints. Adopting modular design as the core approach, the design aims to address the diverse needs across different care scenarios. This design direction is based on an in-depth exploration of dual-user behaviors and requirements within various care contexts.

Furthermore, both domestically and internationally, medical hospital beds generally face the dilemma of balancing cost and functionality, which restricts the popularization and promotion of high-quality nursing beds [14]. To address the core limitations in current nursing bed design—including the neglect of the dual needs of people with disabilities and caregivers, inaccurate mapping between user needs and design elements, and insufficient product adaptability—this study explores how user behavior analysis grounded in the Fogg Behavior Model (FBM), together with the quantification and prioritization of user demands using the KANO-AHP integrated model, can achieve precise mapping from user behaviors to design elements and improve the scientific rigor and user fitness of nursing bed design. Based on the above framework, this study proposes a conceptual design methodology for nursing beds for people with disabilities and validates the feasibility and effectiveness of the integrated model in age-friendly product design through empirical research. In general, taking user behavior analysis as the entry point, this study introduces the Fogg Behavior Model to conduct an in-depth analysis of the behavioral characteristics of people with disabilities and caregivers, comprehensively covering behavioral motivation, ability, and external trigger conditions, thereby accurately capturing the actual usage scenarios and intrinsic needs of target users. To ensure the scientificity of the design, this study further introduces the KANO model and AHP to achieve the quantification of requirements. By means of classification, sorting, and weight assignment, the priority of core requirements is clarified, providing a reliable basis for the design of product functions and characteristics. At the design implementation stage, this study advocates a modular design strategy, decomposing nursing bed functions into interchangeable and expandable modules to enhance the product’s adaptability to diverse needs and care scenarios. Simultaneously, it integrates bionic elements to balance users’ emotional needs with functional requirements, strengthening humanistic care. Furthermore, this study emphasizes the application of Kansei Engineering and Finite Element Analysis (FEA) in practice to ensure the product not only possesses excellent functional performance but also provides high-quality user experience and emotional care. This approach not only offers clear guidance for the development of nursing beds for people with disabilities but also verifies the feasibility and effectiveness of the KANO-AHP model in age-friendly design, providing an innovative perspective for nursing bed design.

2. Research Methods

2.1. Behavioral Design Theory

Behavioral science is a well-established discipline that explores the patterns of human behavior from the perspectives of psychology, sociology, neuroscience, and other related fields. After decades of development, it has formed a complete theoretical framework. Behavioral design is an important applied branch of behavioral science in the design discipline. It integrates theories of behavioral science with design science, user experience, and other disciplines, and guides users to act along prescribed pathways through intentional design interventions. The core purpose of behavioral design is to take user behavior as the starting point, analyze users’ behavioral characteristics and underlying needs based on laws of behavioral science, and optimize user experience and product usability by adjusting design strategies. This theory is highly interdisciplinary, integrating diverse content from multiple fields including psychology, behavioral science, design science, and even user experience. Its purpose is to take user behavior as the starting point and optimize user experience by adjusting design strategies. The theoretical origin of behavioral design lies in persuasive science. The Behavior Design Lab at Stanford University was initially named the Persuasive Technology Lab and was later renamed. Simply put, persuasive science studies the factors that lead us to say “yes” to the requests of others, and this field has a history of more than 60 years. Behavioral design was first applied in the Internet field and was subsequently refined to its current name [15]. Subsequently, Professor Fogg further refined and systematized this theory, thereby proposing the Fogg Behavior Model [16].

In practice, the Fogg Behavior Model is widely adopted. This model posits that, for a behavior to occur, the actor must first possess motivation and ability. If these two conditions are met, the behavior will take place when prompted or triggered. Proposed by Professor Fogg, the model encompasses three core research components: motivation, ability, and triggers. In essence, the occurrence of a behavior is achieved through the combination of these three elements, as illustrated in Figure 1. Overall, this model can be used to determine whether user behavior requires further guidance or is deemed inappropriate. However, it lacks a specific coordinate system, meaning the three elements interrelate and coordinate with one another. Collectively referred to as the three core elements of the Fogg Behavior Model, motivation, ability, and triggers each possess distinct characteristics and classifications [17].

2.2. Applications of Behavioral Design

Behavioral design is increasingly applied in the field of product design. While it is more widely adopted in the Internet sector, it is also frequently utilized in the age-friendly domain to formulate user-centered design strategies. By monitoring user behavior to obtain and analyze user data, researchers can gain a clearer understanding of users’ behavioral habits during operation, thereby identifying areas for improvement in existing products.

When using a product, users primarily rely on its form and function, ultimately achieving the goal of cultivating attention through usage. In the design process based on behavioral design, in addition to considering user factors, functional factors, and appearance factors, it is also necessary to clarify user needs, behavioral characteristics, and product usage scenarios to determine these functional and appearance elements. All these factors must conform to the Fogg Behavior Model, whose three core elements are illustrated in Figure 2. Figure 2 illustrates the core framework of the Fogg Behavior Model, which reveals three essential conditions for behavior occurrence: motivation, ability, and triggers. All three conditions must be satisfied simultaneously to elicit user behavior. The action line in the figure represents the critical threshold for behavior occurrence. When the combined level of motivation and ability lies above the action line, triggers can effectively induce behavior; otherwise, behavior cannot be facilitated.

In the product design process based on Behavioral Design Theory, the first step is definition: identifying behaviors that need to be changed or cultivated, which requires clarifying the specific behavioral process for target users and defining the target behavior. This is followed by processes such as design simplification to facilitate users in completing the behavior. In this study, a combination of questionnaire survey, field interview, and behavioral observation methods will be adopted to establish the behavioral flow chart of target users. After identifying the target behaviors requiring design and research, the Fogg Behavior Grid is used to categorize these target behaviors according to the design procedure. This grid classifies behaviors based on their attributes across three dimensions: the duration of behavior occurrence, users’ familiarity with the behavior, and behavior intensity. The horizontal dimensions define behavior categories as follows: new behaviors, familiar behaviors, increasing behavior intensity, decreasing behavior intensity, and terminating behaviors. The three vertical types are: dot behaviors, span behaviors, and radial behaviors.

Thus, designers can infer the required functions and improvement methods for corresponding behaviors based on the behavior types on the Fogg Behavior Grid and the three elements of motivation, ability, and triggers in the Fogg model. By using the Fogg Behavior Grid to identify users’ behavioral focus when using the product, designers can conveniently formulate corresponding design strategies.

2.3. KANO Model

The KANO Model is a commonly used design tool that analyzes data obtained through questionnaire surveys to prioritize user needs. Its advantage lies in extracting users’ implicit needs through data analysis, without being constrained by the linear relationship between user needs and satisfaction. According to the KANO Model, the needs that influence user satisfaction are categorized into the following types: Must-be Attributes, One-dimensional Attributes, Attractive Attributes, Indifferent Attributes, and Reverse Requirements, as illustrated in Figure 3 [18,19].

The KANO Model is primarily used to screen user needs identified through behavioral design and determine their priority, and this ranking will guide subsequent improvement efforts [20].

In the questionnaire design prior to applying the KANO Model, two dimensions are adopted, and a five-level scale is used to measure users’ satisfaction with target functions. This allows user needs to be categorized into five types: Must-be, One-dimensional, Attractive, Indifferent, and Reverse. The specific categories will then undergo weight analysis via the Analytic Hierarchy Process (AHP), where Must-be attributes carry the highest weight, followed by One-dimensional, Attractive, and Indifferent attributes in sequence [21].

2.4. Analytic Hierarchy Process

The Analytic Hierarchy Process is a multi-criteria decision-making method that combines qualitative and quantitative analyses. Its core lies in decomposing complex multi-objective decision-making problems into a multi-level, multi-factor hierarchical structure, determining the relative importance weights of each factor through pairwise comparisons, and ultimately achieving the optimization of solutions or the allocation of indicator weights. In traditional product form design, the extraction, integration, and induction of elements rely on designers’ innovative thinking and empirical judgment. In contrast, the AHP is an analytical method designed to solve complex decision-making problems. By introducing a quantitative analytical model, it quantifies users’ qualitative evaluations into numerical weights. For the comparison of multi-feature functions in nursing bed design, this method is required to extract weights and streamline practical functions, thereby better supporting designers in making design decisions. Specifically, it employs a tree-like hierarchical structure to conduct horizontal pairwise comparisons between elements at the same level and vertical pairwise comparisons between elements at different levels to identify the optimal solution. The main steps include defining the problem, extracting elements, establishing the hierarchy, constructing judgment matrices, calculating weights, and drawing conclusions based on the final comprehensive weights, thereby pinpointing the critical needs and functional requirements [22].

3. Demand Investigation and Extraction

3.1. Process and Methods of Nursing Bed-Related User Investigation

3.1.1. Confirmation of Research Objectives

This user investigation in this study is grounded in the integrated philosophy of Person-Centered Care (PCC) and Human-Centered Design (HCD). It regards individuals with disabilities and their caregivers as core research subjects rather than mere research objects, and addresses their physiological functional needs, psychological and emotional experiences, as well as social interaction requirements. During the investigation, quantitative questionnaires were used to capture general user perceptions and behavioral patterns, while qualitative methods—in-depth interviews and behavioral observations—were adopted to explore personalized user needs and context-specific pain points. The mixed-methods approach combining quantitative and qualitative research avoids formulaic and oversimplified interpretations of user needs, and ensures that needs identification better conforms to the core requirements of healthy design. Based on the theoretical research presented earlier, this study investigates and analyzes the behaviors of target users when using hospital beds. The primary objective is to conduct surveys on the main users of hospital beds in current hospitals—nursing staff and disabled people—and comprehensively understand product usage from two dimensions: usage behaviors and cognitive behaviors. By analyzing user behavior data, this study summarizes the behavioral characteristics of target users, accurately identifies their implicit needs, and provides a basis for the subsequent proposal of design strategies [23].

3.1.2. Investigation Process

In the design research process, it is divided into three stages based on the design context: the qualitative stage, the model construction stage, and the demand transformation stage, as shown in

Table 1. Research Steps and Methods.

Research Steps	Questionnaire Survey	In-Depth Interview	Behavioral Result Analysis
Research Purpose	Obtain basic information of target users and their cognitive data on hospital bed usage	In-depth understanding of users’ behavioral characteristics and the influencing factors of their behaviors	Conduct multi-dimensional analysis of the survey results
Research Objects	Nursing staff and disabled patients	Nursing staff and disabled patients	Behavioral results
Research Methods	Offline interviews and online questionnaires	Offline interviews and behavioral observation	Main user personas and analysis of behavioral influencing factors

.

The first stage will first select samples and distribute questionnaires for investigation. After recovering the questionnaires, the user behavioral characteristics will be analyzed.

The second stage will conduct qualitative research: interviews will be conducted with target users, as well as professional medical personnel such as nurses and doctors, to gain a preliminary understanding of the research content. Subsequently, typical user behaviors will be screened, and video data will be recorded for observational research.

The third stage is to summarize practical needs. By summarizing and describing the characteristics of the groups divided by the above research, a user checklist with a preliminary user concept will be established to provide a reference for subsequent analysis using the Fogg Model.

3.2. Questionnaire Survey

3.2.1. Questionnaire Design and Distribution

This study adopted a questionnaire survey for quantitative research to grasp the general situation of the use of nursing beds by patients and nursing staff in hospitals. The questionnaire in this stage was mainly divided into four aspects: first, the basic information of users to determine the general information of the user group; then, based on Behavioral Design, it was divided into the ability dimension, motivation dimension, and trigger dimension. The ability dimension included users’ daily behaviors, main functions used, frequency of use, etc.; the motivation dimension mainly included reasons for use, urgent needs, etc.; the trigger dimension mainly included the experience of using nursing beds, personal opinions and suggestions, etc. Based on this, the questionnaire outline shown in

Table 2. Questionnaire Outline.

Dimension	Category	Patient	Nursing Staff
Gender	Male	62	97
	Female	46	163
Age	Ages 18–30	11	74
	Ages 31–40	9	67
	Ages 41–50	16	96
	Ages 51–60	72	23
Degree of disability	Mild disability	36	-
	Moderate disability	48
	Severe disability	24
Department	Department of Neurology	-	64
	Orthopedics		60
	General Surgery		51
	Other Departments		85

was designed. Subsequently, the comprehensive questionnaire data will help to analyze users’ behavioral motivation, behavioral ability, and trigger factors for behavioral occurrence.

Purposive sampling was used in this study. Considering the practical hospital environments and survey feasibility, clear inclusion and exclusion criteria were defined for the study samples. Patient group inclusion criteria: Age 18–60 years; Confirmed diagnosis of physical disability or motor dysfunction, and requiring continuous use of a nursing bed for ≥7 days; Clear consciousness and normal communication ability, able to cooperate with questionnaire completion or interviews; Admitted to departments with a high concentration of disabled patients in Xinhua Hospital and Union Hospital, including Neurology, Orthopedics, General Surgery, and Tuberculosis. Exclusion criteria: Severe cognitive impairment, mental illness, or communication disorder that prevents expression of personal needs; Short-term hospitalization (<7 days) and unfamiliarity with nursing bed use; Refusal to participate or withdrawal during the study. Nursing staff group inclusion criteria: Age 18–60 years; Nursing-related work experience ≥ 1 year, or being an immediate family member of the patient and providing long-term care for ≥3 months; Proficient in operating nursing beds and requiring frequent use of nursing bed functions in daily work; Working in the collaborating hospitals (Xinhua Hospital and Union Hospital) and able to provide objective feedback on usage experience. Exclusion criteria: Nursing work experience < 1 year or family caregiving duration < 3 months, with insufficient experience in nursing bed operation; Temporary nursing staff without long-term exposure to nursing bed operation; Refusal to participate or submission of incomplete questionnaires.

This electronic questionnaire was developed and data were collected using the Wenjuanxing (WJX) online survey platform. Targeted distribution was adopted with two primary delivery approaches. First, the questionnaire was distributed to Peking Union Medical College Hospital (PUMCH) and directly administered to nursing staff for completion. Second, through a nursing service company, the questionnaire was distributed to Xinhua Hospital where Shanghai Qinghao Nursing Company is stationed, and it was completed by nursing personnel from the company and patients in the hospital. The remaining questionnaires were distributed to postgraduate students majoring in industrial design and medicine.

3.2.2. Questionnaire Data Analysis

After distributing the questionnaires and processing the collected data, valid responses were screened out. A total of 312 questionnaires were distributed to mixed research participants in this study, including disabled patients and nursing staff, nurses, attendants, and family caregivers of patients. After excluding invalid questionnaires, 268 independent valid responses were ultimately collected. Given the overlapping characteristics of the research participants, 90 nursing staff who were also family members of patients completed both versions of the questionnaire. As a result, the total number of valid questionnaires when grouped by participant type is higher than the number of independent valid questionnaires. A basic analysis of the questionnaire data was then conducted.

The questionnaire analysis indicates that, overall, patients do not have an overly strong demand for hospital beds, with the majority expressing a neutral attitude—as many as 42 respondents—suggesting that the basic needs of disabled patients are generally met. The numbers of respondents who were very satisfied and very dissatisfied were both small, at only 9 and 7, respectively. However, 18 respondents were somewhat satisfied and 32 were somewhat dissatisfied, indicating that there is still room for improvement and potential directions for optimization.

According to the questionnaires collected from nursing staff, in terms of satisfaction, most were resigned to the status quo. They acknowledged that many genuine pain points exist and cause inconvenience in their work. However, the special nature of nursing tasks allows them to intentionally or unintentionally overlook these difficulties without deliberate reflection. Another major reason, identified through interviews with nursing company managers, is that many caregivers have relatively low educational attainment due to their advanced age, leading to limited reflection on such issues. The questionnaire survey initially clarified the current usage status, core demands, and satisfaction characteristics of nursing beds among nursing staff and disabled patients, laying a quantitative foundation.

For the research objectives outlined in Table 1, the multidimensional analysis specifically entails: “physiological, psychological, behavioral, situational, and social interaction. From the physiological dimension, the physical mobility of individuals with disabilities directly determines their demand for assistive functions of nursing beds, while the work intensity and physical burden of caregivers govern their need for effort-saving operation. From the psychological dimension, patient satisfaction with nursing beds centers mainly on comfort and safety, whereas caregiver satisfaction focuses on operational convenience; both groups express an emotional need to reduce the ‘sterile, clinical impression’ of medical equipment. From the behavioral dimension, core patient behaviors include basic physiological activities such as turning over, feeding, and toileting, while core caregiver behaviors involve nursing actions including assisted turning, cleaning, and transfer. The behaviors of these two user groups exhibit clear coupling. From the situational dimension, user behaviors in hospital settings tend to be more standardized, while those in home settings are more individualized. From the social interaction dimension, the caregiving behaviors of caregivers and the cooperative behaviors of patients interact with each other, and well-designed products can promote positive interactions between them”. Through cross-dimensional analysis of these five aspects, this study achieves a comprehensive exploration of user needs and avoids the one-sidedness inherent in single-dimensional analysis.

However, quantitative data alone cannot capture implicit motivations, contextual details, and potential needs. Therefore, targeted user interviews and behavioral observations are required to deepen the research, verify and supplement the questionnaire findings, and provide more comprehensive support for demand transformation and functional extraction.

3.3. User Interview Research on Nursing Beds

3.3.1. User Interview Design Based on the Fogg Mode

To ensure the coverage of target samples, the samples were divided into three groups: disabled patients, nursing staff, including nurses and nursing assistants, and doctor groups as expert users. To guarantee the interview effect, the disabled patient group also covered three categories: mildly disabled, moderately disabled, and severely disabled individuals.

Since interviews with the disabled population are often conducted in hospitals, the interview outline was developed from three dimensions—ability, motivation, and triggers—based on the three elements in Behavioral Design. Details are shown in

Table 3. User Interview Outline.

Basic Information	Ability Dimension	Motivation Dimension	Trigger Dimension
Name, gender, age, Department	What behaviors do you need to perform in daily life? What daily behaviors are you unable to complete? What daily behaviors require the use of medical bed functions?	What are the current key improvement points you hope for in medical beds? Motivation for adding new functions to medical beds. What reasons lead you to need the relevant functions?	How frequently do you use the functions of nursing beds in daily life? Suggestions for daily life related to nursing bed use. Daily mindset and thoughts regarding nursing bed use.

. Meanwhile, attention should also be paid to the privacy and mental state of patients during the interviews.

After the preliminary interviews, representatives will be selected from each representative user group based on the interview content for video recording and behavioral observation, covering disabled patients, nursing assistants, nurses, and other groups.

As the first step of the interview, a one-on-one semi-structured interview format was adopted. The specific field interview process is illustrated in Figure 4. During the interviews, the behavioral mechanisms were explored, and the characteristics of user behavior were summarized. In addition, professional physicians were interviewed. Finally, in-depth on-site observations of interactions between users were conducted, and relevant conclusions were summarized and analyzed to support the establishment of the user model.

3.3.2. Implementation of On-Site Interviews in Hospitals

Xinhua Hospital was selected as the research site, and interviews were mainly conducted in departments with a high concentration of disabled patients, including Neurology, Orthopedics, General Surgery, and Neurosurgery. Given that most of the participants were middle-aged and elderly individuals, many of whom exhibited sensitive or emotional reactions regarding their current hospitalization status, coupled with concerns about patient privacy, it was not feasible to take on-site photos for documentation.

On-site, tables were compiled together with users based on their behaviors. An analysis was conducted on the pain points and operational experiences caused by nursing beds during these behaviors to refine the observation results. Finally, the observed results were analyzed and summarized, with interview examples presented in

Table 4. Example of User Interview Card.

Basic Information	Ability Dimension	Motivation Dimension	Trigger Dimension
Female, 55 years old, Department of General Surgery	She lives on the hospital bed daily, with her children coming to provide care and companionship. She can get out of bed independently but requires assistance to move around. She frequently uses the bed rails on both sides as support points for getting up or as safety rails during sleep at night.	She hopes to recover and be discharged from the hospital as soon as possible. Due to the inconvenience caused by her disability, she has to utilize various functions to make her lying position more comfortable; however, many daily activities remain difficult. Daily cleaning is performed, but other patients and she hope for air purification and disinfection services, though no one has openly voiced these concerns.	She is dissatisfied with the meal tray, noting that its fixed slot is too shallow, which easily causes lunch boxes to tip over and spill. She even had to rely on her son’s help to find an intact one. She follows medical advice, maintains regular three meals a day, and keeps a calm mindset to facilitate a smoother recovery and discharge.

.

Several key issues with current nursing beds were identified from these user interviews. First, the nursing bed’s meal tray exhibits significant design flaws. It can only be mounted on the tail guardrail and faces inward, which not only occupies bed space but also hinders the ambulation of disabled patients with partial self-care ability. When patients use the tail guardrail for support while getting up, the shape of the meal tray can easily cause instability and falls. Meanwhile, the recesses on both sides of the meal tray are too shallow, making placed items highly prone to tipping over. Additionally, the absence of a fixing mechanism means the tray can easily slide forward or even detach when touched by patients, resulting in serious deficiencies in both safety and practicality. Then, the transfer function offers notable advantages: the fixed brake and movable pedal are designed for convenience and efficiency, allowing switching between fixed and mobile modes simply by stepping on the pedal. During transfer, the side guardrails ensure patient safety, and the head and tail guard boards can serve as push–pull handles for caregivers, improving transfer efficiency. However, the guardrail latches on some hospital beds may become loose or detached due to rust or damage, posing potential safety risks during transfer. Finally, regarding user needs, patients prioritize their recovery progress, with demands for wound healing, surgical outcomes, and discharge time far exceeding those for bed comfort. Consequently, they frequently and proactively request dressing changes. For caregivers, dressing changes are a routine and essential procedure that also serves to comfort patients. In contrast, patient repositioning is physically demanding: when dealing with heavier patients, female caregivers often require assistance, and repositioning is indispensable for care activities such as toileting and daily movement. Overall, the optimization of nursing beds should focus on addressing the design flaws of meal trays and component aging, while also reducing the physical burden on caregivers.

3.4. User Persona Design

Based on different user research types and analytical methods [24], three approaches can be adopted to develop persona models: qualitative personas, quantitative personas, and qualitatively derived personas validated by quantitative data. In this study, the third approach—qualitatively derived personas validated by quantitative data—was employed to construct the persona models, using the data obtained from the preceding research. On this basis,

Table 5. User Segmentation Quick Reference Table.

Segmentation Factors	Physical Condition	Primary Usage Purpose	Main Usage Scenarios	Key Functional Requirements	Basic Requirements
Patient	Disability	Rehabilitation	Passive use due to limited mobility	Posture adjustment, entertainment functions	Safety, comfort
Caregiver	Healthy	Patient care	Frequent use of the hospital bed and familiarity with its operation	Patient cleaning, excretion care, repositioning. Mobility, disassembly, and cleaning functions.	Labor-saving, easy to use Improve safety and enhance treatment efficiency
Nurse				Physical sign monitoring, infusion therapy	Easy to assemble and disassemble, easy to repair

, a User Segmentation Quick Reference Table, was established.

4. Discussion

4.1. Analysis and Summary Based on the Fogg Behavior Model

4.1.1. User Behavior Flowchart Analysis

From the above data, it can be seen that patient users and caregiver users are basically consistent in terms of usage scenarios, but there are significant differences in behavioral cognition. The main differences lie not only in motivation but also in execution ability and triggering factors. However, from the perspective of interaction during bed use, the user behavior paths of patients and caregivers when using the hospital bed are basically consistent. Therefore, user behavior flowcharts can be drawn according to the timeline, as shown in Figure 5.

4.1.2. User Behavior Analysis Based on FBM

Therefore, the actions involved in using the hospital bed are incorporated into the user behavior journey. Combined with the survey data, they are cross-analyzed and presented in the Fogg Behavior Figure, Figure 6. Figure 6 presents the Fogg Behavior Map. Based on this map, user behaviors were categorized into 15 behavioral combinations according to behavior type and duration characteristics. Different colors represent distinct behavior types: green for newly emerged behaviors, blue for familiar behaviors, purple for behaviors with enhanced intensity, gray for behaviors with reduced intensity, and black for terminated behaviors. This figure provides a visual foundation for the subsequent translation of behavior categories into product functions. For instance, green path behaviors correspond to innovative core functions of the product, blue path behaviors correspond to basic core functions, while black point behaviors do not need to be converted into independent functions.

These correspond, respectively, to dot behaviors, whose effect is confined to a single action; span behaviors, whose effect covers behavioral outcomes over a period of time and represents the formation of short-term behavioral habits in users; and path behaviors, which exert long-term influence and indicate that users have established stable behavioral habits. By accurately classifying the behavioral characteristics of the target users—patients and caregivers—

Table 6. Behavioral characteristics of patients.

Behavior Type	Specific Behavior Type	Specific Behavior
Point Behavior	Desirable Point Behavior	Bed allocation; Service ordering
	Neutral Point Behavior	Intravenous infusion; Getting out of bed
	Undesirable Point Behavior	Hospital discharge
Segment Behavior	Neutral Segment Behavior	Undergo inpatient examination; Abdominal binder temporarily restricts movement
	Critical Segment Behavior	Getting in and out of bed; Recreational activities
Path Behavior	Neutral Path Behavior	Dressing change; Turning over; Assisted feeding; Elimination
	Critical Path Behavior	Independent feeding during gradual recovery; Independent elimination during gradual recovery
	Ambiguous Path Behavior	Assisted feeding during gradual recovery; Assisted elimination during gradual recovery

and

Table 7. Behavioral characteristics of nurses.

Behavior Type	Specific Behavior Type	Specific Behavior
Point Behavior	Desirable Point Behavior	Allocate to the patient
	Neutral Point Behavior	Cleaning the surrounding environment; Medication administration; Meal procurement
	Undesirable Point Behavior	Service termination
Segment Behavior	Neutral Segment Behavior	Bed transportation; Patient body cleaning; Intravenous infusion monitoring
	Critical Segment Behavior	Escorting for examinations
Path Behavior	Neutral Path Behavior	Dressing change; Assisted feeding; Toileting care
	Critical Path Behavior	Turning and repositioning; Patient rounds
	Ambiguous Path Behavior	Care during gradual recovery

are derived.

In this study, user behaviors were classified into three categories based on the Fogg Behavior Model: point behavior, segment behavior, and path behavior. According to the three-dimensional criterion of “behavior frequency-behavior importance-context dependence”, different behavior categories were converted into corresponding product functions. The specific rules are as follows: Point behaviors are single-occurrence events with no lasting impact. Such behaviors are converted into auxiliary functions only when their importance is rated as “extremely high”. For instance, the point behavior of “bed allocation” is converted into the “bed identification and positioning” function. Point behaviors with medium or low importance are not transformed into independent functions but integrated into other basic functions. Segment behaviors are short-duration continuous behaviors that form short-term behavioral habits. They are divided into “high-frequency” and “low-frequency” groups based on behavior frequency. High-frequency segment behaviors are converted into core extended functions; for example, the high-frequency segment behavior of “in-hospital examinations” is converted into the “convenient bed transfer” function. Low-frequency segment behaviors are converted into adaptable functions, such as the low-frequency segment behavior of “abdominal strap restricting movement” being converted into the “adjustable bed fixing strap” adaptable function. Path behaviors are long-duration continuous behaviors that form stable behavioral habits. As the core behavior category, all path behaviors are converted into basic or core product functions. Among them, neutral path behaviors are converted into basic functions, e.g., the behaviors of “dressing change and turning over” are converted into the basic function of “bed board-assisted turning over”, while critical path behaviors are converted into core functions, e.g., the behaviors of “independent eating and excretion” are converted into the core functions of “assisted bed exit and convenient toileting”. These conversion rules also incorporate the context dependence of user behaviors, ensuring that the derived functions adapt to the application requirements of multiple scenarios including hospitals and home environments.

4.1.3. Extraction of User Needs and Functions Based on the Fogg Behavior Model

The Fogg Behavior Model performs qualitative analysis of user behavior along three core dimensions: motivation, ability, and triggers. To convert these qualitative insights into quantifiable demand indicators for the KANO model, this study establishes a three-step transformation pathway: extraction of behavioral characteristics, quantification of demand indicators, and verification of indicator measurability. First, core behavioral characteristics are derived from the FBM analysis. For example, “difficulty in turning over” corresponds to the behavioral demand for assisted turning, while “cumbersome dining board operation” corresponds to the demand for a user-friendly dining board. These qualitative behavioral demands are then transformed into quantifiable evaluation indicators, each assigned to one item in the KANO questionnaire and measured using the Better-Worse coefficient. Finally, validation is conducted to confirm that the qualitative behavioral analysis from FBM can be reliably converted into quantitative demand evaluation in the KANO framework.

Since patients and caregivers differ in their goals and specific behaviors, the Fogg Behavior Model is employed to analyze user behaviors, providing a foundation for subsequent behavioral design strategies. Depending on different practices and scenarios, users’ behavioral motivation, ability, and triggers are enhanced or weakened accordingly. It should be noted that although the Fogg Behavior Model originated in the field of digital behavior intervention, its core logic—the interactive relationship among motivation, ability, and trigger conditions—demonstrates cross-context generality. Recent studies in industrial design have validated its effectiveness in analyzing user interactions with physical products, such as the design of age-friendly furniture and rehabilitation devices. In this study, the FBM was adapted and optimized for the specific context of physical interactions between patients and nursing beds. The motivation dimension focuses on physiological needs, e.g., turning over to prevent pressure injuries and psychological needs, e.g., entertainment to relieve anxiety. The ability dimension emphasizes physical operation capacity, e.g., autonomous adjustment of the bed board and learning ability to use functions, e.g., operating the dining board. The trigger dimension highlights product-based triggers, e.g., guardrails for stand-up assistance and context-based triggers, e.g., demand for a dining board during meals, which align closely with the physical interaction scenarios of nursing beds. The above adaptations ensure the applicability of the FBM in this research, rather than applying the original digital-context framework directly.

Based on the Fogg Behavior Model, Figure 7 and Figure 8 are derived, respectively, from which the user requirements are inferred. It should be noted that the points representing behavioral activities in the motivation-ability space in Figure 7 and Figure 8 are only qualitative approximations. They are intended to visually illustrate the characteristic differences among different behaviors, rather than to reflect quantitatively precise coordinate values. The distribution of these points is established based on the qualitative conclusions derived from the preliminary questionnaire surveys and user interviews, so as to ensure consistency with the actual needs of users. This approach does not affect the scientific validity of the subsequent functional extraction.

Based on analytical results from the Fogg Behavior Model, this study identifies significant behavioral coupling between patients with disabilities and their caregivers. Their motivation, ability, and triggering conditions interact and constrain each other, which forms the core logic of dual-user demand design. From the motivation perspective, patients’ rehabilitation motivation directly affects their willingness to use nursing bed functions, which in turn influences caregivers’ caregiving motivation. From the ability perspective, patients’ physical mobility determines the caregiving intensity for caregivers, while the assistive functions of nursing beds can improve both patients’ autonomous operation ability and caregivers’ caregiving ability. From the triggering condition perspective, the functional design of nursing beds acts as the primary external trigger for behaviors of both groups. Rational functional design can simultaneously activate patients’ autonomous behaviors and caregivers’ caregiving behaviors. The behavioral coupling pattern reveals that the design of age-friendly nursing beds must not isolate the behavior or needs of a single user group. Instead, design should be grounded in behavioral coupling to develop functions and structures that fit the behavioral characteristics of both groups, thereby achieving the dual goals of enhancing patients’ autonomy and reducing caregivers’ workload.

Based on the identified user requirements, combined with on-site investigations and the actual functions of hospital beds, as shown in

Table 8. Conversion of Nursing Bed User Needs to Functions.

Needs	Functions
Bed body safety measures Drop-down side rails	Bed with side rails Folding rail-type side rails Streamlined guard plate two-piece side rails
Facilitate patients getting in and out of bed	Assisted bed exit function
Facilitate patients engaging in recreational activities	Equipped with entertainment functions such as audio and television
Safe air purification	Safe and sterile capability
Sufficient IV bottle hangers Integrated IV pole in the bed Hooks for securing drainage tubes	Drainage tube hooks Integrated IV pole in the bed
Facilitate turning	Bed board-assisted turning function
Facilitate bed board control Bed board mobility	Electric control Voice control
Facilitate toileting	Convenient toileting care capability Detachable head and foot boards
Foldable or stowable meal board	Foldable meal board
Easy conversion between mobile and fixed states	Built-in brake switch
Facilitate patient transportation	Horizontal bed board mobility Compatible with transfer beds Detachable head and foot boards
Unobstructed surroundings around the bed	Detachable head and foot boards
Adequate space for related equipment	Headboard storage space Under-bed storage space
Patient status monitoring	Information monitoring and warning function Integrated with patient monitors

, the aforementioned requirements are translated into preliminary functions.

4.2. Demand Classification Based on the KANO Model

4.2.1. Distribute the Designed KANO Questionnaire

Based on the required functions of the hospital bed identified above, a KANO model analysis was conducted to further refine the broad functions. The survey questionnaire targeted disabled patients, patients’ family members, nursing aides, nurses, as well as industrial design and medical postgraduates as research samples. However, since disabled individuals often lack the extra energy to complete online questionnaires, the survey mainly focused on family members, nursing aides, nurses, and other relevant groups.

The questionnaire was designed using Wenjuanxing and distributed through WeChat groups, including nursing aide groups, nurse department groups, and nursing agency groups, to invite targeted users to complete it. A total of 231 questionnaires were distributed, and 220 valid ones were collected, with a questionnaire recovery rate of 95.2%.

4.2.2. Demand Extraction Based on the KANO Model

According to the KANO model, A, O, M, I, and R correspond to the five major demand categories of the model, respectively. A denotes attractive demand, referring to surprise features that users do not explicitly anticipate, which serve as the differentiating highlights of a product. O denotes one-dimensional demand, where the degree of demand satisfaction is linearly positively correlated with user satisfaction, representing needs explicitly put forward by users. M denotes must-be demand, consisting of basic functions that users consider essential for a product, forming the prerequisite for the product’s existence. I denotes indifferent demand, where the presence, absence, or quality of a feature has no impact on user satisfaction and contributes no substantial value to the product. R denotes reverse demand, where the degree of demand satisfaction is negatively correlated with user satisfaction; that is, the stronger the feature, the more dissatisfied users become.

In addition, after initially determining the Kano category of each requirement, the Better-Worse coefficient was introduced for further analysis. This coefficient quantifies the impact of adding or removing a specific function on user satisfaction. User satisfaction is evaluated by calculating the Better-Worse coefficient for each requirement, and the formula for this calculation is as follows:

Satisfaction coefficient after adding a certain function:

$$\mathrm{Better} \mathrm{coefficient}={\displaystyle \frac{\mathrm{A}+\mathrm{O}}{\mathrm{A}+\mathrm{O}+\mathrm{M}+\mathrm{I}}}$$

(1)

Dissatisfaction coefficient after removing a certain function:

$$\mathrm{Worse} \mathrm{coefficient}={\displaystyle \frac{-(\mathrm{M}+\mathrm{O})}{\mathrm{A}+\mathrm{O}+\mathrm{M}+\mathrm{I}}}$$

(2)

Based on the above formulas, after analyzing and calculating the 220 valid questionnaires, the analytical results shown in

Table 9. Analysis Results of the KANO Model.

Number	Functions	A	O	M	I	R	BETTER Coefficient	WORSE Coefficient	KANO Attribute
1	Built-in brake switch	47	74	18	54	24	0.6244	0.4765	Expected
2	Folding rail-type side rails	19	61	48	61	27	0.4212	0.5788	Expected
3	Information monitoring and warning function	38	91	6	50	23	0.6939	0.5244	Expected
4	Drainage tube hooks	107	7	11	90	0	0.5286	0.0859	Attractive
5	Streamlined two-piece side rails	10	82	27	74	23	0.4790	0.5664	Expected
6	Foldable meal board	26	90	10	47	42	0.6719	0.5791	Expected
7	Voice control	69	55	12	71	5	0.5977	0.3218	Indifferent
8	Electric control	79	10	0	117	5	0.4302	0.0465	Indifferent
9	Detachable head and foot boards	52	19	112	32	5	0.3319	0.6113	Essential
10	Integrated with patient monitors	53	82	24	46	2	0.6560	0.5187	Expected
11	Safe and sterile capability	117	10	0	86	2	0.5955	0.0449	Attractive
12	Compatible with transfer beds	64	26	16	100	7	0.4380	0.2047	Indifferent
13	Assisted bed exit function	98	47	25	41	13	0.6892	0.3430	Attractive
14	Equipped with entertainment functions	65	89	14	43	11	0.7325	0.4898	Expected
15	Integrated IV pole in the bed	90	18	65	42	6	0.5032	0.3855	Expected
16	Headboard storage space	57	16	16	126	2	0.3396	0.1497	Indifferent
17	Bed with side rails	16	31	99	63	3	0.2240	0.6234	Essential
18	Convenient toileting care capability	19	12	105	89	2	0.1355	0.5207	Essential
19	Under-bed storage space	36	71	28	68	12	0.5295	0.4872	Expected
20	Horizontal bed board mobility	90	31	7	79	2	0.5862	0.1839	Attractive
21	Bed board-assisted turning function	16	28	88	74	0	0.2153	0.5628	Essential

were obtained.

The Better coefficient is generally positive, indicating that user satisfaction will increase if a product or system provides a certain function or service. A higher value corresponds to a greater improvement in user satisfaction. The Worse coefficient is generally negative, meaning that user satisfaction will decrease if a product or system fails to provide a certain function or service. A more negative value indicates a greater impact on user dissatisfaction and a more significant decline in satisfaction. Therefore, when the values of the Better or Worse coefficients are closer to zero, the impact of the corresponding requirement on user satisfaction is smaller. Based on this, preliminary key requirements can be screened out.

To ensure the reliability and validity of the collected questionnaire data, this study used Cronbach’s α coefficient to examine the reliability and validity of the questionnaire.

According to the formula:

$$\mathsf{\alpha }={\displaystyle \frac{k}{k-1}}\times (1-{\displaystyle \frac{\sum S_i^2}{S^2}})$$

(3)

where k denotes the total number of items in the KANO scale; N denotes the number of valid samples for the KANO scale; $S_i^2$ denotes the variance of each individual item; S² denotes the variance of the total score across all items.

Based on the 21 sets of data above, the calculated Cronbach’s α value falls within the interval 0.7 ≤ α < 0.8, indicating that the internal consistency of the items used for KANO classification meets psychometric requirements and that the classification results are reliable.

4.3. Function Acquisition Based on KANO-AHP

4.3.1. Design of the Analytic Hierarchy Process Questionnaire

To statistically verify the necessity of introducing AHP weighting following KANO classification, this study conducted one-way analysis of variance (ANOVA) on the user importance scores of the four core demand categories in the KANO model. The detailed formulation is given below:

Total sum of squares (SST):

$$SST={\displaystyle {\displaystyle \sum }_{i-1}^k}{\displaystyle {\displaystyle \sum }_{j-1}^{n_i}}{(x_{ij}-\stackrel{-}{x_{..}})}^2$$

(4)

Between-group sum of squares (SSB):

$$SSB={\displaystyle {\displaystyle \sum }_{i-1}^k}{(\stackrel{-}{x_{i.}}-\stackrel{-}{x_{..}})}^2$$

(5)

Within-group sum of squares (SSW):

$$SSW={\displaystyle {\displaystyle \sum }_{i-1}^k}{\displaystyle {\displaystyle \sum }_{j-1}^{n_i}}{(x_{ij}-\stackrel{-}{x_{i.}})}^2$$

(6)

Degrees of freedom:

$$d{f}_T=N-1$$

(7)

$$d{f}_B=k-1$$

(8)

$$d{f}_W=N-k$$

(9)

Mean square between groups (MSB):

$$MSB={\displaystyle \frac{SSB}{d{f}_B}}$$

(10)

Mean square within groups (MSW):

$$MSE={\displaystyle \frac{SSW}{d{f}_W}}$$

(11)

F-statistic:

$$F={\displaystyle \frac{MSB}{MSW}}$$

(12)

where k denotes the number of groups, n_i denotes the sample size of the i-th group, $N={\displaystyle {{\displaystyle \sum }}_{i-1}^k}{n}_i$ denotes the total sample size, x_ij denotes the j-th observation in the i-th group, $\stackrel{-}{x}$_i. denotes the mean value of the i-th group, and $\stackrel{-}{x}$_.. denotes the grand mean.

Substituting the available data into the above formula and consulting the F-distribution table yielded a p-value of less than 0.05. This result indicates extremely significant differences in the perceived importance of functions within the same category. It thus confirms that the analytic hierarchy process, by means of hierarchical modeling and pairwise comparisons, is able to convert such qualitative perceptions of difference into quantitative weight values.

The core value of the KANO model lies in the qualitative classification of user requirements, which categorizes them into Must-be requirements, One-dimensional requirements, Attractive requirements, and Indifferent requirements. However, the model has two major limitations. First, it cannot prioritize requirements within the same category. For example, Must-be requirements include bed side rails, assisted turning, and convenient toileting, yet the KANO model cannot determine their relative importance. Second, it does not account for differences in requirement weights among various user groups. For instance, patients focus more on comfort-related functions, while caregivers prioritize effort-saving functions; the KANO model only provides overall classification and fails to reflect such group differences. As a multi-criteria decision-making method that combines qualitative and quantitative analysis, the Analytic Hierarchy Process can effectively compensate for the limitations of the KANO model. Through hierarchical modeling and pairwise comparison, AHP achieves the quantitative weighting and ranking of requirements within the same category and identifies core design elements. By integrating evaluation results from different user groups (patients, caregivers, and nurses), comprehensive weights are calculated to reflect differences in group demands. Finally, a consistency check is performed with a consistency ratio (CR) of less than 0.1, which ensures the statistical rationality of the weight results and provides operable quantitative support for the implementation of design solutions.

Based on the survey results presented earlier, the evaluation indicators were preliminarily formulated. Subsequently, the evaluation system was optimized by drawing on relevant design indicators, and codes were assigned to each indicator. The specific framework of the evaluation indicator system is shown in the table, which consists of 1 item in the target layer, 3 items in the criteria layer, and 11 items in the indicator layer.

Based on this, an Analytic Hierarchy Process questionnaire was designed and distributed to collect comparative data. During the process of calculating weights via the Analytic Hierarchy Process, a questionnaire survey was conducted to obtain pairwise comparison results from experts regarding the importance of the evaluation indicators. Subsequently, the scoring data from the respondents were organized into judgment matrices and aggregated, and the geometric mean method was adopted to ensure the validity of the consistency check.

On this basis, the secondary demand level was taken as the primary indicator, and the tertiary demand level as the secondary indicator. Finally, the results were compared according to the degree of importance, and the judgment matrices for the primary and secondary indicators were compiled, as shown in Figure 9.

4.3.2. AHP Weight Calculation

(1)

The survey respondents were mainly selected from nurses, nursing aides, disabled patients, patients’ family members, and postgraduate students majoring in industrial design, with a total of 30 participants. Selection criteria for the 30 experts were as follows: Their professional backgrounds cover four core fields: industrial design, geriatric nursing, rehabilitation engineering, and clinical nursing management. They have at least 5 years of working experience in the relevant fields. Informed consent was obtained: they participated in the scoring voluntarily, committed to completing the pairwise comparison of indicators independently and objectively, had no conflicts of interest, and were familiar with the functional design or clinical application scenarios of nursing beds. After ensuring that the judgment matrices of the 30 experts met the consistency requirement (CR < 0.1), the survey results were input into the analytic hierarchy process model. The 30 judgment matrices were then aggregated to obtain the aggregated judgment matrix of primary indicators, and values were assigned according to the different data in

Table 10. Aggregated Judgment Matrix of Primary Indicators.

	Essential Attributes	Expected Attributes	Attractive Attributes	Indifferent Attributes
Essential Attributes	1.0	1.05	1.42	3.6267
Expected Attributes	0.9524	1.0	1.2469	3.1122
Attractive Attributes	0.7042	0.802	1.0	2.1885
Indifferent Attributes	0.2757	0.3213	0.4569	1.0

and

Table 11. Scaling and Its Meanings.

Aij Assignment	Definition
${\mathrm{a}}_{\mathrm{ij}}=1$	Element i and Element j are of equal importance to the higher-level factor.
${\mathrm{a}}_{\mathrm{ij}}=3$	Element i is slightly more important than Element j.
${\mathrm{a}}_{\mathrm{ij}}=5$	Element i is obviously more important than Element j.
${\mathrm{a}}_{\mathrm{ij}}=7$	Element i is much more important than Element j.
${\mathrm{a}}_{\mathrm{ij}}=9$	Element i is extremely more important than Element j.
${\mathrm{a}}_{\mathrm{ij}}=1/$3	Element i is slightly less important than Element j.
${\mathrm{a}}_{\mathrm{ij}}=1/$5	Element i is obviously less important than Element j.
${\mathrm{a}}_{\mathrm{ij}}=1/$7	Element i is much less important than Element j.
${\mathrm{a}}_{\mathrm{ij}}=1/$9	Element i is extremely less important than Element j.
${\mathrm{a}}_{\mathrm{ij}}=2 \mathrm{n}=1$,2,$3$,4	The importance of Element i relative to Element j is intermediate between the two.

.

By calculating the weighted average of the judgment matrix, the weight of each factor in the hierarchical structure is determined. The geometric mean method is adopted to ensure the validity of the consistency check. The scoring matrices formed by each survey respondent are multiplied element-wise, and then the nth root is taken to obtain the unique aggregated matrix $\overline{A}$:

$$\overline{A}={({\displaystyle {\displaystyle \prod }_{k=1}^m}{a}_{ij}^k)}^{{\displaystyle \frac{1}{m}}}$$

(13)

Calculate the relative weights of the judgment matrix. The weights are computed from the unique aggregated matrix using the geometric mean method, and the formula is as follows:

$$W_i={\displaystyle \frac{{({\displaystyle {{\displaystyle \prod }}_{j=1}^n}{a}_{ij})}^{\frac{1}{n}}}{{{\displaystyle \sum }}_{i=1}^n{({\displaystyle {{\displaystyle \prod }}_{j=1}^n}{a}_{ij})}^{\frac{1}{n}}}} , i=1,2,3..,n$$

(14)

$\overline{A}$ Multiply the elements of the matrix row-wise to obtain a new vector; Take the nth root of each component of the new vector; Normalize the resulting vector to obtain the weight vector.

Considering that inconsistent conclusions may arise when conducting pairwise comparisons of indicators in practice, it is necessary to perform a consistency check on the existing judgment matrix to ensure the rationality of the indicator weights. Commonly, CR is used as the criterion for the consistency of the judgment matrix, where CR is the ratio of the consistency index (CI) to the average random consistency index (RI). If CR < 0.1, the matrix meets the requirement and no modification is needed; otherwise, experts should be invited to revise the judgment matrix again, and Steps 1 and 2 should be repeated until the calculated CR < 0.1. The formula for calculating CR is shown in Equation (5).

$$CR={\displaystyle \frac{CI}{RI}}={\displaystyle \frac{{\lambda }_{max}-n}{\left(n-1\right)RI}}<0.1$$

(15)

The formula for CI is shown in Equation (6).

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{\left(n-1\right)}}$$

(16)

λ_max denotes the maximum eigenvalue of the judgment matrix, and its calculation formula is shown in Equation (7). Here, A represents the judgment matrix, W is the weight vector, and [AW]_i refers to the i-th component of the matrix [AW].

$${\lambda }_{max}={\displaystyle {\displaystyle \sum }_{i=1}^n}{\displaystyle \frac{{\left[AW\right]}_i}{n{W}_i}}$$

(17)

Finally, calculations were performed based on the above equations, and the results are presented in

Table 12. First-level Weights, Weight Sums and Consistency Calculation Results.

Weight Calculation Results of Primary Indicators
Essential Attributes	Expected Attributes	Attractive Attributes	Indifferent Attributes
0.3454	0.3141	0.2389	0.1016
Consistency Calculation Results of Primary Indicators
${\mathrm{\lambda }}_{\max }$	CI Value	CR Value	Consistency Test Results
4.0036	0.0012	0.0013	Passed

. The primary indicator weights were confirmed, and the consistency check passed, meeting the required criteria.

(2)

After completing the weight calculation for the criteria layer, the calculation proceeded to the indicator layer. Similarly, after ensuring that the judgment matrices of the 30 experts met the consistency requirement (CR < 0.1), the 30 judgment matrices were aggregated. The calculation method was consistent with that described earlier, and the secondary weight calculation results for each attribute are presented in

Table 13. Second-level Indicator Weight Calculation Results.

Weight Calculation Results of Secondary Indicators for Essential Attributes
A1		A2		A3		A4
0.1234		0.3689		0.33		0.1777
Weight Calculation Results of Secondary Indicators for Expected Attributes
B1	B2	B3	B4	B5	B6	B7	B8
0.0333	0.0624	0.065	0.2092	0.1649	0.3111	0.0358	0.1183
Weight Calculation Results of Secondary Indicators for Attractive Attributes
C1		C2		C3	C4		C5
0.2142		0.3424		0.2572	0.114		0.0721
Weight Calculation Results for Indifferent Attributes
D1		D2		D3		D4
0.2857		0.2188		0.2412		0.2543

.

Consistency checks were performed using the formulas presented earlier, and the results are shown in

Table 14. All Consistency Ratios of the Matrices Pass the Consistency Test.

Consistency Calculation Results of Secondary Indicators for Essential Attributes
${\mathrm{\lambda }}_{\max }$	CI Value	CR Value	Consistency Test Results
4.0206	0.0069	0.0077	Passed
Consistency Calculation Results of Secondary Indicators for Expected Attributes
${\mathrm{\lambda }}_{\max }$	CI Value	CR Value	Consistency Test Results
8.2942	0.042	0.0298	Passed
Consistency Calculation Results of Secondary Indicators for Attractive Attributes
${\mathrm{\lambda }}_{\max }$	CI Value	CR Value	Consistency Test Results
5.0695	0.0174	0.0155	Passed
Consistency Calculation Results of Secondary Indicators for Indifferent Attributes
${\mathrm{\lambda }}_{\max }$	CI Value	CR Value	Consistency Test Results
4.0026	0.0009	0.001	Passed

. All matrices passed the consistency test, as their consistency ratios met the required criteria.

(3)

Weight Summary

Based on the statistics of the indicator judgment matrices provided by the 30 experts mentioned above, the establishment of judgment matrices for both the criteria layer and the indicator layer was completed. The weight summary is presented in

Table 15. Summary of Weight Coefficients for the Judgment Matrix.

Target Layer	Criterion Layer	Relative Weight	Indicator Layer	Relative Weight	Comprehensive Weight
Design Scheme	A Essential Attributes	0.3454	A1 Convenient excretion care capability	0.1234	0.0426
			A2 Bed board auxiliary turning function	0.3689	0.1274
			A3 Guardrails on both sides of the bed	0.33	0.1140
			A4 Removable head and foot boards	0.1777	0.0614
	B Expected Attributes	0.3141	B1 Entertainment functions such as audio and TV	0.0333	0.0105
			B2 Storage space at the bottom of the bed	0.0624	0.0196
			B3 Integration with monitor	0.065	0.0204
			B4 Foldable dining board	0.2092	0.0657
			B5 Streamlined two-piece guardrail	0.1649	0.0518
			B6 Information monitoring and warning function	0.3111	0.0977
			B7 Folding rail-type guardrail	0.0358	0.0112
			B8 Built-in brake switch	0.1183	0.0372
	C Attractive Attributes	0.2389	C1 Auxiliary getting out of bed function	0.2142	0.0512
			C2 Safe and sterile capability	0.3424	0.0818
			C3 Drainage hook setting	0.2572	0.0614
			C4 Infusion stand placed in the bed	0.114	0.0272
			C5 Horizontal movement of the bed board	0.0721	0.0172
	D Indifferent Attributes	0.1016	D1 Electrically controllable	0.2857	0.0290
			D2 Voice controllable	0.2188	0.0222
			D3 Integration with transfer bed	0.2412	0.0245
			D4 Storage space set at the head of the bed	0.2543	0.0258

.

The calculated results of each indicator were substituted into the formula to conduct consistency checks on the evaluation results at each hierarchical level. Through calculation, the consistency ratios of the above judgment matrices were obtained, all of which passed the consistency test.

Based on the weight analysis, it can be found that six evaluation indicators have relatively high weight coefficients: auxiliary turning function of the bed board, guardrails on both sides of the bed, foldable dining board, information monitoring and warning function, safety and sterility performance, and setting of drainage hooks. After comprehensive analysis, functions such as auxiliary getting-out-of-bed function, convenient excretion care ability, and detachable headboard and footboard can also be included in the scope of design to be determined.

At the same time, based on the calculation results, this study reveals significant differences in demand categories and corresponding weights for nursing bed functions across different user groups. These disparities provide a critical foundation for the personalized and modular design of nursing beds. In terms of demand categories, patients prioritize attractive requirements and must-be requirements, with core demands centered on comfort, safety, and rehabilitation. Caregivers focus more on must-be requirements and one-dimensional requirements, emphasizing operational convenience and effort-saving performance. Nurses mainly consider one-dimensional requirements and must-be requirements, with a focus on efficiency and standardization in professional care. Regarding weight scores, patients assign the highest weight to assisted turning functions, caregivers give the highest weight to effort-saving operation functions, and nurses rank vital sign monitoring functions as the most important. These findings on demand differentiation indicate that modular design represents the optimal strategy to accommodate the diverse needs of multiple user groups. Core modules are used to satisfy the must-be requirements of all users, while expandable modules address the personalized demands of different user groups.

5. Design Scheme of Nursing Bed

5.1. Design Principles and Directions of Nursing Bed

5.1.1. Design Principles Based on the Fogg Behavior Model

Based on the three core elements of the Fogg Behavior Model, and combined with previous analyses on the relationships among motivation, ability, triggers, and user behaviors, as well as behavioral characteristics and practical needs derived from prior user surveys, this study summarizes the following design guidelines. These established guidelines are subsequently applied to determine the design principles for the medical care bed presented in

Table 16. Design Criteria for Medical Nursing Beds.

Behavioral Design Requirements	Design Principles	Principle Content
Based on Physiological Characteristics: Improving Usability	Functional Usability	Simplify the operation process; Simplify the functional structure
Based on Physiological Characteristics: Increasing Trigger Conditions	Intention Enhancement	Add rehabilitation functions; Provide a safe environment
Based on Emotional Characteristics: Enhancing Usage Motivation	Emotional Satisfaction	Provide emotional care; Adopt soft styling

.

(1)

Usability Principle

The usability principle will be a core factor for the target user group. Nielsen conducted an in-depth analysis of usability and proposed five fundamental attributes: learnability, efficiency, memorability, error tolerance, and satisfaction.

(2)

Safety Principle

As a medical bed, safety is the fundamental requirement and a critical foundation for the product to fulfill its intended functions. Safety in medical products ensures that patients are protected from harm caused by non-disease-related factors. In design practice, this encompasses both physical safety and psychological safety.

(3)

Interactivity Principle

Interactive design is goal-oriented, prioritizes user experience, and addresses design issues from a human-centered perspective. In a hospital setting, the interaction between disabled patients and caregivers is reflected in whether both parties can maintain a positive state in the environment over the long term. It also concerns whether the design of the nursing bed can integrate both parties into the environment and foster a harmonious and unified relationship between users and the product. Furthermore, given the broad scope of interactions in the usage environment, concepts such as modularity can be integrated into the product’s interaction design to better enhance its interactivity.

5.1.2. Determination of Design Directions for Bionics and Modularity

By summarizing and analyzing the above content, it can be concluded that to enhance emotional care, the product should adopt softer shapes, and thus certain bionic elements can be incorporated into its design. As a technology that applies biological characteristics, bionics is currently widely utilized in product design for specific groups, and can also provide new design concepts and working principles for the research and development of medical products [25,26]. Integrating bionic elements into the product’s shape can emotionally bring a sense of comfort to people and alleviate the sense of tension in the hospital environment.

Meanwhile, based on the weight coefficients, it can be seen that the functional requirements to be met by the nursing bed include 9 functional units: auxiliary turning function, guardrails on both sides of the bed, foldable dining board, information monitoring and warning function, safety and sterility performance, drainage hook configuration, streamlined two-piece guardrails, convenient excretion care capability, and detachable headboard and footboard. Since an excessive number of functions may adversely affect the nursing bed itself, a modular design is adopted to facilitate the upgrading and analysis of the nursing bed for different scenarios while retaining expandability [27,28].

Based on the requirements identified earlier, the nursing bed is designed as a modular product. In the design of modular care-related products, practical implementation should be prioritized first, followed by refined adjustments tailored to different functions. This leads to the modular design shown in Figure 10. However, it should be noted that several key measures are adopted to address structural compatibility. The module connection employs a quick-release buckle and guide-rail positioning structure, with a connection strength of no less than 500 N to prevent module loosening. A safety distance of at least 15 cm is maintained between electrical and mechanical modules, and cables are routed in a concealed manner to avoid damage from mechanical motion. The minimum safety distance between the disinfection module and the patient area is no less than 30 cm. The ultraviolet lamp is equipped with a human body sensing delayed-start function, which automatically switches off upon detection of human presence to eliminate the risk of ultraviolet exposure.

5.2. Ergonomic Analysis of Nursing Bed in Use

After clarifying the design principles and core directions, it is necessary to conduct targeted ergonomic analyses by combining the actual usage scenarios in wards and the interactive behavioral characteristics of caregivers and patients, so as to provide a practical basis for the functional layout and the design of operating mechanisms of the nursing bed. Ergonomics is the key prerequisite to ensure that the nursing bed fits human physiological dimensions, adapts to operational movements, and improves operational convenience and safety [29]. Wards mainly consist of a toilet area and a general ward area. Various devices such as plugs and oxygen outlets are installed on the wall near the bed side, while the opposite side is usually equipped with entertainment facilities like televisions. However, with the increasing popularity of smart mobile devices and the need for a quiet ward environment, the usage frequency of televisions is gradually declining. The area near the window at the far end is usually spacious and serves as a space for daily rest, movement, and exercise. Moreover, in the design of inpatient departments such as Xinhua Hospital, a small space separated by a door panel is set aside next to the window as a dedicated drying area. Therefore, based on field investigations, the ward area can be divided as shown in Figure 11.

Based on behavioral design theory, the design of the nursing bed should adequately consider the interactive behavioral characteristics of caregivers and patients. This suggests that the functional layout of the nursing bed should prioritize both sides of the bed as the primary activity zones, facilitating various nursing operations by caregivers. Such a layout optimizes nursing workflows, enhances care efficiency, and creates a more comfortable and safe care environment for patients. The interaction zone between caregivers and patients designed in Figure 12.

Since the lifting of the bed panel is currently primarily controlled by a rocking lever at the foot of the bed, this function, along with other bed surface movement functions, can be relocated together to the side of the nursing bed. Given the unique factor of caregivers’ high ability but low motivation in human–computer interaction and behavioral design, it is necessary to enhance the motivation for use in the design. Therefore, push buttons are installed on the bed frame near the ground, and an anti-stepping guard is fitted on the foot pedal buttons to prevent accidental activation. The anti-misoperation device for controlling the bed surface foot pedal designed in Figure 13.

5.3. Design and Optimization of the Nursing Bed Scheme

5.3.1. Design Scheme Selection

In terms of form design, according to the FBM, a major pain point for patients using nursing beds lies in motivation. Beyond physiological needs, another critical demand is emotional fulfillment. In contrast to the nursing beds widely used in current hospitals, which predominantly feature metal frames—adopted mainly for cost-effectiveness considerations—the metallic material itself conveys a sense of coldness, imposing additional psychological stress on both patients and caregivers. Therefore, incorporating dynamic shapes such as curves and arcs can enhance the product’s sense of warmth and affinity, transforming it from an impersonal hospital appliance into a gentler therapeutic device.

Taking comprehensive account of the device’s safety, functional characteristics, and the impact of visual attributes on users in design expression, and after integrating multiple considerations, three basic design scheme, as shown in

Table 17. Alternative Scheme Selection.

	Scheme	Design Analysis
A		The overall styling is designed to be robust and mechanical in character. The I-shaped base semantically underscores the sense of stability and security inherent to a hospital bed, while its crisp, rigid lines evoke a modular design esthetic. The vertical sliding rails in the central section clearly communicate their functional intent through the design language. Furthermore, the overall three-dimensional configuration is well-suited for sheet metal fabrication, rendering the manufacturing process simpler, more straightforward, and cost-effective.
B		In terms of styling design, it embraces a traditional style that aligns with the existing design of practical medical beds. The square frame base exudes a more stable and sturdy feel, while the inward-facing brackets convey the function of facilitating easy getting on and off at a glance. Meanwhile, the exposed structures as a whole better reflect the functionality and complexity of the medical bed.
C		In terms of structure, it departs from the frame structure of traditional hospital beds and adopts a more flexible, curved design on the outer side. Meanwhile, to facilitate patients’ getting on and off the bed, the overall height of the styling is lower, thus making the entire appearance exude greater stability. The bottom support design is positioned further inward rather than being directly exposed, minimizing damage to the soft external curves.

, were proposed via 3D modeling to enable deeper analysis and design in subsequent sketch development.

5.3.2. Design Scheme Evaluation

To select the optimal design for the current nursing bed development direction, a wide range of target users were chosen to score each scheme individually. The evaluation grades were Excellent, Good, and Poor, with corresponding scores of 5, 3, and 1 points, respectively. Finally, the evaluation weights were calculated to determine the most suitable scheme, thereby establishing the nursing bed evaluation results, as shown in

Table 18. Evaluation Results of Nursing Bed Appearance Design.

Number	Grade	Evaluation Weight
A	Excellent	0.28
	Good
	Poor
B	Excellent	0.32
	Good
	Poor
C	Excellent	0.38
	Good
	Poor

. “Excellent” indicates a score of 80 or higher, “Good” indicates a score between 60 and 79, and “Poor” indicates a score below 60.

Based on the reliability analysis, the data demonstrates good internal consistency and high reliability.

Based on the table analysis, Scheme C exhibits higher user acceptance. Coupled with its softer and more varied overall form, it aligns with the design selection criteria. Therefore, Scheme C is adopted as the main scheme for further in-depth design.

5.3.3. Form Design of the Design Scheme

Given the selection of Scheme C, its soft, curvilinear design is stylistically more suitable as a medical device in hospital settings and better aligns with the previous research on users’ perceptual imagery.

In the design of the side guardrails of the nursing bed, functional requirements must first be considered. In daily use, the guardrails act as a load-bearing support structure to assist patients in rising from the bed. Figure 14 shows the action differences between patients rising independently and rising with the aid of guardrails. For patients’ movements characterized by low ability but high motivation, it is necessary to enhance their “ability” to ensure successful action completion. Therefore, grip areas should be structurally integrated to improve patients’ ability to rise independently.

The specific form draws to some extent on the imagery of butterflies. Butterfly wings, like the bed’s guardrails, are positioned on both sides and serve as indispensable elements. By drawing on the overall shape and the specific structure of the wings, the general form of the guardrails is derived. The deduction process of the Nursing Bed Side Panel is shown in Figure 15.

As for the bed panels at the head and foot of the nursing bed, their design is based on the human-centered principles of ergonomics, taking into account the overall shape of the guardrails. As a whole, they need to appear softer and more approachable. Continuous iterative simplification is performed on the basis of the initial design, from which the final guardrail design is developed. The deduction process of the Nursing Bed Guardrail is shown in Figure 16.

5.4. Design Scheme Presentation

The overall form centers on the nursing bed, with modular attachments integrated to fulfill diverse functional requirements, while minimizing the visual prominence of complex mechanical components. The surrounding guard panels adopt a bionic design inspired by butterfly imagery: the internal textures and contours of butterfly wings are abstracted and applied to both the guard panels and side guardrails. The hollowed-out sections in the guardrails also double as support grips to assist patients in rising or getting out of bed. The mattress is a segmented inflatable type to prevent pressure ulcers [30]; the segmented design ensures patient comfort even as the bed panels articulate. Furthermore, the bed panels feature a modular hollow construction, which enhances ventilation and breathability while allowing easy removal for disinfection and cleaning. The detailed design specifications are presented in Figure 17, Figure 18, Figure 19 and Figure 20.

6. Conclusions

This study focuses on the design of a medical nursing bed. Behavioral design principles were employed to analyze user behaviors, based on which questionnaires and interview outlines were developed. Subsequently, in-depth interviews and behavioral observations were conducted in field settings to summarize target user behaviors. Combined with the survey data, the Fogg Behavior Model and Fogg Behavior Grid were utilized to derive the broad user needs. The KANO-AHP method was then applied to quantify these needs and rank them by weight, guiding the development of targeted design solutions to address user pain points and finalize the functional structure of the bed. In the design process, drawing on the Fogg Behavior Model from behavioral design, bionics was ultimately selected to enhance the “motivation” aspect of user behavior. Overall, this research and final design were carried out through a systematic approach involving in-depth behavioral observation, progressive categorization, quantification, weight calculation, and functional translation. The outcome is a nursing bed design that effectively resolves user pain points, fulfills user needs, and demonstrates strong practical feasibility.

Through in-depth field investigations and interviews, innovative design directions were derived by applying methods such as behavioral design, and a design process for nursing bed development based on this framework was established.

To improve the nursing efficiency of caregivers for patients, enhance the comfort of patients using the nursing bed, facilitate the process of diagnosis and treatment of patients by nurses and other personnel, conduct in-depth exploration of the needs of target users, and translate practical needs into detailed design guidelines, a modular and multi-functional nursing bed was designed. Although the modular design proposed in this study has verified its conceptual feasibility through basic compatibility analysis, several engineering challenges remain. The electromagnetic compatibility (EMC) of the integrated multi-module system requires further testing. The fatigue strength of the module connection structures under long-term operation needs to be validated by accelerated aging tests. In addition, the ultraviolet dosage of the disinfection module must be precisely calibrated for clinical scenarios. Future work will be conducted in collaboration with a medical device engineering team. Based on the conceptual framework established in this study, prototype development and performance testing of the modular nursing bed will be carried out to ensure its engineering feasibility and clinical applicability.

Through the entire research process of design for nursing beds for people with disabilities, this study derives two novel research insights. First, the design of age-friendly rehabilitation care products should take user behavioral coupling as the core logic. For dual-user groups such as patients with disabilities and their caregivers, their behaviors and needs are not isolated but intercoupled and mutually influential. Product design must consider the behavioral characteristics and demands of both groups to achieve synergistic satisfaction of their needs. Second, the integrated model of behavioral design and KANO-AHP can effectively address the core issues in age-friendly product design, namely inaccurate needs elicitation and unsupported needs translation. The Fogg Behavior Model enables in-depth extraction of users’ latent needs, the KANO model realizes scientific classification of demand types, and the analytic hierarchy process achieves accurate quantification of demand weights. The combination of these three methods constructs a precise mapping system from user behaviors to design elements.

However, in the functional structure design of the medical nursing bed presented in this study, there still exist limitations regarding many specific professional issues.

(1)

The main research focus was concentrated on user behavior analysis during the preliminary design stage. In future work, user experience and performance evaluations will be conducted based on product prototypes, and the design scheme will be continuously refined to achieve a more mature solution suitable for mass production.

(2)

Due to limitations in the professional domain, this study lacks detailed research and design on core technologies such as the bed’s electronic control module and signal control module.

Future research priorities will focus on enhancing user behaviors while ensuring the bed’s basic functions, alongside further deepening functional research. Greater interdisciplinary collaboration will be pursued, with support from professionals to advance in-depth exploration in relevant fields. Knowledge gained from such interdisciplinary exchanges will be fully integrated to unlock further value.

In summary, this study provides a replicable, data-driven methodology for nursing bed development. It not only expands the application of behavioral design in product design but also offers human-centered solutions for dual-user design in an aging society.