Fuzzy Delphi and DEMATEL Approaches in Sustainable Wearable Technologies: Prioritizing User-Centric Design Indicators

Abstract

The rapid advancement of intelligent technologies, including sensing devices, artificial intelligence, and the Internet of Things, has significantly accelerated the progress in industrial technology, particularly within the medical enterprise sector. Wearable innovations for health management have introduced novel approaches to physiological monitoring and early disease detection, contributing to an improved quality of life. In the context of sustainable development, wearable devices demonstrate considerable potential for supporting long-term healthcare solutions, particularly in the post-pandemic era, where the demand for smart health solutions continues to rise. This study aims to identify critical product design indicators for wearable devices that align with sustainable health management goals. Utilizing expert questionnaires and employing a combination of the Fuzzy Delphi Method and the DEMATEL-based Analytic Network Process (ANP), this research systematically evaluates the key factors influencing wearable device design. The findings highlight three primary aspects, six criteria, and 16 design indicators, with pivotal factors including “Compatibility”, “Foresight”, “Integration”, “Comfort”, “Appearance”, “Customization”, and “Intelligence”. These indicators provide a comprehensive framework for developing wearable devices that address diverse user needs while promoting individual well-being and sustainable health management. This study offers valuable insights into the design and development of wearable devices that support sustainable healthcare practices, advance social responsibility, and strengthen preventive care initiatives.

1. Introduction

According to the International Council for Small Business (ICSB), small and medium-sized enterprises (SMEs) account for more than 90% of all businesses worldwide, and Taiwan is no exception. SMEs have historically played a key role in Taiwan’s economy and are now tasked with driving the digital transformation of its industries, amid the rise of the digital economy, in the post-pandemic era. With global uncertainties and the COVID-19 pandemic reshaping the landscape, both the industry and economy have faced significant challenges. These challenges have accelerated technological advancements and transformed the medical care model into a global trend, with an increasing focus on sustainability. The aim is for new technologies to not only conserve medical resources and prevent diseases but also contribute to sustainable healthcare practices by reducing environmental impacts, such as minimizing the carbon footprint of healthcare infrastructure and optimizing resource utilization.

Taiwan has built a strong foundation in the information and electronics industries, creating a conducive environment for the digital transformation of healthcare. Integrating technology and medical care, cultivating interdisciplinary talent, and fostering collaboration between industry experts and healthcare professionals are crucial steps towards achieving both healthcare advancements and sustainability goals. Taiwanese SMEs in medical development and tech startups play a key role in this transformation by developing solutions that not only address medical needs but also prioritize sustainability in their operations and product designs.

In the wearable device market, two key demands dominate: health protection and medical care. Wearable health protection devices come in various forms, such as head-mounted, wristband, wearable, and adhesive types. Among the commercially available products, smartwatches and smart bracelets are the most prevalent. Regarding auxiliary medical devices, which support real-time and preventive care, their role is crucial in aging societies. Wearable medical devices can be categorized into implantable (e.g., brain neurostimulators and defibrillators) and non-implantable devices (e.g., wearable defibrillators and heart rate monitoring patches) [1,2]. These devices are commonly used in telemedicine, allowing doctors to monitor patients remotely. However, wearable medical devices that focus on diagnosis and treatment must undergo clinical trials and national certification, limiting their widespread adoption. Importantly, integrating sustainability into wearable medical devices—through energy-efficient designs, eco-friendly materials, and longer product lifecycles—will be key in meeting global sustainability standards.

Wearable devices offer consumers new experiences, especially with the rapid advancements in sensor technology, artificial intelligence, and internet technologies. These innovations promise more personalized interactions while contributing to sustainable practices through energy efficiency and their reduced environmental impact. Moreover, increasing social awareness of health, enhanced functionalities, rising disposable incomes, and smartphone penetration in developing nations are driving the growing demand for wearable technology. Recent discussions have focused on how technology can improve human well-being while promoting a sustainable, health-conscious lifestyle [3]. Unlike green technology, which primarily targets environmental sustainability, this study emphasizes social responsibility through the use of technology to support human health and well-being. This approach aims to meet the increasing demand for health technology, in line with the United Nations’ Sustainable Development Goals (SDGs), by improving the quality of life, promoting preventive care, and ensuring that technological innovations contribute to long-term sustainability.

This study explores the design and development of wearable devices, with a focus on user-centric safety needs, while addressing the challenges posed by diverse user demands. It analyzes key design factors, examines their relationships and correlations, and constructs a weighted ranking of design indicators. Through this analysis, the study seeks to contribute to the sustainable development of wearable technologies that not only improve individual well-being but also align with the broader goals of social responsibility, risk prevention, and sustainability.

2. Literature Review

2.1. The Product Design and the Current Usage of Wearable Devices

The so-called “wearable devices” detect and analyze physiological data through various sensors, radio frequency identifiers, and positioning systems embedded in clothing, accessories, and even human bodies. These devices are regarded as optimal electronic tools for making intelligent judgments and delivering information [4,5]. With the increasing sophistication of technological tools, the development of wearable devices since the beginning of the 21st century has not only benefited from the monitoring of various physiological signals but also from the integration with the Internet of Things (IoT). Wearable devices and their accompanying systems can interact to establish a mutual communication environment that is increasingly being utilized in future healthcare [6]. Furthermore, within the broader IoT framework, pervasive data can be continuously retrieved from the human body or any connected device, forming a machine-to-machine communication environment that provides personalized, networked health solutions [7].

In the context of sustainability, wearable devices contribute by reducing the environmental impact of healthcare delivery. By enabling real-time monitoring and remote healthcare services, they reduce the need for physical hospital visits, thus decreasing the consumption of medical resources and transportation-related carbon emissions. Moreover, advancements in energy-efficient designs and the use of eco-friendly materials in the production of wearable devices further align with sustainability goals. Sustainable wearable devices, by promoting preventive healthcare, not only enhance individual well-being but also contribute to a more sustainable healthcare system.

At present, wearable devices on the market can be categorized by functionality and product type. Functionally, wearable devices fall into three categories: life health, information consultation, and somatosensory control. Life health products, such as smart bracelets, capture and analyze physiological data; information consultation products, such as smartwatches and smart glasses, provide real-time information access; and somatosensory control products, primarily designed for entertainment, include various motion-sensing controllers [1,4,8]. As for product types, wearable devices have become increasingly miniaturized due to advancements in microelectronics and wireless communication technologies. These wearable monitoring systems, now an integral part of everyday life, include wristband devices (e.g., watches, bracelets, gloves), head-mounted devices (e.g., glasses, helmets), electronic textiles (e.g., coats, underwear, pants), and other body-sensing devices [9,10].

The wearable device market has experienced significant growth in recent years. According to Mordor Intelligence (2020), Asia and Oceania are expected to be the fastest-growing regions for wearable devices until 2024, driven by the rapid aging of the population. While Europe and the United States have shown moderate growth, the adoption in Africa and South America has been slower due to economic factors [11]. Notably, the COVID-19 pandemic in 2020 accelerated the sales of wearable devices, as the crisis heightened public awareness of health metrics. According to Statista, the sales of wearable devices reached a new high in 2020, as people purchased wearable health devices to monitor their well-being [12]. Even after the pandemic, wearable device sales continued to grow steadily, reflecting the increased focus on personal health and preventive care, which aligns with the global shift towards sustainability in healthcare.

2.2. The Connotation and Related Research on Product Design for Wearable Device

The “Product design index” serves as a standard that reflects the market demand and offers guidance in evaluating abstract concepts. By conducting a literature search using keywords such as “wearable device”, “wearable design”, and “wearable technology”, this paper seeks to understand the current state and future direction of wearable devices. Additionally, the study explores the factors considered in wearable product design, encompassing three aspects, six criteria, and 17 indicators.

Wearable devices have rapidly advanced, playing an increasingly vital role in health management and lifestyle enhancement. Their adoption, particularly accelerated by the COVID-19 pandemic, highlights their potential for monitoring health behaviors and addressing diverse needs through multifunctional capabilities. As the field evolves, interdisciplinary collaboration is essential to overcome challenges such as power efficiency and data security, ensuring that these technologies continue to shape sustainable health solutions and future readiness for global health challenges [13,14]. As a response to these crises, some have advocated for a balance between technological progress and human well-being, emphasizing that “people” should remain at the core of all development. This led to the rise of the green movement [15], which promoted environmental protection and spurred the development of Green Technology. Inspired by these movements [16], Wang et al. (2011) proposed a concept that extends green technology to Happiness Technology, Care Technology, and Health Technology [17].

Based on a comprehensive literature review, this study consolidates the aspects, criteria, and indicators relevant to wearable product design. The definitions and supporting literature for these elements are detailed in

Table 1. Aspects of wearable device product design.

Aspects	Definition	References
Happiness Technology	Enhancing positive emotions through information technology and product design, integrating engineering, sociology, and humanities to foster user-friendly experiences and improve interpersonal relationships.	Dewsbury et al. (2003) [18]; Dey et al. (2016) [19]; Soubutts, E. (2023) [20]
Care Technology	Focusing on humanistic thinking, strengthening the connectivity of innovative technologies to enhance positive “human-to-human” and “human-to-machine” interactions.	Roupa et al. (2010) [21]; Peek et al. (2014) [22]; Righi et al. (2015) [23]; Majumder et al. (2017) [24]; Pal et al. (2017) [25]; Sanchez (2017) [26]; Stafford and Baldwin (2018) [27]; Bahadori et al. (2024) [28]; Corti et al. (2024) [29]
Health Technology	Leveraging IT-based devices, instruments, or tools to promote health, prevent diseases, and support treatment, maintaining physical, mental, and spiritual well-being.	Banta (2003) [30]; Heidegger (1977) [31]; Fontrier et al. (2022) [32]; Mbau et al. (2023) [33]

,

Table 2. Criteria for wearable device product design.

Criteria	Definition	References
Quality Appearance	Moderate size and weight to avoid user discomfort during prolonged wear.	Jarusriboonchai and Häkkilä (2019) [34]; Juhlin et al. (2013) [35]; Vatavu and Ungurean (2022) [36]
Bio-Friendliness	Should not cause physiological or psychological harm to users, nor create an environmental burden during production.	Bellekens et al. (2016) [37]; Liang et al. (2018) [38]; Habibipour et al. (2019) [39]
Personalized Design	Adapted to user habits and immediate contexts for real-time and relevant decision-making.	Blasco et al. (2018) [40]; Arias et al. (2015) [41]; Gubbi et al. (2013) [42]; Wang et al. (2017) [43] Tileria (2023) [44]
Function Enhancement	Ensures accurate data detection and reliable performance in various environments.	Gubbi et al. (2013) [42] Degroote et al. (2018) [45]; Hussain et al. (2018) [46]; Loncar-Turukalo et al. (2019) [7]; Kilani et al. (2020) [47]; Kobsar et al. (2020) [48]; Lutz (2020) [49]
Device Extension	Compatibility with older devices during upgrades or updates, ensuring smooth functionality.	Diaz et al. (2019) [50]; Lidynia et al. (2019) [51]; Ahmad et al. (2020) [52]
Future Outlook	Integration and communication between devices, including wearables, smart home, and in-car systems.	Shah et al. (2017) [53]; Sun et al. (2018) [54]

and

Table 3. Indicators for wearable device product design.

Indicators	Definition	References
Appearance	Compact size and lightweight design to increase user willingness to use the product.	Juhlin et al. (2013, 2016) [35,55]; Jarusriboonchai and Häkkilä (2019) [34]; Bigger and Fraguada (2016) [56]
Wearability	Suitable design for long-term wearing, including proper weight, size, shape, and connection methods.	Gemperle et al. (1998) [57]; Knight et al. (2006) [58]; Gao et al. (2020) [59]
Comfortableness	Design considers physiological, psychological, and behavioral comfort through material and ergonomics.	Knight and Baber (2005) [60]; Starner (2014) [61]
Security	Compliance with product safety standards to protect user safety, especially for seniors.	Bellekens et al. (2016) [37]
Eco-Friendliness	Material selection ensures no harm to users or the environment.	Habibipour et al. (2019) [39]
Ease of Use	Designed for intuitive and convenient user operation.	Claudio et al. (2015) [62]; Liang et al. (2018) [38]
Privacy	Ensures data security and prevents personal information leakage.	Arias et al. (2015) [41]; Thierer (2015) [63]; Blasco et al. (2019) [64]
Individualized Design	Customizable appearance and functionality to suit user activity patterns.	Wang et al. (2017) [43]
Extensivity	Digital connections for sharing information with family, caregivers, and doctors in real-time.	Hussain et al. (2018) [46]; Loncar-Turukalo et al. (2019) [7]
Power Efficiency	Enhanced energy efficiency to improve product battery life.	Loncar-Turukalo et al. (2019) [7]; Kilani et al. (2020) [47]
Extra Service Functions	Provides features such as location awareness, emergency alerts, and activity monitoring.	Lidynia et al. (2019) [51]
Interactivity	Enables location tracking and predictive or entertaining interactions based on activity patterns.	Motti (2020) [65]
Smartness/Intelligence	Automatically detects abnormal user conditions and sends alerts.	Gubbi et al. (2013) [42]
Accuracy	High consistency between detected data and actual user physiological values.	Degroote et al. (2018) [45]; Kobsar et al. (2020) [48]
Compatibility	Ensures interoperability between brands and built-in systems.	Cheng (2015); [66] Yang et al. (2016) [67]; Diaz et al. (2019) [50]; Ahmad et al. (2020) [52]
Reliability	Ensures dependable interface connectivity between devices.	Lutz (2020) [49]
Patent	Secures patented technologies to enhance product utility and user satisfaction.	Mück et al. (2019) [68]; Dehghani and Dangelico (2018) [69]

.

The first aspect, Happiness Technology, emphasizes enhancing positive emotions and fostering interpersonal relationships through the integration of technology, sociology, and humanities. This aspect includes the criteria of “Quality Appearance” and “Bio-Friendliness” with the associated indicators such as lightweight, comfort, safety, sustainability, and convenience. These elements, collectively, ensure that wearable products are aesthetically appealing, user-friendly, and environmentally sustainable.

The second aspect, Care Technology, focuses on improving human–machine and interpersonal interactions by incorporating innovative and humanistic thinking. This aspect includes the criteria of “Personalized Design” and “Function Enhancement”, which are supported by indicators such as privacy, customization, intelligence, energy consumption, extension, interactivity, accuracy, and reliability. These indicators address the functionality and adaptability of wearable devices in various usage scenarios.

The third aspect, Health Technology, prioritizes holistic well-being through IT-based tools for health promotion, disease prevention, and treatment. The criteria under this aspect are “Device Extension” and “Future Outlook”, with the indicators including compatibility, additional function, foresightedness, and integration. These features ensure that wearable devices are forward-looking and compatible with other smart systems to meet evolving user needs.

This systematic categorization of aspects, criteria, and indicators provides a robust foundation for wearable device design, aligning technological innovation with user demands and societal impact.

3. Methods

3.1. Research Architecture

Product design is a creative and goal-oriented process. Indicators serve as tools to guide and evaluate, providing qualitative or quantitative standards to assess abstract concepts through clear descriptions, aiding in classification and decision-making [70,71]. This study conducted a literature review and consulted experts in wearable device design to identify and synthesize relevant design components, criteria, and indicators. A preliminary “Wearable Device Product Design Indicator Survey Questionnaire” was developed for a fuzzy Delphi method survey, aiming to select appropriate evaluation indicators with an expert consensus. Subsequently, a Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based network analysis questionnaire was designed to establish the final design indicator scale. Through a comprehensive literature analysis and expert feedback, the study identified three design components, six criteria, and 17 indicators. The research framework and process are illustrated in Figure 1 and Figure 2.

3.2. Research Subjects

The evaluation of usability indicators and weights for wearable device product design demands significant expertise and experience. Consequently, this study employed a purposive sampling approach to ensure the credibility of results. Participants were required to meet strict qualifications, including holding a bachelor’s degree or higher in relevant fields, such as industrial design, healthcare technology, or engineering, along with a minimum of five years of professional experience in wearable technology design, healthcare innovation, or related industries. The panel consisted of five senior industry experts, academics, and healthcare professionals, as well as thirteen additional experts, forming the Fuzzy Delphi and Analytic Network Process groups. This ensured that the insights collected reflected both academic rigor and practical applicability.

3.3. Research Tools

3.3.1. The Fuzzy Delphi Expert Questionnaire

Since most of the multi-criteria decision-making factors need to draft partial criteria for strategy and evaluation via literature and interviews, they may not be completely applicable. Accordingly, this study conducted a fuzzy Delphi questionnaire after constructing a questionnaire based on expert validity. The questionnaire was formed with a 10-point scale, and fuzzy Delphi experts were asked to evaluate the importance of each indicator by filling in “the best single value (optimal value)”, “the lowest acceptable value (minimum value)”, and “the highest acceptable value (maximum value)”, respectively, and an open opinion column was also offered, aiming to establish double triangle fuzzy numbers and gray zone tests, as well as calculate consensus values.

In particular, the “grey zone tests” can effectively examine whether experts show a consistent convergence effect in cognition, so that fuzzy Delphi experts can refer to them to see if they need to revise their opinions until all evaluation items can reach a convergence and obtain the “consensus importance value”. This study conducted two fuzzy Delphi questionnaires in total. After repeated revisions, mergers, additions and subtractions of the questionnaires, the “key factor indicators for the wearable device product design” were finally summarized, organized, and selected.

3.3.2. The Decision-Making Trial and Evaluation Laboratory-Based Analytic Network Process Questionnaire

The Decision-Making Trial and Evaluation Laboratory-based Analytic Network Process (DNAP) method applied by this study is the multi-criteria decision-making (MCDM) model generated by the combination of the Decision-Making Trial and Evaluation Laboratory method and the Analytic Network Process method, as explained below. The exploration of the cause-and-effect relationship and correlation between product design indicators for wearable devices was performed using the Decision-Making Trial and Evaluation Laboratory method.

First, the DANP expert questionnaire was designed with reference to the evaluation scale proposed by Lin and Wu (2008). The evaluation scale was set as “(0) no impact”, “(1) low impact”, “(2) moderate impact”, “(3) high impact”, “(4) extremely high impact” [72]. Then, the influence values of factors judged by experts were collected to establish the average expert opinion matrix, the normalized average expert opinion matrix, and the total impact relationship matrix. Subsequently, the threshold value was set to eliminate the criteria or indicators which had an overly low impact in the total impact relationship matrix, thereby obtaining a simplified total impact relationship matrix, based on which the correlation in the cause-and-effect diagram was plotted. In addition, with the help of the cause-and-effect diagram, decision-makers can also plan appropriate product design strategies based on the cause factors in the criteria or indicators and their interplay relationships. The exploration of the weight value and ranking of product design indicators for wearable devices using the Analytic Network Process method was performed based on the Decision-Making Trial and Evaluation Laboratory method.

In this study, the weighting and ranking of product design indicators for wearable devices were determined by integrating the Analytic Network Process (ANP) proposed by Saaty (1996) with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. The process began with the calculation of the average expert matrix (A) and the normalized average expert opinion matrix (D) using DEMATEL. After performing overall normalization, the total influence relationship matrix ($T_c$) was derived. Subsequently, the normalized benchmark ($f_i$) of the total influence relationship matrix was established to obtain the normalized total influence relationship matrix ($T_c^{*}$), which was transposed to form the unweighted supermatrix (W). The total influence relationship matrix obtained from DEMATEL was then used as the weighting basis for W, resulting in the construction of the weighted supermatrix (S). By leveraging the property that the sum of each column vector in the weighted supermatrix equals 1, the weighted supermatrix (S) was iteratively multiplied by itself until it converged and stabilized. This process yielded the limit supermatrix (L), from which the relative weights and rankings of the indicators were determined [73].

4. Results and Discussions

4.1. The Fuzzy Delphi (FDM) Analysis

4.1.1. The First Fuzzy Delphi Analysis

This study integrated the opinions of scholars and experts with the “double triangular fuzzy number method” proposed by Jeng (2001), examined whether the expert opinions have reached convergence with the “grey zone testing method” [74], and applied the operating procedures of Ishikawa et al. (1993) [75] and Microsoft Excel 2007 software to conduct the pre-test analysis of the development of product design indicators for wearable devices. In the aspect of evaluation criteria, according to the 80/20 rule, the overall average of 6.976 was multiplied by 0.8 to obtain the threshold value of 5.581. Those with values lower than the threshold value were eliminated. As a result, all six measurement criteria were retained, which means that the measurement aspects have expert consistency and importance. As for the design indicators, 80% of the total average expert consensus value of 7.014 was used as the threshold, and the expert consensus threshold value of 5.611 was retrieved. Those with values lower than that were deleted. Consequently, a total of 16 evaluation indicators were retained, and “Additional Function (5.575)” was deleted.

4.1.2. The Second Fuzzy Delphi Analysis

Through the pre-test analysis of the fuzzy Delphi expert questionnaire, the content was revised to three aspects, six criteria, and 16 design indicators. In this way, the post-test analysis of the fuzzy Delphi expert questionnaire was conducted again, hoping to make the construction of product design indicators for wearable devices more precise and critical. According to the 80/20 rule, the overall average of 7.046 was multiplied by 0.8 to obtain the expert consensus threshold value of 5.637, and those lower than that were discarded. The selection results revealed that the six evaluation criteria in the development of product design indicators for wearable devices all reached the threshold value, such that all were retained. Next, in the post-test analysis of the index evaluation of product design indicators for wearable devices, 80% of the total average expert consensus value of 7.252 was used as the threshold value, and the expert consensus threshold value of 5.802 was received. Those with values lower than that were eliminated, and a total of 16 evaluation indicators were retained.

4.2. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) Analysis

This study developed the DANP expert questionnaire based on the results of the fuzzy Delphi analysis to explore the cause-and-effect relationship and correlation between the product design indicators of wearable devices. First, based on the total impact relationship matrix of criteria and indicators, we calculated the column sum and row sum of the total impact relationship matrix T. This study used d + r (correlation degree) as the abscissa and d − r (cause degree) as the ordinate to form a cause-and-effect diagram, in which the criteria and indicators of product design for wearable devices were plotted. In addition, the total impact relationship matrix was simplified based on the criteria and indicators, and then the correlation between the criteria or indicators was depicted in the cause-and-effect diagram to explore whether the impact relationship between the criteria or indicators was one-way (black index line) or two-way (red index line).

In summary, the criterion cause–effect diagram and the index cause–effect diagram (Figure 3) of the product design indicators for wearable devices developed by this study are explained as follows:

(1).

Criteria

In terms of correlation degree, the criteria were sorted by the correlation degree (d + r) in order, including “Personalized Design”, “Function Enhancement”, “Device Extension”, “Future Outlook”, “Quality Appearance”, and “Bio-Friendliness”. Among them, the correlation degrees (d + r) of the “Personalized Design”, “Function Enhancement”, “Device Extension”, and “Future Outlook” criteria were all greater than the average. Therefore, it is learned that they belong to the critical factors of greater importance among the developed product design criteria for wearable devices.

In terms of cause degree, the cause degrees (d − r) of the criteria “Bio-Friendliness”, “Personalized Design” and “Device Extension” were less than 0, such that they belong to the factors that are more easily affected, that is, the “effect” in the cause-and-effect relationship. Regarding the criterion “Quality Appearance”, its correlation degree was not much different from the average, and the cause degree was greater than zero, so it belongs to the cause criterion, that is, the “cause” in the cause-and-effect relationship. However, the correlation degrees of the criteria “Function Enhancement” and “Future Outlook” were greater than the average, and their cause degrees were greater than zero as well. It is clear that these three criteria belong to core criteria which have more active impact in the construction of product design indicators for wearable devices. Through the enhancement of their functions or quality, not only can they help boost the effectiveness of other criteria or indicators, but they can also help achieve the purpose of increasing consumers’ willingness to use the product. Therefore, it is recommended that businesses which intend to develop wearable devices should pay more attention to the criteria of product design, such as “Function Enhancement”, “Future Outlook”, and “Quality Appearance”, or their corresponding indicators.

(2).

Indicators

In terms of correlation degree, the indicators based on the correlation degree (d + r) were sorted in order as “Intelligence”, “Customization”, “Convenience”, “Interactivity”, “Foresight”, “Reliability”, “Connectivity”, “Accuracy”, “Integration”, “Compatibility”, “Security”, “Energy Consumption”, “Privacy”, “Sustainability”, “Comfort”, and “Appearance”. Among them, “Intelligence”, “Customization”, “Convenience”, “Interactivity”, “Foresight”, “Reliability”, “Connectivity”, “Accuracy”, and “Integration” were the indicators whose degrees of correlation (d + r) were greater than the average, so they belong to critical factors of greater importance.

In terms of cause degree, “Comfort”, “Reliability”, “Security”, “Privacy”, “Accuracy”, “Sustainability”, “Convenience” and “Interactivity” belong to the affected indicators (d − r is negative). The indicators “Energy Consumption”, “Compatibility”, and “Appearance” belong to influential indicators because their correlation degrees were not much different from the average, and their cause degrees were greater than zero. The correlation degrees of “Intelligence”, “Foresight”, “Integration”, “Customization”, and “Connectivity” were greater than the average, and their cause degrees were greater than zero. Therefore, it is recommended that businesses which plan to develop wearable devices should pay more attention to “Intelligence”, “Foresight”, “Integration”, “Customization”, and “Connectivity”, followed by “Energy Consumption”, “Compatibility”, and “Appearance”.

4.3. The Decision-Making Trial and Evaluation Laboratory-Based Analytic Network Process (DNAP) Analysis

This study adopted the Decision-Making Trial and Evaluation Laboratory-based Analytic Network Process (DNAP) method to calculate the limit supermatrix, which served as the weight value for “DANP importance calculation” and final ranking. According to the analysis results in

Table 4. The Decision-Making Trial and Evaluation Laboratory-based Analytic Network Process analysis.

Criterion	Criterion Weight Value	Index	Index Weight Value	Goal Weight Value	Importance Ranking
1-1. Quality Appearance	0.1431	1-1-1. Appearance	0.4811	0.0689	5
		1-1-2. Comfort	0.5189	0.0743	4
1-2. Bio-Friendliness	0.1574	1-2-1. Safety	0.3371	0.0531	10
		1-2-2. Sustainability	0.3041	0.0479	11
		1-2-3. Convenience	0.3587	0.0565	9
2-1. Personalized Design	0.1866	2-1-1. Privacy	0.3097	0.0578	8
		2-1-2. Customization	0.3481	0.0650	6
		2-1-3. Intelligence	0.3422	0.0638	7
2-2. Function Enhancement	0.1728	2-2-1. Energy Consumption	0.1807	0.0312	16
		2-2-2. Connectivity	0.1978	0.0342	15
		2-2-3. Interactivity	0.2054	0.0355	13
		2-2-4. Accuracy	0.2042	0.0353	14
		2-2-5. Reliability	0.2118	0.0366	12
3-1. Device Extension	0.1710	3-1-1. Compatibility	1.0000	0.1710	1
3-2. Future Outlook	0.1691	3-2-1. Foresight	0.5069	0.0857	2
		3-1-2. Integration	0.4930	0.0834	3

, the weight rankings of the seven design indicators—“Compatibility”, “Foresight”, “Integration”, “Comfort”, “Appearance”, “Customization”, and “Intelligence”—were determined. These indicators were identified as the most critical factors in wearable device design based on expert evaluations. Each indicator and its implications for wearable device design are discussed below:

(1).

Compatibility

The analysis identifies “Compatibility” as a critical factor in wearable device design, emphasizing its role in ensuring the smooth integration with existing technologies. High compatibility has been shown to positively influence user adoption and retention [67]. Ahmad et al. (2020) further highlighted that compatibility is the primary determinant for the continued use of wearable health devices [52]. These findings confirm that software and hardware compatibility must be prioritized during the design and development process to meet user expectations and technological standards.

(2).

Foresight

“Foresight” reflects the need to anticipate technological evolution and the integration of wearable devices into daily life and healthcare. While current references on wearable device foresight remain limited, interdisciplinary collaboration is crucial for future advancements [76]. This study confirms the importance of envisioning wearable devices not only as tools for data collection but also as platforms for the interaction among individuals and groups, aligning with predictions about embedded wearable technology [77].

(3).

Integration

“Integration” is vital for ensuring that wearable devices can seamlessly expand their functionalities and interact with IoT and artificial intelligence systems. Shah et al. (2017) demonstrated the risks of data inconsistencies across devices, underscoring the need for robust integration mechanisms [53]. This study supports the necessity of developing calibration and data adjustment methods to ensure accurate and reliable device performance when used in diverse environments.

(4).

Comfort

“Comfort” remains a foundational aspect of wearable device design, particularly due to its direct impact on user experience. Early studies by Gemperle et al. (1998) emphasized dynamic wearability as a design criterion [57], a view supported by ergonomics research [78,79]. This study reaffirms that wearable devices must prioritize comfort in terms of materials, form factors, and attachment methods. Future research should extend to embedded devices to ensure their comfort in long-term use.

(5).

Appearance

The analysis reveals that “Appearance” significantly influences user perception and purchasing behavior. Aesthetic design elements enhance the perceived value of wearable devices, making them both functional tools and expressions of personal identity [80,81]. Zhao et al. (2021) demonstrated the application of fuzzy mathematics to optimize product aesthetics, a method that can guide future wearable device design. This study emphasizes that appearance is not merely a competitive advantage but a core driver of user engagement [82].

(6).

Customization

“Customization” is critical for aligning wearable devices with individual user needs, particularly in healthcare applications. Haghi and Deserno (2020) identified the simultaneous monitoring of diverse parameters and seamless connectivity as essential for future devices [83]. This study confirms that highly customizable solutions, adhering to the “4U” principle (unified, unique, ubiquitous, and unobtrusive), are pivotal for enhancing usability and meeting specific user and institutional requirements.

(7).

Intelligence

“Intelligence” underpins the transformative potential of wearable devices in proactive healthcare. Chawla (2020) noted that smart wearable devices support the shift from reactive to preventive care by detecting and alerting users to abnormal health conditions [84]. This study emphasizes that the intelligence of wearable devices, particularly their capacity for real-time monitoring and early warning, is essential for addressing evolving healthcare challenges.

5. Conclusions and Suggestions

5.1. Conclusions

To cater to the challenges of diverse user needs, this study made conclusions based on the research objectives as follows:

(1) In the design of wearable devices, health and home monitoring functions are the primary focus. To achieve enhanced functionality while contributing to sustainability, three major aspects of product design must be considered: providing a pleasant and successful user experience, integrating sensing components with data application, and utilizing human-centered sensing technology. Sustainable design can be achieved by using eco-friendly materials, optimizing energy consumption, and extending the product’s lifecycle, which reduces electronic waste and conserves natural resources. While wearable devices have the potential to influence health behaviors, these changes are not driven solely by the devices themselves. Their successful user experiences and potential health benefits depend largely on the design. In addition to meeting users’ needs for comfort, protection, durability, weight, ease of movement, and aftercare, a human-centered design approach also promotes sustainability by focusing on user retention, reducing the need for frequent replacements.

(2) Experts highlighted the importance of several design indicators, including “Compatibility”, “Foresight”, “Integration”, “Comfort”, “Appearance”, “Customization”, and “Intelligence”. To increase consumers’ willingness to continue using these devices and support sustainable practices, wearable devices should offer a high compatibility with other existing technologies. This allows for seamless upgrades or brand transitions, extending the product’s lifecycle and reducing electronic waste. Moreover, enabling future integration with Internet of Things (IoT) applications can improve resource efficiency by optimizing how devices interact with each other, thereby reducing the environmental impact of manufacturing and disposal. For example, integrating data from multiple devices can create a unified user interface, ensuring that wearable devices continue to meet user needs while minimizing their environmental footprint.

(3) In analyzing the cause-and-effect relationships and correlations between criteria and indicators, the criteria “Function Enhancement”, “Future Outlook”, and “Quality Appearance” were found to significantly impact key indicators such as “Intelligence”, “Foresight”, “Integration”, “Customization”, “Connectivity”, “Energy Consumption”, “Compatibility”, and “Appearance”. These indicators should be prioritized in product design to increase consumer willingness to use wearable devices. In terms of sustainability, reducing energy consumption and enhancing connectivity will decrease the devices’ environmental footprint, making them more eco-friendly while improving user experience.

(4) The integration of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method with the Analytic Network Process (ANP) revealed that while all indicators are important, “Energy Consumption” and “Connectivity” stand out as top priorities for improvement. These indicators not only promote sustainability by optimizing energy use but also enhance the devices’ overall efficiency, allowing wearers to experience the convenience and benefits of smart devices. Although “Comfort” is ranked highly due to its importance in product design, its status as an affected factor suggests that its improvement is tied to the overall sustainability of the product. Thus, reducing energy consumption while maintaining comfort will contribute to the sustainability of wearable devices by promoting user satisfaction and reducing the need for frequent product replacements.

5.2. Suggestions

This study utilized expert questionnaires to extensively explore key product design factors for developing wearable devices in the medical field. However, to create truly sustainable wearable products, the design must not only cater to consumers’ functional diversity, personalization, and technological experiences but also incorporate adaptability for future operating technologies, such as touch panels, voice recognition, eye tracking, limb movements, aerial gestures, projected touch, and brainwave control. These technologies can enable continuous breakthroughs in product functionality and user engagement.

To further bridge the gap between wearable product design and actual user needs, future research should focus on design indicators and user requirements for specific types of wearable devices, such as wristbands, which are currently the most widely used. Additionally, understanding the needs and experiences of different user groups will enhance the sustainability of wearable devices by ensuring that products are both efficient and adaptable to diverse populations over time. By aligning design features with user-centric sustainability goals, future studies can contribute to more eco-friendly production, longer product life cycles, and the promotion of well-being in an increasingly health-conscious society.