Consumer Involvement in the Co-Design of Diabetes Self-Management Smartphone Apps: A Scoping Review

Abstract

Objectives

Consumer involvement in the co-design of diabetes self-management smartphone apps is vital. This scoping review explored how consumers are involved in the co-design processes and methods and approaches guiding this research.

Methods

Our review was guided by Arksey and O'Malley's five-stage framework, PRISMA-ScR guidelines, and Witteman and colleagues' 11-item user-centered design (UCD-11) framework. We searched literature across five databases and examined types of consumer involvement in co-design and frequency of methods and approaches (i.e., co-design approaches, behavioral theories, and other frameworks), synthesizing findings in SPSS and Excel.

Results

Of the 14,206 initial items, 283 articles were included. Most studies were conducted in Asia (33.2%) and focused on type 2 diabetes (43.1%). All articles addressed at least one UCD principle, and prototype evaluation (UCD-3) was the most frequent (82.3%); 85.2% addressed iterative responsiveness (factor 2). Most articles (66.8%) did not report a particular method or approach; 20.5% used design-related approaches, with user-centered design being the most common (7.4%). Few articles (3.9%) utilized social cognitive theory.

Conclusion

Overall, co-design activities were isolated by phase. Consumers were primarily involved in evaluating prototypes and had limited engagement in the early stages. Iterative responsiveness factor activities were underreported or limited in scope. The use of approaches, theories, and frameworks was inconsistent. Consumer involvement in the co-design of diabetes self-management apps is often limited to later phases, with minimal engagement during the critical preprototype phase. To enhance the relevance, effectiveness, and adoption of diabetes self-management apps, app designers should improve the reporting of co-design activities and engage consumers across all co-design phases.

Background and Significance

Involving consumers in designing, developing, and evaluating health interventions is becoming more common and considered good practice. 1 2 However, there is wide variability in the degree of consumer involvement in the different stages of the research process and the methods and frameworks reported and used by authors. 3 4 5 6 To date, reviews of consumer involvement in the creation of digital health interventions have reported mixed definitions, terms, and approaches. 7 8 9 10 11

The terminology surrounding the involvement of consumers in development is varied and nuanced. Consumers are also referred to as end users, patients, clients, community members, citizen scientists, and service users, 12 13 each with implications and assumptions. The involvement of these individuals in the design process further compounds the definition of complexity. Participatory action research is a collaborative process involving persons who are direct recipients of the research, and terms like co-design, co-creation, and co-production are often used interchangeably across this type of research. 14 However, involving the consumer in product ideation, design, or development is not new; it is a crucial component of user-centered design, human-centered design, design thinking, and participatory design, 15 16 17 all of which may be distinct, used in combination, or often used interchangeably. Numerous philosophies and frameworks have emerged to propose, explain, and expand these terminologies, and activities and methods have been outlined to help facilitate their implementation. 18 19 20 Yet, a major challenge for the field is the discrepancy in the nomenclature used to describe the methods and approaches used, making it difficult to clearly distinguish between these terms, which often leads to confusion and inconsistency in their application and interpretation.

Diabetes is an area where consumer health informatics interventions to support self-management have been widely applied. 21 22 23 24 25 Multiple systematic reviews have found that smartphone apps are associated with better glycemic control, increased physical activity, and healthier dietary behavior in people with type 2 diabetes. 26 27 28 However, findings are inconclusive for other types of diabetes, such as type 1 diabetes, due to the limited number of high-quality studies and heterogeneous results. 28 Co-designed mHealth apps generally improve usability and increase user satisfaction and app engagement by aligning closely with users' needs, preferences, and challenges. 29 30 31 This increased engagement may lead to closer adherence to self-management protocols and positive clinical outcomes.

To the best of our knowledge, there has not been a comprehensive study characterizing consumer involvement in the design of diabetes self-management smartphone apps. By characterization, we refer to the extent to which user- or human-centered processes are employed in the design and development of these apps.

Objectives

To address these gaps, the purpose of this study is to describe the formal methods and approaches used in the engagement of consumers in the design and evaluation of diabetes technologies intended for self-management and the extent to which the consumers were engaged.

To gain insights into the state of the science of user- and human-centered (herein user-centered) co-design of smartphone apps for diabetes self-management, we explored the role of the consumer in various aspects of co-design as well as the methods and approaches guiding this research. Specifically, this scoping review addressed the following research questions (RQ): (1) How are people with diabetes engaged in the user-centered co-design of smartphone apps for diabetes self-management? (RQ1) and (2) What methods and approaches were reported in the articles? (RQ2).

Methods

Review Framework and Reporting Guidelines

This scoping review follows Arksey and O'Malley's five-stage framework 32 : (1) identification of the RQ; (2) execution of a comprehensive search; (3) selection of articles for inclusion in the review; (4) data extraction; and (5) synthesis and reporting of the data. To ensure transparency in reporting, we adhered to the PRISMA (preferred reporting items for systematic reviews and meta-analyses) for scoping reviews (PRISMA-ScR) reporting guidelines. 33

Search Strategy and Review Process

We registered the protocol in the Open Science Framework. 34 Using a combination of controlled vocabulary and keyword searching to reflect the concepts of diabetes and smartphone applications, we conducted a comprehensive search across five databases (PubMed, Ovid Medline, Ovid Embase, CINAHL EBSCO, and Cochrane Library) in February 2023 and most recently updated in June 2024. A complete search strategy is available in Supplementary Appendix A (available in the online version only).

We did not place limitations on the language of publication or study design, and we used Google Translate for publications in languages other than English. 35 We compiled and deduplicated results in EndNote 20. 36 We screened articles in 2 stages (title-abstract and full-text) using Rayyan, 37 a web-based tool that facilitates the selection of eligible articles, reporting reasons for exclusion per the PRISMA-ScR Guidelines. 33

The following criteria depict our inclusion criteria:

• Persons of all ages with diabetes (type 1, type 2, or gestational diabetes) or their formal caregiver.

• Smartphone apps (potential, prototype, or final version) designed for diabetes self-management, including comorbidities, regardless of their connection to a wireless device (e.g., glucose monitor, smartwatch).

• Articles that involved consumers in any of the 11 steps of the user and human-centered co-design process, as defined by Witteman and colleagues. 8

• Articles published since 2017, a date selected based on a preliminary search.

• Publications in all languages.

The following criteria depict our exclusion criteria:

• If the population included consumers without diabetes or with prediabetes or metabolic syndromes or exclusively providers.

• Apps not designed for diabetes self-management, including apps for diagnosis or prevention.

• Apps whose functionality was limited to text messaging or SMS or designed for other purposes, such as WhatsApp or MyFitnessPal.

• Apps exclusively tethered to a patient portal, used solely to pass data from a sensor to a system other than a smartphone, and designed for devices other than a smartphone.

• Apps if they were part of a multi-modal intervention, such as an app used in conjunction with health coaching.

• Items if they were published prior to 2017.

• Items if they were any of the following: case studies or reports; commentaries or editorials; conference abstracts; theses or dissertations; protocols; reviews, including systematic and scoping reviews; secondary data analyses; survey validation studies; or clinical trial registrations.

Measures and Operationalization of Terms

To answer RQ1, we used the 11-item measure of user- and human-centered design for personal health tools (UCD-11). 8 The UCD-11 is a descriptive measure measuring how researchers apply and document different aspects, namely consumers' involvement throughout the design and development processes (e.g., human-centered design and co-design). While user and human-centered design are not always used synonymously with co-design, Witteman et al note that their operational definition of user-centered design is “a fully or semistructured approach in which people who currently use or who could in future use a system, service or product are involved in an iterative process of optimizing its user experience,” and that this definition is conceptually and methodologically aligned with co-design. 8

The UCD-11 is a descriptive measure of the extent to which the design process incorporates user-centered principles (see Table 1 for definitions of items). The measure's 11 items are grouped into three distinct factors, each with a factor summative score based on the presence (1 = yes) or absence (0 = no) of the respective characteristics. Preprototype involvement (factor 1) includes UCD-1 and UCD-2. Factor 1 is defined as the early stages of development aimed at understanding prospective users' needs and preferences; factor summary scores range from 0 (no preprototype involvement) to 2 (involvement of both preprototype-related UCDs). Iterative responsiveness (factor 2) includes UCD-3, UCD-4, UCD-5, UCD-6, and UCD-7. Factor 2 is defined as prospective users providing opinions, being observed using the app, and participating in a development process with 3 or more iterative cycles with changes explicitly reported; factor summary scores range from 0 (no iterative cycles) to 5 (addressing all 5 iterative responsiveness-related UCDs). Other expert involvement (factor 3) includes UCD-8, UCD-9, UCD-10, and UCD-11. Factor 3 is defined as health professionals providing opinions, being consulted before and between the development of prototypes, and as the involvement of nonuser expert panels; factor summary scores range from 0 (no expert involvement) to 4 (addressing all 4 expert-related UCDs). Although we report all data in our tables and Supplementary Material S1 (available in the online version only), we primarily focus on preprototype involvement and iterative responsiveness, which most directly involve patients and consumers rather than healthcare providers or expert panels as consumers.

Table 1. Frequency of individual UCD Items ( n  = 283) .

Item	Explanations 1	n	%
UCD-1	Were potential consumers (e.g., patients with diabetes, caregivers) involved in any steps to help understand consumers (e.g., who they are, in what context might they use the app) and their needs?	85	30.0
UCD-2	Were potential consumers involved in any steps of designing, developing, and/or refining a prototype?	37	13.1
UCD-3	Were potential consumers involved in any steps intended to evaluate prototypes or a final version of the app?	233	82.3
UCD-4	Were potential consumers asked their opinions of the app in any way?	120	42.4
UCD-5	Were potential consumers observed using the app in any way?	70	24.7
UCD-6	Did the development process have 3 or more iterative cycles?	19	6.7
UCD-7	Were changes between iterative cycles explicitly reported in any way?	17	6.0
UCD-8	Were health professionals asked their opinion of the app at any point?	44	15.5
UCD-9	Were health professionals consulted before the first prototype was developed?	33	11.7
UCD-10	Were health professionals consulted between initial and final prototypes?	22	7.8
UCD-11	Was an expert panel involved?	20	7.1

To answer RQ2, the research team collaboratively developed a comprehensive list of methods and approaches informed by foundational literature and collective expertise, including social cognitive theory, 38 health belief model, 39 technology acceptance model, and the unified theory of acceptance and use of technology, 40 41 which was then incorporated into a data extraction form. The form also allowed for the addition of other terms that had not been initially identified. Following data extraction, two researchers independently reviewed all methods and approaches that had been identified and grouped each into one of three categories: (1) co-design approaches, methods, or frameworks (hereafter, co-design approaches); (2) behavioral theories, models, or approaches (hereafter, behavioral theories); and (3) other frameworks. In instances where the two researchers were not in agreement and could not reach a consensus, a third researcher served as an arbitrator to resolve discrepancies.

Data Extraction

We created a data extraction form in Qualtrics, 42 piloting and refining it before implementation. Data extraction points included consumer involvement in co-design, defined as the presence or absence of UCD-1 through UCD-11 (RQ1) and any reported approaches, frameworks, or theories (RQ2). To describe article and app attributes, we extracted data regarding the intended consumer, including the type of diabetes and population, and specifics about the app, including its operating system (Android, iOS) and whether it is connected to a wearable device. Two researchers independently screened articles and extracted data; discrepancies were resolved through consensus or by a third party if necessary. 43

Data Synthesis

Following extraction, we cleaned data using OpenRefine. 43 For all analyses, we used Microsoft Excel 44 and Statistical Software for Social Sciences (SPSS), Version 29. 45 When reporting article and app attributes, we did not attempt to combine multiple reports of the same app. The app development process often includes numerous phases and studies, combining the reports was deemed inappropriate due to dissimilar data on participant numbers, demographics, and co-design approaches. All underlying data are publicly available through the Open Science Framework. 34

Results

Study Selection

Our comprehensive literature searches yielded 14,206 items. Before screening, we excluded 6,559 duplicates and 2,058 conference abstracts or clinical trial registrations. During the title-abstract stage, we screened 5,589 articles; in the full-text stage, we screened 939 articles. Based on our eligibility criteria, screening resulted in 283 articles. ( Fig. 1 )

Fig. 1.

PRISMA flow diagram.

Overview of Eligible Articles: Article and App Attributes

Most eligible articles described studies taking place in Asia (33.2%), Europe (28.3%), or North America (23.7%; Table 2 ). Most articles were supported by grant funding from governmental funding agencies, nonprofit organizations, universities, and hospitals (61.8%), while 14.8% were supported by industry sponsors and 5.7% were supported both by grant funding and industry sponsors. Eight point one percent received no external funding and 9.5% did not report whether any funding was received. Most articles focused on a single type of diabetes (79.2%). Of these, type 2 diabetes was the most frequently represented (43.1%), followed by type 1 diabetes only (27.9%). Most articles included only adults (75.3%), multiple genders (82.0%), and did not report comorbidities (84.5%). Most articles (85.9%) involved an app (whether developed or under development). Of the articles that involved an app, 33.7% reported the app's connectivity to a wireless device, and 51.0% reported the operating system type, with Android only (24.7%) or Android and iOS/Apple (22.2%) as the most common. Eleven point seven percent of the included articles reported studies that would provide participants with devices while 37.5% reported studies in which participants used their own previously acquired devices and 50.9% did not report how participants obtained devices ( Supplementary Appendix B , available in the online version only).

Table 2. Article attributes ( n  = 283) .

Article attributes	n	%
Continents
Asia	94	33.2
Europe	80	28.3
North America	67	23.7
Oceania	19	6.7
Africa	7	2.5
South America	4	1.4
Various Continents	11	3.9
Not reported	1	0.4
Diabetes type
One type of diabetes	224	79.2
Type 2 diabetes	122	43.1
Type 1 diabetes	79	27.9
Gestational diabetes	14	4.9
Diabetes, otherwise unspecified	9	3.2
Multiple types of diabetes	59	20.8
Age
≥18	213	75.3
<18	18	6.4
Both	32	11.3
Not reported	20	7.1
Gender
Female only	17	6.0
Male only	1	0.4
Multiple genders	232	82.0
Not reported	33	11.7
Comorbidities
Not reported	239	84.5
Reported	44	15.5
Articles involving apps a
Yes	243	85.9
Not applicable	40	14.1

^aApp previously developed or currently under development.

Research Question 1: Consumer Engagement in App Co-Design Process

All 283 full-text articles reviewed addressed at least 1 of the UCD principles involving consumers (UCD-1 to UCD-7; ( Table 1 ). The most frequently reported item was UCD-3 (Were potential consumers involved in any step intended to evaluate prototypes or a final version of the app?), with 82.3% of articles indicating the use of this principle. The 2 least frequently reported items were UCD-6 (Did the development process have 3 or more iterative cycles?) and UCD-7 (were changes between iterative cycles explicitly reported in any way?) at 6.7 and 6.0%, respectively.

A little over a third of articles (34.3%) fulfilled at least 1 principle within preprototype involvement (factor 1), indicating attention to consumer engagement in the formative phase of technology design. Most of these articles addressed only 1 of the 2 possible preprototype involvement principles. In contrast, 85.2% of articles fulfilled at least 1 principle within iterative responsiveness (factor 2), suggesting that most articles seek to respond to consumer feedback at some point in the development or evaluation of technology. Similarly, most articles address only 1 (41.9%) or 2 (35.3%) of the five possible iterative responsiveness principles. ( Fig. 2 )

Fig. 2.

Frequency and percentage of each factor by score.

Research Question 2: Approaches, Frameworks, and Theories

Most articles (66.8%) did not report the use of a method, while 17.3% reported the use of one method, and 15.9% reported using more than 1 method. Less than a third (20.5%) of articles utilized a co-design approach, 15.9% utilized a behavioral theory, and 3.5% utilized a different framework, such as an implementation or social sciences framework. Among the subset of articles that used a co-design approach, the three most frequent were user-centered design (7.4%), participatory design (3.2), and co-design (2.8%). Among the subset of articles that used behavioral theories, the three most common were the social cognitive theory (3.9%), the health belief model (2.8%), and the transtheoretical model (1.8%). Of the articles that reported using another framework, the frequency was less than 1%. ( Table 3 )

Table 3. Frequency of the most commonly reported methods and approaches.

Method type	Method	n	%
Co-design approaches	User-centered design	21	7.4
	Participatory design	9	3.2
	Co-design	8	2.8
	Technology acceptance model	6	2.1
	Unified theory of acceptance and use of technology	5	1.8
	System usability scale	4	1.4
	Co-creation	3	1.1
	ADDIE framework	2	0.7
	Double-diamond framework	2	0.7
	Health information technology acceptance model	2	0.7
	Patient engagement	2	0.7
	User engagement	2	0.7
	All other approaches ( n  = 22) a	19	6.7
	Total b	58	20.5
Behavioral theories	Social cognitive theory	11	3.9
	Health belief model	8	2.8
	Transtheoretical model	5	1.8
	Information motivation behavioral skills model	3	1.1
	Self-determination theory	3	1.1
	Theory of planned behavior	3	1.1
	Bandura's theory of self-efficacy	2	0.7
	Behavior change wheel	2	0.7
	Health action process approach	2	0.7
	Self-determination theory	2	0.7
	All other behavioral theories ( n  = 24) a	21	7.4
	Total b	45	15.9
Other frameworks	Interpretative phenomenological analysis framework	2	0.7
	All other frameworks ( n  = 9) a	8	2.8
	Total b	10	3.5

^aAll other approaches were used in one article (0.4% of all articles).

^bTotal is greater than the number of articles, as some articles reported more than one method.

Discussion

Our scoping review aimed to examine how consumers are involved in co-designing smartphone apps for diabetes self-management and explore the reported co-design approaches, behavioral theories, or other frameworks. To gain a holistic understanding of consumer engagement, we used the UCD-11 measure to evaluate the frequency and percentages of each factor by score, focusing on preprototype involvement and iterative responsiveness. Additionally, we examined the frequencies of individual UCDs to gain insight into reported co-design activities. We explored related approaches, theories, and frameworks to identify strategies guiding the co-design process and to determine how different methodologies influenced the extent to which these strategies involved consumers in co-design.

Researchers engaged persons with diabetes in the co-design process; this engagement, however, appeared to have been primarily related to components of iterative responsiveness. The most frequently reported co-design activities involved potential consumers in any steps intended to evaluate prototypes of a final version of the app and observing potential consumers using the app. 8 Approximately half of these studies reported engaging with a co-design activity, possibly indicating that the iterative processes may have been present; however, they were limited in scope, potentially involving only a single round of refinement, or were not reported. Previous reviews of diabetes self-management interventions have found that user experience testing was the most reported activity in the development of digital health interventions for young people, 46 and that users were typically involved only once during the electronic health record development. 47 Noteworthy, however, is that previous research on co-design and usability assessment in mHealth found incomplete reporting of activities. 11 48 It is possible that co-design activities were more extensive than reported. Future research should explore developing an extension to current reporting guidelines to ensure a more comprehensive account of co-design activities.

In contrast, consumers were involved in co-design before the design, development, or refinement stages of an app's prototype or final version. This minimal involvement suggests that many of the app developers may not have made efforts to deeply understand the needs of potential consumers and potentially did not consider how diabetes may have impacted one's health status, self-manage efficacy, or aspects of daily life, including factors affecting their overall well-being. The minimal involvement of consumers may have also resulted in a missed opportunity to fully understand the consumers and their needs early on or throughout the entirety of the co-design process. Existing literature illustrates that consumer involvement in intervention development improves the acceptability and efficacy of interventions 49 50 ; further exploration is needed to understand why consumers were not involved during the formative stages of co-design. While consumer engagement in design is common, these findings highlight the incomplete nature of co-design across eligible articles and suggest that it may be isolated in individual activities rather than emphasizing a more comprehensive co-design approach.

Finally, our exploration of the types of reported co-design approaches, behavioral theories, and other frameworks highlighted the lack of consistency. The design research was guided by various approaches, theories, and frameworks; few of which were rigorously defined. Only a small subset of articles reported using a specific approach, theory, or framework. While these findings parallel those of previous research, 11 it should be noted that co-design approaches may be incorporated but not formally reported. Approaches, theories, and frameworks can play a meaningful role in the co-design process by providing structure and guiding principles and ensuring the research and design process is grounded in established best practices and existing knowledge. Their definition, use, and reporting are an essential first step to furthering the current state of the science.

The prominence of behavioral theories in our included articles is not surprising, given that such theories can be integrated into co-design processes to positively influence behavioral outcomes of interventions. 51 Since diabetes self-management is strongly associated with lifestyle behaviors, it is not surprising that a subset of articles reported using behavioral theories. Yet, more research is needed to understand if and how behavioral theories impact co-design processes.

While our review found broad consumer engagement in mHealth app development, it is necessary to consider if and how these consumers reflect the population of potential users. While the United States and China were the most represented countries in our review, both with substantial populations of diagnosed and undiagnosed diabetes, countries with the highest age-adjusted comparative diabetes prevalence, such as Pakistan and Kuwait, 52 were underrepresented among our eligible articles.

Our review also found that studies more frequently require participants to use their own previously acquired devices rather than providing devices to participants. While smartphone ownership has expanded rapidly over the past two decades, it remains less common with older adults, racialized people, those with lower income levels, and individuals who do not have postsecondary education. 53 These populations have also been previously found to have lower levels of both health literacy and digital literacy, 54 55 56 57 both of which would directly impact an individual's ability to fully participate in healthcare decision-making and self-management activities. Furthermore, these populations have also reported disparities in access to and use of remote and telehealth services. 58 59 60 As such, study eligibility requirements for smartphone ownership may create barriers to full participation by a representative sample of people with diabetes. To avoid perpetuating inequities and ensure meaningful app engagement, it is essential to comprehensively understand how systemic issues may impact diabetes self-management among vulnerable populations involved in co-design. 61 Future research should consider how to engage a more representative sample of potential users in the co-design process.

Limitations

We intentionally focused our scoping review on the involvement of consumers in the co-design of diabetes self-management smartphone apps. Due to the many available diabetes self-management apps, we excluded apps not designed for diabetes care, such as WeChat or MyFitnessPal, that may be used in diabetes self-management. We also purposefully omitted apps that were part of multimodal interventions and developed them to be used in conjunction with other self-management interventions. These criteria may have resulted in the exclusion of examples of co-design intended for diabetes self-management.

While we placed no restrictions on the publication language, we excluded case studies or reports; commentaries or editorials; conference abstracts; theses or dissertations; survey validation studies, and clinical trial registrations due to the limited information available in these resources. As a result, we may have omitted ongoing projects that would have otherwise met the criteria. By their nature, scoping reviews are intended to provide an overview of a broad area of literature, helping to define terminology and identify trends and gaps. They do not typically include an assessment of the methodological quality of the included articles, nor do they assess the effectiveness of an intervention or course of action. As a result, we cannot conclude the impact of co-design on the design and development process or, ultimately, on the health outcomes of patients.

Conclusion

In conclusion, our scoping review reveals that consumer involvement in the co-design of diabetes self-management apps remains limited in scope, with most articles engaging consumers in evaluating a prototype or final version of the app. Preprototype involvement—critical for tailoring app design based on consumer needs, preferences, and potential for adoption—was far less prevalent. Iterative responsiveness was more prevalent; many articles reported engaging with consumer feedback, reflecting an effort to adapt the app based on input received during development and evaluation. Furthermore, inclusive and systematic co-design approaches are needed to adequately address health disparities. The limited use of co-design approaches and behavioral theories may have also impacted the depth and quality of the design of diabetes self-management apps. Researchers and app developers should consider engaging consumers across all 11 steps of the co-design process to improve the adoption, reach, and efficacy of diabetes self-management apps.

Clinical Relevance Statement

Consumer engagement in the co-design of diabetes self-management apps is critical yet limited. User-centered co-design ensures that these apps are tailored to meet the needs, preferences, and unique contexts of the intended users. Involving consumers throughout the entirety of the co-design process has the potential to enhance app adoption, usability, engagement, and overall clinical effectiveness.

Multiple-Choice Questions

1. Witteman and colleagues' 11-item user-centered design (UCD-11) framework comprises the following three factors:

   1. Preprototype, prototype, and postprototype

   2. Co-design, implementation, and evaluation

   3. Preprototype involvement, iterative responsiveness, and other expert involvement

   4. Assessment, interpretation, and synthesis

   Correct Answer: The correct answer is option c. The UCD-11 framework includes preprototype involvement (factor 1), iterative responsiveness (factor 2), and other expert involvement (factor 3).

2. Which of the following factors is aimed at understanding prospective users' needs and preferences?

   1. Factor 1 (preprototype involvement: UCD-1, UCD-2)

   2. Factor 1 (preprototype involvement: UCD-1, UCD-2, UCD-3)

   3. Factor 2 (iterative responsiveness: UCD-4, UCD-5, UCD-6)

   4. Factor 2 (iterative responsiveness; UCD-3, UCD-4, UCD-5, UCD-6, UCD-7)

   Correct Answer: The correct answer is option a. Factor 1, preprototype involvement, aims to understand user needs and preferences. It includes UCD1 (were potential consumers involved in any steps to understand consumers and their needs?) and UCD2 (were potential consumers involved in any steps of designing, developing, and/or refining a prototype?)

Funding Statement

Funding None.