1 Introduction

Mobile health (mHealth) applications are an effective way to track health benefits for older people who need medical treatment. As technologies evolve and mature, they tend to be used by an increasingly diverse set of users, including older people. There is an urgent need for attention and focus on user experience (UX) because good experiences critically encourage the continued use of an application. In medical contexts, UX can sometimes mean the difference between life and death. Due to advances in ubiquitous computing in recent years, the scientific community has developed numerous technical systems across domains including healthcare [1], education [2], entertainment [3], and transportation [4]. These developments help older people to be more independent and improve their quality of life. However, older people still face barriers that prevent them from using technology effectively, with mobile applications often representing obstacles due to their lack of usability and inaccessible design [5]. Therefore, considering the characteristics of older people’s cognitive abilities when designing for this population can help designers make the right decisions to improve UX, influence perceptions of user friendliness, and improve users’ cognitive abilities.

Meanwhile, lack of medication adherence is a serious problem worldwide. Studies conducted in Saudi Arabia have found that rates of non-adherence to antihypertensive medications range from 54 to 72% [6]. In [7], the authors conducted a study in Saudi Arabia that revealed an extremely high (96.62%) rate of medication non-adherence among patients with chronic diseases. Therefore, improving medication adherence is critical, with several studies indicating that technological interventions could represent a solution [8]. Technology interventions can improve medication adherence by allowing for timely monitoring and providing useful information about the patient’s status and commitment to the healthcare provider [9]. However, there is scarce research on the design of applications suitable for use by older Arabs. To the best of the authors’ knowledge, no Arabic-language applications exist that can help older people easily adhere to their medication regimens.

This paper aims to empirically investigate the effects of designing an interactive mobile user interface (UI) based on a conceptual model of older Saudis that can effectively improve their medication adherence and reduce errors. The paper contributes theory-driven guidelines and incorporates cognitive attributes and user preferences into a model that encourages further engagement with the developed solution. The objectives of this research are as follows:

  • To analyze the existing guidelines for designing UIs for older Saudi people, classify them according to cognitive process attributes, and identify any contradictions between guidelines;

  • To design an interactive medication adherence application suitable for older Saudis people by considering the characteristics of the Saudi population and the cognitive attributes of older people;

  • To empirically validate the proposed design by testing the quality-in-use characteristics of the design in terms of effectiveness, error safety, and productivity;

  • To validate the desirability of the design.

The remainder of the paper is structured as follows. Section 2 discusses related work concerning technology designed for older people. Section 3 presents the details of the research methodology used to collect and analyze the data. Section 4 proposes the application design. Section 5 evaluates the proposed application design. Section 6 summarizes the results. Section 7 discusses the findings, and Sect. 8 concludes the study and suggests directions for future research.

2 Related work

This section discusses related work in two areas: (1) medication adherence systems and (2) designing technological solutions for older people.

2.1 Medication adherence systems

Medication adherence systems have been reviewed in terms of hardware and software. Hardware concerns include existing pill containers and wearable devices. The software discussion considers the various software designs, applications, and features in the field.

2.1.1 Hardware devices

Various studies have proposed using a sensor-based approach to improve medicine intake and adherence monitoring by fixing sensors to pill boxes, pill bottles, or cabinets to collect data and assess medication-taking activity. Other studies have used wristband devices to detect hand gestures related to pill-taking to provide a medication-taking guarantee. Some of these studies have considered older people a target user group [10] [11] [12]. For example, Jia et al. [13] suggested a smart pillbox managed with a mobile application. However, one limitation is that the individual’s smartphone must always be in the zone of the pillbox in order to access the stored data. If not, the connection will be lost, precluding access to the data. Wu et al., [14] used technology to record medication information directly using radio frequency identification (RFID). To serve people who depend on the senses of hearing and sight, [11, 13, 15, 16], and [17] designed a pillbox that reminds users to take their medication by showing lights and making sounds. Some systems have taken the approach of collecting data about users via sensors, with recording vital data [11] and collecting fine-grained data on user activity [18], requiring no user intervention when collecting data. The smart pillbox proposed by Shinde et al., [12] required users to enter the number of pills removed each time, which may be difficult for older users.

None of these studies or their solutions have been able to address the demands of medication adherence—these devices cannot confirm whether users have taken their medication. Moreover, only two studies have even considered this challenge, with Aldeer et al. [18] proposing an approach to validating medication intake based on detecting patient hand movement patterns while interacting with pill bottles. Shinde et al. [12] set the pillbox’s alarm to continue until the patient pressed a button on the pillbox, validating medication intake via the patient’s physical interaction with the pillbox. However, this result does not accurately measure the extent of medication adherence, with user efforts to stop the alarm or open the medicine bottle and retrieve the medication not necessarily meaning that the user has ingested the medication. Notably, SmartMATES [19], which comprises two sensors that are worn on the wrist and a mobile application, is not intrusive, meaning that if the system detects that the patient has not taken their medication for a certain period, a reminder alert is sent to the individual’s mobile phone. Although wearable devices are characterized by accuracy, there are major limitations, with users often finding such devices intrusive, uncomfortable, and annoying. Meanwhile, for older users, there are limitations pertaining to user acceptance [20] [21].

2.1.2 Software applications

Adherence applications are a novel approach to facilitating older people’s medication adherence, with the rapid spread and increased accessibility of smartphones making such applications appealing to many. Gashu et al. [22] developed a web-based reminder system for tuberculosis treatments. The system reminds patients about medication scheduling and refills by sending short message service (SMS) messages, available for both basic and smartphones. The system is characterized by low-resource needs, which makes it suitable for regions that do not have extensive technologies infrastructure. Sherif et al. [23] contributed to improving older people’s medication adherence using a LoRa-driven medical adherence system comprising an embedded hardware device with medication alerts for patient home use. However, the system can store and track only a limited number of medications. Furthermore, the alarms run only once, meaning that the system mostly performs monitoring functions and cannot follow up with the patient. Alsswey and Al-Samarraie [24] developed an mHealth user interface to keep older people aware of schedules, dosages, and directions for their medications. General information about illnesses in the Arab world was also included in the proposed mHealth app, and the application design was based on Arabic culture, language, customs, and values. Although the authors considered color, font size, and type, they did not focus on older people’s cognitive processes or preferences regarding the use and acceptance of specific design features (e.g., color, language, style, images) from a cultural perspective. In 2016, Heldenbrand et al. [25] evaluated the features of medication adherence applications, their functionality, and level of health literacy. Their survey confirmed that 461 adherence applications were available, of which 367 were unique applications that were evaluated after eliminating “lite” or trial versions. They listed the features that should be included in a medication adherence application (option for languages other than English, ability to monitor whether a dose has been missed or taken, ability to “snooze” reminders, refill alerts, recognition of adverse effects, ability to order refills, and ability to work without Internet access). Later in 2016, Rizal Mohd et al. [26] reconstructed the findings of Heldenbrand et al. [25] by reviewing four of the main medication adherence applications. In addition, another adherence-related feature was introduced: verification of adherence, which requires patients to scan a QR code each time they take a medication as proof of adherence.

This literature review has recognized the importance of increasing medication adherence and validating medication intake by harnessing the potential different hardware and software setups (e.g., smart pillboxes, mobile and web-based applications, wearable devices) and summarized the most common features of medication adherence applications.

2.2 Cognitive design for older users

The human body undergoes age-related changes as a person gets older, including changes in visual, auditory, motor, and perception control [27]. Technology providers, such as designers, must consider these changes when designing a product or service for a community of older users. To overcome these obstacles, special applications must be designed that fulfill the needs of older people in terms of usability, one of the most significant factors in older people accepting new technology [28]. Usability is affected by many factors, including familiarity with technology [29] and geographical area [30]. Familiarity is obtained from prior experience with technology and concerns the relationship between an older person and a technology with which they have experience [31]. Knowledge of existing technologies helps older people become familiar with newly introduced technologies. Moreover, designing a usable interface for older people means designing an interface that matches their existing knowledge and competence. The heterogeneity in familiarity levels and cognitive abilities among older people makes it challenging to design for this population [29].

Several researchers have presented guidelines and principles for designing UIs suitable for older users. However, guidelines that focus on cognitive aging remain rare [32]. Design challenges associated with the physical limitations of users have generally been easier to address than those associated with cognitive limitations [33]. Alnanih [34] addressed the importance of considering a user’s cognitive abilities when designing a UI and provided guidance for establishing design guidelines for UIs. The author mapped cognitive process attributes, including attention, thinking, memory, perception, learning, planning, and decision-making, to the capacity of cognitive UIs to plan in the context of uncertainty, supporting implications, learning based on experience, and autonomous adaptation to change. Sharp et al. [35] identified six categories of cognitive process attributes: attention; perception and recognition; memory; learning; reading, speaking, and listening; and problem-solving, planning, reasoning, and decision-making.

For this section, the authors reviewed studies concerning the cognitive guidance of mobile applications for older users. Upon reviewing 48 studies, the authors extracted 17 studies [32] [33] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] that could be analyzed to extract guidelines and recommendations. They classified these guidelines according to the aforementioned cognitive attribute taxonomy [35] in relation to designing UIs for older people. The 63 extracted guidelines are available at (https://gitfront.io/r/user-2924583/2fLh3Sracgsb/Design-guidelines/).

3 Research methodology

This study has adopted a top-down research methodology that begins with collecting data and analyzing the requirements, a step that is followed by the proposal of a prototype and then the testing and interpretation of the results. The following subsections detail each step.

3.1 Data collection

The following three data collection methods were used as inputs:

  1. (1)

    Literature review: The authors extracted the features and cognitive design guidelines most suitable for consideration in the context of medication adherence applications for older people;

  2. (2)

    Interviews with medical doctors: The authors conducted a face-to-face group interview with two expert Saudi medical doctors working at the College of Pharmacy at King Abdulaziz University (KAU). The interview took place after arranging a formal appointment with the participants at their office during business hours. The interview lasted approximately 30 min. A thorough set of questions concerning how older Saudi patients adhered to their medication regimens was prepared. Interviewing multiple doctors in a group setting provided several benefits to the process, including saving time, enabling explanations of the same ideas from different perspectives, and enabling explanations of the challenges of ensuring older people adhere to their medication regimens by presenting different scenarios to different participants;

  3. (3)

    Questionnaire survey of older Saudis: The questionnaire was designed to be either self-completed or filled out by an older person’s caregiver. The questionnaire was divided into three tracks, with Table 1 presenting the details of each track. The questionnaire was developed in English and translated into Arabic. According to World Meter’s elaboration of the latest United Nations data, the current population of Saudi Arabia is 35,023,049 people [51], with older people 65 + age group comprising 2.8% of that total. The sample size was calculated with a 5% margin of error, 95% confidence interval, and 50% response distribution equivalent, giving 385 [52]. However, the survey ultimately exceeded that base requirement to reach 602 people.

Table 1 Description of questionnaire tracks
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3.2 Data analysis

This section analyzes the data obtained using both qualitative and quantitative methods.

3.2.1 Analysis of interviews with medical doctors

The researchers received substantial insight from the interviews with medical doctors regarding older Saudis’ medication adherence:

  • One of the most common mistakes among older Saudis was taking expired medicine. Thus, designers should consider expiration date as an essential field when adding medication information;

  • The number of inputs should be reduced. There is no need to calculate the drug’s position in the regimen each time; instead, the application should be designed to ask the user to enter the number of doses and the time of the first dose, after which the application can calculate the number of periods remaining;

  • The side effects of drugs should be considered;

  • Application designers should consider sending motivational phrases to users.

The analysis of interviews helps designers think and experience the world as users and better understand their needs in terms of medication adherence.

3.2.2 Analysis of questionnaire survey

The three tracks have been analyzed and a statistical description provided.

3.2.2.1 Track 1: demographic information

This track produced categorical variables classifying individuals into one of several groups. The data set for this section was first presented as frequencies and percentages (Table 2). The age group and average monthly income were chosen because, according to the Saudi Ministry of Health and the United Nations Population Fund, older age begins between the ages of 60 and 65 years [54] [55], and the average monthly income for Saudi families in 2021 was approximately 16,700 S.R. [56].

Table 2 Summary of participant demographics
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3.2.2.2 Track 2: adherence to medication

The reliability of the eight-item scale in Track 2 was determined to be an acceptable level, with an alpha value of 0.7. Of the overall sample (N = 602), 476 participants (79%) were considered non-adherent (scores < 6), 112 (19%) were considered moderately adherent (scores of 6 or 7), and only 14 (2%) were considered highly adherent (score = 8) (Table 3).

Table 3 Adherence level
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3.2.2.3 Track 3: user preferences

In the user preferences track, the results confirmed that target users demonstrated the following preferences, categorized in terms of the cognitive attributes reflected:

  • Attention: Most users preferred the primary font color to be blue or red (38.2% and 36.5%, respectively) and the secondary font color to be pink (67%). The preferred primary background colors were blue (49.2%) and turquoise (45.7%);

  • Perception: Most older Saudis recognized Sunday as the first day of the week (60.5%) rather than Saturday. Most users (over 95%) preferred redundant menu layouts (i.e., icons supported with text) over word-based (i.e., text-only) layouts;

  • Memory: Most users preferred grid menu structures (85.9%) over vertical menu structures. This option is consistent with user choices pertaining to the perception attribute;

  • Learning: Most participants (81%) preferred rectangles over circles. The former resemble the shape of pillboxes, which had been used by 40% of participants (although 61% did not use a pillbox to organize their medications at home);

  • Reading, writing, and speaking: Most respondents preferred large font sizes (18 points) for reading on the UI (90%) and chose to hear reminder alarms that used a family member’s voice (55.8%).

The researchers also investigated how respondents took their medications (Table 4):

Table 4 Methods for adhering to medication regimens
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75% (56% + 19%) depend on themselves to take their medication at the right time;

45% (21% + 24%) depend on other people living in the same home;

26% (17% + 9%) use mobile alarms to remind them when to take their medication;

10% depend on the medication’s application.

4 Design of proposed application

The main problem demonstrated in the data collected was non-adherence to medication: 79% of older Saudis did not adhere to their medication regimen (Track 2, Table 3). Recognition of this led the authors to consider the existing methods participants used to take medications, because 75% of those in our sample depended on themselves to take medication and 45% depended on caregivers (Table 4). Non-adherence among older people indicates the ineffectiveness of these current approaches. Most older people can easily perform basic tasks but have difficulty performing advanced tasks. Based on the results of the data analysis (Sect. 3), participants were categorized into one of two classes:

Independent older people: Able to perform basic and advanced mobile tasks;

Dependent older people: Able to perform only basic mobile tasks and dependent on the assistance of a caregiver or other family member to perform more advanced tasks.

These two types of older people were considered in the design of the proposed application, Teryaq. For the dependent group, which represents most older Saudis, the researchers relied on basic functions to design the screens, with a caregiver connected to perform more advanced tasks. Meanwhile, for the independent group, the application screens have been designed with all the functionalities matched to their technical level, enabling individuals to use the application independently.

The conceptual model shown in Fig. 1 provides a general idea of the shift from the problem space to the solution space. The model distinguishes two cases that represent the two types of older people.

Fig. 1
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Conceptual model for the classification of older people

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4.1 Design of proposed application

The design of the Teryaq application is based on outputs generated from the three data inputs detailed, namely, a literature review, a questionnaire survey of older Saudis, and an interview with medical specialists (see Sect. 3.1). The outputs from the analysis focused on two aspects: (1) Adherence to medication functionalities must be provided to serve this age group and map their familiarity with mobile technologies; (2) certain UI design elements must be considered to properly perform the functionalities of older people.

This section details these two aspects, with subSect. 4.1.1 explaining the application’s functionalities and subSect. 4.1.2 describing the technical design guidelines.

4.1.1 Application functionalities

The functional requirements elicited represent the needs of older people and their caregivers. Older people, whether dependent or independent, can interact with the system and perform a set of basic tasks including showing their pillbox, checking their medications, and snoozing or skipping alarms. However, independent users can perform more tasks at the advanced level, including adding medications and displaying daily and monthly reports, with dependent users requiring that a caregiver performs the advanced tasks for them.

4.1.2 Application interfaces

The design implications extracted from each input have enabled: (1) The application of design guidelines classified by authors based on cognitive attributes (Appendix A details the applied guidelines); (2) the study of older people’s preferences from a design perspective to (a) resolve contradictions in the guidelines of previous studies and (b) determine older people’s preferences in terms of designing certain elements (e.g., colors and tones) (see Appendix B describes all of the applied preferences); and (3) the consideration of the expertise of specialists in the field of medicine in the development of the design. Regarding the latter point, the experts interviewed mentioned an important aspect of supporting and motivating older users, namely, delivering motivational phrases to patients after they confirm that they have taken their medication.

The outputs in terms of design implications have been mapped to aspects of the design using mapping codes. The codes are divided into three segments: (1) Letters G, P, and S indicate guidelines, preferences, or specialist comments, respectively; (2) a number that refer to a cognitive attribute, under which design implications are classified; (3) a serial number. Figure 2 provides a visual demonstration of the mapping code format.

Fig. 2
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Mapping codes

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The final screen designs of the Teryaq prototype correspond to three user types (dependent older people, independent older people, and caregivers). Figure 3 shows tasks in the advanced-level screens, (a) medication screen and (b) new medication screen. Figure 4 shows tasks in the basic-level screens, (a) weekly pillbox screen, (b) daily pillbox screen, and (c) confirmation screen (Arabic).

Fig. 3
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Caregiver and independent older user screens

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Fig. 4
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Dependent older user screens

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5 Design evaluation

As a preliminary step, pilot testing was conducted prior to the actual test to resolve design issues and ensure the clarity of the prototype and the task workflow. Table 5 lists the tasks mapped to the older people model, the level of functionality (basic or advanced), the guidelines considered in the design (Appendix A), and the cognitive attributes.

Table 5 Task list
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5.1 Pilot testing

The authors conducted pilot tests with three types of users: doctors, UX/UI experts, and older people. The feedback and results from this step were considered to improve the proposed Teryaq design before conducting actual usability testing.

5.1.1 Doctors

A 30-min pilot test was conducted in person with two doctors from the pharmacy college at KAU. Each doctor performed the tasks and identified existing errors. Most of the doctors’ suggestions related to inputs, such as adding additional entries on the “Add medication” screen. The designers modified the prototype in response to these comments before pilot testing with UX/UI experts was conducted.

5.1.2 UX/UI experts

The pilot test was conducted online with four UX/UI experts (20 min per participant on average). The test moderator encouraged the UX experts to think aloud and ask questions if there was any confusion. During the test, the experts read the predefined tasks (basic and advanced) and then performed them. Expert comments were evaluated based on two factors: priority and severity. The high priority and high severity comments were acted upon.

5.1.3 Older users

The pilot test was conducted in person with a sample of four older users. According to [57], a sample of four to five participants is sufficient to identify 80% of usability flaws. The test took about five minutes for basic users and ten minutes for advanced users. Their levels were determined, and then the appropriate test was conducted. The test moderator observed the behavior of the older people during the test and then analyzed them based on their cognitive attributes to make the appropriate adjustments.

5.2 Case study testing

The effectiveness of designing a mobile UI was investigated based on cognitive attributes and the level of functionality of the proposed prototype in terms of improving older people’s adherence to their medication regimens.

First, the proposed design was validated by evaluating older people’s task performance using the quality-in-use characteristics of effectiveness, productivity, and error safety. Second, the proposed design was validated by whether participants expressed a desire to use the proposed design to adhere to their medication regimen. The application’s functionality was tested based on a model involving two classes of older people:

  • Dependent (basic level): To measure participant familiarity with performing the basic mobile application tasks required by the proposed design;

  • Independent (advanced level): To measure participant familiarity with performing advanced mobile application tasks.

Pairs of hypotheses were formulated to meet the goal of the experimental design as follows:

  • Null hypothesis: There is no significant difference in the average quality-in-use characteristics between the basic and advanced groups;

  • Alternative hypothesis: There is a significant difference in the average quality-in-use characteristics between the basic and advanced groups.

5.2.1 Participants

A total of 50 Saudi citizens over the age of 60 years old were selected randomly. Participants were asked to sign consent forms, confirming that the collected information would be used for research purposes only. Control of subject selection methods involved assigning participants with similar levels of background knowledge to one of the two cohorts (basic or advanced) based on an initial examination comprising a set of items covering both basic and advanced mobile functionalities. Older users were considered advanced if they were able to perform 60% or more of the tasks on the advanced tasks list or 80% of those on the total list (basic and advanced). After the assessment, participants were divided into two groups: 36 in the basic group and 14 in the advanced group.

5.2.2 Testing procedure

Each participant was given a pre-test questionnaire to collect demographic information. Then, a usability test was conducted by briefing the participant on the application concept and the tasks. Two methods were used to collect data during the test: interviews and observations. The experiment evaluated each participant’s ability to perform tasks sequentially on mobile devices using these methods. The basic group received four tasks related to the basic level, and the advanced group received four tasks related to the advanced level of functionality. The researchers collected independent variables, such as time taken to complete each task, number of correct actions for each task, and number of incorrect actions for each task. A post-test questionnaire obtained participant feedback on the application via four items related to cognitive attributes and one item related to adherence to medication. Table 6 shows the post-test questionnaire statements.

Table 6 Post-test questionnaire
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6 Results

6.1 Step 1: pre-test questionnaire results

Table 7 summarizes participant information for both groups.

Table 7 Demographic results
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6.2 Step 2: usability testing results

To investigate the hypothesis posed in Sect. 5.2, the three characteristics of the new quality-in-use model for each group were computed for each participant for all tasks based on the metrics presented in Table 8 [58]. After calculating descriptive statistics for all characteristics, the following steps were performed to verify the results. First, the normality of the data was checked. As Table 9 shows, the data are normal because the mean and median for all characteristics in both groups are almost identical.

Table 8 Quality-in-use factors, metrics, and interpretation
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Table 9 Mean and median of both groups of older users
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Table 9 indicates that the means of effectiveness and error safety are close to 1 in both groups. This means that older people in both groups perform well in terms of task effectiveness (i.e., selecting the minimum number of correct actions required) and safety (i.e., reducing the number of incorrect actions). The productivity levels of both groups were close to one another (0.29 and 0.27) and closer to 0 than a larger number, indicating that both groups took more time to complete the tasks successfully. Second, an F-test was performed to determine the variance between the two independent groups––whether different or equal––by verifying the following hypotheses: “The two population variances are equal” (null) and “The two population variances are not equal” (alternative). Because the p-values for effectiveness, productivity, and error were 0.08, 0.48, and 0.5, respectively, and none is smaller than 0.05, the null hypothesis cannot be rejected. As such, the two groups’ variances are equal.

6.3 Step 3: user satisfaction results

The results of the post-test questionnaire revealed that both groups of older people gave the rating “agree” to all items, as Figs. 5 and 6 show. Statistically, after applying a two-sample t-test assuming equal variances, there were no differences between the two groups regarding agreement with all items. By comparing each item separately, it becomes clear that the advanced group demonstrated higher levels of agreement than the basic group for items 1, 2, and 4. However, for item 3 (familiarity of the design compared with other applications used), the basic group recorded higher levels of agreement than the advanced group. For the last item, both groups (94 and 93%) agreed to use the proposed design to improve their adherence to their medication regimens.

Fig. 5
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Post-test questionnaire for the basic group

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Fig. 6
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Post-test questionnaire for the advanced group

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7 Discussion

The performances of basic and advanced older users were evaluated via direct assessment (a usability test) of their ability to achieve a set of tasks designed based on cognitive attributes and mapped to the developed model of older people (dependent vs. independent). In addition, an indirect assessment (a survey) was distributed to both groups after they performed the tasks to obtain their impressions regarding using the proposed design.

The usability test results make clear that both groups performed all the tasks included in the design prototype. The average effectiveness levels for the basic and advanced groups were 0.94 and 0.95, respectively. This result is considered excellent because it is close to 1 according to the interpretation (Table 8). Meanwhile, average error safety levels were 0.94 and 0.96 for the basic and advanced groups. This result is also considered excellent. Thus, both groups achieved the tasks effectively and with few errors.

Regarding the productivity factor, the averages were 0.29 and 0.27, respectively, for the basic and advanced groups. To interpret this result, the authors decided to compare the task performances of the two groups of older users with the performance of a sample of expert users who are familiar with mobile technology, namely, young people and users with a computer science background.

This sample’s six expert user was given all the tasks (basic and advanced). Then, their average was calculated for the three quality-in-use factors. Based on the technique suggested by [59], the performance of the six experienced users was adopted as the baseline for judging the study participants. The average performance of the six expert users for the three factors was considered excellent, with instances of needing more time, correct actions, and incorrect actions considered acceptable and instances of needing substantially more time considered unacceptable. Table 10 shows an example of the attributes collected for task 1 (“Confirm taking medication”) from the expert users.

Table 10 Establishing baseline attributes for tasks (quality benchmarks)
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Each task was measured for each expert, and the mean of all tasks for each factor was then computed. Table 11 shows the average results of the expert users for the three factors for both the basic and advanced tasks. According to [59], the limits of the acceptable and unacceptable ranges are 0.66 and 0.33 for effectiveness and error safety and 0.65 and 0.45 for productivity. The indicative baseline range appears in Table 12.

Table 11 Average results of expert users for basic and advanced tasks
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Table 12 Baseline range for quality factors
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The results of the participating older users were compared with the baseline ranges documented in Table 12, which classify user performance as either excellent, acceptable, and unacceptable. The results show that the performance of both groups of older users was excellent for the effectiveness and error safety factors, meaning that designing an application for older people that considers their cognitive attributes based on the proposed model allowed them to perform tasks with the minimum number of correct actions and fewer incorrect actions. For the productivity factor, older users recorded scores of 0.29 and 0.27, respectively; as Table 12 shows, this is unacceptable. However, this result aligns with the physical characteristics of older users, who require more time than younger people to perform tasks due to the physical effects of aging.

8 Conclusion

This paper aimed to reduce the distance between older people and technology by understanding older users’ cognitive attributes and adapting UI design accordingly. This paper has presented novel design guidelines based on the cognitive attributes that promote older people’s adherence to their medication regimens. The study developed a model including two classes of older people based on mobile technology capability: (1) a dependent class, which maps to the basic level of mobile capability, and (2) an independent class, which maps to both the basic and advanced levels of mobile capability. The usability testing results confirmed that designing the mobile app based on the proposed model of older people and cognitive attributes improved effectiveness and error safety in the context of performing mobile tasks and promoted adherence to medications as a proof of concept. Regarding productivity, the results align with the physical characteristics of older people, who require more time than younger people to perform tasks demanding motor skills. In future work, the authors aim to add doctors as another user type and develop all features that relate to this type to improve communication between doctors and patients.