Addressing Diabetes Distress and Usability in Arabic mHealth: User Experience, Psychological Reassurance, and Age-Related Dynamics

Introduction

In recent years, the widespread adoption of smartphones has transformed mobile health applications into essential components of chronic disease management.¹ When effectively implemented, these digital resources facilitate uninterrupted self-tracking, supply crucial medical education, and empower individuals to actively participate in their treatment protocols.² However, coping with a lifelong disease such as diabetes transcends basic physical compliance. It demands continuous cognitive vigilance and significant emotional strength—a psychological toll frequently characterized in clinical research as “diabetes distress.” ³⁻⁴ Inadequately designed technological interventions can exacerbate this condition by introducing unnecessary cognitive strain and digital fatigue into a patient’s daily life.⁵ Conversely, carefully optimized applications can streamline disease management, minimize mental load, and provide a sense of emotional security rather than becoming a burdensome obligation.³^˒⁶

Despite the rapid expansion of the global mobile health sector, there is a persistent lack of platforms specifically customized for the linguistic and cultural nuances of Arabic-speaking communities, leading to reliance on poorly localized mHealth interfaces that fail to address regional linguistic, clinical, and cultural nuances.⁷ Within the Iraqi healthcare landscape specifically, individuals managing type 2 diabetes frequently encounter substantial hurdles related to self-management education and routine clinical practices.⁸ Furthermore, research underscores the critical necessity of accurately assessing and enhancing both the quality of life⁹ and the reliability of medication adherence¹⁰ among this specific patient demographic. Recent clinical investigations have proven that integrating pharmacist-directed teleconsultations with targeted mHealth applications can markedly optimize glycemic outcomes, specifically HbA1c levels,¹¹ while simultaneously boosting self-care behaviors and overall life quality in Iraqi patients.¹² The “Edarat Alsukari” Android application was engineered to address this exact deficiency by offering native-language support to diabetic patients.¹³ The ultimate effectiveness of such digital tools, however, relies intrinsically on user acceptance and practical intuitiveness.¹⁴ To systematically measure these factors, the mHealth App Usability Questionnaire (MAUQ) serves as a standardized metric for evaluating functional simplicity, interface satisfaction, and the user’s perception of the application’s overall value.¹⁵

Demographics—particularly age—play a critical role in dictating how individuals interact with digital health interfaces.¹⁶ Older populations generally face more complex chronic health challenges and theoretically possess the highest potential to benefit from mHealth interventions Nevertheless, a widespread lack of digital literacy among older diabetics, combined with age-related functional barriers, creates a significant “digital divide”.¹⁷ Nevertheless, diminished digital fluency and functional barriers often create a “digital divide,” causing senior patients to view mobile platforms as tedious chores rather than supportive resources.¹⁶⁻¹⁷ Consequently, analyzing age-related differences in user experience is a foundational requirement for refining and maximizing the impact of these digital health interventions.¹³

The current investigation seeks to measure the usability, user satisfaction, and perceived clinical utility of the “Edarat Alsukari” application among Arabic-speaking individuals diagnosed with diabetes.¹³ By stratifying responses across distinct age brackets and interpreting the findings through a biopsychosocial framework, this research aims to illuminate how specific software design elements influence both practical navigation and the actual psychological comfort derived by the patients.¹³

Literature Review

Mobile health (mHealth) technologies have established a pivotal role in chronic disease self-management, particularly for Type 2 Diabetes Mellitus (T2DM), where continuous cognitive, behavioral, and emotional self-monitoring frequently leads to diabetes distress ¹⁻³. Although mHealth interventions have proven effective at enhancing self-care behaviors and clinical outcomes ², significant disparities persist in regional platform availability and linguistic and cultural localization ⁷˒²². Within Arabic-speaking populations across the Middle East, a shortage of culturally and linguistically customized mHealth tools forces reliance on generic or poorly localized interfaces, which limits patient engagement and undermines clinical utility ⁷˒²².

Application usability and user experience (UX) are primary determinants of long-term technology adoption in digital health ¹⁴. Validated evaluation frameworks, such as the mHealth App Usability Questionnaire (MAUQ), assess essential domains including ease of use, interface satisfaction, and perceived clinical usefulness ¹⁵. However, user perception across these domains is significantly moderated by demographic variables, particularly age and digital literacy ¹⁶˒²³. Older adults frequently encounter physical, visual, and cognitive friction during app interaction—a challenge highlighted in aging-focused usability models such as the MOLD-US framework ¹⁷. While operational usability ratings often show minimal variance across gender and sex ¹⁶, broader sociotechnical dynamics influence overall technology acceptance. Synthesizing these factors underscores the necessity of evaluating localized mHealth tools through a multidimensional lens that accounts for age-related dynamics and psychological reassurance alongside traditional usability metrics ³˒¹³.

Materials and Methods

Study Design, Setting, and Participants

A cross-sectional evaluation was conducted at the Endocrinology and Diabetes Center located in Baghdad’s Al-Rusafa district, Iraq. The study was carried out over a six-month duration, spanning from October 2023 to March 2024. The evaluation cohort consisted of 76 active Android users with a documented medical diagnosis of diabetes, recruited via convenience sampling during routine outpatient visits. The baseline demographic characteristics and eligibility criteria for the participant sample are summarized in Table 1.

Table 1: Baseline Demographic and Input Characteristics of Study Participants (N = 76)

Instrument

Following an uninterrupted six-month trial period, users evaluated the “Edarat Alsukari” platform utilizing the validated 18-item mHealth App Usability Questionnaire (MAUQ).⁸ Participants rated each statement on a 7-point Likert scale, ranging from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”).⁸ The questionnaire is structured around three primary domains:

• Ease of Use (5 items): Measures functional straightforwardness and the reduction of cognitive barriers.⁸
• Interface and Satisfaction (7 items): Gauges visual presentation, navigational fluidity, and generalized user comfort.⁸
• Usefulness (6 items): Assesses the subjective clinical benefit, the quality of digital feedback, and integration with healthcare systems.⁸

Data Analysis

Statistical evaluations were performed to generate descriptive data, including means, standard deviations, frequencies, and percentages for individual questionnaire statements, domain subscales, and the aggregate usability score.⁸ The resulting subscale metrics were categorized as Low (1.00–3.00), Moderate (3.01–5.00), or High (5.01–7.00).⁸ Subsequent subgroup testing was applied to observe score fluctuations based on sex and age distributions.⁸

Results

Overall Usability and Subscale Performance

The general reception of the “Edarat Alsukari” application was highly positive, yielding an aggregate total usability index mean rating of 5.37 (SD = 0.3). To evaluate performance, the 18 questionnaire items were grouped into three core evaluation Variables: Ease of Use (Items 1–5), Interface and Satisfaction (Items 6–12), and Usefulness (Items 13–18). Composite scores for each Variable were calculated as the arithmetic mean of all constituent items in that group per participant, whereas individual item scores reflect standalone statement ratings.

When evaluating the primary Variables, the platform excelled predominantly in operational functionality. The Ease of Use composite variable achieved the highest rating (mean = 5.94, SD = 0.4; High), closely followed by Interface and Satisfaction (mean = 5.40, SD = 0.3; High). Alternatively, the Usefulness composite variable scored lower, falling into the moderate category (mean = 4.86, SD = 0.5), as detailed in Table 2.

Table 2: MAUQ Performance Across Core Evaluation Variables (N = 76)

Item-Level Usability Analysis

An examination of individual statements revealed distinct trends: users demonstrated high satisfaction with interface fluidity and expressed a strong likelihood of continued app usage, but identified notable deficiencies regarding clinical connectivity and automated feedback. Because Table 3 details standalone statement ratings, individual item scores differ mathematically from the aggregated composite Variable means reported in Table 2.

For example, while the overall Ease of Use composite variable averaged 5.94, individual statement ratings within this domain ranged from 5.19 for learning facility (Item 2) to 6.68 for navigational consistency across screens (Item 3). Similarly, within the Interface and Satisfaction domain (composite mean = 5.40), individual item scores varied from social setting comfort (Item 9, mean = 4.41) to temporal efficiency (Item 10, mean = 6.59) and repeat usage intent (Item 11, mean = 6.78).

The lowest individual scores occurred within the Usefulness domain, specifically concerning healthcare access improvement (Item 14, mean = 1.72, SD = 0.6) and progress acknowledgment (Item 8, mean = 2.49, SD = 1.0). A comprehensive breakdown of all 18 standalone items is displayed in Table 3.

Table 3: Descriptive Statistics of Standalone Questionnaire Items (N = 76)

Demographic Variations in Usability

Sex-based differences in usability perception were negligible.⁸ Both men (mean = 5.36, SD = 0.3) and women (mean = 5.38, SD = 0.3) indicated similarly high levels of overall satisfaction, sharing identical categorizations across all evaluated subscales (Table 4).⁸

Table 4: MAUQ Scores Comparison by Sex

Age distributions, however, revealed a divergent pattern.⁸ Although metrics for Ease of Use and Interface and Satisfaction remained robust across all age brackets, perceptions of Usefulness declined sequentially with advancing age.⁸ Individuals in the youngest cohort (30–45 years) categorized the app’s utility as High (mean = 5.30), whereas the middle (46–60 years) and senior (61–75 years) groups rated it as Moderate (means of 4.79 and 4.62, respectively).⁸ Consequently, total usability averages mirrored this age-related decline, as illustrated in Table 5.⁸

Table 5: MAUQ Scores Comparison by Age Groups

Response Frequency Distribution

An analysis of response frequencies corroborated the statistical averages.⁸ A significant majority, 60 participants (78.9%), strongly agreed they would continue using the software (Item 11).⁸ In contrast, 49 individuals (64.5%) disagreed, and 25 (32.9%) strongly disagreed that the platform successfully expanded their access to clinical care providers (Item 14).⁸ Table 6 outlines the complete response distribution for these highlighted items.

Table 6: Response Frequency Across 7-Point Likert Scale (N = 76)

 Discussion

The Usability-Utility Gap and Psychological Reassurance

A primary observation from this study is the distinct discrepancy between the app’s strong operational performance and its perceived utility among patients.⁸ Users reported minimal cognitive strain while navigating the application (Ease of Use mean = 5.94).⁸ This type of predictable layout and visual coherence is fundamentally critical for mitigating digital anxiety, allowing users to perform necessary tasks without feeling cognitively overburdened.⁵˒¹³

However, the application exhibited significant shortcomings regarding its psychosocial and medical benefits.⁸ Effective diabetes management is intricately linked to continuous self-assessment and prevalent emotional fatigue.³⁻⁴ Individuals utilize mobile health platforms not merely for data entry, but to seek reassurance and maintain a psychological bridge to their medical providers.⁶ Because “Edarat Alsukari” functions primarily as an isolated digital diary rather than a networked clinical care portal,⁸ its design limitations are clearly reflected in the negative evaluations of healthcare accessibility (Item 14 mean = 1.72)⁸ and progress acknowledgment (Item 8 mean = 2.49).⁸ In the absence of responsive feedback mechanisms, an observational tracking app can easily devolve into an additional chore, failing to alleviate disease-induced stress.³˒¹⁴ To bridge this usability-utility gap, future mHealth platforms must incorporate strategic user engagement frameworks.²⁴ Specifically, proactive cognitive load reduction—achieved through automated data entry, streamlined navigation, and clutter-free summaries—can prevent user fatigue.²² Furthermore, integrating Just-In-Time Adaptive Interventions (JITAIs) enables the delivery of personalized behavioral prompts and clinical support at critical decision-making points (such as during glucose spikes or planned meal times).²² Finally, incorporating empathetic gamification mechanics (such as supportive progress badges, milestone celebrations, and non-judgmental messaging) can enhance intrinsic motivation and emotional reassurance without inducing anxiety or compliance pressure.²³

Age Dynamics and Cognitive Accessibility

The perceived value of the application decreased inversely with age, dropping from a high of 5.30 in the youngest demographic to 4.62 in the oldest cohort.⁸ This trajectory perfectly mirrors trends documented in extensive digital health research.¹¹⁻¹² Older patients frequently navigate intricate pharmacological regimens alongside deeply ingrained lifestyle routines.¹² Consequently, when presented with a platform that passively logs metrics without generating actionable insights or linking directly to their physicians, these older individuals correctly perceive a lower return on their digital investment.¹¹˒¹⁵

Conversely, patients aged 30 to 45—who possess greater baseline comfort with smartphone analytics—derived tangible benefits from the app’s self-tracking mechanics.⁸ To optimize accessibility dynamics for older diabetics, software architectures must explicitly address age-related visual, physical, and cognitive changes.²⁴ Physical adjustments and visual enhancements—including scalable typography, high-contrast color palettes, enlarged touchable targets, and generous white space—are vital to alleviate visual strain and precision challenges.²⁴ In parallel, incorporating cognitive scaffolding (such as step-by-step visual onboarding, contextual tooltips, conversational voice assistance, and structured task prompts) can guide senior users seamlessly through multi-step workflows.²⁴˒²⁵ To effectively cater to senior populations, who experience greater morbidity yet higher digital apprehension, future software iterations must transition from static data storage to dynamic care extensions. Such tools must feature automated, encouraging feedback loops and seamless communication channels with medical professionals.¹²˒¹⁶

Sex-Gender Dynamics and Physical/Hardware Considerations

In analyzing the broader determinants of mobile health adoption, sex and gender dynamics play a nuanced role ³. In the present study, overall usability ratings between male (mean = 5.36, SD = 0.3) and female (mean = 5.38, SD = 0.3) participants were virtually identical across all evaluated variables. This lack of gender disparity aligns with emerging digital health literature suggesting that when mobile applications offer equivalent operational simplicity, functional usability barriers are minimized across sexes.¹⁶˒²⁸ However, gender-specific sociotechnical factors—such as varying health-seeking behaviors, household caregiving burdens, and differing health literacy levels—can subtly influence how men and women integrate continuous digital self-monitoring into their daily routines.²⁸ Tailoring mHealth features to accommodate these social dynamics can further optimize long-term engagement for both men and women.²⁸

Beyond software usability and disease management, the clinical integration of mHealth ecosystem components—including smartphones, Bluetooth-enabled continuous glucose monitors, handsfree equipment, and wearable digital sensors—introduces concurrent health considerations.³ While these hardware peripherals enable seamless automated data capture, excessive or prolonged reliance on mobile digital devices can trigger unexpected physiological and psychological side effects.²⁹ High screen exposure and continuous interaction with mobile interfaces frequently cause digital eye strain, visual fatigue, and musculoskeletal stiffness.²⁹ Furthermore, constant peripheral connectivity and repetitive notification alerts can generate digital fatigue, heightened stress, and cognitive overwhelm, potentially worsening the very diabetes distress the application aims to alleviate.³^˒²⁹ Developers and clinicians must therefore balance technological convenience with physical ergonomics, promoting balanced usage patterns, muted passive alerts, and streamlined hardware synchronization to prevent physical discomfort and digital fatigue.²⁹

Limitations

Certain methodological constraints must be acknowledged.⁸ Primarily, the sample size was limited to 76 subjects from a single clinical facility in Baghdad. Furthermore, the reliance on convenience sampling introduces potential selection bias, as participants recruited during clinic visits may possess higher baseline health motivation or digital device familiarity than the broader population of diabetic patients. Additionally, the absence of a control group receiving standard care or an alternative intervention limits the ability to establish definitive causal inference regarding the app’s isolated impact, thereby constraining the external validity of the conclusions. Thereby restricting the broader epidemiological generalizability of the conclusions.⁸ Furthermore, the cross-sectional framework only measured subjective user sentiment at a specific temporal juncture, omitting long-term clinical data such as sustained glycemic control (HbA1c levels) or quantifiable changes in psychological distress.⁸ Subsequent investigations should adopt longitudinal designs to determine how interface optimizations directly impact biochemical markers and mental wellness over extended durations.⁶˒⁸

Conclusion

In summary, the “Edarat Alsukari” platform delivers an accessible and highly intuitive interface, effectively minimizing the navigational effort required by Arabic-speaking patients managing diabetes. However, a significant gap remains between this operational simplicity and the application’s subjective clinical value. Because the software lacks active progress reinforcement and direct communication channels with medical professionals, its capacity to alleviate disease-related anxiety is substantially restricted. This limitation is particularly detrimental to older users, who typically experience higher levels of diabetes distress. Moving forward, if mobile health interventions are to successfully support chronic care, developers must prioritize integrating real-time clinical feedback, telehealth connectivity, and robust support networks. Implementing these features will ensure that digital health tools mitigate psychological burdens for all age groups, rather than solely benefiting younger, digitally fluent populations.

Acknowledgment

The authors would like to acknowledge the Diabetes and Endocrinology Center at Al-Kindi Teaching Hospital for their support and facilitation during the study.

Funding Sources

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interest

The authors have no conflicts of interest.

Data Availability Statement

The manuscript incorporates all datasets produced or examined throughout this research study.

Ethics Statement

The study protocol was reviewed and approved by the University of Baghdad-College of Pharmacy Research Ethics Committee (Ref/Approval Number: 2023/104).

Informed Consent Statement

Informed consent was obtained from all participants before screening, and it conforms to the standards currently applied in the country of origin. The privacy rights of human subjects must always be observed.

Clinical Trial Registration

This research does not involve any clinical trials.

Permission to reproduce material from other sources

Not Applicable

Author Contributions

• Zahraa Alkhafaje: Conceptualization, methodology, investigation, patient recruitment coordination, data curation, formal analysis, visualization, writing—original draft preparation, writing—review and editing, and project administration.
• Fadya Al-Hamadani: Methodology, formal analysis, statistical analysis, validation, interpretation of data, writing—review and editing, and supervision.
• Anmar Thiaa Aldeen: Clinical investigation, patient recruitment, participant eligibility assessment, clinical supervision, resources, data acquisition, writing—review and editing, and supervision.

References

1. Steinhubl SR, Muse ED, Topol EJ. Can mobile health technologies transform health care? JAMA. 2013;310(22):2395-2396.
   CrossRef
2. Free C, Phillips G, Galli L, et al. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med. 2013;10(1):e1001362.
   CrossRef
3. Fisher L, Gonzalez JS, Polonsky WH. The clinical relevance of diabetes distress: What we know and what we need to know. Diabetes Care. 2014;37(6):1678-1686.
4. Polonsky WH, Fisher L, Earles J, et al. Assessing psychosocial distress in diabetes: Development of the Diabetes Distress Scale. Diabetes Care. 2005;28(3):626-631.
   CrossRef
5. Sweller J. Cognitive load theory, learning difficulty, and instructional design. Learn Instr. 1994;4(4):295-312.
   CrossRef
6. Cahn A, Akirov A, Raz I. Digital health technology and diabetes management. J Diabetes. 2018;10(1):10-17.
   CrossRef
7. Alanzi T. mHealth applications for healthcare delivery in Saudi Arabia: general overview and literature review. J Health Informatics Dev Ctries. 2018;12(1):1-15.
8. Mikhael EM, Hassali MA, Hussain SA, Shawky N. Self-management knowledge and practice of type 2 diabetes mellitus patients in Baghdad, Iraq: a qualitative study. Diabetes Metab Syndr Obes. 2018;11:1-17. doi:10.2147/DMSO.S183776
   CrossRef
9. Mikhael EM, Hassali MA, Hussain SA, Shawky N. The development and validation of quality of life scale for Iraqi patients with type 2 diabetes mellitus. J Pharm Bioallied Sci. 2020;12(3):262-268. doi:10.4103/jpbs.JPBS_190_19
   CrossRef
10. Mikhael EM, Hussain SA, Shawky N, Hassali MA. Validity and reliability of anti-diabetic medication adherence scale among patients with diabetes in Baghdad, Iraq: a pilot study. BMJ Open Diabetes Res Care. 2019;7(1):e000658. doi:10.1136/bmjdrc-2019-000658
    CrossRef
11. Alkhafaje Z, Al-Hamadani F, Al-Deen AT. Effect of a pharmacist-led mHealth and teleconsultation intervention on HbA1c in adults with type 2 diabetes in Iraq: A single-centre randomized controlled trial. Int J Body Mind Cult. 2026;13(5):e2026-1373. doi:10.61838/ijbmc.v13i5.1373
12. Alkhafaje Z, Al-hamadani F. Arabic mHealth and pharmacist teleconsultation for medication adherence, self-care, and quality of life in Iraqi adults with type 2 diabetes: A randomized controlled trial. Int J Body Mind Cult. 2026;13(6):e2026-1358. doi:10.61838/ijbmc.v13i6.1358
13. Al-Musa MH. Evaluating an Arabic mHealth App: Usability, Satisfaction, and Age-Related Variations. [Unpublished manuscript / Primary Study Data], Baghdad, Iraq, 2026.
14. Zapata BC, Fernández-Alemán JL, Idri A, Toval A. Empirical studies on usability of mHealth apps: a systematic literature review. J Med Syst. 2015;39(2):1.
    CrossRef
15. Zhou L, Bao J, Setiawan IM, Saptono A, Parmanto B. The mHealth App Usability Questionnaire (MAUQ): development and validation study. JMIR Mhealth Uhealth. 2019;7(4):e11500.
    CrossRef
16. Gordon NP, Hornbrook MC. Differences in access to and preferences for using patient portals and other eHealth technologies based on race, ethnicity, and age: a database and survey study of seniors in a large health plan. J Med Internet Res. 2016;18(3):e50.
    CrossRef
17. Wildenbos GA, Peute L, Jaspers M. Aging barriers influencing mobile health usability for older adults: a literature-based framework (MOLD-US). Int J Med Inform. 2018;114:66-75.
    CrossRef
18. Krebs P, Duncan DT. Health app use among US mobile phone owners: a national survey. JMIR Mhealth Uhealth. 2015;3(4):e101.
    CrossRef
19. Cui M, Wu X, Mao J, Wang X, Nie M. T2DM self-management via smartphone applications: a systematic review and meta-analysis. PLoS One. 2016;11(11):e0166718.
    CrossRef
20. Lyles CR, Nelson EC, Frampton S, et al. Using electronic health record portals to improve patient engagement: research priorities and best practices. Ann Intern Med. 2020;172(11_Supplement):S123-S129.
    CrossRef
21. Radhakrishnan K, Xie B, Berkley A, Kim M. Barriers and facilitators for sustainability of tele-homecare programs: a systematic review. Health Serv Res. 2016;51(1):48-75.
    CrossRef
22. Nahum-Shani I, Smith SN, Spring BJ, et al. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Ann Behav Med. 2018;52(6):446-462.
    CrossRef
23. Schmidt-Kraepelin M, Warsinsky S, Thiebes S, Sunyaev A. Gamification in Health Behavior Change Support Systems: A Synthesis of Unintended Negative Effects. Int J Med Inform. 2024;181:105286.
24. Harrington CN, Wilcox L, Connelly K, Rogers Y, Rivera-Pelayo V. Designing mHealth Applications for Older Adults: A Review of Usability Guidelines and Design Principles. ACM Trans Comput-Hum Interact. 2022;29(4):1-32.
25. Boumans R, van de Sande Y, Thill S, Bosse T. Virtual Agents and Cognitive Scaffolding in Digital Health Interventions for Older Adults. J Med Internet Res. 2023;25:e41021.
26. Senitan, M., & Alotaibi, A. (2024). Assessment of Quality Attributes Impacting End-Users’ Experience with Mobile Health Applications in Saudi Arabia: A Systematic Review and Meta-Analysis. Population Medicine, 6. https://doi.org/10.18332/popmed/199564
    CrossRef
27. Kim M, Kim B, Park S. Social Support, eHealth Literacy, and mHealth Use in Older Adults With Diabetes: Moderated Mediating Effect of the Perceived Importance of App Design. J Med Internet Res. 2024;26:e50123.
    CrossRef
28. Neter E, Brainin E. Gender and the Digital Divide in Health Information: Appraisal of Online Health Information and Confidence in Digital Skills. J Med Internet Res. 2023;25:e42011.
29. Al-Khlaiwi T, Meo SA. Association of Mobile Phone Usage with Digital Fatigue, Ocular Strain, and Musculoskeletal Symptoms Among Patients with Chronic Conditions. Int J Environ Res Public Health. 2022;19(15):9120.