Usability Evaluation of the Pharmacist-Integrated <i>Sahabat DM</i> Application for Type 2 Diabetes Mellitus Management in Indonesian Primary Care Using SEQ and SUS Instruments

Asti Rindarwati; Lia Amalia; Hikmat Permana

doi:10.12688/f1000research.174228.1

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Research Article

Usability Evaluation of the Pharmacist-Integrated Sahabat DM Application for Type 2 Diabetes Mellitus Management in Indonesian Primary Care Using SEQ and SUS Instruments

[version 1; peer review: 1 approved with reservations]

Asti Rindarwati¹, Lia Amalia¹, Hikmat Permana²

PUBLISHED 16 Apr 2026

Author details Author details

¹ Pharmacy, Institut Teknologi Bandung Sekolah Farmasi, Bandung, West Java, Indonesia
² medical, Universitas Padjadjaran Facultas Kedokteran, Bandung, West Java, Indonesia

Asti Rindarwati
Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Lia Amalia
Roles: Conceptualization, Data Curation, Investigation, Methodology, Supervision, Writing – Review & Editing

Hikmat Permana
Roles: Conceptualization, Investigation, Methodology, Supervision, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Health Services gateway.

Abstract

Background

Type 2 diabetes mellitus (T2DM) is a major global public health challenge, characterized by rapidly increasing prevalence, morbidity, and mortality. In line with the increasing trend, the number of T2DM cases in Indonesia is projected to reach 28.6 million by 2045, ranking among the highest worldwide. To address this growing challenge, digital health application offer promising strategies to enhance diabetes self-management through continuous monitoring, medication reminders, and patient education, particularly in community-based settings. Therefore, this study aimed to evaluate the usability and user experience of Sahabat DM digital health application for T2DM management in Indonesian community health centers using the Single Ease Question (SEQ) and System Usability Scale (SUS) instruments.

Methods

A descriptive usability evaluation was conducted among 20 adult patients with T2DM recruited from community health centers. Participants performed 10 predefined tasks representing major application features, followed by SEQ assessments after each task and a SUS questionnaire upon completion. Subsequently, descriptive statistics such as mean, median, and standard deviation values were calculated for both instruments, and usability classifications were determined according to established scoring standards.

Results

The mean SEQ score was 5.5 ± 0.22, indicating that participants perceived the application “fairly easy” to operate. The average SUS score was 72.0 ± 7.1, classified as “Good” usability on Bangor’s adjective scale. High SEQ and SUS ratings showed the application’s intuitive layout, clear task flow, and efficient navigation. However, the lowest SEQ rating was observed in the “Report Side Effects” task, suggesting potential opportunities for optimization in data entry and user guidance.

Conclusion

The Sahabat DM application demonstrated good usability and user acceptance among patients with T2DM, confirming the potential to support pharmacist-led diabetes self-management in Indonesian community health centers. Moreover, further large-scale and longitudinal studies are recommended to validate clinical outcomes and optimize the design for broader implementation.

Keywords

Community Health Centers, Diabetes Mellitus Type 2, Digital Health, System Usability Scale, Usability Testing 

Corresponding author: Lia Amalia

Competing interests: No competing interests were disclosed.

Grant information: This study was supported by the Indonesian Education Scholarship Program (BPI) grant no 202209091648, Education Financing Service Center (Puslapdik), Ministry of Education and Culture, Republic of Indonesia (Kemendikbud), Center for Higher Education Funding and Assessment (PPAPT), and Indonesia Endowment Fund for Education Agency (LPDP)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2026 Rindarwati A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Rindarwati A, Amalia L and Permana H. Usability Evaluation of the Pharmacist-Integrated Sahabat DM Application for Type 2 Diabetes Mellitus Management in Indonesian Primary Care Using SEQ and SUS Instruments [version 1; peer review: 1 approved with reservations]. F1000Research 2026, 15:537 (https://doi.org/10.12688/f1000research.174228.1) First published: 16 Apr 2026, 15:537 (https://doi.org/10.12688/f1000research.174228.1) Latest published: 16 Apr 2026, 15:537 (https://doi.org/10.12688/f1000research.174228.1)

Introduction

Type 2 diabetes mellitus (T2DM) is one of the fastest-growing global health problems and a leading cause of cardiovascular disease, stroke, renal failure, blindness, and lower-limb amputation (OECD & The World Bank, 2023). In 2021, the International Diabetes Federation (IDF) estimated that 537 million adults aged 20–79 years were living with diabetes worldwide, with a projection to increase by 46% in 2045 (Kumar et al., 2024). More than 1.2 million children and adolescents are affected by diabetes, with a continuous increase in the prevalence rate annually (Goyal & Vanita, 2025). Currently, Indonesia ranks fifth globally for the highest number of diabetes patients, with 19.5 million cases in 2021, projected to reach 28.6 million by 2045 (Kusuma et al., 2025). These figures highlight the need for urgent public health action, as the disease imposes a substantial clinical and economic burden on the healthcare system and the population (Kusuma et al., 2025).

Effective diabetes management extends beyond pharmacological treatment with insulin or oral hypoglycemics (Nishida & Watada, 2024). This disease requires proper management, such as continuous self-management behaviors, including regular blood glucose monitoring, adherence to treatment, dietary control, physical activity, smoking cessation, and consistent follow-up with healthcare professionals (Nico Octario Sotya Negara et al., 2025). However, in many low- and middle-income countries such as Indonesia, limited health literacy, fragmented patient follow-up, and inadequate healthcare infrastructure often impede optimal diabetes management (Faizi et al., 2024). These challenges show the need for innovative, accessible, and sustainable interventions to support patient self-care and continuity of care within the community (Cornet et al., 2020).

Recent advances in information and communication technology have increased the growth of digital health and mobile health (mHealth) solutions, particularly during the COVID-19 pandemic due to restricted access to healthcare facilities (Williams et al., 2020). According to IQVIA Digital Health Trends 2021, more than 350,000 digital health applications were available globally, with over 90,000 new applications launched in 2020 (Morrison et al., 2022). These applications offer flexibility and accessibility beyond conventional clinic-based diabetes education, allowing patients to monitor health, receive reminders, and access educational content easily (V. Sharma et al., 2024). Digital health services are not intended to replace traditional clinical practice but rather to complement it by enabling early detection, continuous monitoring, and teleconsultation, particularly for chronic conditions such as T2DM (Mbanugo, 2020). In Indonesia, digital applications have also started supporting the Program Pengelolaan Penyakit Kronis (Prolanis), the national chronic disease management program, by facilitating communication between patients and healthcare providers, including pharmacists (Nappoe et al., 2023). Pharmacists play an essential role in providing medication counseling, monitoring drug-related problems, and supporting patient adherence. However, relatively few digital interventions explicitly integrate pharmacists’ roles into patient care workflows. Despite the continuous rise in the development of mHealth applications, published studies evaluating the usability of diabetes-related applications in the context of Indonesian primary care remain limited, particularly using standardized and validated usability instruments.

The success of digital health interventions depends significantly on usability. According to the International Organization for Standardization (ISO 9241-210), usability refers to the extent to which a product can be applied by specific users to achieve goals effectively, efficiently, and satisfactorily in a defined context of use (Jokela et al., 2003). High usability enhances user acceptance, adherence, and engagement, while poor usability leads to frustration and inaccurate data input, which can compromise clinical outcomes (Jansson et al., 2022). To ensure that digital health applications are user-centered and context-appropriate, usability testing is recognized as an essential phase during the development process (Rodriguez et al., 2023). In this context, System Usability Scale (SUS) has gained significant attention for measuring overall user perception of a system, while Single Ease Question (SEQ) assesses task-specific ease of use immediately after task completion (Lewis & Sauro, 2018). The combination of these tools provides both macro- and micro-level insights into system performance and user experience (Sedig et al., 2013).

The Indonesian healthcare system depends significantly on community health centers (Puskesmas) and pharmacists as frontline providers of medication counseling, diabetes education, and patient follow-up (Rusli et al., 2025). However, there is still a knowledge gap in patient–provider communication, adverse drug reaction (ADR) reporting, and continuity of care (Li et al., 2022). This shows the need for locally developed, culturally adapted, and user-friendly digital health tools (Whitehead et al., 2023) such as Sahabat DM application, which integrates several features like medication tracking, laboratory result documentation, ADR reporting, and quality-of-life monitoring, to strengthen collaboration between patients and pharmacists within Indonesia primary healthcare system.

Several usability studies have been conducted internationally, such as the MyPal for Adults application for cancer care (Bonotis et al., 2025), the eHealthResp online course for antibiotic stewardship among pharmacists (Moura et al., 2021), and the Primagravida application for pregnancy monitoring in rural Indonesia (Lismidiati et al., 2023). However, there is still limited information on the usability of digital health applications specifically within Indonesian community health centers. Recent studies like AirPredict for asthma management (Atzeni et al., 2025), Electronic Health Report Forms (eHRF) in Swedish youth clinics (Lostelius et al., 2023), and digital tools in general practice (Albrink et al., 2022) have validated the SUS and SEQ as robust usability measures across diverse clinical settings. To the best of our knowledge, few published studies have comprehensively assessed the usability of pharmacist-integrated digital service for diabetes management in Indonesia, particularly at the primary care level. This shows a significant gap in the current literature, as the contextual, linguistic, and infrastructural characteristics of Indonesian healthcare can influence user experience differently than in high-income countries.

Based on the description above, this study aims to evaluate the task-specific ease of use and overall usability of the Sahabat DM digital health application for T2DM management in Indonesian community health centers using the Single Ease Question (SEQ) and System Usability Scale (SUS). The results are expected to provide empirical data on the application usability, guide further design optimization, and support integration into pharmacist-led chronic disease management programs in Indonesia.

Methods

Study design and participants

This study used a cross-sectional descriptive usability evaluation to assess the ease of use and perceived usability of a digital health service designed for T2DM management in Indonesian community health centers. A total of 20 adult participants diagnosed with diabetes were recruited using convenience sampling. In line with the established usability testing standards, a sample size of 15–20 participants was considered sufficient to identify the majority of usability issues in interactive systems, supporting the adequacy of the sample size used in this study. Each participant installed the Android-based application on personal smartphones and performed a series of predefined tasks simulating typical user interactions (González-Pérez et al., 2022). Eligible participants were required to (1) be ≥18 years old, (2) have a confirmed diagnosis of T2DM, (3) be able to read and understand Indonesian, and (4) own an Android smartphone. However, participants with severe visual, cognitive, or physical impairments that could interfere with app use were excluded. Each participant installed Sahabat DM application on personal Android smartphones and completed a series of predefined task scenarios simulating typical user interactions within the application. After completing each task, participants rated the experience using validated usability questionnaires. The overall study workflow, including participant recruitment, application installation, usability task completion, and usability evaluation using SEQ and SUS instruments, is illustrated in Figure 4.

Figure 1. Login and main menu interfaces of the SahabatDM application.

Legend: Left: login screen with phone number and PIN fields. Right: main menu with weekly monitoring, emergency access, and icons for Drug Information, Report Side Effects, Laboratory Results, Quality of Life, and Consultation.

Figure 2. Clinical data and self-reporting interfaces.

Legend: Left: Adverse Drug Effects entry form (drug name, concomitant medications, administration, dosage, time, side-effect description, history). Middle: Laboratory Results input (glucose measures, lipid profile, renal parameters). Right: Quality of Life questionnaire prompt for overall life and health over the past four weeks.

Figure 3. Distribution of SUS Scores, SUS Grades, and Mean SEQ Scores per Task for Sahabat DM.

Legend: application. (A) Distribution of SUS scores from 53 to 85, with most scores in the 70–75 range; (B) Distribution of SUS grades showing the highest number of participants rated the app as “Good”; (C) Mean SEQ scores per task, with Task 1 (Login) having the highest score (5.95) and Task 7 (Adverse Effect Reporting) the lowest (5.20).

Figure 4. Flowchart of participant recruitment and usability evaluation procedure.

Legend: Adult patients with type 2 diabetes mellitus (T2DM) were recruited from community health centers. Participants installed the Sahabat DM application on their Android smartphones and completed ten predefined usability tasks. Task-level usability was evaluated using the Single Ease Question (SEQ) after each task, followed by overall usability assessment using the System Usability Scale (SUS). Descriptive statistical analysis was then conducted.

Usability evaluation procedures

A total of two standardized instruments were used to evaluate usability, namely SEQ and SUS. Specifically, SEQ was used to measure the perceived ease of completing each task immediately after completion. This single-item method was selected for efficiency and reliability in capturing user perceptions without causing fatigue. SUS was applied to assess the overall usability and acceptance of the application interface, serving as a well-established, validated, and widely adopted scale for evaluating interactive systems and digital health applications.

The usability evaluation was conducted in a controlled indoor environment to minimize external distractions. Participants used personal Android smartphones with a minimum screen size of 5 inches, running Android version 8.0 or higher, and connected to a stable internet. During the evaluation, the observer recorded usability issues and user feedback, identifying difficulties and inefficiencies in task completion. This observation was supplemented with verbal reports from participants, who were encouraged to “think aloud” while completing tasks.

SEQ Assessment

Participants completed 10 predefined task scenarios (coded as F1–F10), designed to represent major functional components of the application. These tasks were developed based on a clinical workflow and verified by healthcare professionals, including pharmacists and physicians, to ensure clinical relevance. After completing each task, participants rated the task difficulty using a 7-point Likert scale ranging from 1 (Very Difficult) to 7 (Very Easy). Subsequently, mean and median SEQ scores were calculated using the following equations:

(1)

SEQ = \frac{Sum of task scores}{Number of participants}

(2)

Median = \frac{X_{n} + X_{n + 1}}{2}

Higher SEQ values indicate that participants perceived the tasks as easier to perform. The interpretation of SEQ ratings was provided in Appendix A (Ridho et al., 2023).

SUS assessment

After completing all tasks, participants filled out the 10-item SUS questionnaire. Items were rated on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree). Odd-numbered items were positively worded, while those with even numbers were negatively worded. Scoring followed the standard SUS algorithm:

(3)

Score for Odd item score = X - 1

(4)

Score for Even item score = 5 - X

(5)

Total SUS Score = (Sum of item scores) \times 2.5

Total SUS scores range from 0 to 100, with higher values showing better perceived usability. Interpretation followed Bangor et al.’s adjective rating scale (Appendix B) (Lewis & Sauro, 2018). This scale was used to facilitate interpretability of usability categories.

Data analysis

Descriptive statistics (mean, median, and standard deviation) were computed for both SEQ and SUS scores using IBM SPSS. Usability classifications were derived from established SEQ and SUS benchmarks to identify areas of strength and features requiring improvement. Given the small sample size, non-parametric tests were applied for exploratory analysis only.

Results

Participant characteristics

A total of 20 T2DM patients participated in the usability assessment, recruited from community health centers in Indonesia, with demographic characteristics summarized in Table 1. Ages ranged from 30 to 80 years, with most participants (35%) being 51–60 years. A total of 12 participants (60%) were male, and eight (40%) were female. Most had completed senior high school (60%), 10% held postgraduate degrees. The most common occupations were housewife (40%) and retired (40%), followed by merchant (20%).

Table 1. Participant characteristics (n = 20).

Variable	Category	n	%
Age (years)	30–40	5	25
	41–50	3	15
	51–60	7	35
	61–70	3	15
	71–80	2	10
Gender	Male	12	60
	Female	8	40
Education	Elementary school	1	5
	Junior high school	3	15
	Senior high school	12	60
	Bachelor’s degree	2	10
	Postgraduate	2	10
Occupation	Merchant	4	20
	Housewife	8	40
	Retired	8	40

SEQ

Participants completed 10 task scenarios covering the application’s core functions, such as login, weekly tracking, medication information, side-effect reporting, laboratory input, quality-of-life form, and consultation. After each task, perceived ease was rated on a 7-point scale (1 = very difficult; 7 = very easy). Mean SEQ scores per task ranged from 5.20 to 5.95 ( Table 2), showing tasks were perceived as between fairly easy and easy. The overall mean across tasks was 5.50, showing low perceived difficulty. Tasks including authentication (T1) and consultation (T10) were easiest, while the adverse-effect submission (T7) had the lowest mean.

Table 2. Mean SEQ scores, SD, and median by task (n = 20).

Task	Description	Mean	SD	Median	Interpretation
T1	Login and authentication	5.95	0.62	6.0	Easy
T2	Access weekly monitoring page	5.55	0.71	5.5	Fairly easy
T3	View medication information	5.50	0.69	5.0	Fairly easy
T4	Enter new medication data	5.45	0.77	5.0	Fairly easy
T5	Access therapy history	5.35	0.73	5.0	Fairly easy
T6	Record side effect report	5.45	0.75	5.0	Fairly easy
T7	Submit adverse effect form	5.20	0.82	5.0	Slightly difficult
T8	Input laboratory results	5.50	0.69	5.5	Fairly easy
T9	Complete quality-of-life form	5.50	0.71	5.5	Fairly easy
T10	Perform consultation task	5.70	0.63	6.0	Easy
Overall		5.50	0.22		Good usability

SUS

Post-task SUS scores ranged from 53 to 85, with a mean (SD) of 72.0 (7.1) ( Table 3). Based on Bangor’s adjective ratings, scores clustered mainly in Good (B) and OK (C) categories, with two Excellent (A) ratings. One participant scored 53 (D; Poor), while there was no score at ≤50.

Table 3. Individual SUS Scores and corresponding grades.

Participant	Raw SUS score	SUS (0–100)	Letter grade	Adjective rating
P1	28	70	B	Good
P2	27	68	B	Good
P3	29	73	B	Good
P4	26	65	C	OK
P5	27	68	B	Good
P6	25	63	C	OK
P7	25	63	C	OK
P8	21	53	D	Poor
P9	28	70	B	Good
P10	31	78	B	Good
P11	30	75	B	Good
P12	30	75	B	Good
P13	30	75	B	Good
P14	29	73	B	Good
P15	34	85	A	Excellent
P16	31	78	B	Good
P17	31	78	B	Good
P18	32	80	B	Good
P19	34	85	A	Excellent
P20	28	70	B	Good
Mean (SD)		72 (7.1)	B	Good

SUS results showed that all participants rated the application within the acceptable range (≥ 51), with a mean overall usability score of 72 ± 7.1. As shown in Table 4, 65% of participants classified the application as Good (mean = 75.2), 10% as Excellent (mean = 85.0), 20% as OK (mean = 66.2), and one (5%) as Poor (score = 53). However, no participant reported a usability score below 50, confirming that Sahabat DM application met satisfactory usability standards across all groups.

Table 4. Distribution of SUS scores, letter grades, and adjective ratings (n = 20).

SUS score range	Letter grade	Adjective rating	n	%	Mean ± SD
85–100	A	Excellent	2	10	85.0 ± 0.0
71–84	B	Good	13	65	75.2 ± 4.1
63–70	C	OK	4	20	66.2 ± 2.9
51–62	D	Poor	1	5	53.0 ± −
≤ 50	E	Awful	0	0	–
Overall			20	100	72.0 ± 7.1

Spearman and kruskal-wallis test results

To evaluate the relationship between SEQ and SUS scores, Spearman’s rank correlation and Kruskal-Wallis tests were applied to explore the associations between variables such as education, occupation, and usability outcomes. The results are summarized in Table 5.

Table 5. Correlation and grouping results (n = 20).

Test	Grouping variable	Statistic	p-value	Interpretation
Spearman Correlation	Mean SEQ vs SUS	ρ = −0.368	0.110	Not significant
Kruskal-Wallis	Education vs SUS	χ² = 1.63	0.803	Not significant
Kruskal-Wallis	Occupation vs SUS	χ² = 8.69	0.013	Significant

The Spearman correlation between mean SEQ and SUS scores was not statistically significant (ρ = −0.368, p = 0.110), indicating that no statistically significant association was observed between task difficulty ratings and overall system usability perceptions. Additionally, the Kruskal-Wallis test found no significant difference in SUS scores based on education level (χ² = 1.63, p = 0.803), while occupation showed a a statistically significant difference across occupational groups (χ² = 8.69, p = 0.013). These analyses were conducted to explore potential patterns across user characteristics and usability outcomes, which should be interpreted as descriptive rather than inferential based on the exploratory nature of the study.

Application interface overview

Sahabat DM application showed a structured and intuitive interface design that supported efficient navigation through the tested modules ( Figures 1 and 2). The login and main menu provided direct access to weekly monitoring, an emergency shortcut, and primary modules. Furthermore, data-entry interfaces supported reporting of adverse drug effects, recording of laboratory values, and completion of a quality-of-life questionnaire.

SUS grades, SUS scores, and mean SEQ per task

Figure 3 shows the Distribution of SUS Grades for Sahabat DM application. The majority of participants (65%) rated the application as “Good” (B), with 10% as “Excellent” (A), and 20% as “OK” (C). However, only 5% rated the application as “Poor” (D), indicating that most users showed high acceptable usability. As presented in Figure 3A, the distribution of SUS Scores ranged from 53 to 85, with the highest concentration of scores between 70 and 75. This suggested that most users found the application usable, although some reported areas for improvement. In Figure 3B, mean SEQ score per Task ranged from 5.2 to 5.95, with Task 1 (Login and Authentication) having the highest value of 5.95, showing it was perceived as very easy to complete. Task 7 (Submit Adverse Effect Form) received the lowest mean score of 5.20, suggesting that the application was slightly more difficult for participants to complete.

Discussion

To the best of our knowledge, this is among the first studies to evaluate the usability of Sahabat DM digital health application for T2DM management using SEQ and SUS in Indonesian community health centers. This dual method enabled a comprehensive evaluation of task-specific ease and overall usability, providing insights into how users interacted with the application. The results showed that Sahabat DM achieved a mean SEQ score of 5.5. This showed that the application was perceived as “fairly easy” to use, with a mean SUS score of 72.0 (SD 7.1), categorizing it as “good” usability according to Bangor’s adjective scale. Furthermore, the application provided an intuitive and acceptable user experience, supporting diabetes self-management tasks without imposing excessive cognitive or operational burden on users. The high ratings across the modules showed that the application was accessible to users with various educational and technological backgrounds (Saidelles et al., 2023).

In line with the results from other diabetes-related mHealth tools, SUS score of 72 is consistent with the value ranging from 68 to 80, commonly interpreted as “good usability” (Singhal et al., 2023). This suggests that Sahabat DM performs similarly to other validated diabetes applications, such as mySugr and GlucoMe, with values between 70 and 78 (Debong et al., 2019). Similarly, SEQ results showed that participants found the application navigation to be intuitive, with mean scores above five, indicating low cognitive effort (Akpinar et al., 2025). However, the slightly lower SEQ score for Task 7 (Adverse Effect Reporting) suggests that tasks requiring free-text input or multi-step procedures tend to receive lower usability ratings (Myka et al., 2019). This finding is consistent with prior usability research showing that text-based data entry increases cognitive load and reduces perceived ease of use. Comparable patterns were also observed in studies of electronic medication adherence systems and ADR reporting platforms, where manual entry and scrolling sequences were identified as key usability challenges (Al-Worafi, 2023). These suggest that the small variability in Sahabat DM’ SEQ scores reflects expected differences in cognitive demand between simple navigation tasks and data-entry-intensive features rather than fundamental interface problems (Arana-De Las Casas et al., 2023).

The consistently high SEQ and SUS scores across modules show the user-centered design of Sahabat DM, which includes a clear information architecture, efficient task flow, and consistent iconography (Johansson, 2024). The interface presents logical groupings of modules, Drug Information, Laboratory Results, Quality of Life, Consultation, and an easily accessible emergency contact button, minimizing navigational confusion (Chigudu, 2018). These design features are consistent with the ISO 9241-210 guidelines for usability, which emphasize effectiveness, efficiency, and satisfaction (Farinango et al., 2018). The simplicity and predictability of the interface also minimize user error and improve task completion speed (Niu et al., 2025). However, the lower performance in tasks like Adverse Effect Reporting presents the challenge of designing input-heavy interfaces for populations with varying digital literacy levels (Klouche, 2019). Incorporating features such as auto-complete menus, voice input, or drop-down selections can enhance usability and accessibility in future versions (Cao et al., 2025).

From a pharmacy practice perspective, Sahabat DM has several clinical implications. A user-friendly interface promotes patient engagement in medication management, ADR reporting, and other activities that typically depend on pharmacists’ follow-up (Liyanage et al., 2025). This finding supports the integration of usability principles into pharmacist-led digital interventions. Furthermore, the integration of structured ADR reporting allows pharmacovigilance at the community level, which facilitates the early detection of drug-related problems (Anestina et al., 2025). The application also simplifies self-reporting of laboratory results and quality-of-life assessments, empowering patients to play a more active role in care. This supports the role of pharmacists as mediators of safe and effective medication use, particularly in low-resource settings, where there is limited access to healthcare professionals (Yousif et al., 2024).

The usability and accessibility shown by Sahabat DM are highly relevant for clinical pharmacy practice. In primary care, pharmacists are central to medication counseling, adherence monitoring, and patient education (Merks et al., 2021). Therefore, digital applications such as Sahabat DM facilitate real-time, asynchronous communication between patients and pharmacists, allowing continuous monitoring and early intervention (Awala & Olutimehin, 2024). The ability to document medication use, laboratory parameters, and perceived well-being within a single digital platform creates opportunities for continuous therapeutic monitoring and early intervention (Aapro et al., 2020). In rural or resource-limited settings with restricted follow-up, the application serves as a digital extension of pharmacy services, which improves continuity of care and reduces preventable complications (Andrade et al., 2025). Integration with electronic medical records (EMRs) and pharmacy information systems can further improve data interoperability, thereby enhancing collaboration between pharmacists and physicians in chronic disease management (Al Kulayb et al., 2024). This cross-functional integration improves treatment adherence, reduces duplication of care, and enhances the overall efficiency of Indonesia’s primary healthcare network (Oktaviana et al., 2025).

The results obtained using Sahabat DM application show the need for iterative design improvements, particularly in the side-effect reporting module. Features like dropdown menus, voice recognition, and automated drug database integration can reduce user burden and improve data accuracy (Getov et al., 2025). Moreover, future studies should expand the sample size to include a more diverse population, representing different age groups and digital literacy levels (Ma et al., 2023). Longitudinal studies are also essential to determine whether consistent usage leads to clinical improvements, such as glycemic control, adherence rates, or the frequency of ADR reporting (Ye et al., 2022). The impact of pharmacist-led digital literacy programs should also be explored, focusing on app adoption and sustained user engagement (Bober et al., 2024). By coupling usability optimization with behavioral reinforcement strategies, Sahabat DM could evolve into a scalable tool supporting both patient self-management and pharmacist-driven monitoring (Gatwood et al., 2019).

Despite the significant contributions, this study has several limitations, which include a relatively small sample size (n = 20) and the convenience sampling method that can introduce bias toward participants with higher motivation or better digital literacy (Sarkar, 2024). In line with established usability testing guidelines, a sample size of 15–20 participants is generally considered sufficient to identify the majority of usability issues in interactive systems and inform iterative design improvements. The short-term interactions also limit insights into long-term engagement or behavior change. Although SEQ and SUS are validated instruments, the reliance on self-reported responses introduces subjectivity and does not capture objective usability metrics such as task completion time or error rates (S. Sharma & Kumar, 2025). Another limitation is the inclusion of participants from Indonesian community health centers, which may limit generalizability to populations in other cultural or healthcare contexts. However, the convergence of SEQ and SUS findings suggests that Sahabat DM application demonstrates acceptable usabilit. This study provides a foundation for future multicenter trials, iterative system refinement, and investigations linking digital usability to clinical outcomes in diabetes care.

Conclusions

In conclusion, this study suggests that Sahabat DM digital health application demonstrates good usability and user acceptance among patients with T2DM in Indonesian community health centers, as reflected by a mean SEQ score of 5.5 and a SUS of 72. The results indicate that the user-centered design of the application effectively supports ease of navigation, clarity of information, and efficiency in performing self-management tasks such as medication monitoring, ADR reporting, and laboratory data entry. By integrating usability principles with pharmacy-led care functions, Sahabat DM shows strong potential to enhance patient engagement, improve pharmacovigilance, and strengthen pharmacist–patient communication in community settings. Moreover, future large-scale and longitudinal studies are recommended to validate the clinical impact and optimize the design for broader implementation across diverse healthcare environments.

Ethical considerations

All participants provided written informed consent prior to participation. The study obtained ethical approval from the Research Ethics Committee of Universitas Padjadjaran (Approval No. 635/UN6.KEP/EC/2023, issued May 2023). Additional operational permits included Kesbangpol Bandung (Permit No. PK.03.04.05/1664-BKBP/VIII/2023, August 2023) and Bandung City Health Office (Permit No. B/PP.06.02/15934-Dinkes/VIII/2023, August 2023). Participant anonymity and confidentiality were maintained throughout data collection and analysis.

Data availability

The datasets supporting the results of this study are openly available in Usability Evaluation of the Pharmacist-Integrated Sahabat DM Application for Type 2 Diabetes Management in Indonesian Primary Care Using SEQ and SUS Instruments [Data set]. Zenodo at https://doi.org/10.5281/zenodo.18825312 (Yunia et al., 2026a).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Reporting guidelines

This study complies with the STROBE guidelines for observational studies and the SRQR guidelines for qualitative experiments.

Completed STROBE and SRQR checklists, including a one-page summary and detailed page/section mapping, are available in an online repository.

Repository name: Zenodo.

Title: STROBE and SRQR reporting checklist for Sahabat DM usability study.

DOI: https://doi.org/10.5281/zenodo.18239382 (Yunia et al., 2026b).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgements

The authors are grateful to the Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Indonesia Education (BPI), Education Financing Service Center (Puslapdik), Science and Technology of the Republic of Indonesia, and the Indonesia Endowment Fund for Education Agency (LPDP) of the Republic of Indonesia, for their invaluable support and generous funding of this study. Furthermore, the authors are grateful to all individuals and Institutions who provided insights, technical assistance, and encouragement throughout the study process.

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Author details Author details

¹ Pharmacy, Institut Teknologi Bandung Sekolah Farmasi, Bandung, West Java, Indonesia
² medical, Universitas Padjadjaran Facultas Kedokteran, Bandung, West Java, Indonesia

Asti Rindarwati
Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Lia Amalia
Roles: Conceptualization, Data Curation, Investigation, Methodology, Supervision, Writing – Review & Editing

Hikmat Permana
Roles: Conceptualization, Investigation, Methodology, Supervision, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This study was supported by the Indonesian Education Scholarship Program (BPI) grant no 202209091648, Education Financing Service Center (Puslapdik), Ministry of Education and Culture, Republic of Indonesia (Kemendikbud), Center for Higher Education Funding and Assessment (PPAPT), and Indonesia Endowment Fund for Education Agency (LPDP)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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https://doi.org/10.12688/f1000research.174228.1

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Rindarwati A, Amalia L and Permana H. Usability Evaluation of the Pharmacist-Integrated Sahabat DM Application for Type 2 Diabetes Mellitus Management in Indonesian Primary Care Using SEQ and SUS Instruments [version 1; peer review: 1 approved with reservations]. F1000Research 2026, 15:537 (https://doi.org/10.12688/f1000research.174228.1)

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Version 1

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Reviewer Report 06 May 2026

Anand K Gavai, University of Twente, Enschede, The Netherlands

Approved with Reservations

https://doi.org/10.5256/f1000research.192111.r476048

Summary of the Article
This study presents a usability evaluation of the Sahabat DM digital health application designed to support type 2 diabetes mellitus (T2DM) management in Indonesian primary care settings. The application integrates pharmacist-supported functionalities, including medication tracking, ... Continue reading

Summary of the Article
This study presents a usability evaluation of the Sahabat DM digital health application designed to support type 2 diabetes mellitus (T2DM) management in Indonesian primary care settings. The application integrates pharmacist-supported functionalities, including medication tracking, laboratory data entry, adverse drug reaction (ADR) reporting, and consultation features.

A cross-sectional usability study was conducted with 20 adult participants diagnosed with T2DM. Participants completed ten predefined tasks representing key application features. Task-level usability was assessed using the Single Ease Question (SEQ), while overall system usability was evaluated using the System Usability Scale (SUS).
The reported results indicate a mean SEQ score of 5.5 and a mean SUS score of 72, suggesting “fairly easy” task performance and “good” overall usability. The authors conclude that the application demonstrates acceptable usability and potential to support pharmacist-led diabetes self-management, while recommending further large-scale and longitudinal studies.

General Assessment
The manuscript is clearly written and methodologically sound within the scope of usability evaluation. The use of validated instruments (SEQ and SUS) and a structured task-based design aligns with established usability testing practices. The study also addresses a relevant and underexplored context—digital health adoption in Indonesian primary care settings with pharmacist integration.

However, the manuscript operates within a relatively narrow conceptual framing of digital health, equating usability with broader notions of effectiveness and impact. While this is common in early-stage evaluations, the current presentation risks overstating the implications of the findings. The main limitations of the work are therefore not technical, but conceptual and interpretative.

Major Comments (Must Be Addressed)
1. Clarify the distinction between usability and clinical or behavioral effectiveness
The manuscript frequently implies that good usability translates into improved diabetes self-management, adherence, or pharmacovigilance. However, the study does not measure behavioral outcomes, engagement, or clinical indicators (e.g., glycemic control).

Required revision:

Explicitly distinguish usability from effectiveness in both the Discussion and Conclusion.
Reframe claims regarding “supporting self-management” as potential or hypothesized, rather than demonstrated outcomes.
Clearly state that usability is a necessary but not sufficient condition for clinical impact.

2. Strengthen the discussion of limitations
While some limitations are acknowledged, their implications are not fully developed.
Required revision:

Expand discussion on:
- Small sample size (n = 20) and convenience sampling bias
- Short-term, task-based interaction vs. real-world usage
- Absence of longitudinal engagement data
- Lack of objective usability metrics (e.g., task time, error rates)
Explicitly discuss how these limitations constrain generalizability and interpretation.

3. Avoid overinterpretation of results
The conclusions extend beyond what the data directly support, particularly in relation to pharmacist-led care and system-level impact.
Required revision:

Moderate claims related to:
- Improved adherence
- Enhanced pharmacovigilance
- Strengthened patient–provider communication
Ensure all claims are directly tied to measured outcomes (SEQ/SUS only).

Minor Comments (Should Be Addressed)

4. Broaden engagement with current digital health literature
The manuscript cites relevant usability and mHealth studies, but does not sufficiently engage with more recent developments in digital health research.
Suggested improvement:

Incorporate literature that:
- Examines long-term engagement and adherence in digital interventions
- Explores behavior change frameworks in mHealth
- Discusses adaptive, personalized, or interaction-driven digital health systems

This would help position the study within a broader and evolving research landscape.

5. Expand interpretation of usability findings
The results are reported clearly but could be interpreted more critically.
Suggested improvement:

Discuss what a SUS score of ~72 implies in practical deployment contexts
Reflect on variability across users (e.g., the participant with “poor” usability)
Provide deeper analysis of why specific tasks (e.g., ADR reporting) performed worse

6. Elaborate on pharmacist integration
The inclusion of pharmacists is a strength, but remains under-theorized.
Suggested improvement:

Clarify how the application changes or enhances pharmacist–patient interaction
Discuss whether this integration is informational, communicative, or workflow-based
Situate this within broader care delivery models

Overall Recommendation
The study is methodologically sound and suitable for indexing, but requires moderate revision to ensure that conclusions are appropriately aligned with the evidence and that the contribution is clearly positioned within the broader digital health landscape.
The required revisions primarily concern:

Conceptual clarity
Interpretation of results
Strengthening of limitations and discussion

Addressing these points will significantly improve the scientific rigor and relevance of the manuscript.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Gavai A, Meuwissen M: Agentic AI as a coordination paradigm in digital health and agri-food systems. Patterns. 2026; 7 (3). Publisher Full Text
2. Gavai A, van Hillegersberg J: AI-driven personalized nutrition: RAG-based digital health solution for obesity and type 2 diabetes. PLOS Digital Health. 2025; 4 (5). Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Digital health, artificial intelligence in healthcare (including retrieval-augmented generation systems), personalized nutrition, chronic disease management (with a focus on metabolic disorders such as type 2 diabetes), and data-driven health systems. I also have expertise in usability and evaluation of digital health interventions, as well as in the design of adaptive, interaction-driven health technologies.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Anand K Gavai, University of Twente, Enschede, The Netherlands

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Reviewer Report

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06 May 2026 | for Version 1

Anand K Gavai, University of Twente, Enschede, The Netherlands

6 Views Cite this report Responses(0)

Approved With Reservations

Summary of the Article
This study presents a usability evaluation of the Sahabat DM digital health application designed to support type 2 diabetes mellitus (T2DM) management in Indonesian primary care settings. The application integrates pharmacist-supported functionalities, including medication tracking, laboratory data entry, adverse drug reaction (ADR) reporting, and consultation features.

A cross-sectional usability study was conducted with 20 adult participants diagnosed with T2DM. Participants completed ten predefined tasks representing key application features. Task-level usability was assessed using the Single Ease Question (SEQ), while overall system usability was evaluated using the System Usability Scale (SUS).
The reported results indicate a mean SEQ score of 5.5 and a mean SUS score of 72, suggesting “fairly easy” task performance and “good” overall usability. The authors conclude that the application demonstrates acceptable usability and potential to support pharmacist-led diabetes self-management, while recommending further large-scale and longitudinal studies.

General Assessment
The manuscript is clearly written and methodologically sound within the scope of usability evaluation. The use of validated instruments (SEQ and SUS) and a structured task-based design aligns with established usability testing practices. The study also addresses a relevant and underexplored context—digital health adoption in Indonesian primary care settings with pharmacist integration.

However, the manuscript operates within a relatively narrow conceptual framing of digital health, equating usability with broader notions of effectiveness and impact. While this is common in early-stage evaluations, the current presentation risks overstating the implications of the findings. The main limitations of the work are therefore not technical, but conceptual and interpretative.

Major Comments (Must Be Addressed)
1. Clarify the distinction between usability and clinical or behavioral effectiveness
The manuscript frequently implies that good usability translates into improved diabetes self-management, adherence, or pharmacovigilance. However, the study does not measure behavioral outcomes, engagement, or clinical indicators (e.g., glycemic control).

Required revision:

Explicitly distinguish usability from effectiveness in both the Discussion and Conclusion.
Reframe claims regarding “supporting self-management” as potential or hypothesized, rather than demonstrated outcomes.
Clearly state that usability is a necessary but not sufficient condition for clinical impact.

2. Strengthen the discussion of limitations
While some limitations are acknowledged, their implications are not fully developed.
Required revision:

Expand discussion on:
- Small sample size (n = 20) and convenience sampling bias
- Short-term, task-based interaction vs. real-world usage
- Absence of longitudinal engagement data
- Lack of objective usability metrics (e.g., task time, error rates)
Explicitly discuss how these limitations constrain generalizability and interpretation.

3. Avoid overinterpretation of results
The conclusions extend beyond what the data directly support, particularly in relation to pharmacist-led care and system-level impact.
Required revision:

Moderate claims related to:
- Improved adherence
- Enhanced pharmacovigilance
- Strengthened patient–provider communication
Ensure all claims are directly tied to measured outcomes (SEQ/SUS only).

Minor Comments (Should Be Addressed)

4. Broaden engagement with current digital health literature
The manuscript cites relevant usability and mHealth studies, but does not sufficiently engage with more recent developments in digital health research.
Suggested improvement:

Incorporate literature that:
- Examines long-term engagement and adherence in digital interventions
- Explores behavior change frameworks in mHealth
- Discusses adaptive, personalized, or interaction-driven digital health systems

This would help position the study within a broader and evolving research landscape.

5. Expand interpretation of usability findings
The results are reported clearly but could be interpreted more critically.
Suggested improvement:

Discuss what a SUS score of ~72 implies in practical deployment contexts
Reflect on variability across users (e.g., the participant with “poor” usability)
Provide deeper analysis of why specific tasks (e.g., ADR reporting) performed worse

6. Elaborate on pharmacist integration
The inclusion of pharmacists is a strength, but remains under-theorized.
Suggested improvement:

Clarify how the application changes or enhances pharmacist–patient interaction
Discuss whether this integration is informational, communicative, or workflow-based
Situate this within broader care delivery models

Overall Recommendation
The study is methodologically sound and suitable for indexing, but requires moderate revision to ensure that conclusions are appropriately aligned with the evidence and that the contribution is clearly positioned within the broader digital health landscape.
The required revisions primarily concern:

Conceptual clarity
Interpretation of results
Strengthening of limitations and discussion

Addressing these points will significantly improve the scientific rigor and relevance of the manuscript.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Gavai A, Meuwissen M: Agentic AI as a coordination paradigm in digital health and agri-food systems. Patterns. 2026; 7 (3). Publisher Full Text
2. Gavai A, van Hillegersberg J: AI-driven personalized nutrition: RAG-based digital health solution for obesity and type 2 diabetes. PLOS Digital Health. 2025; 4 (5). Publisher Full Text

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Digital health, artificial intelligence in healthcare (including retrieval-augmented generation systems), personalized nutrition, chronic disease management (with a focus on metabolic disorders such as type 2 diabetes), and data-driven health systems. I also have expertise in usability and evaluation of digital health interventions, as well as in the design of adaptive, interaction-driven health technologies.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

Responses (0)

[1] Aapro M, Bossi P, Dasari A, et al.: Digital health for optimal supportive care in oncology: Benefits, limits, and future perspectives. Support. Care Cancer. 2020; 28(10): 4589–4612. PubMed Abstract | Publisher Full Text | Free Full Text

[2] Akpinar AE, Gencturk M, Pournik O, et al.: User experience and usability evaluation of the CAREPATH adaptive integrated care platform.Mantas J, Hasman A, Gallos P, et al., editors. Studies in Health Technology and Informatics. IOS Press; 2025. Publisher Full Text

[3] Al Kulayb SH, Alyami YH, Alyami AH, et al.: Information systems in pharmacy: Enhancing patient safety and outcomes. J Int Crisis Risk Commun Res. 2024; 7(S11): 60.

[4] Albrink K, Joos C, Schröder D, et al.: Obtaining patients’ medical history using a digital device before consultation in primary care: Study protocol for a usability and validity study. BMC Med. Inform. Decis. Mak. 2022; 22(1): 189. Publisher Full Text

[5] Al-Worafi YM: Technology for drug safety: Challenges.Al-Worafi YM Technology for drug safety. Springer International Publishing; 2023; pp. 129–152. Publisher Full Text

[6] de Andrade JBC , Fagundes TP, Katsuyama E, et al.: Digital health in low-resource settings: Comprehensive challenges and opportunities with a focus on stroke care. Stroke. 2025.

[7] Anestina N, Obiageli C, Anthony O, et al.: Ensuring drug safety: Comprehensive pharmacovigilance strategies for public health.2025.

[8] Arana-De Las Casas NI, De La Riva-Rodríguez J, Maldonado-Macías AA, et al.: Cognitive analyses for interface design using dual n-back tasks for mental workload (MWL) evaluation. Int. J. Environ. Res. Public Health. 2023; 20(2): 1184. Publisher Full Text

[9] Atzeni M, Cossu L, Gaiotti S, et al.: AirPredict: An eHealth platform for asthma management leveraging wearable sensors, digital diaries, and air quality monitoring to optimize patient outcomes. Front. Digital Health. 2025; 7: 1573342. PubMed Abstract | Publisher Full Text | Free Full Text

[10] Awala EV, Olutimehin D: Revolutionizing remote patient care: The role of machine learning and AI in enhancing tele-pharmacy services.2024.

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Usability Evaluation of the Pharmacist-Integrated Sahabat DM Application for Type 2 Diabetes Mellitus Management in Indonesian Primary Care Using SEQ and SUS Instruments

Abstract

Background

Methods

Results

Conclusion

Keywords

Introduction

Methods

Study design and participants

Figure 1. Login and main menu interfaces of the SahabatDM application.

Figure 2. Clinical data and self-reporting interfaces.

Figure 3. Distribution of SUS Scores, SUS Grades, and Mean SEQ Scores per Task for Sahabat DM.

Figure 4. Flowchart of participant recruitment and usability evaluation procedure.

Usability evaluation procedures

SEQ Assessment

(1)

(2)

SUS assessment

(3)

(4)

(5)

Data analysis

Results

Participant characteristics

Table 1. Participant characteristics (n = 20).

SEQ

Table 2. Mean SEQ scores, SD, and median by task (n = 20).

SUS

Table 3. Individual SUS Scores and corresponding grades.

Table 4. Distribution of SUS scores, letter grades, and adjective ratings (n = 20).

Spearman and kruskal-wallis test results

Table 5. Correlation and grouping results (n = 20).

Application interface overview

SUS grades, SUS scores, and mean SEQ per task

Discussion

Conclusions

Ethical considerations

Data availability

Reporting guidelines

Acknowledgements

References

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