Mobile health intervention to reduce HbA1c in type 2 diabetes: a quasi-experimental study in Indonesian primary care

Mobile health intervention to reduce HbA1c in type 2 diabetes: a quasi-experimental study in Indonesian primary care Asti Rindarwati1, Lia Amalia1, Hikmat Permana2 1 Institut Teknologi Bandung, Bandung, Indonesia 2 Universitas Padjadjaran, Sumedang, Indonesia Corresponding authors: Asti Rindarwati ([email protected]); Lia Amalia ([email protected]) Received 24 May 2025♦ Accepted 7 September 2025♦ Published 10 October 2025 Citation: Rindarwati A, Amalia L, Permana H (2025) Mobile health intervention to reduce HbA1c in type 2 diabetes: a quasi-experimental study in Indonesian primary care. Pharmacia 72: 1–9. https://doi.org/10.3897/pharmacia.72.e160083 Abstract Background: The global burden of type 2 diabetes mellitus (T2DM) continues to rise, with Indonesia projected to reach over 28 million cases by 2045. Limited access to continuous education and follow-up care in low-resource primary care settings necessitates scalable digital health solutions to support self-management. Objective: This study aimed to evaluate the effectiveness of a mobile health (mHealth) application in reducing HbA1c levels among T2DM patients in Indonesian primary care. Methods: A quasi-experimental, two-arm study was conducted over 24 weeks across 10 public health centers (Puskesmas) in Bandung. One hundred patients with T2DM (HbA1c >8%) were assigned to either an intervention group (mHealth application supported by integrated pharmacist consultation) or a control group (standard care). The primary outcome was the change in HbA1c levels measured at baseline, 12, and 24 weeks. Results: The intervention group showed a significant reduction in HbA1c from 9.31% to 8.13% (p = 0.001), while the control group showed minimal change (from 8.99% to 8.96%, p = 0.021). Glycemic trends in the intervention group were consistent and progressive, indicating improved adherence and self-management. Boxplot analysis demonstrated reduced variability and tighter glycemic control in the intervention group. High usability and engagement were observed among older adults and participants with low educational backgrounds. Conclusion: The mHealth intervention significantly improved glycemic control and promoted consistent self-care among Indonesian T2DM patients. These findings suggest that scalable digital solutions may enhance diabetes management in primary care systems in lowand middle-income countries. Keywords diabetes mellitus, Indonesia, mobile applications, primary health care, telemedicine Copyright Rindarwati A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Pharmacia 72: 1–9 DOI 10.3897/pharmacia.72.e160083 Research Article Rindarwati A et al.: Mobile health intervention to reduce HbA1c in type 2 diabetes2 Introduction Type 2 diabetes mellitus (T2DM) remains one of the most pressing global health challenges, contributing significantly to morbidity, mortality, and healthcare expenditures worldwide (International Diabetes Federation 2025). As of 2021, the International Diabetes Federation (IDF) estimated that 537 million adults were living with diabetes globally, a figure projected to rise to 783 million by 2045, with more than three-quarters residing in lowand middle-income countries (LMICs) (Sun et al. 2022). The disease burden is particularly severe in Indonesia, where diabetes prevalence continues to rise sharply, and the national health system faces substantial barriers to delivering accessible, consistent, and patient-centered care (Soewondo et al. 2013). The IDF predicts that Indonesia alone may account for over 28 million diabetes cases by 2045, underscoring the urgent need for scalable, cost-effective interventions to support self-management and improve glycemic outcomes at the primary care level (International Diabetes Federation 2025). T2DM self-management, encompassing medication adherence, dietary control, blood glucose monitoring, and physical activity, is fundamental to achieving glycemic control (De Groot et al. 2021). However, patients often struggle to sustain these behaviors within conventional care models, especially where patients have limited access to ongoing education, monitoring, and healthcare-provider interaction (Al-Badri and Hamdy 2021). In this context, mobile health (mHealth) technologies offer promising, patient-centered tools to bridge these gaps (Agarwal et al. 2019). Recent reviews have highlighted that digital interventions can significantly enhance medication adherence and self-care behavior, particularly among patients with chronic illnesses such as T2DM (Georgieva et al. 2023). Smartphone-based applications can deliver continuous support through interactive educational content, automated reminders, and communication with healthcare professionals (Al-Badri and Hamdy 2021). Furthermore, diabetes monitoring systems and digital platforms have demonstrated the potential to improve glycemic control and clinical decision-making, particularly when integrated into healthcare settings (Kamusheva et al. 2021). A growing body of systematic reviews and meta-analyses has consistently demonstrated that mHealth interventions contribute to significant improvements in glycemic control among individuals with type 2 diabetes, with meta-analytic estimates ranging from 0.4% to 0.5% mean reduction in HbA1c compared to control conditions (Liang et al. 2011; Cui et al. 2016; Hou et al. 2016). Despite these promising findings, the effectiveness of mHealth interventions varies widely across studies and settings. For example, a multicenter pragmatic RCT of the BlueStar mobile app in the United States showed no significant difference in HbA1c reduction between intervention and control groups after 3 months (Agarwal et al. 2019), largely due to low and variable app engagement. Similarly, studies from South Korea and Europe confirm that contextual factors, including digital literacy, healthcare infrastructure, and patient-provider interaction, critically influence outcomes (Yang et al. 2020; Bretschneider et al. 2022). Most of these trials have been conducted in high-income countries, with limited evaluation in LMICs, where mobile health innovations may be most impactful. In Indonesia, evidence regarding the feasibility and clinical effectiveness of mHealth interventions in routine primary care settings remains scarce (Damayanti et al. 2021; Kusuma et al. 2023, 2024). To address this gap, we designed a quasi-experimental study to evaluate the effectiveness of a culturally adapted mobile health application in improving glycemic control among patients with T2DM in public health center (puskesmas) settings in Bandung, Indonesia. The intervention integrated multimedia diabetes education, automated reminders, and real-time pharmacist consultations to support daily self-management. This study provides novel insights into the real-world utility of mobile app–based digital interventions in an LMIC context and explores their potential for scalability within national diabetes care strategies. Methods Ethical approval and permissions Ethical approval and administrative permissions for this study were obtained prior to data collection. Ethical clearance was granted by the Health Research Ethics Committee of Universitas Padjadjaran, Indonesia (Approval No. 635/UN6.KEP/EC/2023), issued in May 2023. In addition, research permits were obtained from relevant regional authorities to conduct the study in selected public health centers (puskesmas) in Bandung. These included approval from the National Unity and Political Agency (KesBangPol) of Bandung (Permit No. PK.03.04.05/1664-BKBP/ VIII/2023, issued in August 2023) and from the Bandung City Health Office (Permit No. B/PP.06.02/15934-Dinkes/ VIII/2023, also issued in August 2023). These procedures ensured that the study adhered to all applicable ethical and regulatory standards for research involving human subjects. Study design This 24-week, open-label, two-arm quasi-experimental study was conducted from December 2023 to June 2024 in 10 public health centers (puskesmas) across Bandung, Indonesia. Participants were assigned to either an intervention group, which received a mobile health (mHealth) application designed to support diabetes self-management, or a control group receiving standard diabetes care. A total of 100 participants were enrolled, with 50 individuals in each group. The study design and workflow are illustrated in Fig. 1. Pharmacia 72: 1–9 3 Measurement of primary outcome (HbA1c) Glycated hemoglobin (HbA1c) levels were measured in percentage (%) using high-performance liquid chromatography (HPLC) with a Bio-Rad Variant II Turbo Hemoglobin Testing System (Bio-Rad Laboratories, Hercules, CA, USA). This system is certified by the National Glycohemoglobin Standardization Program (NGSP) and traceable to the International Federation of Clinical Chemistry (IFCC) reference method. All samples were processed at certified clinical laboratories under standard operating procedures. The primary outcome was the change in HbA1c from baseline to the end of the intervention period, calculated as: ΔHbA1c (%) = HbA1c_post – HbA1c_pre Education delivery and implementation The educational intervention was delivered through a mobile health application called Sahabat DM, which contains structured educational video modules on diabetes self-management. These materials were developed by a multidisciplinary team, including internal medicine specialists, pharmacists, and academic researchers in public health and behavioral science, and were validated by a panel of experts. During the intervention period, the pharmacist played a central role as the health facilitator and patient counselor. Specifically, the pharmacist was responsible for: –Introducing and guiding patients on how to use the Sahabat DM app. –Monitoring patient engagement with educational content. –Providing reinforcement and clarification during scheduled follow-up counseling sessions (either in-person or by phone). –Encouraging medication adherence and lifestyle modifications in accordance with clinical guidelines. These roles were performed in collaboration with primary care physicians as part of integrated chronic disease management at the primary healthcare center. Eligibility criteria Participants included adults aged 18 to 70 years who had been diagnosed with type 2 diabetes mellitus (T2DM), had a baseline HbA1c value greater than 8%, and had been undergoing treatment for at least 1 year. All participants were enrolled in Indonesia’s national chronic disease management program. Additional inclusion criteria were the ability to read and write and access to an Android-based smartphone (version 3.0 or higher) with a minimum of 4 Figure 1. Workflow of the study. Rindarwati A et al.: Mobile health intervention to reduce HbA1c in type 2 diabetes4 GB RAM, either directly or through a caregiver. Exclusion criteria included pregnancy or the presence of serious comorbidities such as advanced heart failure, end-stage renal disease, or malignancy. Sample size The total sample size of 100 participants (50 per group) was determined based on feasibility and program implementation considerations. A formal sample size calculation was not performed due to feasibility constraints. The sample size was considered adequate for detecting medium effect sizes in non-randomized designs. The study was designed to provide preliminary evidence of the effectiveness of mobile health interventions for diabetes management in Indonesian primary care settings. This study did not employ random allocation. Participants were assigned to the intervention or control group based on health center location and logistical feasibility. As a non-randomized controlled trial, statistical methods were applied during analysis to address potential differences between groups at baseline. Intervention The intervention group received access to a mobile application developed collaboratively with users to support diabetes self-management. The application provided educational content on diet, physical activity, medication, and complications of diabetes in various formats, including video, audio, infographics, and animations. It also included automated reminders for blood glucose monitoring, medication adherence, and HbA1c testing. Additional features enabled real-time communication with pharmacists via text, voice, or video calls. Participants received personalized daily motivational challenges related to lifestyle behaviors, as well as a reporting feature for medication side effects. The application included a dashboard displaying laboratory results and therapy history. Prior to the intervention period, participants and caregivers were trained on app usage and provided a 1-week trial to ensure usability. Control group Participants in the control group received standard care provided by the public health centers (puskesmas). This included fasting blood glucose and 2-hour postprandial glucose testing every 4 weeks, as well as HbA1c measurements every 12 weeks. Therapeutic decisions were made based on laboratory findings, and general education was delivered during scheduled clinical visits. No mobile application or digital tools were provided to this group. Primary and secondary outcomes The primary outcome of this study was the change in HbA1c levels from baseline to week 24. Secondary outcomes included changes in fasting blood glucose levels and 2-hour postprandial glucose levels. Additional observational outcomes included qualitative assessment of self-management behavior changes in the intervention group. Measurements Laboratory measurements were conducted at three time points: baseline (T0), week 12, and week 24. Blood samples were collected by certified health professionals following standard operating procedures and analyzed in certified laboratories using standardized immunoassay techniques. Participant demographics and clinical data were collected at baseline through structured interviews and data forms. Statistical analysis Statistical analysis was performed using SPSS version 26 (IBM Corp.). Normality of the data was assessed using the Shapiro–Wilk test. As the HbA1c data in the intervention group were not normally distributed (W = 0.884, p = 0.0002), nonparametric testing was applied. The Mann–Whitney U test was used to compare HbA1c levels between groups at each time point. A p-value <0.05 was considered statistically significant. Descriptive statistics were also used to summarize demographic characteristics and changes in blood glucose indicators over time. Results Participant flow and baseline characteristics A total of 100 eligible participants were enrolled from 10 community health centers in Bandung, Indonesia. Fifty participants were assigned to the intervention group, which received the mobile application, while the remaining 50 received standard care. All participants completed the 24-week intervention period, and no dropouts were reported. Baseline demographic and clinical characteristics are presented in Table 1. The majority of participants were aged between 51 and 70 years, with 34% aged 51–60 and 32% aged 61–70. Most participants were female (78%), and the dominant occupational background was housewife (62%). In terms of education level, the largest group completed junior high school (48%), followed by senior high school (36%). Primary Outcome: Change in HbA1c Levels. The results of the secondary outcomes focused on evaluating the usability of the Sahabat DM application. Usability was measured using two tools: the Single Ease Question (SEQ) and the System Usability Scale (SUS). Task-based usability testing using SEQ was conducted on 10 tasks (T1–T10) performed within the application. The average SEQ score across these tasks was 5.0, which corresponds to the “Somewhat easy” category based on the SEQ interpretation scale, as shown in Table 3. Pharmacia 72: 1–9 5 The overall SUS score for the Sahabat DM application was 72, which falls into the “Acceptable” range of usability according to standard SUS benchmarks. These findings indicate that the application was generally perceived as user-friendly by the participants. Details are presented in Table 4. The mean HbA1c levels for both groups across time points are summarized in Table 2. At baseline, the control group had a mean HbA1c of 8.99%, while the intervention group showed a slightly higher baseline of 9.31%. At week 12, the control group’s HbA1c marginally increased to 9.04%, whereas the intervention group demonstrated a reduction to 8.76%. At the end of the 24 weeks, the control group’s HbA1c remained largely unchanged at 8.96%, while the intervention group showed a substantial decrease to 8.13%. Both groups exhibited statistically significant changes in HbA1c; however, the reduction was notably greater in the intervention group (p = 0.001) compared to the control group (p = 0.021). A boxplot in Fig. 2 illustrates the distribution of HbA1c values at week 24, highlighting a lower median and narrower interquartile range in the intervention group. Pharmacists in education In this study, pharmacists played a pivotal role in both the development and implementation of education and consultation services via the Sahabat DM mobile application. Their contributions included designing and validating 60 concise, 2-minute educational videos covering key aspects of diabetes self-management–such as medication adherence, healthy eating, physical activity, understanding HbA1c, the mechanism of metformin, and identification of adverse drug reactions and interactions. These videos, hosted within the app’s “Education” feature, were mandatory for participants in the intervention group to complete over the 24-week study period. Moreover, pharmacists provided real-time, patient-centered consultation through integrated chat and call features in the application. This allowed participants to receive timely, evidence-based guidance on medication usage, side effects, lifestyle adjustments, and other treatment-related concerns. The continuous involvement of pharmacists ensured personalized support, contributing to improved patient engagement and glycemic outcomes in the intervention group. HbA1c reduction trend analysis As shown in Fig. 3, the intervention group experienced a consistent and progressive decline in HbA1c from baseline through week 24. In contrast, the control group exhibited fluctuating and relatively stable values over the same period. The trajectory of HbA1c reduction further supports the clinical benefit of the mobile health application. Table 1. Baseline demographic characteristics of participants. Characteristic N (%) Age (years) 30–40 4 (4%) 41–50 12 (12%) 51–60 34 (34%) 61–70 32 (32%) 71–80 18 (18%) Gender Male 22 (22%) Female 78 (78%) Educational Level Elementary School 8 (8%) Junior High School 48 (48%) Senior High School 36 (36%) Bachelor’s Degree 6 (6%) Postgraduate Degree 2 (2%) Occupation Merchant 8 (8%) Housewife 62 (62%) Retired 30 (30%) Table 2. HbA1c levels (%) over time in control and intervention groups. Group Baseline (T0) Week 12 Week 24 p-value Control 8.99 ± SD 9.04 ± SD 8.96 ± SD 0.021 Intervention 9.31 ± SD 8.76 ± SD 8.13 ± SD 0.001 Figure 2. Comparison of week 24 HbA1c between control and intervention groups. Table 3. SEQ interpretation scale. Score Interpretation 7 Very Easy 6Easy 5Somewhat easy 4 Neutral 3 Fairly difficult 2 Difficult 1 Very difficult Table 4. SUS interpretation scale. Respondents’ Average Total Score Letter Grade Adjective Rating >80.3 A Excellent 68-80.3 B Acceptable 68 C Ok 51-67 D Poor <51 F Awful Rindarwati A et al.: Mobile health intervention to reduce HbA1c in type 2 diabetes6 Discussion To the best of our knowledge, this is the first quasi-experimental study in Indonesia to evaluate the effectiveness of a mobile health (mHealth) application that integrates pharmacist consultation, multimedia education, and personalized blood glucose monitoring reminders in the management of type 2 diabetes mellitus (T2DM) in a primary care setting. Over 24 weeks, participants in the intervention group experienced a substantial and statistically significant reduction in HbA1c from 9.31% to 8.13%. In contrast, the control group, which continued receiving routine care, showed negligible change (from 8.99% to 8.96%). These findings align with existing clinical evidence suggesting that a reduction of ≥1% in HbA1c is associated with a 21% reduction in diabetes-related mortality, a 14% reduction in myocardial infarction, and a 37% reduction in microvascular complications (Bukhsh et al. 2018). These figures, derived from UKPDS and ADA guidelines, reinforce the clinical importance of our observed outcomes and support the role of digital interventions in achieving meaningful therapeutic benefits in glycemic control (Eikenhorst et al. 2017). The pattern of HbA1c decline was both progressive and sustained. Improvements were observed as early as week 12 and maintained through week 24, suggesting long-term behavioral change and adherence to therapy rather than short-lived novelty effects (Berget et al. 2020). In contrast, the control group exhibited slight variability with no meaningful trend, indicative of the inherent limitations of conventional care models (Arnold et al. 2023). These typically offer education in static, infrequent formats, often lacking interactivity or patient-centered follow-up (Alba and Tunsi 2016). The digital intervention, by contrast, provided daily cues, weekly content refreshes, and individualized support (Takenouchi et al. 2016). This type of structured and persistent reinforcement is critical for chronic disease management, as emphasized by Marden et al. (2017), who argue that sustainable health outcomes depend on systems that reinforce behavior in real time and across diverse contexts. The superior outcomes in the intervention group can be attributed to the multifaceted behavioral mechanisms embedded within the app (Azelton et al. 2021). Functioning as a “digital health coach,” the platform incorporated evidence-based features such as medication reminders, glucose monitoring prompts, pharmacist consultation through chat or video calls, and adaptive educational modules (Azelton et al. 2021). These components align with the Information–Motivation–Behavioral Skills (IMB) model, which posits that sustainable health behavior change occurs when individuals are adequately informed, internally motivated, and provided with the behavioral skills needed to act (Camacho et al. 2023; Coventry et al. 2019). For instance, reminders and prompts acted as environmental cues, reducing reliance on memory and fostering habit formation, while motivational content increased perceived benefits and self-efficacy (Avalos et al. 2024). Self-regulation theory further supports the idea that frequent feedback–especially when personalized–closes the gap between intention and behavior, enabling patients to sustain complex regimens such as blood glucose monitoring and dietary adjustment (Castro et al. 2022). In line with Social Cognitive Theory, the intervention’s real-time bidirectional feedback mechanisms, particularly pharmacist interaction, likely enhanced patients’ self-efficacy and capacity for autonomous disease management (Kooij et al. 2017). Several recent meta-analyses have confirmed that digital interventions incorporating real-time provider interaction are more effective in reducing HbA1c compared to static educational tools (Bennett et al. 2017). Michie et al. (2017), for instance, reported that mHealth apps with interactive features achieved up to 0.8% greater HbA1c reduction compared to control conditions. This suggests that immediacy and personalization in feedback not only maintain patient engagement but also empower users to adjust behavior in a timely and context-sensitive manner (Knight et al. 2016). Importantly, our results demonstrated not only a reduction in average HbA1c but also improvements in glycemic stability (Garcia et al. 2017). As illustrated in Fig. 2, the intervention group displayed a lower median HbA1c, narrower interquartile range, and fewer outliers at week 24, indicating reduced variability in glucose control (Seok et al. 2015). This is clinically important because glycemic variability, independent of mean HbA1c, is linked to increased oxidative stress, inflammation, and vascular complications (Liu et al. 2020). Huang et al. (2024) demonstrated that fluctuations in glucose levels contribute more to endothelial dysfunction than sustained hyperglycemia alone. The enhanced glycemic stability observed in our study may reflect the timely, dynamic nature of the app’s intervention, allowing patients to anticipate and mitigate disruptions in their diabetes management (Okumura et al. 2016). The feasibility and usability of the intervention were further supported by its successful implementation across a demographically diverse sample (Chew et al. 2025). The majority of participants were aged 51–70 years, and many had only secondary-level education, yet they engaged meaningfully with the app (Rodríguez et al. 2019). This is notable because older adults and individuals with limited educational backgrounds often exhibit lower digital Figure 3. Comparison of the reduction in HbA1c levels in the control and intervention groups. 0 5 10 15 20 25 30 35 40 45 Baseline week 12 week 24 HbA1C Treatment control group intervention group Pharmacia 72: 1–9 7 literacy, posing a barrier to technology adoption (Chew et al. 2025). Our intervention overcame this through intuitive interface design, caregiver integration, and the use of familiar communication tools like WhatsApp (Rodríguez et al. 2019; Kim et al. 2021). These design elements align with the Technology Acceptance Model (TAM), which emphasizes that perceived ease of use and perceived usefulness are key predictors of technology uptake (Tsai et al. 2017). The app’s success across varied user groups suggests strong potential for scalability within Indonesia’s public health infrastructure and other low-resource settings (Chou et al. 2024). The cumulative impact of reduced HbA1c, improved glycemic consistency, high user engagement, and wide demographic applicability underscores the intervention’s potential as a transformative tool for T2DM self-management (Bonet et al. 2023). The study was conducted in a real-world primary care setting involving 10 public clinics, with full retention across the 24 weeks, enhancing the ecological validity of our findings (Bentley et al. 2016). The app itself was developed using user-centered design principles and underwent pre-deployment usability testing, ensuring cultural and technological fit (Bonet et al. 2023). Additionally, HbA1c, used as our primary outcome, represents a validated and objective measure of long-term glycemic control, enhancing the scientific rigor of our evaluation (Terauchi et al. 2017). Despite these strengths, this study has several limitations. The quasi-experimental design without randomization introduces the possibility of selection bias, although baseline comparability between groups was maintained. HbA1c was the sole clinical endpoint; we did not assess complementary health indicators such as BMI, blood pressure, or lipid profiles, which limits the scope of interpretation. The 24-week duration, while sufficient to observe intermediate outcomes, does not allow for an assessment of the long-term sustainability of the intervention’s effects. Additionally, external confounders such as concurrent use of other digital health tools or informal support from family members were not controlled for and may have influenced results. Conclusion This quasi-experimental study provides strong preliminary evidence that a culturally tailored mobile health application, integrated with pharmacist support, behavioral reinforcement, and multimedia education, can significantly improve glycemic control and support sustained self-management in patients with type 2 diabetes mellitus in Indonesian primary care settings. The significant reductions in HbA1c, enhanced glycemic stability, and high engagement across diverse user groups underscore the intervention’s feasibility, usability, and potential for broader implementation. Future randomized controlled trials are needed to validate long-term effectiveness, cost-efficiency, and scalability. Acknowledgments The authors would like to express their sincere gratitude to the Center for Higher Education Funding and Assessment (PPAT), Ministry of Higher Education, 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 research. Their contributions were instrumental in facilitating the successful completion of this study. The authors also extend their appreciation to all individuals and institutions who provided insights, technical assistance, and encouragement throughout the research process. Additional information Conflict of interest The authors have declared that no competing interests exist. Ethical statements The authors declared that no clinical trials were used in the present study. The authors declared that experiments on humans or human tissues were performed for the present study. Informed consent from the humans, donors or donors’ representatives: The study protocol received approval from the Faculty of Medicine Ethics Committee at Padjadjaran University, with ethical clearance number 635/UN6/KEP/EC/2023, dated May 19, 2023. All participants provided written informed consent after being informed about the objectives, procedures, risks, and benefits of the study. Participation was voluntary, and participants retained the right to withdraw from the study at any time without penalty. The authors declared that no experiments on animals were performed for the present study. The authors declared that no commercially available immortalised human and animal cell lines were used in the present study. Use of AI No use of AI was reported. Funding This research 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). Author contributions Conceptualization, AYR, HP, LA; methodology, AYR, HP, LA; investigation, AYR, HP, LA; data curation, AYR, LA; writing – original draft preparation, AYR; writing – review and editing, AYR, HP, LA; supervision, HP, LA; funding acquisition, AYR. All authors have read and agreed to the published version of the manuscript. 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