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Factors affecting medication adherence among older adults using tele-pharmacy services: a scoping review

Archives of Public Health volume 80, Article number: 199 (2022) Cite this article

Abstract

Background

Medication adherence among older adults (aged 60 and above), particularly those with chronic conditions who take several medications, is critical, and tele-pharmacy services are a way to improve medication adherence. This study sought to determine the factors influencing medication adherence (MA) in older adults using tele-pharmacy services.

Method

The Joana Briggs Institute scoping review methodology was implemented. Searches were conducted in databases PubMed, Scopus, ProQuest, Web of Science, and Embase from 2000 to the present day, to identify both qualitative and quantitative studies focusing on the use of tele-pharmacy by older people. Factors impacting MA were extracted and analyzed into themes using a qualitative approach. A concept map was also designed summarising these factors.

Results

Of 7495 articles obtained in the initial search, 52 articles met the inclusion criteria. The analysis resulted in 5 themes and 21 sub-themes representing factors that impacted MA with tele-pharmacy. These themes are divided broadly into technology and user related factors. Technology factors included design of the tele-pharmacy intervention, commercial aspects, and adherence measurement method. User factors included user-health constraints, behaviors and perceptions.

Conclusion

Industry, policymakers, and stakeholders should consider using tele-pharmacy services for improving medication adherence among older adults; however, ensuring interventions facilitate communication between patients and health care teams, and are accompanied by user training and support, is essential for technology uptake and effectiveness.

Peer Review reports

Background

Nonadherence to medications, particularly in older people, is associated with an increased risk of hospitalization and death [1] and a significant financial burden on the health care system [2]. Moreover, poor medication adherence (MA) is one of the strongest predictors of all-cause mortality in patients with chronic diseases such as heart failure and diabetes [3, 4]. Nonadherence to medications among older people is not uncommon, with a study of medication adherence among patients aged > 60 years revealing that less than 50% of people were adherent to medications [5]. The drivers for nonadherence are multi-factorial, with the World Health Organization (WHO) acknowledging that it is not only the patients themselves that contribute to poor adherence but that health service providers also play a role in improving MA [6].

The shortage of health professionals and the closure of pharmacies in remote areas is a growing problem worldwide that health systems must address to improve medication adherence among patients [7]. These issues are challenging in older patients (those over 60 years) due to their limited mobility and time and distance constraints, which may result in a reluctance to consume their medications at the appropriate time [8].Older adults are more likely to suffer from chronic diseases and take multiple medications daily (polypharmacy) than younger patients, necessitating regular and quick access to medications [9]. Also, the high prevalence of cognitive impairments among older people makes MA more challenging for this population [10]. The use of new technology, such as tele-pharmacy, could be a way to address some of these access barriers and improve MA.

The term tele-pharmacy describes the use of information and communication technology to deliver the components of pharmacy practice, including support to clinical services, remote education, patient counselling and monitoring, dispensing prescriptions and reconciliation of drug therapies. Tele-pharmacy is not a new initiative. Following the closure of several rural pharmacies, the first practical implementation of technology facilities in pharmacy service began in the early 2000s in North Dakota (USA). In recent years, the uptake of tele-pharmacy has increased exponentially, with strong support from relevant associations like the American Association of Health-System Pharmacists (ASHP) [2]. Despite the growing interest and uptake in tele-pharmacy research in recent years, no studies have comprehensively studied the facilitators and barriers of using tele-pharmacy services in older adults and the technology's impact on MA. The current scoping review investigated the key factors that affect the MA in older adults using tele-pharmacy interventions by analyzing papers reporting tele-pharmacy experiences, particularly in older patients. In addition, the benefits and challenges of tele-pharmacy for older people were examined to provide policymakers with enough information to make critical implementation decisions regarding the use of tele-pharmacy in this population.

Methods

The present scoping review was conducted based on the JBI manual of evidence synthesis in 9 steps, as shown in Table 1 [11].

Table 1 Scoping review methodological steps
Full size table

The Participants, Concept and Context (PCC) were defined as follows: the population was adults older than 60 years old. The concept included medication adherence in older adults, and the context included all tele-pharmacy services. All full-text articles and conference papers reporting a qualitative or quantitative study of tele-pharmacy services in older people were included. Review papers were excluded. The search strategy was designed around the three keywords: older people, medication adherence, and tele-pharmacy (Table 3, Additional file 1). Non-English papers were excluded on the title and abstract review. We included articles from 1st January 2000 to 10th August 2021 to capture any studies of the initial implementation of tele-pharmacy in the US.

The systematic search and screening process results are shown step by step in Fig. 1 [12]. The search strategy was applied to five databases, including PUBMED, SCOPUS, ISI Web of Science, PROQUEST, and EMBASE. The first ten pages of Google Scholar were also hand searched for any grey literature. After removing duplicates, FE and AG independently reviewed all titles and abstracts, and the full-text of relevant articles was retrieved. Another researcher (PB) checked the articles based on the inclusion criteria and the study questions to ensure the included articles were eligible. The result of the systematic search has been shown step by step in Fig. 1. using PRISMA [12]. As described in step 6 of Table 1, the data were extracted and subsequently charted descriptively (Table 4, Additional file 2).

Fig. 1
figure 1

PRISMA flow chart of study selection

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In several steps, extracted data were analyzed using a qualitative thematic analysis via the Thomas method. First, the extracted data were reviewed multiple times. Next, data units that best fit the study focus were highlighted and designated as initial codes that were evaluated and integrated to achieve the final codes. Subsequently, coding was done again to categorize the sub-themes per the study's purpose; this process helped identify key themes from sub-sub-themes, which were displayed in a table [13]. MAXQDA software (VERBI, MAXQDA 2020. Berlin) was used to analyze the data.

A thematic network was created applying VISIO plan software (Microsoft Corporation, 2018. Microsoft Visio), and the final map was reviewed by three experts in public health and pharmacy. The results were presented and discussed via the virtual panel to ensure the comprehensiveness and robustness of the results and accuracy of the final map. Furthermore, a 10th step was added, as recommended by Ghanbarzadegan et al. [14], to illustrate the included publications over time. In this step, the location and intervention of focus for the publications was mapped over time to determine how the tele-pharmacy interventions in different countries changed over time.

Results

Initially, searches returned a total of 7495 articles for the title and abstract screening (Table 3, Additional file 1). Then, 1897 duplicate articles and 115 review articles were excluded and 5484 studies remained for title screening. 521 studies were retrieved after removing the irrelevant titles and proceeded to the abstract screening phase. During abstract screening, 131 studies were selected as eligible studies for full text screening. 50 studies met the criteria of the scoping review and were included in our study, and two additional studies were identified through grey literature, resulting in a total of 52 studies for data extraction (Fig. 1). The extracted data are presented in Table 4, Additional file 2. Thematic analysis of the included studies led to the extraction of 5 main themes, 21 sub-themes and eighteen sub-sub-themes (Table 2). All references are presented in Table 5 in Additional file 3, and the main themes are described below.

Table 2 Factors impacting medication adherence in older adults using tele-pharmacy services
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Design of the tele-pharmacy intervention

One critical factor that impacted MA in tele-pharmacy was the interaction between patient and health care professionals. Both patients and health care professionals, such as physicians, pharmacists, and nurses, were actively involved in the tele-pharmacy intervention process in nearly half of the studies that resulted in improved MA, highlighting the importance of patient-health care professional interaction in a tele-pharmacy intervention’s success [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38]. Patients' desire to participate in an adherence intervention was also important. Various methods of remote communication were identified, including electronic medical device [18, 25, 39], mobile application [40,41,42], web application [27, 43], text message [23, 44, 45], postal mail [46], phone call [26, 47, 48], electronic-prescription (e-prescription) [34, 49] and remote dispensing [50]. Electronic medical devices used in studies included interventions like in-home electronic medication dispensing systems and electronic dosage forms like blisters, eye drops and sprays to improve the MA among older adults. In addition to electronic devices, e-prescription or remote dispensing by the pharmacist were investigated in some trials, but only one study reported an improvement in MA [34, 49, 50]. Patients tended to use mobile applications rather than other tools like the message, postal mail, or telephone calls. Furthermore, access to user manuals to guide patients using the tele-pharmacy services improved MA [29, 30, 41, 51]. Other design factors that impacted MA included device size, usability, and medicine storage capacity, which improved the utilization of the tools and improved MA in older patients [29, 34, 37, 41, 48, 51].

Commercial and market aspects

The commercial and economic aspects played essential roles when investigating the utilization of a tele-pharmacy intervention. Patients mentioned the affordability of the intervention as a factor that facilitated use [42, 48]. Moreover, government subsidies improved patients' willingness to use the interventions, leading to better medication adherence [37]. However, there were some concerns about patients' health data privacy and security [37, 48].

Adherence measurement

A tele-pharmacy intervention's success was impacted by the MA measurement method. For instance, in a study investigating the MA of older people with heart failure using a telemonitoring medication system (MedSentry), the device data indicated high MA rates (95-99%) in the intervention group versus the usual care group. However, the self-report data showed no significant improvement in the telemonitoring group compared to the control group [21]. Similarly, another study showed considerable improvement in MA via Medication Event Monitoring System (MEMS) in older patients with coronary heart disease when the device data was used, but not self-reported data [52]. Although the Morisky scale and the proportion of days covered were the most common scales to measure the MA [28, 44, 50, 53], other scales were considered an indicator of the MA in several studies such as medication delay [45], refill delay [24], timing adherence [18], non-adherence [47] and non-compliance level [32].

User health constraints

The use of tele-pharmacy to improve MA in older people was impacted by a wide range of individual health variables. One of the most important factor was found to be the disease itself. For example, older patients with chronic diseases such as diabetes, hypertension, hyperlipidemia, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD) and heart diseases were more committed to taking their medications, as were those with cancer [27, 29, 42, 43, 52] . In contrast, no significant MA improvement was observed in patients with HIV and pain [54, 55]. As a large proportion of older adults experienced the aforementioned chronic diseases simultaneously, polypharmacy was found to be a general characteristic among older people, which led to increased utilization of the MA tools and, as a result, improved MA outcomes. Surprisingly, in two studies on patients with several chronic diseases, older adults showed a higher MA rate than younger adults [42, 45]. While in another study on patients with hypertension, no significant MA change was reported in the older age group [56].

Notably, disability was examined in a few studies, limiting the utilization of technology tools in geriatrics tracking adherence [37, 39]. A few studies also examined the role of family members or caregivers in interventions to assist older people in using technology. However, there was no substantial impact on MA; the patients' awareness and knowledge about their medications improved [57]. Furthermore, training sessions conducted by caregivers resulted in higher intention to use a mobile app reminder in older patients [30]. Finally, patient remoteness was also identified as a factor impacting MA. One study showed that providing health care via text, video, or phone call to older people in remote rural clinics can improve MA [48].

User behavior and perceptions

The last main theme identified was user behavior and perceptions, which included four sub-themes: baseline MA, patient behavior, preference, and long-term use of interventions. Notably, participants who initially maintained a high level of adherence before the tele-pharmacy intervention reported no significant change in their adherence following the use of the tele-pharmacy services [51, 57, 58]. For example, in a study evaluating the effectiveness of mobile apps and electronic pillboxes on MA in older people, there was no significant change in MA, attributed to the very high levels of MA initially [59].

Several studies have demonstrated the importance of individual behavior in using the technology tools to improve MA [20, 23, 51]. For instance, Dugas et al. used the regulatory mode theory to test the role of locomotion and assessment to describe the effectiveness of mobile health (mHealth) tools among veterans with diabetes. They created DiaSocial, a mHealth tool to motivate veterans to take their medicine. In a 13-week pilot trial, low locomotion was linked to no change in MA, whereas high locomotion was linked to an improvement in MA rate [20]. A qualitative study identified four behavioral elements that enhance MA via tele-pharmacy service: commitment, awareness, feelings of involvement, and power to take medicine at the correct dose and time [23]. However, another study found no significant increase in MA despite patients' satisfaction with their increased awareness and commitment to taking medication [51].

In addition to individual behavior, patient preference was another sub-theme that emerged from included papers. In one study, patients were pleased with the smart pillbox bottle's usability. Still, they were dissatisfied with its size and functionality because they already used weekly pill organizers to manage their medications [34]. In another study, participants preferred mobile apps to text message reminders because of their greater interactivity [30], and in another study, patients preferred mobile apps over the electronic pill box [59].

The other important issue impacting MA was the long-term effect of the tele-pharmacy intervention. In several studies, MA improved in the short term; however, this improvement was not persistent over time, and people stopped using the tele-pharmacy intervention over time. Studies associated this decline with poor long-term commitment to the interventions [17, 31, 34]. For instance, following up on the older HIV patients using mobile app reminders for 48 weeks showed no change in MA [54], and in another study, cancer patients showed only short-term improvements in adherence to medications when using a mobile app [43]. In contrast, one study reported a higher MA rate with a six-month rather than the three-month intervention using a multimodal strategy, including an electronic pillbox and telephone counselling. Surprisingly, a study reported an adherence rate of 98.35% ± 2.15% over the 26 weeks follow-up [25].

Concept map of factors impacting medication adherence with tele-pharmacy

The idea map depicted in Fig. 2 was built based on the major themes, the documented rational correlations in the included studies, and consensus among the study team. It conceptualizes the factors influencing MA in older adults using tele-pharmacy interventions. . The concept map is divided into two distinct sections: the technology and the user. The technology section consists of design, commercial and market considerations, and adherence assessment, whereas the user section consists of user-health restrictions and user behaviors and perceptions. The five major themes are connected with using tele-pharmacy tools to improve medication adherence in older people. Thus, the effect of tele-pharmacy services on older patients' medication adherence is influenced by their underlying sub-themes.

Fig. 2
figure 2

Concept map of factors affecting older adults’ medication adherence using telepharmacy interventions

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Trend analysis

Figure 3 depicts the time trend of research on tele-pharmacy intervention approaches. Each color represents a distinct type of intervention in these streamgraphs, including in home electronic medical device, postal mail, mobile app, SMS, phone call, e-prescription, and remote dispensing by a pharmacist. As illustrated in Fig. 3, studies over time tended to focus on mobile application technology compared to other methods. Additionally, the majority of papers were published in 2019. As shown in Fig. 4, more than half of the studies included in our review were done in the United States. Additionally, American research has focused on various intervention strategies, whereas work in other countries has focused on a more limited number of intervention methods (Fig. 4).

Fig. 3
figure 3

Stream graph of trend analysis (Intervention by publication year)

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Fig. 4
figure 4

Bar chart of trend analysis (Publications count by location)

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Discussion

This review identified several factors related to the technology and user that affects MA in older people using tele-pharmacy services. Overall, older patients, particularly those with chronic diseases, demonstrated good MA when using tele-pharmacy services. However, this effect might not be sustained. In several studies, the improvement in MA observed when using tele-pharmacy interventions was short-lived, and after a few months of using the service, MA declined. The one exception to this appeared to be medication dispensing systems (MDS), with a study showing that high MA was maintained for up to 26 weeks [25]. MDS include various features that may contribute to this sustained impact on MA, including reminders that alert users about medications and changes to symptoms and a feature that links patients with pharmacists and health care teams to help resolve any medication-related issues. However, studies also showed that the success of MDS was impacted by participants understanding the device functionalities, which highlights the importance of user support, training, and user manuals [29, 30, 41, 51].

An exciting result from this review was the key role that health professionals play in MA among older people, regardless of the tele-pharmacy technology used (text, phone call, app, device). This is likely because health professionals are a trusted source for patients and are often the first point of call for older people with a medication-related question or concern [60]. This suggests that tele-pharmacy interventions are likely more effective in supporting MA if they facilitate communication between patients and health professionals.

Our trend analysis showed that older patients are becoming more comfortable with using mobile applications over time. In addition, the majority of the included studies were completed between 2019 and 2021, indicating a recent surge in interest in this topic. Although there is a common perception that older people will not use technology for health, our review showed that this is rapidly changing, and in some cases, MA was higher in older groups (over 65 years) than in younger groups (less than 65 years) when using tele-pharmacy interventions [42, 45].

This review showed that many smartphone apps, particularly those that include interactive features, have been effective in improving MA for older patients. Although patients found text message reminders helpful in taking their medications, they preferred mobile applications because they were interactive and provided individualized health monitoring and personalized medication information [29]. Although mobile apps are gaining momentum, there remain issues that need to be adequately addressed to ensure apps are easy to use and useful for older people. For example, in one study, patients were unfamiliar with the generic name of medications in the application, and the design needed to be modified to auto-link generic and brand names to solve this problem [41]. Studies also showed that in the absence of strong support services and training sessions, older people could become confused when using apps [61].

In contrast to mobile apps, we found fewer papers focusing on other types of tele-pharmacy interventions. Although in recent years, e-prescriptions and remote dispensing by pharmacists are increasingly being used to improve medication adherence, only a small number of studies investigated the utilization of these tools in older people [34, 49, 50]. The bulk of studies in our review were also conducted in the USA, suggesting that other countries may be slower in adopting tele-pharmacy services. Our positive findings indicate high MA can be achieved with tele-pharmacy, and we recommend other nations, particularly those with a higher population of older people, consider these new technologies. Overall, tele-pharmacy services can significantly motivate patients to take their medications. Only a few trials reported poor MA after implementation of the intervention, and that is likely to be because of high baseline adherence prior to intervention [57,58,59], low numbers of participants [30, 58], and self-reported adherence measures, which are unreliable for determining the effectiveness of an intervention [21, 62].

There was no consistent approach used for measuring MA in studies, which made comparison difficult across the studies. Investigating medication adherence was not the primary purpose of some studies and MA was reported only as a secondary outcome. However, we included those studies as their outcomes aligned with the aim of the scoping review.

Conclusion

In conclusion, tele-pharmacy can improve MA in older people, however, several factors impact the achievement of this goal. These factors relate to the intervention, including design, commercial aspects, and adherence measurement, and to the user, including health constraints behaviors, and perceptions. Communication with the health care team and sufficient user training appear necessary for tele-pharmacy interventions to enhance MA. In addition, intervention designers should focus on the design, size, storage capacity, and security of technologies. Government and policymakers should provide subsidies for such interventions to encourage uptake among older users. There is no one-size-fits-all strategy, and each patient's physical and mental characteristics should be considered when recommending the best treatment option, including tele-pharmacy interventions.

Availability of data and materials

All data generated or analyzed during this study are included in this published article [and its supplementary information files]. Additional file 2 includes all included studies and extractions.

Abbreviations

CKD:

Chronic Kidney Disease

COPD:

Chronic Obstructive Pulmonary Disease

E-prescription:

Electronic Prescription

MA:

Medication Adherence

MDS:

Medication Dispensing System

MEMS:

Medication Event Monitoring System

MHealth:

Mobile Health

WHO:

World Health Organisation

References

  1. Walsh CA, Cahir C, Tecklenborg S, Byrne C, Culbertson MA, Bennett KE. The association between medication non-adherence and adverse health outcomes in ageing populations: A systematic review and meta-analysis. Br J Clin Pharmacol. 2019;85(11):2464–78.

    Article  Google Scholar 

  2. Pathak S, Haynes M, Qato DM, Urick BY. Peer Reviewed: Telepharmacy and Quality of Medication Use in Rural Areas, 2013–2019. Prev Chronic Dis. 2020;17:200012.

  3. Pallangyo P, Millinga J, Bhalia S, Mkojera Z, Misidai N, Swai HJ, et al. Medication adherence and survival among hospitalized heart failure patients in a tertiary hospital in Tanzania: a prospective cohort study. BMC Res Notes. 2020;13(1):1–8.

    Article  Google Scholar 

  4. Kim Y-Y, Lee J-S, Kang H-J, Park SM. Effect of medication adherence on long-term all-cause-mortality and hospitalization for cardiovascular disease in 65,067 newly diagnosed type 2 diabetes patients. Sci Rep. 2018;8(1):1–7.

    Google Scholar 

  5. Shruthi R, Jyothi R, Pundarikaksha HP, Nagesh GN, Tushar TJ. A Study of Medication Compliance in Geriatric Patients with Chronic Illnesses at a Tertiary Care Hospital. J Clin Diagn Res. 2016;10(12):Fc40-fc3.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Sabaté E, Sabaté E. Adherence to long-term therapies: evidence for action. Geneva: World Health Organization; 2003.

  7. Angaran DM. Telemedicine and telepharmacy: current status and future implications. Am J Health-Sys Pharm. 1999;56(14):1405–26.

    Article  CAS  Google Scholar 

  8. Christopher C, KC B, Shrestha S, Blebil AQ, Alex D, Mohamed Ibrahim MI, et al. Medication use problems among older adults at a primary care: A narrative of literature review. Aging Med.n/a(n/a).

  9. Bastani P, Bikineh P, Mehralian G, Sadeghkhani O, Rezaee R, Kavosi Z, et al. Medication adherence among the elderly: applying grounded theory approach in a developing country. J Pharm Policy Pract. 2021;14(1):55.

    Article  Google Scholar 

  10. Chudiak A, Uchmanowicz I, Mazur G. Relation between cognitive impairment and treatment adherence in elderly hypertensive patients. Clin Interv Aging. 2018;13:1409–18.

    Article  Google Scholar 

  11. Aromataris E MZ. JBI Manual for Evidence Synthesis. JBI. 2020.

  12. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

    Article  Google Scholar 

  13. Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol. 2008;8(1):1–10.

    Article  Google Scholar 

  14. Ghanbarzadegan A, Bastani P, Luzzi L, Brennan D. Inequalities in utilization and provision of dental services: a scoping review. Syst Rev. 2021;10(1):1–11.

    Article  Google Scholar 

  15. Ajit RR, Fenerty CH, Henson DB. Patterns and rate of adherence to glaucoma therapy using an electronic dosing aid. Eye. 2010;24(8):1338–43.

    Article  CAS  Google Scholar 

  16. Antonicelli R, Testarmata P, Spazzafumo L, Gagliardi C, Bilo G, Valentini M, Olivieri F, Parati G. Impact of telemonitoring at home on the management of elderly patients with congestive heart failure. J Telemed Telecare. 2008;14(6):300–5.

    Article  Google Scholar 

  17. Ammenwerth E, Woess S, Baumgartner C, Fetz B, van der Heidt A, Kastner P, Modre-Osprian R, Welte S, Poelzl G. Evaluation of an Integrated Telemonitoring Surveillance System in Patients with Coronary Heart Disease. Methods Inf Med. 2015;54(5):388–97.

    Article  CAS  Google Scholar 

  18. Boeni FH, KE Arnet I. Success of a sustained pharmaceutical care service with electronic adherence monitoring in patient with diabetes over 12 months. BMJ Case Reports. 2015;2015:bcr2014208672.

  19. Desteghe L, Vijgen J, Koopman P, Dilling-Boer D, Schurmans J, Dendale P, Heidbuchel H. Telemonitoring-based feedback improves adherence to non-Vitamin K antagonist oral anticoagulants intake in patients with atrial fibrillation. Eur Heart J. 2018;39(16):1394–403.

    Article  CAS  Google Scholar 

  20. Dugas MC, K.; Gao, G. G.; Xu, T.; Agarwal, R.; Kruglanski, A. W.; Steinle, N. Individual differences in regulatory mode moderate the effectiveness of a pilot mHealth trial for diabetes management among older veterans. PLos One. 2018;13(3).

  21. Hale TMJ, K.; Kandola, M. S.; Saldana, F.; Kvedar, J. C. A Remote Medication Monitoring System for Chronic Heart Failure Patients to Reduce Readmissions: A Two-Arm Randomized Pilot Study. J Med Int Res. 2016;18(5).

  22. Kim M. Effects of Customized Long-Message Service and Phone-Based Health-Coaching on Elderly People with Hypertension. Iran J Public Health. 2019;48(4):655–63.

    PubMed  PubMed Central  Google Scholar 

  23. Kassavou AAC, C. E.; Chauhan, J.; Brimocombe, J. D.; Bhattacharya, D.; Naughton, F.; Hardeman, W.; Mascolo, C.; Sutton, S. Assessing the acceptability of a text messaging service and smartphone app to support patient adherence to medications prescribed for high blood pressure: A pilot study. Pilot and Feasibility Studies. 2020;6(1).

  24. Kooy MJvW, B. L. G.; Heerdink, E. R.; de Boer, A.; Bouvy, M. L. Does the use of an electronic reminder device with or without counseling improve adherence to lipid-lowering treatment? The results of a randomized controlled trial. Frontiers Pharmacol. 2013;4.

  25. Mubashir Aslam AA, Ahmad; Chiu, Venus; Kembel, Lorena. Medication adherence support of an in-home electronic medication dispensing system for individuals living with chronic conditions: a pilot randomized controlled trial. BMC Geriatrics. 2021;21:1-16.

  26. Ownby RL, Hertzog C, Czaja SJ. Tailored Information and Automated Reminding to Improve Medication Adherence in Spanish- and English-Speaking Elders Treated for Memory Impairment. Clin Gerontol. 2012;35(3):221–38.

    Article  Google Scholar 

  27. Panuccio VM, Salvatore; Villa, Antonino; Versace, Maria Carmela; Mercuri, Sergio; Vigni, Maurizio Li; Tripepi, Giovanni; Torino, Claudia. Smit-Ckd: A Mobile App To Improve Adherence To Therapy In Ckd Patients. A Pilot Study. Piscataway: The Institute of Electrical and Electronics Engineers, Inc. (IEEE); 2020. p. 1492-7.

  28. Park H, Adeyemi A, Wang W, Roane TE. Impact of a telephonic outreach program on medication adherence in Medicare Advantage Prescription Drug (MAPD) plan beneficiaries. J Am Pharm Assoc. 2017;57(1):62-6.e2.

    Article  Google Scholar 

  29. Park LGN, Fion; K. Shim Janet; Elnaggar, Abdelaziz; Villero, Ofelia. Perceptions and experiences of using mobile technology for medication adherence among older adults with coronary heart disease: A qualitative study. Digital Health. 2020;6.

  30. Park DY. A Theoretically Informed mHealth Intervention to Improve Medication Adherence by Adults with Chronic Conditions: Technology Acceptance Model-Based Smartphone Medication Reminder App Training Session [Ph.D.]. Ann Arbor: Indiana University - Purdue University Indianapolis; 2019.

  31. Qvist I, Lindholt JS, Søgaard R, Lorentzen V, Hallas J, Frost L. Randomised trial of telephone counselling to improve participants’ adherence to prescribed drugs in a vascular screening trial. Basic Clin Pharmacol Toxicol. 2020;127(6):477–87.

  32. Schmidt S, Sheikzadeh S, Beil B, Patten M, Stettin J. Acceptance of telemonitoring to enhance medication compliance in patients with chronic heart failure. Telemed E-Health. 2008;14(5):426–33.

    Article  Google Scholar 

  33. Sheilini M, Hande HM, Prabhu MM, Pai MS, George A. Impact of multimodal interventions on medication nonadherence among elderly hypertensives: a randomized controlled study. Patient Preference Adherence. 2019;13:549–59.

    Article  Google Scholar 

  34. Toscos T, Drouin M, Pater JA, Flanagan M, Wagner S, Coupe A, Ahmed R, Mirro MJ. Medication adherence for atrial fibrillation patients: Triangulating measures from a smart pill bottle, e-prescribing software, and patient communication through the electronic health record. JAMIA Open. 2020;3(2):233–42.

    Article  Google Scholar 

  35. JYFWu, Leung WY, Chang S, Lee B, Zee B, Tong PCY, Chan JCN. Effectiveness of telephone counselling by a pharmacist in reducing mortality in patients receiving polypharmacy: Randomised controlled trial. Br Med J. 2006;333(7567):522–5.

    Article  Google Scholar 

  36. Zárate-Bravo EG-V, Juan-Pablo; Torres-Cervantes, Engracia; Ponce, Gisela; Andrade, Ángel G.; Valenzuela-Beltrán, Maribel; Rodríguez, Marcela D. Supporting the Medication Adherence of Older Mexican Adults Through External Cues Provided With Ambient Displays: Feasibility Randomized Controlled Trial. JMIR mHealth and uHealth. 2020;8(3).

  37. Faisal S, Ivo J, McDougall A, Patel T. Stakeholder Feedback of Electronic Medication Adherence Products: Qualitative Analysis. J Med Internet Res. 2020;22(12):e18074.

    Article  Google Scholar 

  38. Patton DEF, J. J.; Clark, E.; Smith, F.; Cadogan, C. A.; Ryan, C.; Hughes, C. M. A pilot study of the S-MAP (Solutions for Medications Adherence Problems) intervention for older adults prescribed polypharmacy in primary care: Study protocol. Pilot and Feasibility Studies. 2019;5(1).

  39. Ahmad A, Chiu V, Arain MA. Users’ Perceptions of an in-Home Electronic Medication Dispensing System: A Qualitative Study. Med Devices-evidence Res. 2020;13:31–9.

  40. Brath H, Morak J, Kastenbauer T, Modre-Osprian R, Strohner-Kastenbauer H, Schwarz M, Kort W, Schreier G. Mobile health (mHealth) based medication adherence measurement - a pilot trial using electronic blisters in diabetes patients. Br J Clin Pharmacol. 2013;76:47–55.

    Article  Google Scholar 

  41. Chew SL, P. S. M.; Ng, C. J. Usability and Utility of a Mobile App to Improve Medication Adherence Among Ambulatory Care Patients in Malaysia: Qualitative Study. JMIR MHEALTH AND UHEALTH. 2020;8(1).

  42. Criner GJ, Cole T, Hahn KA, Kastango K, Eudicone J, Gilbert I. The Impact of Budesonide/Formoterol pMDI Medication Reminders on Adherence in Chronic Obstructive Pulmonary Disease (COPD) Patients: Results of a Randomized, Phase 4, Clinical Study. Int J Chronic Obstruct Pulmon Dis. 2021;16:563–77.

    Article  Google Scholar 

  43. Graetz I, Mckillop CN, Stepanski E, Vidal GA, Anderson JN, Schwartzberg LS. Use of a web-based app to improve breast cancer symptom management and adherence for aromatase inhibitors: a randomized controlled feasibility trial. J Cancer Survivorship. 2018;12(4):431–40.

    Article  Google Scholar 

  44. Foreman KF, Stockl KM, Le LB, Fisk E, Shah SM, Lew HC, Solow BK, Curtis BS. Impact of a Text Messaging Pilot Program on Patient Medication Adherence. Clin Ther. 2012;34(5):1084–91.

    Article  Google Scholar 

  45. Huang CYN, P. A. A.; Clinciu, D. L.; Hsu, C. K.; Lu, J. C. R.; Yang, H. C.; Wu, C. C.; Tsai, W. C.; Chou, Y. C.; Kuo, T. B. J.; Chang, P. L.; Jian, W. S.; Li, Y. C. J. A personalized medication management platform (PMMP) to improve medication adherence: A randomized control trial. Computer Methods Programs Biomed. 2017;140:275-81.

  46. Borah BJ, Qiu Y, Shah ND, Gleason PP. Impact of provider mailings on medication adherence by Medicare Part D members. Healthcare. 2016;4(3):207–16.

    Article  Google Scholar 

  47. Elliott RA, Barber N, Clifford S, Horne R, Hartley E. The cost effectiveness of a telephone-based pharmacy advisory service to improve adherence to newly prescribed medicines. Pharm World Sci. 2008;30(1):17–23.

    Article  Google Scholar 

  48. Kobb R, Hoffman N, Lodge R, Kline S. Enhancing elder chronic care through technology and care coordination: report from a pilot. Telemed J E Health. 2003;9(2):189–95.

    Article  Google Scholar 

  49. Forestal DA, Klaiman TA, Peterson AM, Heller DA. Initial Medication Adherence in the Elderly Using PACE Claim Reversals: A Pilot Study. J Managed Care Specialty Pharm. 2016;22(9):1046–50.

    Article  Google Scholar 

  50. Guadamuz JS, McCormick CD, Choi S, Urick B, Alexander GC, Qato DM. Telepharmacy and medication adherence in urban areas. J Am Pharm Assoc. 2021;61(2):E100–13.

  51. Sterns AAS, H. L. Medication Reminding for Older Adults Using Personal Digital Assistants. Promoting Independence Older Persons Disabil. 2006;18:231–4.

    Google Scholar 

  52. Park LG, Howie-Esquivel J, Chung ML, Dracup K. A text messaging intervention to promote medication adherence for patients with coronary heart disease: a randomized controlled trial. Patient Educ Couns. 2014;94(2):261–8.

    Article  Google Scholar 

  53. Redfern JC, G.; Mulley, J.; Scaria, A.; Neubeck, L.; Hafiz, N.; Pitt, C.; Weir, K.; Forbes, J.; Parker, S.; Bampi, F.; Coenen, A.; Enright, G.; Wong, A. N.; Nguyen, T.; Harris, M.; Zwar, N.; Chow, C. K.; Rodgers, A.; Heeley, E.; Panaretto, K.; Lau, A.; Hayman, N.; Usherwood, T.; Peiris, D. A digital health intervention for cardiovascular disease management in primary care (CONNECT) randomized controlled trial. NPJ DIGITAL MEDICINE. 2020;3(1).

  54. Puig J, Echeverría P, Lluch T, Herms J, Estany C, Bonjoch A, Ornelas A, Paris D, Loste C, Sarquella M, Clotet B, Negredo E. A Specific Mobile Health Application for Older HIV-Infected Patients: Usability and Patient’s Satisfaction. Telemed E-Health. 2021;27(4):432–40.

  55. Wilkie DJ, Yao Y, Ezenwa MO, Suarez ML, Dyal BW, Gill A, Hipp T, Shea R, Miller J, Frank K, Nardi N, Murray M, Glendenning J, Perez J, Carrasco JD, Shuey D, Angulo V, McCurry T, Martin J, Butler A, Wang ZJ, Molokie RE. A Stepped-Wedge Randomized Controlled Trial: Effects of eHealth Interventions for Pain Control Among Adults With Cancer in Hospice. J Pain Symptom Manage. 2020;59(3):626–36.

    Article  Google Scholar 

  56. Varleta P, Acevedo M, Akel C, Salinas C, Navarrete C, Garcia A, Echegoyen C, Rodriguez D, Gramusset L, Leon S, Cofre P, Retamal R, Romero K. Mobile phone text messaging improves antihypertensive drug adherence in the community. J Clin Hypertens. 2017;19(12):1276–84.

    Article  CAS  Google Scholar 

  57. Lien DK, Kiki; Vijgen, Johan; Koopman, Pieter; Dilling-Boer, Dagmara; Schurmans, Joris; Dendale, Paul; Hein, Heidbuchel. The Health Buddies App as a Novel Tool to Improve Adherence and Knowledge in Atrial Fibrillation Patients: A Pilot Study. JMIR mHealth and uHealth. 2017;5(7).

  58. Desteghe LK, K.; Vijgen, J.; Koopman, P.; Dilling-Boer, D.; Schurmans, J.; Dendale, P.; Heidbuchel, H. The Health Buddies App as a Novel Tool to Improve Adherence and Knowledge in Atrial Fibrillation Patients: A Pilot Study. JMIR MHEALTH AND UHEALTH. 2017;5(7).

  59. Goldstein CM, Gathright EC, Dolansky MA, Gunstad J, Sterns A, Redle JD, Josephson R, Hughes JW. Randomized controlled feasibility trial of two telemedicine medication reminder systems for older adults with heart failure. J Telemed Telecare. 2014;20(6):293–9.

    Article  Google Scholar 

  60. Donohue JM, Huskamp HA, Wilson IB, Weissman J. Whom do older adults trust most to provide information about prescription drugs? Am J Geriatr Pharmacother. 2009;7(2):105–16.

    Article  Google Scholar 

  61. Holender A, Sutton S, De Simoni A. Opinions on the use of technology to improve tablet taking in >65-year-old patients on cardiovascular medications. J Int Med Res. 2018;46(7):2754–68.

    Article  Google Scholar 

  62. Lee J-AE, Lorraine S.; Moore, Alison A.; Juth, Vanessa; Guo, Yuqing; Gago-Masague, Sergio; Lem, Carolyn G.; Nguyen, Michelle; Khatibi, Parmis; Baje, Mark; Amin, Alpesh N. Feasibility Study of a Mobile Health Intervention for Older Adults on Oral Anticoagulation Therapy. Gerontol Geriatr Med. 2016;2.

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Acknowledgements

Proposal of the study got the approval from Shiraz University of Medical Sciences (registration code: 23790).

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Authors and Affiliations

  1. Health and Clinical Sciences Unit, The University of South Australia, Adelaide, Australia

    Fatemeh Emadi

  2. Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia

    Fatemeh Emadi

  3. Australian Research Centre for Population Oral Health, Adelaide Dental School, The University of Adelaide, Adelaide, Australia

    Arash Ghanbarzadegan

  4. Menzies Centre for Health Policy and Economics, School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

    Arash Ghanbarzadegan

  5. Health Policy Research Centre, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran

    Sulmaz Ghahramani

  6. Faculty of Health and Behavioral Sciences, School of Dentistry, University of Queensland, Brisbane, Australia

    Peivand Bastani

  7. Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

    Melissa T Baysari

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Contributions

All authors participated in all methodological steps, they have read and revised the final manuscript. FE and AG searched the relevant studies and selected and charted them. PB and SG analyzed the content. MB and FE revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sulmaz Ghahramani.

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This project has approved by Shiraz University of Medical Sciences Human Ethics Committee (IR.SUMS.REC.1400.507)

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Supplementary Information

Additional file 1: Table 3.

Search Strategy.

Additional file 2: Table 4.

Included articles extracted information.

Additional file 3: Table 5.

Determinants of elders’ medication adherence in telepharmacy services with references.

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Emadi, F., Ghanbarzadegan, A., Ghahramani, S. et al. Factors affecting medication adherence among older adults using tele-pharmacy services: a scoping review. Arch Public Health 80, 199 (2022). https://doi.org/10.1186/s13690-022-00960-w

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Keywords

Archives of Public Health

ISSN: 2049-3258