| Literature DB >> 32003748 |
Sara Chew1, Pauline Siew Mei Lai1, Chirk Jenn Ng1.
Abstract
BACKGROUND: To date, several medication adherence apps have been developed. However, the existing apps have been developed without involving relevant stakeholders and were not subjected to mobile health app guidelines. In addition, the usability and utility of these apps have not been tested with end users.Entities:
Keywords: medication adherence app; usability testing; utility testing
Mesh:
Year: 2020 PMID: 32003748 PMCID: PMC7055750 DOI: 10.2196/15146
Source DB: PubMed Journal: JMIR Mhealth Uhealth ISSN: 2291-5222 Impact factor: 4.773
Figure 1The conceptual framework for the design and development of Med Assist based on the Theory of Planned Behavior and the Nielson Usability Model.
Summary of the preferred features and utilities of Med Assist.
| Utility | Description |
| Specific medication reminder | Users will be able to set specific medication reminders with a personalized tone |
| Specific medication refill reminders | Users can set specific medication refill reminders, which prompt users to procure a prescription refill before running out of medications. |
| Complex medication regime | Ability to aid patients in managing complex medication regime. |
| Adherence scoring system | Users are able to calculate their adherence to medications. A 100% adherence to medications is displayed as five stars. |
| Multiple user support | Users can enter another individual’s list of medications in addition to their own. |
| Third party reminder or “pill buddy” | This function allows a family member to receive text message notification stating that the user has missed three consecutive medication reminders. |
| Contact clinic or pharmacy by email | Users can contact the clinic receptionist via email to postpone an appointment or the outpatient pharmacy to check on medication availability. This maximizes appointment schedules and allocates last-minute vacant slots to other patients. |
| Available in dual language |
Figure 2The start, registration, homepage, and adherence score of Med Assist (vP4).
Figure 3Flowchart of Med Assist design and development.
Demographic characteristics of participants recruited for beta testing.
| IDa | Gender | Age (years) | Ethnicity | Level of education | Number of medication(s) | Patient/carer | iPhone/android user | eHEALSb score (%) |
| P1 | Male | 66 | Chinese | Secondary | 3 | Patient/carer | iPhone | 84.4 |
| P2 | Female | 29 | Eurasian | Secondary | –c | Carer | Android | 75.0 |
| P3 | Female | 43 | Chinese | Tertiary | 4 | Patient | Android | 59.4 |
| P4 | Female | 55 | Indian | Secondary | 2 | Patient | Android | 75.0 |
| P5 | Female | 72 | Malay | Tertiary | 2 | Patient | Android | 75.0 |
| P6 | Male | 56 | Indian | Tertiary | 3 | Patient | iPhone | 90.6 |
| P7 | Male | 72 | Malay | Secondary | 4 | Patient | Android | 68.7 |
| P8 | Male | 62 | Chinese | Tertiary | 6 | Patient | Android | 75.0 |
| P9 | Female | 42 | Malay | Tertiary | 3 | Patient | Android | 46.9 |
| P10 | Female | 64 | Malay | Secondary | 2 | Patient/carer | Android | 78.0 |
| P11 | Female | 57 | Indian | Tertiary | 2 | Patient | Android | 50.0 |
| P12 | Female | 27 | Malay | Tertiary | 4 | Patient | Android | 56.0 |
| P13 | Male | 44 | Malay | Tertiary | 5 | Patient | Android | 75.0 |
aID: identification.
beHEALS: electronic health literacy scale.
cNot available.