Sage Journals: Discover world-class research

Abstract

Background:

Information technology can be used to improve the management of non-communicable diseases, such as diabetes. This study aims to evaluate the willingness of older outpatients with type 2 diabetes to use mobile phones to support medication adherence and receive text message appointment reminders and investigated the factors associated with this willingness.

Methods:

This study was a cross-sectional study conducted at the outpatient department of Dong Da General Hospital. Participants aged 60 and over managed and treated for type 2 diabetes were asked about mobile phone usage. Data were also collected on sociodemographic information, diabetes characteristics, and medical history.

Results:

In the 584 study participants recruited, the mean age was 73.2 (SD: 8.3) years. Approximately 80% patients with diabetes had medium or high treatment adherence and 52.4% had hemoglobin A1c (HbA_lc) < 7.5%. In the multilevel logistic regression analysis, the following factors were significantly associated with willingness to using phones to support medication adherence: college, university, or higher level (odds ratio [OR] = 2.35, 95% confidence interval [CI] = 1.10, 4.99), current smoking (OR = 5.40, 95% CI = 1.01, 28.94), whether they had a mobile phone and type of phone (basic phone: OR = 2.47, 95% CI = 1.42, 4.30; smartphone: OR = 17.93, 95% CI = 8.81, 36.47) . The following factors were significantly associated with willingness to receive these appointment reminders via mobile phone: whether they had a mobile phone and type of phone (basic phone: OR = 2.79, 95% CI = 1.70, 4.59; smartphone: OR = 9.61, 95% CI = 4.61, 19.99) and HbA_1c < 7.5 (OR = 0.65, 95% CI = 0.43, 0.99).

Conclusions:

Our study would suggest that there is potential value in using mobile phone to improve the management of diabetes in community living older people but this alone cannot be relied upon.

Keywords

mobile phone usage text message appointment outpatients with diabetes Vietnam

Introduction

In recent years, although infectious diseases have caused many difficulties and challenges for health systems, economies, and societies around the world, non-communicable diseases (NCDs), such as cardiovascular disease, diabetes, cancer, and chronic lung disease now account for approximately 60% of all deaths worldwide.¹ According to worldwide estimates by the International Diabetes Federation (IDF), in 2019, there were about 463 million people aged 20 to 79 years old with diabetes (9.3%).² This was estimated to increase to 578 million people (10.2%) by 2030 and 700 million (10.9%) by 2045.² Vietnam is in a period of epidemiological transition with the burden of NCDs, especially diabetes, increasing rapidly.³

Diabetes prevention and management is one of the National Health Target Programs in Vietnam. Patient adherence to diabetes treatment and engagement with providers of diabetes care is one of the key strategies of this program. Currently, adherence with best practice diabetes management is poor for a large proportion of the population in Vietnam.⁴ In addition, Vietnam has a rapidly aging population and as a result the prevalence of older people with diabetes is increasing. Strategies to improve adherence with diabetes management and comprehensive geriatric assessment in Vietnam therefore need to account for the needs of older people.⁴

Information technology can be used to improve the management of NCDs, such as diabetes.⁵-⁸ There have been several studies that have investigated the use of information technology in the management of sending text message services (SMSs) NCDs. Saleh et al⁹ investigated the value of SMS to 1000 patients with NCDs. They found that 93.9% of patients found receiving text messages useful, and easy to read and understand. This intervention resulted in a decrease in the complications caused by treatment of diabetes and hypertension. A relatively simple intervention, such as text message information, can be used to improve adherence with diabetes management.⁹

Despite rapid advances in health care technology, older people often face challenges and barriers in using this technology due to physical impairment (poor vision or poor motor control), cognitive difficulties, lack of technological skills, and lack of perceived ability and time.¹⁰ It is therefore important to know more about the perspectives of older people toward adopting new technology-based interventions aimed at improving the adherence with the treatment of chronic conditions, such as diabetes. Therefore, we conducted a study to evaluate the willingness of older outpatients with diabetes to use mobile phones to support medication adherence and receive appointment reminders by mobile phone text message and investigated the factors associated with this willingness.

Material and Methods

Research Design and Location

This study was a cross-sectional study conducted at the outpatient Diabetes Clinic of the Department of Medical Examination, Dong Da General Hospital, from September 2020 to August 2021.

Participants

The study participants were selected through convenience sampling based on whether they met the following inclusion criteria: managed and treated for type 2 diabetes as an outpatient at the hospital for at least one year, aged 60 years and above, able to hear and respond to interview questions and willing to participate in the study.

Data Collection

Data were obtained from face-to-face interviews and audit of medical records.

Data from face-to-face interviews

Participants were interviewed using a pre-designed questionnaire. The interviews were conducted in a separate room and lasted from 10 to 15 minutes. Willingness to use a mobile phone to receive messages to support medication adherence and reminders about next clinic appointments was determined using the following questions, respectively: are you willing to use a mobile phone to receive messages to remind you about your medication schedule? (yes/no) and would you like to receive text messages about your next clinic appointment via a mobile phone (yes/no).

Current Diabetes Treatment adherence was classified based on the Morisky Medication Adherence scale.¹¹ This questionnaire includes eight items, the first seven being dichotomous (yes/no) to identify adherence or non-adherence. The eighth question asks patients to respond using a five-point Likert scale. The total score ranges from 0 to 8 points. Scores ranging from 0 to 5 were classified as low treatment adherence, 6 as medium treatment adherence, and 7 to 8 as high treatment adherence. This scale has been validated in Vietnamese and it is short and suitable for this study.¹² The Cronbach alpha of this is 0.6.

Participants were asked about mobile phone usage. This included questions about whether they used or owned a mobile phone, if so, whether it was a basic phone (only call and text messages function) or smartphone (call, text, network connection allowing access to internet, and able to use email application) and whether they used the following features on the phone: Wi-Fi, text message, internet, or email.

The following sociodemographic information was obtained: age group (60-69, 70-79, and 80+ years old), gender (male/female), educational level (secondary school or lower/high school/college, university or higher), income level (lowest quartile/second quartile/third quartile/highest quartile), alcohol consumption status (not drinking/occasionally drinking/drinking one to two glasses daily), and smoking status (never smoker/former smoker/current smoker).

Cognitive was assessed by Mini-Cog test (three word recall, and clock drawing and three word recall).¹³

Data collected from medical records

Diabetes control was determined based on HbA_1c, where <7.5% indicated good control and >7.5% indicated poor control.¹⁴ Hemoglobin A_1c was collected from routine blood test results at the most recent time point within three months prior to the time of recruitment.

The following medical information was obtained: number of other chronic diseases (< 2/≥ 2), duration of diabetes illness (< 10 years/≥ 10 years), treatment method (only medication/only insulin injection/both medication and insulin injection).

Statistical Analysis

Descriptive analysis was employed to determine the frequency distribution of categorical variables. Multivariate logistic regression was utilized to identify factors associated with (1) willingness to utilize mobile phones for medication adherence support and (2) the willingness to receive text message appointment reminders via phone as dependent variables in separate models. Variables that have been demonstrated to be associated with these variables in previous studies or “a priori” hypothesis were considered independent variables and included in the models.^10,15 Statistical analysis was performed using STATA 15. In this study, a significance level of .05 was used for all statistical tests.

Research Ethics

The study was conducted in accordance with the Declaration of Helsinki and ethics approval was obtained from the Hanoi Medical University (decision no. 241/GCN-HDDDCNCYSH-DHYHN dated November 17, 2020). Written informed consent was obtained from all participants or their representatives/guardians. Participants could withdraw their consent at any time. Participant information was kept confidential and used only for research purposes.

Results

Table 1 shows the characteristics of the included 584 study participants. The mean age was 73.2 (8.3) years old. Prevalence of good HbA_1c control was 52.4% and approximately 80% of participants had medium or high treatment adherence.

Table 1.

Characteristics of Study Participants (n = 584).

Characteristics	n	%
Age
60-69	226	38.7
70-79	188	32.2
≥ 80	170	29.1
Gender
Male	206	35.3
Female	378	64.7
Educational level
Secondary school or lower	301	51.5
High school	204	34.9
College, university, or higher	79	13.6
Living status
Living with family	580	99.3
Living with caregiver	1	0.2
Living alone	3	0.5
Alcohol consumption status
Not drinking	442	75.7
Occasionally drinking	123	21.1
Drinking one to two glasses daily	19	3.3
Smoking status
Never smoker	470	80.5
Former smokers	84	14.4
Current smokers	30	5.1
Number of chronic diseases
<2	101	17.3
≥2	483	82.7
Duration of diabetes illness
<5 years	131	22.4
≥5 years	453	77.6
Treatment method
Only medication	397	68.0
Only insulin injection	38	6.5
Both medication and injection	145	24.8
Number of medications
<5 drugs	414	70.9
≥5 drugs	170	29.1
Current Diabetes Treatment adherence (MORISKY Score)
High (7-8)	191	32.7
Medium (6)	260	44.5
Low (0-5)	133	22.8
HbA_1c
< 7.5%	306	52.4
≥ 7.5%	278	47.6

Among all participants, 241 (41.3%) were willing to use a mobile phone to support medication adherence and 239 (40.9%) were willing to use mobile phone to receive appointment reminders by text message (Table 2).

Table 2.

Mobile Phone Usage Pattern in Study Population.

Characteristics	n	%
No phone	205	35.1
Basic phone	218	37.3
Smartphone	161	27.6
Basic phone and smartphone users (n = 379)
Make phone calls (both basic phone and smartphone)
Yes	329	86.8
No	50	13.2
Send text messages (both basic phone and smartphone)
Yes	193	50.9
No	186	49.1
Smartphone users (n = 161)
Use a phone to connect to Wi-Fi
Yes	141	87.6
No	20	12.4
Search for information on the internet
Yes	135	83.9
No	26	16.1
Check email
Yes	44	27.3
No	117	72.7
Willingness (n = 584)
Willingness for using mobile phones to support medication adherence
Yes	241	41.3
No	343	58.7
Willingness to use mobile to receive appointment reminders by text message
Yes	239	40.9
No	345	59.1

Table 3 shows the factors associated with willingness for using mobile phones to support medication adherence. In the multilevel logistic regression analysis, aged ≥ 80 (odds ratio [OR] = 2.15, 95% confidence interval [CI] = 1.10, 4.20), education level (college, university, or higher: OR = 2.35, 95% CI = 1.10, 4.99), smoking status (current smokers: OR = 5.40, 95% CI = 1.01, 28.94), whether they had a mobile phone and type of phone (basic phone: OR = 2.47, 95% CI = 1.42, 4.30; smartphone: OR = 17.93, 95% CI = 8.81, 36.47) were significantly associated with willingness for using phones to support medication adherence. There was no statistically significant association between the treatment adherence or diabetes control status and willingness for using mobile phones to support medication adherence.

Table 3.

Factors Associated With Willingness for Using Mobile Phones to Support Medication Adherence Among All Participants (n = 584).

Variables		Willingness for using mobile phones
Variables	n	%	Crude OR (95% CI)	aOR (95% CI)
Age
60-69	127	56.2	1	1
70-79	65	34.6	0.41 (0.28, 0.61)	0.69 (0.41, 1.16)
≥ 80	53	31.2	0.35 (0.23, 0.54)	2.15 (1.10, 4.20)*
Gender
Male	106	51.5	1	1
Female	139	36.8	0.55 (0.39, 0.77)	1.24 (0.66, 2.34)
Educational level
Secondary school or lower	92	30.6	1	1
High school	98	48	2.1 (1.45, 3.04)	1.19 (0.76, 1.86)
College, university, or higher	55	70.5	5.43 (3.15, 9.37)	2.35 (1.10, 4.99)*
Income (VND)
Lowest quartile	84	42.4	1	1
Second quartile	32	38.1	0.84 (0.50, 1.41)	1.21 (0.65, 2.25)
Third quartile	57	32.4	0.65 (0.43, 0.99)	0.69 (0.40, 1.17)
Highest quartile	66	62.9	2.30 (1.41, 3.73)	1.03 (0.54, 1.98)
Alcohol consumption status
Not drinking	168	38	1	1
Occasionally drinking	63	51.2	1.71 (1.15, 2.56)	1.35 (0.59, 3.08)
Drinking one to two glasses daily	14	73.7	4.57 (1.62, 12.91)	1.12 (0.24, 5.30)
Smoking status
Never smoker	186	39.6	1	1
Former smokers	33	39.3	0.99 (0.61, 1.59)	1.04 (0.44, 2.43)
Current smokers	26	86.7	9.93 (3.41, 28.9)	5.40 (1.01, 28.94)*
Cognition impairment
No	227	41.9	1	1
Yes	18	43.9	1.09 (0.57, 2.06)	1.06 (0.47, 2.38)
Other chronic diseases
< 2	55	54.5	1	1
≥ 2	190	39.3	0.54 (0.35, 0.84)	0.90 (0.52, 1.58)
Duration of diabetes illness
< 10 years	119	42.7	1	1
≥ 10 years	126	41.3	0.95 (0.68, 1.32)	0.77 (0.51, 1.17)
Treatment method
Only medication	168	42.3	1	1
Only insulin injection	18	47.4	1.23 (0.63, 2.39)	0.70 (0.29, 1.65)
Both medication and injection	59	40.7	0.94 (0.64, 1.38)	0.88 (0.53, 1.47)
Type of phone
No use	45	22	1	1
Basic phone	71	32.6	1.72 (1.11, 2.65)*	2.47 (1.42, 4.30)*
Smartphone	129	80.1	14.33 (8.62, 23.85)*	17.93 (8.81, 36.47)*
Treatment adherence
Low (Morisky 0-5)	42	31.6	1	1
Medium (Morisky 6)	35	35	1.17 (0.67, 2.02)	0.90 (0.45, 1.80)
High (Morisky 7-8)	168	47.9	1.99 (1.31, 3.03)*	1.46 (0.86, 2.49)
Diabetes control
No (HbA_1c ≥ 7.5%)	116	41.9
Yes (HbA_1c < 7.5%)	128	41.8	1.00 (0.72, 1.39)	0.95 (0.62, 1.47)

Abbreviations: aOR = adjusted odds ratio; OR = odds ratio; CI = confidence interval; VND = Vietnam Dong.

p < 0.05.

Table 4 shows the multilevel logistic regression analysis of factors associated with willingness to receive reminders about next clinic appointment via mobile phone text. Willingness to receive these reminders via mobile phone was significantly associated with the whether they had a mobile phone and type of phone (basic phone: OR = 2.79, 95% CI = 1.70, 4.59; smartphone: OR = 9.61, 95% CI = 4.61, 19.99), diabetes control status (HbA_1c < 7.5%: OR = 0.65, 95% CI = 0.43, 0.99).

Table 4.

Factor Associated With Willingness to Receive Reminder About Next Clinic Appointment Via Mobile Phones Text Messages Among All Participants (n = 584).

Variables	n	Willingness to receive appointment reminders via phone
Variables	n	%	Crude OR (95% CI)	aOR (95% CI)
Age
60-69	177	78.3	1	1
70-79	111	59	0.40 (0.26, 0.61)	0.61 (0.37, 1.02)
≥ 80	97	57.1	0.37 (0.24, 0.57)	1.36 (0.72, 2.57)
Gender
Male	150	72.8	1	1
Female	235	62.2	0.61 (0.42, 0.89)	1.3 (0.69, 2.42)
Educational level
Secondary school or lower	175	58.1	1	1
High school	144	70.6	1.73 (1.18, 2.52)	1.11 (0.71, 1.74)
College, university, or higher	66	84.6	3.96 (2.05, 7.63)	2.27 (0.98, 5.25)
Income (VND)
Lowest quartile	122	61.6	1	1
Second quartile	54	64.3	1.12 (0.66, 1.91)	1.51 (0.83, 2.77)
Third quartile	112	63.6	1.09 (0.72, 1.66)	1.32 (0.79, 2.19)
Highest quartile	84	80	2.49 (1.43, 4.35)	1.45 (0.73, 2.88)
Alcohol consumption status
Not drinking	281	63.6	1	1
Occasionally drinking	87	70.7	1.39 (0.90, 2.14)	0.67 (0.29, 1.54)
Drinking one to two glasses daily	17	89.5	4.87 (1.11, 21.35)	1.12 (0.18, 7.18)
Smoking status
Never smoker	298	63.4	1	1
Former smokers	58	69	1.29 (0.78, 2.12)	2.11 (0.88, 5.02)
Current smokers	29	96.7	16.74 (2.26, 123.96)	6.07 (0.63, 58.18)
Cognition impairment
No	359	66.2	1	1
Yes	26	63.4	0.88 (0.46, 1.71)	0.97 (0.44, 2.16)
Other chronic diseases
< 2	77	76.2	1	1
≥ 2	308	63.8	0.55 (0.34, 0.90)	0.81 (0.45, 1.45)
Duration of diabetes illness
< 10 years	181	64.9	1	1
≥ 10 years	204	66.9	1.09 (0.78, 1.54)	0.91 (0.61, 1.36)
Treatment method
Only medication	261	65.7	1	1
Only insulin injection	27	71.1	1.28 (0.62, 2.66)	0.78 (0.34, 1.8)
Both medication and injection	96	66.2	1.02 (0.68, 1.53)	0.81 (0.50, 1.32)
Type of phone
No use	99	48.3	1	1
Basic phone	143	65.6	2.04 (1.38, 3.02)	2.79 (1.70, 4.59)
Smartphone	143	88.8	8.51 (4.85, 14.91)	9.61 (4.61, 19.99)
Treatment adherence
Low (Morisky 0-5)	83	62.4	1	1
Medium (Morisky 6)	61	61	0.94 (0.55, 1.61)	0.68 (0.36, 1.27)
High (Morisky 7-8)	241	68.7	1.32 (0.87, 2.00)	0.95 (0.57, 1.58)
Diabetes control
No (HbA_1c ≥ 7.5)	195	70.4	1	1
Yes (HbA_1c < 7.5)	189	61.8	0.68 (0.48, 0.96)	0.65 (0.43, 0.99)

Abbreviations: aOR = adjusted odds ratio; OR = odds ratio; CI = confidence interval; VND = Vietnam Dong.

Discussion

Our study of older patients with diabetes found that around a third did not use a mobile phone. We found that age, educational level, smoking status, and whether they had a mobile phone and type of phone (basic or smart) were significantly associated with willingness to receive messages to remind them about their medication schedule. Whether they had a mobile phone and type of phone (basic or smart) were significantly associated with willingness to receive text message appointment reminders.

Mobile Phone Usage Overview

The proportion of participants in our study using smartphone to connect to the Wi-Fi for searching information on the internet was lower than in study conducted in Iran.¹⁵ This may be explained by the fact that the average age of the population in our study (73.2 ± 8.3) was higher than the study in Iran (53.18 ± 15.5). Almost a half of participants used text message, but a lower proportion used email. Of those with mobile phone, a larger proportion made phone calls with it suggesting that voice calls could be used to support medication adherence and remind people about future clinic appointments. Current smoking was significant associated with the willingness to use a mobile phone to support medication adherence in our study. This is an unexpected finding. This relationship could be further explored with qualitative studies. We speculate that some smokers may think that if they are more compliant with diabetes treatment this may counter the adverse effects of smoking.

Medication Adherence

A large proportion of participants (41.3%) were willing to use mobile phone to support medication adherence. Not surprisingly, a higher level of education was associated with a greater willingness to use mobile phones to support medication adherence. Similarly, findings from Ethiopia also indicated that higher education was positively associated with willingness to use a mobile phone.^16,17 The percentage of participants willing to use mobile phones to support medication adherence was consistent with other studies from the United States (56.7%)¹⁸ and Japan (50%).¹⁹

Appointment Reminders

Having any mobile phone (compared with no phone) was significantly associated with willingness to clinic appointment reminders via receive text messages and having a smart phone compared with basic phone was associated with higher odds of this willingness.

Implications for Mobile Phone-Based Interventions for Medication Adherence

Our study suggests that there may be value in designing a mobile phone based app that can be used to provide information to enhance diabetes self-management, diabetes management adherence, and clinic follow-up attendance. This particularly applies to older people with higher education and access to smartphones. Our study, however, also shows that we need to think about ways to achieve these outcomes, which does not rely only on mobile phone technology as a large proportion of our participants did not have a mobile phone. Even among those who had a mobile phone many did not use their mobile phone for anything but voice calls. For mobile phone technology to reach a broader audience, health care providers may need to implement educational interventions on use of mobile phones and consider simpler reminder options, such as voice calls, for those less comfortable with digital platforms. It is important to consider sociodemographic characteristics and affordability of mobile phone usage as these are important consideration in older people adapting to digital technology.²⁰

Strengths and limitations

The strengths of our study were that it was conducted in a general hospital outpatient clinic with a large number of patient visit. However, there are a number of limitations of the study. The study was conducted during COVID-19 period, thus, we could not reach all outpatients with diabetes. Our findings about factors associated with willingness to use mobile phone technology are based on regression analysis of cross-sectional data rather than longitudinal data. But we tend to do a longitudinal study on this population in the future. In addition, the study population was from an urban area. Our findings are specific to a Vietnamese population, so that, cultural differences might limit the generalizability of our findings to older people in other countries.

Conclusions

Our study would suggest that there is potential value in using mobile phone technology to improve the management of diabetes in community living older people but this alone cannot be relied upon.

Footnotes

Acknowledgements

The authors are genuinely thankful to all the participants who gave their time to participate in this study.

Abbreviations

IDF, International Diabetes Federation; NCDs, non-communicable diseases; SMS, sending text message services; VND, Vietnam Dong

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: One of the co-authors— TTHN was funded by the Master, PhD Scholarship Program of Vingroup Innovation Foundation (VINIF), code VINIF.2023.TS.122.

ORCID iD

Huyen Thi Thanh Vu

Data Availability

The data sets of this study are available from the corresponding author on reasonable request.

References

WHO. Global Status Report on Noncommunicable Diseases. Geneva, Switzerland: WHO; 2010.

Saeedi

Petersohn

Salpea

, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019;157:107843.

THLBT

Tran

Pham

Lai

Minh Hoang

. Comorbidities of diabetes and hypertension in Vietnam: current burden, trends over time, and correlated factors. BMC Public Health. 2023;23(1):2419.

HTT

Nguyen

, et al. Comprehensive geriatric assessment in older patients with diabetes mellitus in Hanoi, Vietnam. Gerontology. 2022;68(10):1132-1138.

Hussein

ESE

A-SA Ramadan

RME

. Applications of medical digital technologies for noncommunicable diseases for follow-up during the COVID-19 pandemic. Int J Environ Res Public Health. 2022;19(19):12682.

Monaco

Palmer

Marengoni

, et al. Integrated care for the management of ageing-related non-communicable diseases: current gaps and future directions. Aging Clin Exp Res. 2020;32(7):1353-1358.

Zin

KSLT KS

Kim

H-S

Feyissa

. A study on technology acceptance of digital healthcare among older Korean adults using extended tam (Extended Technology Acceptance Model). Admin Sci. 2023;13(2):42.

Hwang

Dronina

. Mediating effects of smartphone utilization between attitude and willingness to use home-based healthcare ICT among older adults. Healthc Inform Res. 2021;27(2):137-145.

Saleh

SFA

El Arnaout

Dimassi

, et al. mHealth use for non-communicable diseases care in primary health: patients’ perspective from rural settings and refugee camps. J Public Health (Oxf). 2018;1(40):ii52-ii63.

10.

Wildenbos

Peute

Jaspers

. Aging barriers influencing mobile health usability for older adults: a literature based framework (MOLD-US). Int J Med Inform. 2018;114:66-75.

11.

Morisky Medication Adherence Research L. https://www.moriskyscale.com. Accessed June 10, 2024.

12.

Nguyen

Cao

HTK

Quach

, et al. Translation and cross-cultural adaptation of the brief illness perception questionnaire, the beliefs about medicines questionnaire and the Morisky Medication Adherence Scale into Vietnamese. Pharmacoepidemiol. 2015;24:159-160.

13.

Borson

Scanlan

Watanabe

Lessig

. Improving identification of cognitive impairment in primary care. Int J Geriatr Psychiatry. 2006;21(4):349-355.

14.

Group AtCCRiDF-OAESGatAtCCRiDF-OAS. Persistent effects of intensive glycemic control on retinopathy in type 2 diabetes in the action to control cardiovascular risk in diabetes (ACCORD) follow-on study. Diabetes Care. 2016;39(7):1089-1100.

15.

Rangraz Jeddi

Nabovati

Hamidi

, et al. Mobile phone usage in patients with type II diabetes and their intention to use it for self-management: a cross-sectional study in Iran. BMC Med Inform Decis Mak. 2020;20:24.

16.

Jemere

Yeneneh

Tilahun

, et al. Access to mobile phone and willingness to receive mHealth services among patients with diabetes in Northwest Ethiopia: a cross sectional study. BMJ Open. 2019;9:e021766.

17.

Kebede

Zeleke

Asemahagn

Fritz

. Willingness to receive text message medication reminders among patients on antiretroviral treatment in North West Ethiopia: a cross-sectional study. BMC Med Inform Decis Mak. 2015;15:65.

18.

Humble

Tolley

Krukowski

Womack

Motley

Bailey

. Use of and interest in mobile health for diabetes self-care in vulnerable populations. J Telemed Telecare. 2016;22(1):32-38.

19.

Shibuta

Waki

Tomizawa

, et al. Willingness of patients with diabetes to use an ICT-based self-management tool: a crosssectional study. BMJ Open Diabetes Res Care. 2017;5(1):e000322.

20.

Nguyen

TXH

Tran

TBN

Dao

, et al. Elderly people’s adaptation to the evolving digital society: a case study in Vietnam. Soc Sci. 2022;11:324.

Mobile Phone Usage and Willingness to Use Mobile Phones to Support Medication Adherence and Receive Text Message Appointment Reminders in Older Outpatients With Type 2 Diabetes

Abstract

Background:

Methods:

Results:

Conclusions:

Keywords

Introduction

Material and Methods

Research Design and Location

Participants

Data Collection

Data from face-to-face interviews

Data collected from medical records

Statistical Analysis

Research Ethics

Results

Discussion

Mobile Phone Usage Overview

Medication Adherence

Appointment Reminders

Implications for Mobile Phone-Based Interventions for Medication Adherence

Strengths and limitations

Conclusions

Footnotes

Acknowledgements

Abbreviations

Declaration of Conflicting Interests

Funding

ORCID iD

Data Availability

References