Mobile phone apps for health knowledge and management (mHealth) are becoming more integrated into people's everyday lives worldwide, potentially enhancing health care, reducing inequalities and optimising health systems. mHealth knowledge concerns being ‘aware or unaware of mHealth technology’ and the use of mobile devices to assess healthcare, including treatment, services, emergency medical response and education (Seidenberg et al, 2012). mHealth presents intriguing options for delivering services that may enhance overall healthcare quality (Vo et al, 2019).
mHealth applications and their use worldwide are projected to grow (Vo et al, 2019; Sharma et al, 2022).
Mobile application technology has aided tremendously in time management, convenience and cost savings for healthcare, in areas from hospital visits to individual patient consultations with a practitioner. With the advance of mobile technology and the growing number of mobile users, new opportunities for mobile inpatient care, such as mHealth applications, have arisen (Sharma et al, 2022).
However, after being downloaded, healthcare applications frequently appear to be underused (Vo et al, 2019).
Background
Chronic kidney disease (CKD) is a global, non-communicable disease, responsible for 2.4 million deaths per year. It is now the sixth fastest-growing cause of death in Malaysia, with more than 7000 new diagnoses in the country every year (Murugesan, 2019).
Chronic kidney disease (CKD) has been a major public health concern in Malaysia for the past decade. The prevalence of CKD in Malaysia was 15.48% in 2018, an increase on 2011 when the prevalence of CKD was 9.07%. In 2018, an estimated 3.85% of Malaysians had stage 1 CKD, 4.82% had stage 2, and 6.48% had stage 3, while 0.33% had stage 4—5 CKD (Saminathan et al, 2020).
Living with CKD is a challenge, as patients need sufficient knowledge to be able to self-manage and treatment adherence is important (Yangöz et al, 2021) .CKD patients’ personal, subjective and objective knowledge of their condition is often insufficient, and a better understanding of this could improve self-management skills, so this essential (Inkeroinen et al, 2021).
Patients with CKD are often not satisfied with their ability to communicate with healthcare providers and are mostly unaware of their diagnosis, care management required and the implications of both of these (Greer et al, 2011). As the CKD population is increasing (Saminathan et al, 2020), the need for knowledge on managing the condition is growing too (Yangöz et al, 2021).
An mHealth application could help solve issues related to the CKD population. In contemporary society, the number of mHealth applications is increasing rapidly and they are being made available to people with different health conditions. They have become commonplace in healthcare and are used to carry out many tasks. They can provide information on care management, services and knowledge sources, be used for communication, consulting and monitoring, and reduce patients’ illness-related burden (Aungst, 2013; Grossman et al, 2018).
The World Health Organization (2011) Global Observatory for eHealth defines mHealth as medical and public health practice supported by mobile devices. mHealth applications are increasingly employed to facilitate unprecedented access to specialist clinical diagnostics and treatment advice, to optimise health systems, to improve care and health and to reduce health inequalities (Vo et al, 2019).
Despite Malaysia's rapid transition from analogue to digital informatics and rising CKD rates (Saminathan et al, 2020), there are a lack of data on patient readiness and ability to use mHealth applications, particularly for those with CKD.
Furthermore, mHealth applications are part of a global trend in promoting self-management of long-term conditions (Whitehead and Seaton, 2016). Therefore, it has become imperative to survey CKD patients’ readiness and ability to use them. In addition, despite patients’ growing interest in mHealth applications, there has been little discussion on potential users’ views regarding their readiness, particularly patients with CKD.
Aims
This study aimed to assess CKD patients’ views of their readiness and ability to use mHealth applications in a tertiary teaching hospital on the north-east coast of Peninsular Malaysia.
Methods
Design
A cross-sectional study was carried out in Hospital Universiti Sains Malaysia (Hospital USM), located on the north-east coast of Peninsular Malaysia.
Participants
The study population comprised CKD inpatients from three medical wards in Hospital USM. A convenience sampling technique was used to recruit the participants.
The inclusion criteria were: adults aged >18 years; diagnosed with CKD with an estimated glomerular filtration rate of less than 60 ml/min/1.73 m2 as per the Modification of Diet in Renal Disease equation; be able to read and write in Bahasa Malaysia (the official Malay language) or English; and able to give written consent. The sample size was calculated based on a value of standard normal distribution of 1.96. Assuming an α (two-tailed) of 0.05, β of 0.20 and an expected correlation coefficient (r) of 0.3, the calculated sample size required was 85. After considering a likely dropout rate of 20%, it was calculated that 102 CKD patients were required for this study.
Setting
The study was conducted in Hospital USM, which was selected as it is a tertiary teaching and referral hospital. In addition, patients visit this hospital from all over the north-east coast of Peninsular Malaysia as well as from other states in Malaysia.
Instrument
The instrument was designed based on literature examining mHealth and mobile health devices for health literacy (Jung et al, 2012; Lee et al, 2020a) to fit the Malaysian CKD patient context and be self-administered.
The questionnaire had three sections—A, B, and C. Section A consisted of sociodemographic data (sex, ethnicity, educational level, employment status, monthly household income and type of mobile device). Section B contained five items to collect CKD patients’ views regarding their ability to use an mHealth application on a mobile device (independently using a mobile device, searching for information on the internet, downloading the mHealth application, making calls and checking and sending emails and text messages) and rated on a four-point Likert scale. Section C consisted of nine items about readiness to use mHealth applications. For every ‘yes’ response, one point was added, and the ‘no’ response was 0. The maximum total score was nine points.
The total points obtained were converted into a percentage from the score using the formula (score x 100)/9. Ability to use an mHealth app was assessed using the percentage scores obtained, which were classified into three categories: poor (<50%); moderate (≥50% to <80%); and good (≥80%).
The questionnaire was piloted among a convenience sample of 10 CKD patients who were not included in the study. A Cronbach's alpha of 0.712 was obtained, indicating a good level of item reliability (Taber, 2018).
The questionnaire was also translated into Bahasa Malaysia to suit participants using forward and backward translation by four independent reviewers (two physicians, one nursing lecturer and one language expert) who were fluent in Bahasa Malaysia and English.
The final translated instrument was used in this study.
Data collection
Data collection ran from February 2021 to March 2021. The research was explained verbally by the first researcher. Each questionnaire took 10-15 minutes on average to complete.
All completed questionnaires were kept by the researcher's supervisor in a lockable room. During the study, the data were stored on the researcher's computer, with access restricted by a code known only to the researcher; they will be destroyed two years after this study's publication.
Ethical considerations
Ethical approval was obtained from USM's human research ethics committees (USM/JEPeM/20120646). The study was performed according to the principles of the Declaration of Helsinki and institutional requirements.
All participation was voluntary. No information was collected regarding participants’ identities. Completed consent forms and responses were stored separately and coded numbers were used during data collection to ensure confidentiality and anonymity.
Data analysis
All data were entered and analysed using SPSS v 26.0 for Windows. First, the accuracy of data entry was checked. Then, descriptive statistics were used to identify frequencies/valid percentages and measures of central tendency and dispersion. Finally, Spearman's correlation analysis test was performed to determine the relationships between participants’ readiness and ability to use mHealth applications. P≤0.05 was considered significant.
Results
Sociodemographic characteristics
A total of 102 CKD patients were initially recruited for the study; two were excluded because they were experiencing fatigue at the time of the survey, giving a final sample of100. The mean age was 62.06 years (SD 27.50) and 77% were aged >40 years. The majority (62%) were male and 38% were female. Regarding ethnicity, 97% were Malay. Fewer than half had no education beyond secondary school (44%), and similar proportions worked in the private (41%) and government (40%) sectors. Almost half (48%) reported a monthly household income of less than MYR1000 (£187.50). Most (80%) owned a smartphone. Demographic details are shown in Table 1.
Table 1. Sociodemographic characteristics of participants (n=100)
| Variables | Mean (SD) | n | % |
|---|---|---|---|
| Age (years) | 62.06 (27.50) | ||
| 18-28 | 9 | 9 | |
| 29-39 | 14 | 14 | |
| 40 and above | 77 | 77 | |
| Sex | |||
| Male | 62 | 62 | |
| Female | 38 | 38 | |
| Ethnicity | |||
| Malay | 97 | 97 | |
| Chinese | 1 | 1 | |
| Indian | 2 | 2 | |
| Education | |||
| Secondary | 44 | 44 | |
| Diploma | 37 | 37 | |
| Bachelor's degree | 17 | 17 | |
| Postgraduate degree | 2 | 2 | |
| Employment status | |||
| Self-employed | 19 | 19 | |
| Private sector | 41 | 41 | |
| Public (government) sector | 40 | 40 | |
| Type of mobile phone | |||
| Ordinary phone | 6 | 6 | |
| Smartphone | 80 | 80 | |
| Tablet | 14 | 14 | |
| Monthly income (MYR) | |||
| <1000 | 48 | 48 | |
| 1001-3000 | 14 | 14 | |
| 3001-5000 | 25 | 25 | |
| »5001 | 13 | 13 | |
MYR: Malaysian ringgit. MYR1=£0.19
Ability to use mHealth applications
Of the 100 participants, 58% reported using their mobile phones to connect to wifi. More than half reported they could search for information on the internet (53%) and were able to download an mHealth application (52%) and check emails (52%). In addition, more than three-quarters reported being able to make calls and send text messages (SMS) (81%). See Table 2.
Table 2. Ability to use mobile phone
| Ability to use mobile phone | Mean | SD | Strongly disagree (n) | Disagree (n) | Agree (n) | Strongly agree (n) |
|---|---|---|---|---|---|---|
| I can connect to wifi | 2.72 | 1.33 | 32 | 10 | 12 | 46 |
| I can search for information on the internet | 2.66 | 1.37 | 34 | 13 | 6 | 47 |
| I can download the mHealth application | 2.62 | 1.39 | 37 | 11 | 5 | 47 |
| I can check emails | 2.59 | 1.39 | 38 | 10 | 7 | 45 |
| I can make calls and send SMS | 3.43 | 0.98 | 8 | 11 | 11 | 70 |
Readiness to use mHealth applications
Most (80%) participants reported using a smartphone, while the remainder (6% and 14%) used ordinary phones and tablets respectively Nearly one in five (18%) reported they actively used health applications.
More than three-quarters (78%) indicated that they were ready to use the mHealth application and would actively use it to manage their condition, to remind them to take medication and to record glucose levels, while 76% would use it to look up health or medical information.
Of the 100 CKD patients, 77% could find medical information about CKD and 78% could share health or medication information via a mobile health application.
Data on this are shown in Table 3.
Table 3. Readiness to use mHealth applications
| Items | Yes |
|---|---|
| Do you currently use a smartphone? | 80 |
| Do you actively use any health application on your smartphone? | 18 |
| If there was a mobile health application to help you manage your sickness, would you use it actively? | 78 |
| If there was a mobile application on your smartphone to remind you to take your medication, would you use it actively? | 78 |
| If there was a mobile health application for you to record glucose levels, would you use it actively? | 78 |
| If there was a mobile health application to help you change your lifestyle for the better (eg promote healthy eating habits and exercise), would you use it actively? | 78 |
| Do you ever use your mobile phone to look up health or medical information? | 76 |
| Would you like to be given medical information on a mobile health application about your sickness? | 77 |
| Would you share health or medication information via a mobile health application? | 78 |
Correlations between readiness and ability to use mHealth applications among CKD patients
The Spearman correlations showed significant correlations (r=0.4; P<0.05) between readiness and ability to use mHealth applications among CKD participants (Table 4).
Table 4. Correlations between readiness and ability to use mHealth applications among participants
| Variables | Level of readiness | Correlation coefficient (r) | P* | |
|---|---|---|---|---|
| Ready (n) | Not ready (n) | |||
| Level of ability in using mHealth applications | 78 | 22 | 0.4 | 0.000 |
*Spearman's correlation test; P<0.05
Discussion
This study reports the readiness and ability ofpatients in Malaysia with CKD to use mHealth applications. The mean age of CKD participants was 62.06 years.
The proportion of older CKD patients has risen over recent decades. This study's findings concur with those of previous research showing that older people were inclined to adopt the mHealth app to manage health issues (Shah et al, 2018). Nonetheless, a previous study in Malaysia found that younger individuals were more technology savvy than older people and more open to adopting it (Lee et al, 2020a). Results of this study also concur with those of a previous study conducted in Malaysia that showed Malaysians were receptive to using technology to seek health-related information, including that on medical conditions, symptoms and treatment options (Lee et al, 2019; Kua et al, 2019).
Although the present study showed the use of mHealth applications among CKD patients aged ≥60 years is becoming more common, people with CKD were still unable to use mHealth applications properly because they lacked the capability. This difficulty experienced by older adults in using mHealth applications was similar to that found by Lee et al (2020b). Support appears to play a role in patients’ capacities to use the mHealth application. This builds patients’ confidence and enables them to improve their digital skills and knowledge.
Furthermore, patterns and challenges in using mHealth applications vary. Therefore, while this study might reflect CKD participants’ ability and readiness to use mHealth applications, it does not necessarily imply they were properly using all facets of their mHealth applications. Therefore, further study is needed to examine the trials involving elderly people regarding mHealth application use and to examine all personal and other factors associated with mHealth application use.
The participants in this study were ethnically diverse, and generally reflected Malaysia's multicultural population. A high number of Malays were recruited. The population of Malaysia is 32.6 million, with Malays the largest ethnic group (69.6%). The USM hospital is located in Kelantan; Malays form 95% of Kelantan's population, making them the largest ethnic group (Department of Statistics Malaysia, 2021).
This explains the ethnic diversity and why Malays were the predominant ethnic group in this study. However, because not all ethnic groups were sufficiently represented, the association between ethnicity and degree of readiness and ability to use mHealth applications could not be examined. Therefore, there is a need to include non-Malay CKD patients from Chinese, Indian and other ethnic groups in future research.
Digital transformation provides new avenues for all. However, our study showed the digital gender divide. More men than women had a mobile phone. According to the Connected Women. The Mobile Gender Gap Report 2020 (GSMA, 2020), men in South Asia are more likely to own a mobile phone than women and use the internet on a mobile. Additionally, a Pew Research Center survey of 30 133 people in 27 countries carried out between 14 May and 12 August 2018 showed that sex has a minor influence on technology use in most countries with inconsistent gender smartphone ownership (Silver, 2019). The survey reported that women and men generally used mobile phones for the internet and social media for comparable amounts in advanced and emerging economies. For example, in Japan, mobile phone ownership among men was 69% and 63% among women. In many countries, mobile phone ownership is similar in men and women. However, the gender gap in mobile phone use has remained in many countries. For example, in Brazil in 2015, 38% of women and 43% of men owned mobile phones.
The findings of this study concur with those in India that men are more likely than women to own mobile phones (Silver, 2019). In the context of cultural norms that control and limit women's use of technology in South Asia, men's mobile ownership growth was higher than women's (Sambasivan et al, 2018; Iqbal, 2021). The present study's results are in line with the International Telecommunication Union's (2022) statistics showing that a mobile gender gap exists. The plausible explanation could be that women have less autonomy regarding mobile phone acquisition. Much remains to be explored about the gender gap in mobile phone ownership.
In this study, most participants who had completed secondary education but no higher owned a mobile phone. These findings were consistent with the results of studies in various countries on the ownership and use of mobile phone and their health and wellness strategies’ benefits (Kamis et al, 2015; Silver, 2019). These findings suggest that mobile phone ownership is important even among the most vulnerable CKD patients and the B40 category of the Malaysian population; the latter is the bottom 40% regarding household income, who earn <MYR4850 per month (£909.40). This is dissimilar to the study by Olamoyegun et al (2020) of patients with diabetes in south-west Nigeria, which found mobile phone ownership was highest among those who had received tertiary education. Further study is needed to determine how participants’ education affects their use of mobile phones in mHealth applications.
More than half of CKD participants in this study in the B40 income category owned a smartphone although smartphone ownership is prohibitively expensive for billions worldwide (Lieser, 2020). Although it could be inferred that B40 category Malaysians are underprivileged with limited spending power, the present study showed that smartphone ownership is increasing among this population. It could be that smartphone ownership is seen as a necessity rather than a luxury. The possible explanation could be that mobile technology has intensified connectivity among populations capable of accessing the internet and running apps. People consider they need to own a mobile phone to access the internet and social media.
Mobile phone ownership varies greatly between developed and developing countries. As Silver (2019) reported in the Pew Research Center Survey, around nine in 10 South Koreans, Israelis and Dutch people own mobile phones such as smartphones. Ownership rates are closer to six in 10 in developed countries such as Poland, Russia and Greece. In emerging economies, smartphone ownership rates vary substantially, from highs of 60% in South Africa and Brazil to around four in 10 in developing nations such as Indonesia, Kenya and Nigeria. Ownership is the lowest in India, with only 24% of people reporting having a smartphone. In emerging economies, patterns of smartphone ownership are different. Across all age groups, smartphone ownership is lower than in advanced economies. While most adults aged >50 years in many advanced economies own smartphones, no emerging economy surveyed had smartphone ownership rates above 35% among this older group (Silver, 2019). This variation may be attributable to differences in age as well as socioeconomic and cultural standards.
The findings of this study showed that most participants who were able to use a smartphone were demographically similar to those in Lee et al's (2019) study. It could be that the research participants believed mobile phones helped them personally in various ways, as Lee et al (2020a) reported. In Lee et al's (2019) research, the participants were familiar with mobile technology, especially mHealth, and were more or less aware of the potential benefits of the application. By being aware of the advantages of using an mHealth application, users can better prepare themselves and have reasonable expectations of its usefulness. This explains the users’ ease of readiness and ability to use the mHealth application in the present study. However, this study also showed that fewer than half had a good command of mHealth apps. As Mahmood et al (2019) mentioned, certain groups, such as older adults, are most affected by a digital divide and this and their apprehension about using technology limit their ability to maximise the potential of mHealth apps. This explains why some people cannot take advantage of these tools.
Cosco et al's (2019) study about mobilising mHealth data collection in older adults and problems and opportunities around this indicated that older adults experience issues such as being more concerned with data security than younger individuals. Therefore, caution over downloading applications could be a factor.
In addition, a 2017 Royal Australian College of General Practitioners technology survey in Australia reported that most doctors lacked knowledge of applications as well as the time and skills to evaluate, find and recommend evidence-based apps. Therefore, they were recommending apps to patients sparingly. A study of mHealth app awareness and use in England found that, despite the growing number ofmHealth apps, patients’ and pharmacists’ awareness and usage remained relatively low, highlighting the need to raise public and health professionals’ awareness and knowledge of mHealth apps (Kayyali et al, 2017).
Research among these populations is needed to establish the ease of using and integrating approved mHealth apps for patients.
Strengths and limitations
The self-administered survey questionnaire has good reliability with a Cronbach's alpha value of0.712 (Taber, 2018). However, the current study has limitations. It was conducted in a Malaysian rural state and a tertiary teaching hospital, so the findings may not represent CKD patients across the country and the implications for other populations in other areas remain unknown.
In addition, the study was conducted from the perspective of patients with CKD rather than health practitioners.
Most participants were Malay, limiting the results’ generalisability. The authors suggest that future research recruits a larger sample of diverse CKD patients from several hospitals with similar demographic factors.
Finally, a comparison of the results of this study with those in other contexts, national or international, is impossible because of a lack of data.
Conclusion
Fewer than half of patients with CKD had a good command of mHealth apps. Therefore, support to use mHealth apps is needed to improve outcomes, and healthcare management needs to consider this.
Key points
- Little information regarding the readiness and ability to use mHealth applications among Malaysian patients with chronic kidney disease is available
- Fewer than half of the patients with chronic kidney disease had a good command of mHealth applications
- Mobile phone ownership is high among people in the bottom 40% of the Malaysia's population by income
- Readiness and ability to use mHealth applications influence whether patients will use them
- Patients with chronic kidney disease need support to make the most of mHealth applications
CPD reflective questions
- How can mHealth apps be made easier to use for patients with chronic kidney disease (CKD)?
- How can mHealth apps become a solution in areas of healthcare?
- What are CKD patients’ expectations of a mHealth app before use?
- How could mHealth apps be tailored to CKD patients to provide support and information, promote interventions and compliance with treatment, assist with CKD management and facilitate discussions with practitioners?