Version Changes
Revised. Amendments from Version 2
The revised version of the article incorporates a clearer description of the variable entry procedure for the multivariate regression analysis and the procedure for selecting healthcare facilities.
Abstract
Background
In India, nearly one-fourth of the population has hypertension, and only 17.5% of them have their blood pressure under control. Utilizing a smartphone application for hypertension care could potentially address the unmet needs within the existing healthcare delivery system. However, individuals with hypertension are the primary stakeholders for the implementation of such smartphone application-based care. Therefore, identifying the factors that influence the acceptance of a smartphone application-based care among individuals with hypertension is a prerequisite.
Aim
This study aimed to identify factors influencing acceptance of smartphone application among people with hypertension using the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model with age, gender, education, digital health literacy, m-health experience, status of blood pressure and its risk factors.
Method
In this cross-sectional survey, 134 individuals with hypertension, aged 30–79 years, were recruited from public and private healthcare facilities located in both urban and rural areas. One hundred and thirty-four participants completed the self-administered questionnaire.
Results
One hundred and twenty-four participants’ responses were analysed. Primary analysis revealed that individuals who perceived the application to be easy to use were 8.55 (95% CI: 1.973–37.008, p-0.004) times, more likely to accept smartphone application-based care. However, in the sensitivity analysis, it was found to be insignificant (aOR = 3.52, p = 0.08), whereas social influence (aOR = 8.20, 95% CI: 2.835-23.719, p <0.001) was identified as a significant predictor of acceptance of smartphone-based care. Other variables had insignificant influence on smartphone application-based care.
Conclusion
Ease of use was found to be a significant predictor of smartphone application acceptance among individuals with hypertension in the primary analysis however it was not retained in the sensitivity analysis
Implications
These studies’ findings suggest that individual, technological, and social predictors influence acceptance of smartphone application-based hypertension care, and future studies should confirm their independent contributions.
Keywords: Hypertension, m-health, smartphone application, predictors, self-care
Plain Language summary
The burden of hypertension is escalating globally. Approximately 75% of the global population with hypertension resides in low-to middle-income countries. Notably, India is home to 50% of the low-to middle-income countries’ population with uncontrolled hypertension. To address the burden of hypertension, various care delivery approaches beyond standard care have recently been employed in India. However, the majority of these approaches have been deployed in public healthcare facilities. Thus, limiting access to 80% of the population who seek care from private hospitals and incur out-of-pocket expenses. This highlights a need for a care-delivery approach that is accessible and scalable. Integrating smartphone technologies into care delivery practices presents a significant opportunity as two-thirds of the Indian population owns a smartphone and has internet access.
Nonetheless, the introduction of a new form of care delivery should be acceptable to the intended population. Hence, in this study, we identified a factor that influences acceptance of smartphone application-based care. We found preliminary evidence that ease of use significantly influenced the acceptance of the smartphone application among people with hypertension.
Background
In the past three decades, the burden of hypertension (HTN) has doubled globally, causing almost half of the heart disease or stroke-related deaths ( NCD Risk Factor Collaboration (NCD-RisC), 2021; Jeemon et al., 2021). India is no exception to this, with nearly a quarter(24.2%) of its population having HTN. Among the Indian population with HTN, only 17.5% have their blood pressure under control ( Koya et al., 2022) despite the recommendation of essential HTN care in resource-constrained settings by the International Society of Hypertension ( Unger et al., 2020) and the World Heart Federation ( Jeemon et al., 2021).
Poor HTN control is due to individual-level, healthcare professionals, and system-level barriers to implementing essential HTN care in India ( Gupta et al., 2019). Recent hypertension control programs like the India Hypertension Control Initiative (IHCI) ( Kaur et al., 2024) and the mPower Heart project ( Ajay et al., 2016) offer potential models for implementation; however, they are limited to public healthcare facilities. In countries like India, most of the population depends upon healthcare delivery by private hospitals with out-of-pocket expenses ( Das et al., 2021).
In addition, the current rate of adherence to the antihypertensive treatment is variable and ranges from 10%–90% among the population with HTN ( Unger et al., 2020; Dalal et al., 2021). Furthermore, non- adherence underlying HTN is multifactorial at the patient and healthcare system level ( Unger et al., 2020). Therefore, key barriers associated with suboptimal blood pressure control are limited access and poor adherence to recommended care ( Koya et al., 2022). A systematic care delivery strategy that prioritizes the unmet needs of people with HTN may be required to address these barriers.
Smartphone technology has enormous potential to address these barriers and assist those suffering from HTN ( Unger et al., 2020) given that more than half of India’s population uses smartphones and has internet accessibility ( Telecom Regulatory Authority of India, 2022). Even the National Digital Health Mission(2020) of India has recognized smartphones as a critical tool for digital healthcare infrastructure and patients as the key stakeholders in smartphone application development. However, to create a successful smartphone application, it is crucial to comprehend the targeted population acceptance of the application while considering the country-specific requirements ( Smith et al., 2015).
Many theories were proposed to understand and predict the acceptance and use of smartphone technology for industry, commerce, and healthcare. Our study used one of the most well-known models: the Unified Theory of Acceptance and Use of Technology (UTAUT) to explain acceptance, which is willingness or behavioural intention to use the smartphone application-based care ( Venkatesh et al., 2003). This model includes the following factors: 1) ease of use (effort expectancy, EE), 2) benefits (performance expectancy, PE) achieved by using a smartphone application, 3) belief of a person’s significant others related to their use of a smartphone application (social influence, SI), and 4) perception of a person related to the availability of the resources and support in using an application (facilitating conditions, FC) ( Venkatesh et al., 2003).
Other models explain technology acceptance, such as the Technology Acceptance Model, which focuses on perceived usefulness and ease of use, and the Theory of Planned Behaviour, which focuses on attitudes, perceived behavioural control, and subjective norms. Although these models have contributed substantially to understanding technology acceptance, they do not account for social, infrastructural, and organizational factors, which are particularly essential for executing smartphone application-based care in real-world settings. Therefore, the UTAUT model was chosen for its ability to explain social, infrastructural, organizational, behavioural, and technological factors. The appropriateness of the UTAUT model in predicting acceptance of technology in healthcare, however, is dubious, especially in low to middle-income nations ( Venkatesh et al., 2011). Previous studies examining acceptance of smartphone apps for hypertension care have found perceived usefulness and ease of use as significant predictors of acceptance; some have even found digital health literacy and social influence to play a significant role in technology acceptance. However, the majority of this evidence belongs to high-income nations ( Alessa et al., 2019; Breil, Salewski and Apolinário-Hagen, 2022). This limits its applicability in low- to middle-income nations or low-resource settings due to differences in access to healthcare, digital literacy, and sociocultural contextual factors. In India, though, the number of smartphone users increased, creating opportunities for digital hypertension care; there is limited evidence of acceptance of such care, especially in the private healthcare sector. As a result, context-specific factors should be included to increase the UTAUT model predictability ( Ahlan & Ahmad, 2014).
In this study, therefore the influence of age and gender on smartphone application acceptance needs to be investigated considering the wide range of age groups (30–79 years of age) and both men and women being affected by HTN ( Jeemon et al., 2021). Furthermore, most people who have HTN are not aware of their HTN status or its risk factors ( Jeemon et al., 2021). Thus, an investigation is needed to determine the influence of blood pressure status and the number of risk factors on smartphone application acceptance. Additionally, skills and experience to navigate and analyse the information provided by the smartphone application is required to make an informed decision ( Taylor & Todd, 1995; Norman & Skinner, 2006). Hence, necessitating investigation to determine the influence of education, digital health literacy, and m-health experience on acceptance of the smartphone application.
Thus, this study’s objective was to identify factors influencing the acceptance of a smartphone application among people with hypertension using the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model with age, gender, education, digital health literacy, m-health experience, status of blood pressure and its risk factors.
Method
Study design & setting
People with HTN were recruited from 1 public and 3 private healthcare facilities located in urban and rural areas of two districts of an Indian state in this cross-sectional study. These facilities were selected based on convenience and institutional permission to conduct the study. The study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) ( Vandenbroucke et al., 2007). This study received approval from the Institutional Ethics Committee of the first author’s affiliated university.
Eligibility criteria
Inclusion criteria
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1.
Participants of both genders, 30–79 years of age ( Jeemon et al., 2021).
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2.
Participants who were diagnosed with hypertension(≥ a year) by a physician.
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3.
Participants who use an Android-based smartphone(≥ a year).
Exclusion criteria
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1.
Participants who had malignant hypertension, gestational hypertension or learning disability.
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2.
Participants who cannot read and understand English and/or Gujarati vernacular.
Sample size
A priori power analysis was performed using G*Power version 3.1.9.7. Logistic regression was applied to estimate the required sample size to achieve 80% statistical power at a 5% significance level, considering an increase in odds of 1.67 among individuals with three or more hypertension risk factors as reported in a previous study ( Cherfan et al., 2020). The calculated minimum sample size was 111. The R 2 of other X was 0.04, and a 20% non-response rate was anticipated due to incomplete questionnaires ( Bujang, 2021). A convenience sample of 134 individuals with hypertension was recruited between February 2023 and December 2023.
Procedure
A Physiotherapist(s) with a bachelor’s degree in Physiotherapy and a minimum of one year of experience in managing people with hypertension collected the data. Three Physiotherapists received training in data collection from the principal investigator. Eligible people received a letter detailing the study and a notification that they would be contacted to discuss participation. Figure 1 shows the study flow diagram. The data collection occurred face-to-face with the participants. At recruitment, the eligible person’s written informed consent was obtained. A blood pressure measurement was carried out using a sphygmomanometer, following the recommendation of the International Society of Hypertension ( Unger et al., 2020).
Figure 1. Study flow diagram.
We collected data on age, gender, education, risk factors of hypertension, access to the internet, prescribed medicine & adherence them, preferred type of exercise, day and time to exercise. All the questions measuring the dependent variable (acceptance of smartphone application) and predictor variables (digital health literacy (EHL), facilitating conditions (FC), effort expectancy (EE), social influence (SI), and performance expectancy (PE)) were adapted from the previously validated instruments ( Supplementary Table 1). The constructs effort expectancy (EE), social influence (SI), performance expectancy (PE), and acceptance were measured on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree), with higher score indicating greater endorsement of the respective construct. They exhibited satisfactory indicator reliability (ranging from 0.710 to 0.976), composite reliability (ranging from 0.822 to 0.935), and average variance extracted (AVE) exceeded the 0.50 threshold ( Dou et al., 2017). Digital health literacy was assessed using the e-HEALS questionnaire (8 items) developed by Norman & Skinner (2006). It is measured on a 5-point Likert scale(1 = strongly disagree to 5 = strongly agree). The total score ranges from 8 to 40, with a higher score indicating greater perceived digital health literacy. Scoring does not include the 2 introductory questions regarding digital health resources usefulness and importance in the e-HEALs questionnaire. The e-HEALS questionnaire exhibits moderate test-retest reliability (intraclass correlation r = 0.64) and excellent internal consistency (Cronbach’s α = 0.90) ( Norman & Skinner, 2006).
The response was recorded in three categories for each item: “agree” (score of 4–5), “undecided” (score of 3), and “disagree” (score of 1–2) ( Rojanasumapong et al., 2021). The score interpretation used an item-level agreement count and a majority-response threshold. This methodological approach was used and reported by Rojanasumapong et al. (2021) and Walker et al. (2021). The interpretation of the dependent variable- acceptance and predictor variable- effort expectancy was as follows: 1) High (agreed to at least 3/5 questions) and 2) Low (agreed to less than 3/5 questions). Social influence and Facilitating conditions were defined as follows: 1) High (agreed to at least 2/3 questions) and 2) Low (agreed to less than 2/3 questions). Performance expectancy is classified as 1) high (agreed to ≥3/4 questions) or 2) low (agreed to <3/4 questions) ( Rojanasumapong et al., 2021). Digital health literacy is interpreted as high for a score of ≥26 out of 40 & low for a score of <26 out of 40 ( Richtering et al., 2017).
Originally written in English, the master scale was translated into an Indian language (Gujarati) as per the World Health Organization (2010) guidelines. The original English questionnaire was translated by two bilingual translators whose native language was Gujarati. One translator was an expert in the field of Physiotherapy, while another was a professional language expert. The research team prepared the reconciled Gujarati version by comparing the two versions and resolving any discrepancies through consensus. The reconciled Gujarati version was translated into English by two bilingual translators who had a background in Physiotherapy. The research team compared the two versions with the original English questionnaire and resolved the conceptual discrepancies. A meeting was convened between the research team and the group of translators to review all the versions. The suggested modifications were incorporated in the pre-final Gujarati version of the questionnaire. The questionnaire was field-tested with adults with hypertension. They were subsequently interviewed by the primary investigator, who inquired about the clarity, wording, and comprehension of each questionnaire item. Thereafter, the final Gujarati version of the questionnaire was produced. All participants completed the self-administered questionnaire.
Statistical analysis
Data were analyzed using the Statistical Package for Social Sciences software (SPSS), 23rd edition, IBM, United States. A predefined data dictionary was created for data entry. The data validity was assessed by the proofreading method ( Thakar et al., 2023). Before the analyses, the data were checked for completeness and coded. The mean and standard deviation were used to describe the continuous data. Percentages and frequencies were used for the categorical data. The dependent (acceptance of a smartphone application) and independent variables (age, gender, education, number of risk factors, facilitating conditions, effort expectancy, social influence, and performance expectancy) were categorical. Predictors of acceptance of smartphone-based care were identified using logistic regression. Predictors were examined using univariate logistic regression. Predictors (p ≤ 0.05) were thereafter entered into the hierarchical logistic regression analysis in blocks based on their conceptual relevance to acceptance of smartphone application. The first block consists of core UTAUT predictors followed by contextual and finally clinical predictors. The magnitude of the relationship between dependent and predictor variables was explained using the odds ratio (OR) and 95% confidence interval (CI) at a 5% significance level. Sensitivity analysis was performed to assess the robustness of findings obtained in the primary analysis.
Results
All analyses were performed on the pooled dataset from all centers. Our study participants’ mean age was 62 years (SD 10.2, range 30-79). The gender distribution was roughly balanced, with 54.8% female and 45.2% male. Most of our participants had at least received primary school-based education(80, 64.5%), as shown in Table 1.
Table 1. Sociodemographic characteristics of participants.
| Characteristics | Category | Frequency (n = 124) | Percentage(%) |
|---|---|---|---|
| Age (years) | <65 | 72 | 58.1 |
| ≥65 | 52 | 41.9 | |
| Gender | Male | 56 | 45.2 |
| Female | 68 | 54.8 | |
| Education | College based | 44 | 35.5 |
| School based | 80 | 64.5 | |
| Location | Urban | 66 | 53.2 |
| Rural | 58 | 46.8 | |
| m-health use | Used | 27 | 21.8 |
| Not used | 97 | 78.2 |
All participants had access to the internet and were owners of Android-based smartphones. SMS and other messengers(110, 88.7%), music and audio(94, 75.8%), videos(80, 64.5%) were the features of the smartphones commonly used by the participants. Email(101, 81.5%), website(89, 71.8%) and health applications(97, 78.2%) were not used by our participants. The mean digital health literacy was 24(SD 7.5).
Each participant received a prescription for medicine; among them, 83(66.9%) received a prescription for monotherapy. One hundred and nine(87.9%) participants stated complete adherence to prescribed medicines. Participants also had various risk factors associated with hypertension. Seventy six participants(61.3%) were overweight/obese, 53(42.7%) were diabetic, 25(20.2%) consumed tobacco, and 4(3.2%) consumed alcohol occasionally, as shown in Table 2. One hundred and fourteen participants preferred to adopt the walking program because of hypertension and its risk factors. They didn’t have any preference for the day of the week, but they did like to choose the time of the day when to go for walks.
Table 2. Hypertension related characteristics of participants.
| Characteristics | Category | Frequency (n = 124) | Percentage(%) |
|---|---|---|---|
| BMI (kg/m 2) | >30 | 20 | 16.1 |
| 25 to 29.9 | 56 | 45.2 | |
| 18.5 to 24.9 | 46 | 37.1 | |
| <18.5 | 2 | 1.6 | |
| Diabetes | Have | 53 | 42.7 |
| Do not have | 71 | 57.3 | |
| Alcohol consumption | Heavy drinking | 0 | 0 |
| Drank in the past | 3 | 2.4 | |
| Light-moderate drinking | 4 | 3.2 | |
| Never drinking | 117 | 94.4 | |
| Tobacco consumption | Current | 25 | 20.2 |
| Past | 9 | 7.3 | |
| Never | 90 | 72.6 | |
| Number of risk factors | 3 or more | 13 | 10.5 |
| 2 | 36 | 29 | |
| 1 | 58 | 46.8 | |
| 0 | 17 | 13.7 | |
| Medicine adherence(%) | 100 | 109 | 87.9 |
| 75 | 11 | 8.9 | |
| 50 | 1 | 0.8 | |
| 25 | 1 | 0.8 | |
| 0 | 2 | 1.6 | |
| Blood pressure control | Controlled | 67 | 54 |
| Uncontrolled | 57 | 46 |
The majority of the respondents(84.7%) of this study showed interest in using a smartphone application-based care for HTN. This study’s findings indicate that significant predictors of acceptance of smartphone application-based care were effort expectancy, facilitating conditions, digital health literacy, and blood pressure. There was insufficient information (frequency in one of the cells was less than 5) to suggest that social influence was associated with acceptance of smartphone application-based care. Age, gender, education, m-health experience, number of risk factors, and performance expectancy were not associated significantly with acceptance of smartphone application-based care.
Table 3 shows the findings of the simple binary logistic regression. In this study, people with high facilitating conditions were 5.6(95%CI: 1.86–16.80, p-0.002) times more likely to accept application-based care. People who perceived that smartphone applications would be easy to use (High effort expectancy) were 13.97(95% CI: 3.78–51.55, p- < 0.001) times more likely to accept smartphone application-based care as compared to those who perceived application would be troublesome to use. Moreover, the odds of accepting application-based care increased by 1.06(95%CI: 1.01–1.10, p-0.012), 1.07(95%CI: 1.001–1.14, p-0.047), and 1.11 times(95%CI: 1.03–1.19, p-0.004) respectively; among those with high systolic and diastolic blood pressure, as well as those with high digital health literacy; depicting relatively very small effect estimates ( Di Lorenzo et al., 2014).
Table 3. Predictors of the acceptance of smartphone application-based care.
| Predictors | B | SE for B | OR | p-value | 95% CI | |
|---|---|---|---|---|---|---|
| LL | UL | |||||
| Facilitating conditions | 1.72 | 0.56 | 5.60 | 0.002 | 1.866 | 16.803 |
| Digital health literacy | 0.11 | 0.04 | 1.11 | 0.004 | 1.034 | 1.192 |
| Effort Expectancy | 2.63 | 0.66 | 13.97 | <0.001 | 3.789 | 51.554 |
| Systolic blood pressure | 0.06 | 0.02 | 1.06 | 0.012 | 1.012 | 1.107 |
| Diastolic blood pressure | 0.07 | 0.03 | 1.07 | 0.047 | 1.001 | 1.139 |
Table 4 shows the strength of the association of the predictor with acceptance of smartphone application-based care. Multivariate logistic regression was performed to find the most significant predictors among different factors influencing acceptance of application-based care. In the 3-step model, predictors were entered in blocks based on their conceptual relevance to smartphone application acceptance. Technology acceptance constructs within the extended UTAUT framework were entered first (Effort expectancy and Facilitating conditions), followed by digital health literacy, and then clinical variables- systolic and diastolic blood pressure were entered. Effort expectancy was the most significant predictor (aOR = 8.55, 95%CI: 1.973–37.008, p-0.004) in the multivariate analysis. The odds of accepting smartphone application-based care increased by 8.55 times for individuals who perceived that the applications would be user-friendly, considering their access to technical expertise and resources.
Table 4. Multivariate logistic regression.
| Predictors | B | SE for B | OR | p-value | 95% CI | R 2 | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| LL | UL | |||||||||
| Step 1 | 30.7 | |||||||||
| Constant | -2.79 | 1.03 | ||||||||
| Effort Expectancy | 2.28 | 0.71 | 9.79 | 0.001 | 2.449 | 39.163 | ||||
| Facilitating Conditions | 0.86 | 0.63 | 2.37 | 0.169 | 0.693 | 8.090 | ||||
| Step 2 | ||||||||||
| Constant | -3.30 | 1.16 | 31.9 | |||||||
| Effort Expectancy | 2.11 | 0.72 | 8.22 | 0.004 | 1.996 | 33.822 | ||||
| Facilitating Conditions | 0.70 | 0.65 | 2.01 | 0.281 | 0.565 | 7.164 | ||||
| Digital health literacy | 0.05 | 0.04 | 1.05 | 0.304 | 0.960 | 1.141 | ||||
| Step 3 | 41.6 | |||||||||
| Constant | -14.60 | 4.53 | ||||||||
| Effort Expectancy | 2.15 | 0.75 | 8.55 | 0.004 | 1.973 | 37.008 | ||||
| Facilitating Conditions | 0.67 | 0.69 | 1.96 | 0.331 | 0.505 | 7.571 | ||||
| Digital health literacy | 0.07 | 0.05 | 1.08 | 0.141 | 0.976 | 1.187 | ||||
| Systolic blood pressure | 0.06 | 0.03 | 1.06 | 0.055 | 0.999 | 1.128 | ||||
| Diastolic blood pressure | 0.03 | 0.04 | 1.04 | 0.411 | 0.953 | 1.124 | ||||
Table 5 shows a sensitivity analysis. This analysis was performed to determine whether the findings of the primary analysis were influenced by dichotomization of the 5-point scale score. In this analysis, continuous mean scores were entered for the predictor variables, and acceptance (dependent variable) was retained as a binary variable. In this analysis, social influence was significantly positively associated (aOR = 8.20, 95%CI: 2.835-23.719, p < 0.001) with acceptance of smartphone application-based care, whereas effort expectancy had an insignificant positive association (aOR = 3.52, p-0.08). Other variables (digital health literacy, performance expectancy, and facilitating conditions) were not significantly associated with acceptance of smartphone application-based care.
Table 5. Sensitivity analysis.
| Predictors | B | SE for B | OR | p-value | 95% CI | R 2 | |
|---|---|---|---|---|---|---|---|
| LL | UL | ||||||
| Step-1 | 47.6 | ||||||
| Constant | -6.32 | 1.66 | |||||
| Social Influence | 2.21 | 0.47 | 9.09 | <0.001 | 3.607 | 22.912 | |
| Step-2 | 51.7 | ||||||
| Constant | -9.84 | 2.63 | |||||
| Social Influence | 2.10 | 0.49 | 8.14 | <0.001 | 3.124 | 21.207 | |
| Effort Expectancy | 1.36 | 0.69 | 3.89 | .049 | 1.009 | 15.000 | |
| Step-3 | 51.8 | ||||||
| Constant | -9.90 | 2.65 | |||||
| Social Influence | 2.04 | 0.51 | 7.70 | <0.001 | 2.835 | 20.886 | |
| Effort Expectancy | 1.26 | 0.73 | 3.53 | 0.09 | 0.840 | 14.849 | |
| Facilitating Conditions | 0.16 | 0.41 | 1.17 | 0.70 | 0.527 | 2.613 | |
| Step-4 | 52.1 | ||||||
| Constant | -8.90 | 3.24 | |||||
| Social Influence | 2.11 | 0.53 | 8.24 | <0.001 | 2.918 | 23.244 | |
| Effort Expectancy | 1.28 | 0.73 | 3.59 | 0.08 | 0.860 | 15.004 | |
| Facilitating Conditions | 0.21 | 0.42 | 1.24 | 0.62 | 0.542 | 2.818 | |
| Performance Expectancy | -0.33 | 0.65 | 0.72 | 0.61 | 0.202 | 2.549 | |
| Step-5 | 53.0 | ||||||
| Constant | -10.02 | 3.55 | |||||
| Social Influence | 2.10 | 0.54 | 8.20 | <0.001 | 2.835 | 23.719 | |
| Effort Expectancy | 1.26 | 0.73 | 3.52 | 0.08 | 0.845 | 14.658 | |
| Facilitating Conditions | 0.10 | 0.43 | 1.10 | 0.82 | 0.473 | 2.567 | |
| Performance Expectancy | -0.22 | 0.65 | 0.81 | 0.74 | 0.225 | 2.889 | |
| Digital health literacy | 0.40 | 0.41 | 1.49 | 0.33 | 0.670 | 3.303 | |
Discussion
This study aimed to identify key factors influencing the acceptance of smartphone application-based care among individuals with hypertension. The primary analyses in this study revealed that effort expectancy was the most significant predictor of acceptance. We found that individuals with hypertension were more likely to accept application-based care if they perceived it to be easy to use. Our study finding is consistent with the findings of the study by Alalwan et al. (2017). They also found effort expectancy to be a significant predictor of acceptance.
Facilitating conditions was another important construct influencing the acceptance of smartphone application-based care in this study. Similar findings were obtained in a study conducted in Bangladesh ( Alam et al., 2020). However, the multivariate analysis revealed it to be insignificant predictor of acceptance. We did not find performance expectancy as a significant predictor despite prior research suggesting its impact on the acceptance of care. We attribute this finding to the participants’ perception that smartphone application-based care will offer no advantage over in-person consultations with healthcare professionals. We also had inconclusive results related to social influence, as fewer than five persons represented both the characteristics of “having social support” and “not being interested in the future acceptance of smartphone application-based care”.
Although effort expectancy and facilitating conditions were significantly associated with acceptance of smartphone application-based care in the primary analysis, the sensitivity analysis revealed that they were insignificantly associated with acceptance of smartphone application-based care. This may have resulted from Likert-scale responses dichotomization, leading to some information loss and variation in the strength and significance of association between predictors and acceptance of smartphone application-based care. Additionally, in the sensitivity analysis, social influence, when analysed continuously, was significantly associated with acceptance of smartphone application-based care; however, in the primary analysis it could not be estimated reliably due to fewer cell frequencies. This suggests that endorsement of family and friends might influence the acceptance of smartphone application-based care among people with hypertension.
Furthermore, during the primary analysis, we also found poor digital health literacy among our participants. These findings are consistent with the findings of the studies conducted in Japan ( Mitsutake et al., 2016), whereas the study conducted in Thailand reported higher digital health literacy ( Rojanasumapong et al., 2021). Comparing the scores among different countries’ populations may not be appropriate here, considering they have different demographics, socioeconomic status, internet availability, and accessibility. In addition, digital health literacy had negligible influence, and the m-health experience did not influence the acceptance of smartphone application-based care. We believe this occurred because individuals with hypertension expected to receive training for this newer form of care.
We did not find age, gender, and education influencing the acceptance of smartphone application-based care, even though previous studies reported it ( Norman & Skinner, 2006; Zhao et al., 2018). Smartphone application-based care is still in its early stages in India; therefore, we believe we did not obtain any difference in acceptance between age groups, levels of education, and genders.
The odds ratio was close to 1 for blood pressure, which is suggestive of the negligible influence of it on the acceptance of smartphone application-based care ( Di Lorenzo et al., 2014). We also did not find the impact of multiple risk factors on the acceptance of smartphone application-based care. One of the possible contributing factors to this may be participants’ lack of awareness about hypertension and their status of blood pressure control ( Jeemon et al., 2021). All participants in this study received a medication prescription consistent with the recommendations of the International Society of Hypertension ( Unger et al., 2020). Despite this, uncontrolled hypertension was prevalent in 46% participants, 12.1% had poor adherence to the prescribed medication, and 86.3% had risk factors for hypertension.
This occurrence is a result of the current care delivery system. They are focused on pharmacological intervention, even though exercise and medicine are equally effective in reducing blood pressure ( Naci et al., 2019). Therefore, to include additional essential hypertension management domains in future smartphone application-based care, participants in this study were asked about their preferred exercise behavior. The majority of the participants preferred to adopt a walking program (91.9%) and showed interest in using smartphone application-based care in the future (84.7%).
The identified factors in this study are the preferences of people residing in peculiar socio-cultural and geographical locations. Therefore, the characteristics of the participants in this study provide information about who will be eligible to use the novel smartphone application-based care. In this study, participants were recruited from urban and rural areas who had access to the internet. All participants were Android-based smartphone users and have used SMS, audio, and video features of the smartphone.
Strengths and limitations
Among the several strengths, this study sample includes participants from urban and rural areas, public and private healthcare facilities, those with controlled and uncontrolled hypertension, and those who adhere to or do not adhere to the prescribed medication. Moreover, most participants had not utilized smartphones for health-related activities, highlighting the necessity for creating smartphone-based hypertension management programs.
Our study has many limitations. The length of the questionnaire prevented us from further investigating other factors, including perceived risk and trust. The translated questionnaire did not undergo formal psychometric evaluation. Self-reporting bias may have been introduced by self-reported responses. In this study, only existing Android smartphone users were recruited using convenience sampling. Univariate analysis yielded a wider confidence interval, while multivariate analysis, which adjusted for another factor, offered a credible confidence interval for effort expectancy in this investigation. Dichotomization of the constructs may have resulted in some information loss and influenced the strength and significance of association of the predictors with acceptance. Social desirability bias may have contributed to the majority of participants’ interest in using the smartphone application-based care in the future.
Implications
In the primary analysis, an antecedent factor integral to the acceptance of a smartphone application for hypertension was identified: effort expectancy. Nonetheless, effort expectancy was found to be an insignificant predictor of acceptance of smartphone-based care during sensitivity analysis. Therefore, findings of association between the predictor and acceptance of smartphone-based care should be considered preliminary and not proof of independent effect. Thus, while creating and commissioning smartphone application-based programs to address self-care among people with hypertension, app developers and policymakers may focus on making the application convenient and easy for users to adopt. Additionally, in the sensitivity analysis, social influence was found significantly associated with acceptance of smartphone-based care; however, in the primary analysis, it could not be reliably estimated due to sparse data. This suggests that the influence of family and friends requires further investigation in future research on smartphone-based hypertension care.
The development of smartphone applications considering the requirements of the person with hypertension may play a pivotal role in improving access to health-related information and services for people with hypertension and load-sharing for healthcare professionals. In addition, evidence-based information and recommendations via smartphone applications in the future may contribute to secondary prevention in chronic conditions like hypertension.
Conclusion
Effort expectancy (ease of use) was identified as an important contributor to the acceptance of smartphone-based hypertension care in the primary analysis. However, social influence emerged as an important contributor, and effort expectancy was not retained in the sensitivity analysis. These findings suggested that individual, technological, and social factors may influence acceptance of smartphone-based hypertension care, and that future research should confirm their independent contributions.
Ethics approval
This study received approval from the Institutional Ethics Committee of the first author’s affiliated university (CHA/IEC/ADM/23/04/410). Ethical approval for the study was obtained on 19 th January 2023 from the Institutional Ethics Committee and no participant was recruited or data were collected prior to ethics approval.
Acknowledgement
The main & corresponding author is a doctoral candidate who conducted this doctoral research project under the supervision of co-authors. The authors thank Wellcome Trust DBT India Alliance for supporting this work. The authors thank Dr. Dhruv Dave, Dr. Dipali Rana, Dr. Tarpan Shah and Dr. Mitva Thakkar for their assistance in translation of the questionnaire, Dr. Anjali Manjar, Dr. Jeel Velani, and Dr. Niki Patel, for their assistance during data collection.
Funding Statement
This work was supported by a Wellcome Trust DBT India Alliance [Grant IA/CPHE/16/1/502650], awarded to Dr. Sureshkumar Kamalakannan.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
[version 3; peer review: 2 approved]
Data availability
Extended data
Figshare: Reporting (STROBE) checklist and dataset for: Predictors of Acceptance of Smartphone Application among People with Hypertension in India: A Cross-Sectional Study, https://doi.org/10.6084/m9.figshare.31923069.v2 ( Thakar et al., 2026).
This project contains the following extended data:
Supplementary Table 1. Measurement items and participants’ responses in percentage.
STROBE Statement—Checklist of items for this cross-sectional study.
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
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