Information | Free Full-Text | Leveraging the TOE Framework: Examining the Potential of Mobile Health (mHealth) to Mitigate Health Inequalities
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1. Introduction
1.1. mHealth Potential to Reduce Health Disparities
1.2. Theoretical Framework and Hypotheses Development
1.2.1. Technological Factors (TF)
The relative advantage of mHealth has a significant influence on mHealth adoption.
The compatibility of technology has a statistically significant influence on mHealth adoption.
1.2.2. Organizational Factors (OF)
Management support has a statistically significant influence on mHealth adoption.
Organizational readiness has a statistically significant influence on mHealth adoption.
1.2.3. Environmental Factors (EF)
External support has a statistically significant influence on mHealth adoption.
Government regulation has a statistically significant influence on mHealth adoption.
mHealth adoption has a statistically significant influence on the reduction in health disparities.
1.3. Study Objectives
The objectives were to (i) assess and authenticate the technology–organization–environment (TOE) theoretical framework, (ii) identify TOE factors impacting the adoption of mHealth technology among healthcare professionals (doctors and nurses), and (iii) gauge the impact of mHealth adoption on reducing health disparities in resource-limited settings in Pakistan.
2. Materials and Methods
2.1. Participants and Procedure
A cross-sectional survey was carried out at the six public and private hospitals in two districts (Lodhran and Multan) of Punjab, Pakistan. The population of the study comprised registered regular physicians and nurses working full-time in the participating hospitals. These hospitals are recognized and regularized by the Punjab Medical and Dental Council (PMDC), and the Pakistan Nursing and Midwifery Council (PNMC).
2.2. Research Instrument
To ensure the questionnaire’s quality, three professionals in information management, public health, and health communication conducted a pre-test, leading to suggested changes including statement rearrangements and rephrasing. A pilot test was performed on the first 20 responses to assess the validity of the questionnaire. However, we found no ambiguities that might lead to inaccurate data collection. The findings showed that the statements under each construct accurately assess the construct they aim to measure. The accuracy of data collection was assured by performing internal consistency checks within a respondent’s answer, while each respondents’ anonymity and confidentiality were also ensured. The questionnaire’s reliability was assessed using Cronbach’s alpha. The Cronbach’s alpha scores were as follows: 0.865 for the four statements on management support (MS); 0.612 for the three items under organizational readiness (OG); 0.863 for the four items under relative advantage (RA); 0.895 for the four items loaded on compatibility (CP); 0.861 for the three statements on external support (ES); 0.763 for the statements on the government regulations construct (GR); 0.854 for the four statements about mHealth adoption (AD); and 0.85 for the five statements on health disparity (HD) reduction. The overall Cronbach’s alpha value for the 31 statements across eight constructs was 0.85, indicating good reliability of the questionnaire.
2.3. Data Collection and Analysis Procedure
The survey for the study was distributed to participants using purposive sampling. A three-member research team, consisting of research students, voluntarily collected the data through personal visits to hospitals, distributing printed copies via surface mail, and sending a questionnaire link to participants’ WhatsApp numbers and email IDs. In total, 500 questionnaires (both online and printed) were distributed, and after three follow-ups with two-week intervals 314 completed questionnaires were received, representing a response rate of 62.8%. All 314 questionnaires were deemed valid for data analysis. Data collection took place between March 2023 and May 2023.
We used the “statistical package for social sciences” (SPSS software v26) for the analysis of collected participants’ data. Missing values in the dataset were addressed using expectation–maximization (EM) techniques, a widely accepted method for handling missing data. EM involves selecting random values for missing data points and estimating a second set of data based on those values.
Demographic information from the respondents was subjected to Chi-squared tests. For structural equation modeling (SEM), the analysis of moment structures (AMOS) was employed. SEM was utilized to estimate correlations between latent variables, assess the influence of exogenous variables on endogenous variables following the TOE theoretical framework pathways, and validate hypotheses. The significance level was set at <0.05.
2.4. Ethical Approval
The research commenced following the receipt of ethical approval from the Departmental Research Committee, Department of Information Management, the Islamia University of Bahawalpur, Pakistan, approval number: 4/DoIM, dated 16 December 2022. Informed consent was obtained from all questionnaire respondents.
4. Discussion
In summary, the results of the structural equation modeling (SEM) analysis confirmed the validity of only two hypotheses, namely, the positive and statistically significant influence of management support (MS) and external support (ES) on mHealth adoption (AD). Conversely, the impact of relative advantage (RA), compatibility (CP), organizational readiness (OR), and government regulations (GR) on mHealth adoption (AD), as well as the influence of mHealth adoption on reducing health disparities, were found to be positive but not statistically significant. Therefore, these five proposed hypotheses could not be validated due to the absence of statistical significance. Additionally, the SEM findings demonstrated acceptable goodness of fit indices, with χ2 = 2.050, df = 412; p = 0.000; IFI = 0.920; TLI = 0.909; CFI = 0.920; and RMSEA = 0.058, indicating that our proposed mHealth adoption model is deemed acceptable. This suggests that the model has the potential to contribute to mHealth adoption and the reduction in health disparities in low-resource settings.
Informed by our study’s findings, we believe that enhancing mHealth adoption is dependent upon emphasizing educational efforts, such as conducting practical training sessions for healthcare professionals to enhance their digital health literacy. Additionally, explaining the potential advantages of the utilization of mobile health solutions is crucial. Given the limited digital health literacy of many practitioners in low-resource settings, we recommend prioritizing simplicity and user-friendly interfaces in mHealth apps to cater to healthcare professionals with varying technological proficiency. Collaboration between healthcare providers, mobile app developers, and local communities is also essential for creating culturally relevant and accessible mHealth solutions. The development of mobile apps should be tailored to specific community health needs, incorporating local languages and cultural nuances for better resonance.
The study suggests that for mHealth adoption to be successful it needs to be endorsed by trusted figures, such as local community leaders, reputable influencers, and senior healthcare professionals. Moreover, our research emphasizes the need for a holistic approach that considers cultural norms, economic circumstances, and technological access factors in order to ensure the widespread adoption of mHealth within a community.
4.1. Study Limitations
Data were gathered through a survey method, relying on respondents’ knowledge and self-reports. This approach introduces the potential for respondents’ self-reported statements to deviate from the actual situation. To mitigate the questionnaire’s limitations, it underwent pre-testing by two specialists in public health and health communication. The reliability of the questionnaire was assessed using Cronbach’s alpha, resulting in a value of 0.85 for the 31 elements categorized into eight constructs, indicating its reliability.
The study’s lack of generalizability is acknowledged as a limitation due to variations in settings, demographic characteristics, technology infrastructure availability, and levels of digital health literacy among different populations. Therefore, caution is advised when extrapolating the findings to other populations or settings, such as tertiary or secondary healthcare populations.
4.2. Note to Readers
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