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The ultimate goal of any prescribed medical therapy is to achieve desired outcomes of patient care. However, patient nonadherence has long been a major problem detrimental to patient health, and thus is a concern for all health care providers. Moreover, nonadherence is extremely costly for global medical systems because of unnecessary complications and expenses. Traditional patient education programs often serve as an intervention tool to increase patients’ self-care awareness, disease knowledge, and motivation to change patient behaviors for better adherence. Patient trust in physicians, patient-physician relationships, and quality of communication have also been identified as critical factors influencing patient adherence. However, little is known about how mobile patient education technologies help foster patient adherence.
This study aimed to empirically investigate whether and how a mobile patient education system (MPES) juxtaposed with patient trust can increase patient adherence to prescribed medical therapies.
This study was conducted based on a field survey of 125 patients in multiple states in the United States who have used an innovative mobile health care system for their health care education and information seeking. Partial least squares techniques were used to analyze the collected data.
The results revealed that patient-physician communication and the use of an MPES significantly increase patients’ trust in their physicians. Furthermore, patient trust has a prominent effect on patient attitude toward treatment adherence, which in turn influences patients’ behavioral intention and actual adherence behavior. Based on the theory of planned behavior, the results also indicated that behavioral intention, response efficacy, and self-efficacy positively influenced patients’ actual treatment adherence behavior, whereas descriptive norms and subjective norms do not play a role in this process.
Our study is one of the first that examines the relationship between patients who actively use an MPES and their trust in their physicians. This study contributes to this context by enriching the trust literature, addressing the call to identify key patient-centered technology determinants of trust, advancing the understanding of patient adherence mechanisms, adding a new explanation of the influence of education mechanisms delivered via mobile devices on patient adherence, and confirming that the theory of planned behavior holds in this patient adherence context.
The primary goal of any prescribed medical therapy is to achieve the desired medical outcomes of patient care, which requires a certain level of patient adherence, a critical measure of quality care. Previous studies have shown that nonadherence results in an economic burden of approximately US $100 to US $300 billion per year in the United States [
Poor patient adherence can obscure a clinician’s assessment of therapeutic effectiveness and result in avoidable hospitalization, increased mortality risk, and increased health care costs [
The World Health Organization Multidimensional Adherence Model identifies five interrelated dimensions of patient medication adherence: (1) social and economic factors (eg, limited access to health care facilities), (2) health care system factors (eg, provider-patient relationship and providers’ communication skills), (3) medical condition–related factors (eg, severity of symptoms), (4) therapy-related factors (eg, duration of a therapy), and (5) patient-related factors (eg, patient age, gender, and knowledge of a disease) [
However, patient education is not always
With the rapid advances and prevalence of the latest ubiquitous computing and mobile communication technologies, mobile health (mHealth) systems have been partially or fully implemented and embedded in current health care systems to foster patient-centered care. As such, increasing research has started to explore whether mHealth technologies can help improve patients’ adherence behavior. In this study, we define mHealth technologies as medical and public health practices and services, such as health care–related reminders, advice, and information delivered through mobile devices such as mobile phones [
Therefore, in this study, we aimed to fill such a knowledge gap to identify and empirically examine in-depth mechanisms through a lens of theory of planned behavior (TPB) on how and why such patient-physician trust is formed and leveraged by a mobile patient education system (MPES), leading to increased actual adherence to prescribed medical therapies. Thus, we conducted a field survey of 125 patients in multiple states in the United States who have used an innovative mHealth system for their health care education, information seeking, and communication with physicians. Our main finding was that patient-physician communication and the use of an MPES significantly increased patients’ trust in their physicians, which further influenced patient attitude, intention, and actual behavior toward treatment adherence.
In this subsection, we propose our theoretical model and develop hypotheses to explain the role of an MPES in a health care setting, where an MPES affects patients’ trust in physicians, which further influences their adherence. Our model is presented in
Trust has been widely studied and recognized as a cornerstone of effective patient-physician relationships [
Next, we focus on discussing 3 key factors and their underlying mechanisms associated with patients’ trust: patients’ general satisfaction, communication quality, and communication barriers between physicians and patients. Patients’ general satisfaction reflects their perceptions and attitudes toward physicians and medical care in general [
Communication also plays an essential role in the patient-physician relationship, especially in the effectiveness of this relationship [
Our next hypothesis predicts that mobile patient education can help increase patients’ trust in their physicians. Our contextual assumption for the design of this study was that patients would use an MPES to learn about the treatment that they were seeking in a just-in-time manner in their physicians’ waiting room right before seeing a physician about the treatment. During this time, patients can learn the key terms, procedures, issues, risks, and benefits involved in a treatment and, thus, are able to communicate with a physician regarding their treatment plan with more knowledge and confidence. Furthermore, such an educational artifact allows basic questions to be answered ahead of time and, thus, enables patients to use the limited time with their physicians more effectively. Better and more effective communication enhances patients’ confidence and trust in their physicians.
To further explain and justify this prediction, we used several lines of reasoning and evidence. First, one of the biggest problems in trust formation between patients and physicians is poor communication and misunderstandings between them because of information asymmetry [
Second, patients judge the competencies of physicians in a multifaceted way [
Third, a key issue in the relationship between patients and physicians is too little time spent together, which undermines communication and trust [
Fourth, we also assert that physicians who provide such an MPES with personalized patient materials and tools in their waiting rooms not only better prepare their patients to communicate with them about their treatments but also provide a positive signal of service quality and empathy that can facilitate patient-physician communication and trust formation [
In summary, using an MPES is predicted to reduce information asymmetries between patients and physicians, empower patients taking an active role in clinical decision-making processes, facilitate effective communication in a time-constrained visit, and send a message of care and empathy from the physicians, thus leading to a better patient-physician relationship and enhancing patients’ confidence and trust in their physicians. Therefore, we propose that the level of trust is likely to be higher when the level of patient use of a mobile patient education app is higher. As such, we posit that an increase in patients’ use of a mobile patient education app designed to increase their knowledge of a specific medical treatment will increase their trust in their physicians (hypothesis 4).
A trustful relationship between patients and health care service providers is key to patient adherence. In particular, a healthy relationship is established based on patients’ trust in physicians and empathy from physicians [
The research model. H1: hypothesis 1; H2: hypothesis 2; H3: hypothesis 3; H4: hypothesis 4; H5: hypothesis 5; H6: hypothesis 6; H7: hypothesis 7; H8a: hypothesis 8a; H8b: hypothesis 8b; H9a: hypothesis 9a; H9b: hypothesis 9b.
We used the TPB to account for the formation of attitudes from beliefs, norms, and self-efficacy, which can then be used to predict subsequent behaviors [
Next, we followed the TPB assumption that normative beliefs will influence actual adherence behaviors. Normative beliefs represent a person’s perceived social pressure to comply with a recommendation as informed by their valued social referents for the context [
According to the TPB, norms affect individuals’ intentions and behaviors [
Next, we followed the TPB assumption that one’s actual behaviors will be influenced by one’s efficacy. As a long-established component of the TPB, self-efficacy is highly important in the medical treatment adherence context as it covers patients’ basic self-assessment regarding their ability to effectively follow medical advice and whether they believe that a recommended treatment is efficacious [
The MPES on which we focused in this study was codeveloped by the first author’s research group and ABC Company (anonymized), which is a software company whose main products are health care systems aiming to address the communication and trust issues between patients and physicians to improve patient adherence behavior. The MPES has been successfully sold and deployed in many clinics and hospitals in North America, South America, and Asia. Patient users can access patient education materials in physicians’ clinics or anywhere else through different mobile devices. Its web portal interface is easy to navigate, and the educational contents are customized according to each patient’s health situation.
This study was approved by the institutional review board of the Southern Utah University (approval number: 15-052013). We worked with the ABC Company to obtain their support for conducting this research with their patients. With the assistance of the company’s attorney, we carefully followed the US Health Insurance Portability and Accountability Act's (HIPAA) Privacy, Security, and Breach Notification Rules to protect patients’ rights. Finally, after obtaining all approvals, we were able to post a web-based flyer on the ABC Company’s patient web portal to invite interested patients who had used the MPES to participate in our field study. We provided a US $10 honorarium for each survey respondent who provided valid and complete responses.
We conducted a field study with real patients from multiple states in the United States to test our research model. These patients were able to use an MPES designed to improve patient education and experience at their physicians’ clinics or hospitals. Patient participation was completely voluntary. They were given web-based instructions to fill out a web-based questionnaire distributed solely inside the MPES, where only active patient users could see our project flyer and answer our questions on their perceptions and assessments of using the MPES and its influence on their adherence behavior. During a period of 2 and a half months, after both plastic surgery and obstetrics versions of the MPES were launched, we received a total of 126 patient responses. We excluded 0.8% (1/126) of responses from a male patient from further data analysis because most questions were unanswered, resulting in 125 valid responses, all of which were not surprisingly from female patients, adequately representing the patient population that we reached out to. After the initial data collection, several duplicate responses were identified and removed before data analysis.
After the initial development of the questionnaire, we made it accessible on the web. We then circulated it among 26 senior students at a US university to obtain feedback on the relevance and clarity of the survey questions and on whether the web-based questionnaire could be accessed properly through different mobile devices, such as iPads, iPhones, and other types of smartphones and tablets. As we planned to deploy the questionnaire on the web, the first author also conducted a 20-minute face-to-face meeting with each student to verify the clarity of the web-based questionnaire instructions in that no face-to-face contact was expected to take place between the researchers and the actual patient respondents. According to the feedback that we obtained from these pilot sessions, we were able to further refine a few ambiguous questions and adjust the web-based questionnaire interface to better fit heterogeneous mobile platforms.
Of the 125 valid survey respondents, 110 (88%) patients were from the plastic surgery field, 9 (7.2%) were from obstetrics, and 6 (4.8%) were from other medical fields. All the respondents (125/125, 100%) were female. The average age was 39.6 (SD 12.9) years.
For the study constructs and measures, we adapted existing validated psychometric scales and measurement items from established research. We then tailored the questions to fit the context of this study.
Items for a patient’s general satisfaction with physicians were taken from the
As all our constructs were reflective and our research model contained both first-order and second-order constructs, partial least squares path modeling was used to examine our research model [
In this study, we used the marker variable technique suggested by Lindell and Whitney [
Demographic information of the participants (N=125).
Variable and category | Participants, n (%) | ||
|
|||
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Female | 125 (100) | |
|
Male | 0 (0) | |
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<20 | 2 (1.6) | |
|
20 to 29 | 30 (24) | |
|
30 to 39 | 33 (26.4) | |
|
40 to 49 | 35 (28) | |
|
50 to 59 | 14 (11.2) | |
|
≥60 | 11 (8.8) | |
|
|||
|
Lower than high school or secondary school | 0 (0) | |
|
High school or secondary school | 21 (16.8) | |
|
Some university but had not completed a degree | 33 (26.4) | |
|
Associate degree | 16 (12.8) | |
|
Bachelor’s degree | 38 (30.4) | |
|
Master’s degree | 13 (10.4) | |
|
Doctorate or PhD | 4 (3.2) | |
|
|||
|
Poor | 0 (0) | |
|
Fair | 7 (5.6) | |
|
Good | 28 (22.4) | |
|
Very good | 53 (42.4) | |
|
Excellent | 37 (29.6) | |
|
|||
|
Never | 0 (0) | |
|
Seldom | 0 (0) | |
|
Occasionally | 7 (5.6) | |
|
Frequently | 14 (11.2) | |
|
Always or every day | 104 (83.2) | |
|
|||
|
Never | 2 (1.6) | |
|
Seldom | 18 (14.4) | |
|
Occasionally | 54 (43.2) | |
|
Frequently | 46 (36.8) | |
|
Always or every day | 5 (4) |
We first tested internal consistency and then examined convergent and discriminant validity of the measurement items. Composite reliability and average variance extracted (AVE) [
Furthermore, to verify discriminant validity, we also computed the square root of the AVE for all latent variables and compared them against their correlations with other constructs [
As all factor measures loaded highly (
In this study, we selected and examined several control variables related to our dependent variable,
Results of research model testing. *
In this paper, we aimed to systemically examine how and why patient-physician trust is formed and how patient education delivered via an MPES influences patients’ trust and patient adherence. We leveraged the TPB along with fundamental concepts of trust to propose a model that explains how an MPES, along with other factors, can help foster trust of patients in physicians and eventually increase treatment adherence. We first confirmed that general satisfaction with physicians, communication quality, and use of an MPES jointly facilitate and foster patients’ trust in physicians, whereas communication barriers decrease trust. We also found that patients’ trust in their physicians was indeed a significant determinant of positive attitude formation. We found compelling evidence for our expanded model based on the TPB. Attitudes toward treatment adherence were positively related to intentions toward treatment adherence, and these intentions were positively related to actual treatment adherence behaviors. In addition to behavioral intention toward adherence, we found that response efficacy and self-efficacy enhance actual treatment adherence behaviors. A key exception in our model was the insignificant influence of social norms (ie, subjective and descriptive norms). This was particularly surprising in the context of plastic surgery visits, in which we expected social norms to play a stronger role. As most of our study participants were patients of plastic surgery who primarily sought treatment to improve their physical appearance instead of for medical reasons, the insignificant results associated with social norms were likely caused by their privacy concerns, which requires further research. Our research model explained 61.9% of the variance (
First, as one of the first studies that systematically examine the relationship between the use of mobile technologies and patients’ trust in physicians in the context of patients being active users of technologies, we found that patient use of an MPES positively influences patient trust in physicians. Previous studies have examined patients’ trust in technologies used solely by providers, such as electronic health records, electronic monitors, and web-based health communities supported by the internet [
Second, our study proposed an integrated model to explain how communication, patient satisfaction, MPES use, and trust foster the degree of actual treatment adherence. We examined these 4 factors in the patient adherence context. We found that communication, patient satisfaction, and MPES use jointly influence trust, which further fosters actual patient adherence. Overall, the integration of factors from multiple streams of the literature to explain patient adherence behavior is one of our core theoretical contributions, especially from a TPB theoretical lens. This finding advances our understanding of underlying patient adherence mechanisms.
Third, we examined an MPES in a clinical setting. Although the focus of our study, patient education mechanisms, is not new to the patient adherence literature, previous relevant patient adherence research has not examined patient education mechanisms delivered through mobile systems. Given the popularity and availability of mobile devices, patient education is increasingly delivered and communicated through mobile devices. Previous patient education studies have primarily focused on engaging patients in mHealth intervention programs to improve their adherence behaviors regarding medication [
Finally, we extended and empirically tested the TPB in the context of patient adherence. From a theoretical standpoint, few empirical studies have examined the TPB in the context of patient adherence [
It is critical for physicians to understand how to enhance patient adherence to their treatment recommendations. Our study indicates that an approach that may help is for physicians to proactively leverage mobile technologies such as an MPES to enhance the provider-patient relationship and foster patient trust in physicians. We recommend that physicians consider the trust-building process both on the web and in the office. Web-based trust can be developed by providing quality apps for patients to adopt and use while considering patients as active partners in the care process. Offline trust can be built through 2-way effective conversations with patients by addressing their personalized treatment needs. This study also highlights the importance of choosing the appropriate mobile technologies and apps for patients to use given that a message of care and empathy to patients disseminated through an MPES can further increase patients’ trust in physicians and enhance patient adherence.
Our results also imply that additional changes to clinical workflows in hospitals and clinics may enhance patient-centered care. For example, the Mayo Clinic, as the leading hospital system in the United States, has implemented a secure patient message portal system to improve communication quality between physicians and patients, patient engagement, and patient-centered care [
Our study has several limitations owing to the nature of the field study but also provides new opportunities for future research. First, this field study was restricted to physicians who had adopted the MPES in their practices and were willing to offer us access to their patients. Although all valid survey respondents were female, they were representative of the population in the related medical practices—plastic surgery and obstetrics.
Second, after going through many complicated legal and research coordination processes, we were only allowed to conduct the study with patients who had tried the MPES; thus, we were not able to reach patients who were still using traditional patient education systems as a control group. Patient adherence behavior has not been well studied across populations, diseases, and settings, thus making it difficult for health professionals and patients to know which strategies work and which do not [
Third, according to the behavior model of persuasive design by Fogg [
In summary, extensive research has been conducted that examines factors associated with patient adherence, some of which has examined the relationship between patient education and adherence. However, as highlighted by extant literature, the underlying relationship between patient education and adherence is complex, and no studies to date have been conducted that explore and explain the underlying mechanisms of patient education delivered through mobile devices on patient adherence [
In this study, our MPES provided additional patient care at the physician’s office or clinic through a real-time mobile personalized patient education intervention program, which enabled 2-way physician-patient communication beyond the patients’ in-person office visits. Our study participants were active patient users of the MPES, on which they could access their individual patient education materials and directly interact with their physicians and caregiver teams on the web. The results of our study imply that the MPES can be effectively leveraged by physicians’ offices or clinics for more seamless high-quality care. It can also be a trade-off for physicians’ offices or clinics to handle additional workload to provide more personalized services to their patients through an MPES. Nevertheless, our study findings indicate that the extended service lines provided on the MPES beyond regular in-person office visits may significantly improve patient-physician communication quality and increase patients’ trust in their physicians, thus leading to more optimal health outcomes such as enhanced patient adherence to their therapy or treatment plans.
In conclusion, our study is one of the first that examines the relationship between patients who actively use an MPES and their trust in their physicians. This study contributes to this context by (1) enriching the trust literature addressing the call to identify key patient-centered technology determinants of trust, (2) advancing the understanding of patient adherence mechanisms, (3) adding a new explanation for the influence of education mechanisms delivered through mobile devices on patient adherence, and (4) confirming that the TPB holds in this patient adherence context.
Patient survey instrument and questions and measures for the theoretical model.
Measurement model statistics and validity details.
average variance extracted
mobile health
mobile patient education system
theory of planned behavior
The authors would like to acknowledge the funding support provided by the University of South Carolina (USC), Columbia, South Carolina, United States (grant 80002838), and partial support from the USC Big Data Health Science Center, a USC excellence initiative program (grant BDHSC-2021-14 and BDHSC-2022); the USC Advancing Chronic Care Outcome through Research and Innovation Center (ACORN-2022); and a Utah Science Technology and Research Initiative grant (USTAR-TCG). The content is solely the responsibility of the authors and does not necessarily represent the views of the funding agencies.
Access to the data set analyzed in this study may not be available to researchers because of Health Insurance Portability and Accountability Act and institutional review board constraints.
None declared.