A study “Sociotechnical Factors Affecting Patients’ Adoption of Mobile Health Tools: Systematic Literature Review and Narrative Synthesis”, published in JMIR mHealth and uHealth, investigated the literature to understand the factors affecting patients’ adoption of mHealth tools from technical, social, and health perspectives.
- Mobile health tools have emerged as a promising health care technology that may contribute to cost savings, better access to care, and enhanced clinical outcomes; however, it is essential to ensure their acceptance and adoption to harness this potential. Patient adoption has been recognized as a key challenge that requires further exploration.
- The study analyzed 147 peer-reviewed articles published over the last ten years to identify potential factors impacting patient adoption of mHealth tools and their implications for healthcare practices.
- Technical factors such as the tool’s usefulness, ease of use, data privacy, cost, technical issues, and user experience are key for adoption.
- However, social and personal factors such as a patient’s demographics, personal characteristics, and social and cultural aspects were even more prominent.
- Health-related factors – the specific nature of the disease or health condition that the patient is suffering from, health consciousness and literacy, health behavior, relation to other therapies, integration into patient journey, the patients’ insurance status, and the care team’s role – also had a clear impact on patients’ decision to adopt a mHealth tool.
- The findings suggest that technology providers alone are not likely to achieve success by focusing on creating technically superior tools; social, health care, and policy-related factors must be considered, underlining the central role of care teams and health care policy in promoting adoption.
Combating the digital divide with truly inclusive design and overall fit into the patient journey
The study showed that many demographic factors are typically not the root cause for the lack of adoption per se, but rather other underlying causes were at play, pointing back to a lack of skills and literacy that were typically correlated to older patients, those with a lower level of education, or those belonging to lower socioeconomic classes.
Designing for inclusivity takes into account the unique features, environments, and cultural contexts of users, implying that many aspects of the digital divide may be addressed through an inclusive design that incorporates cultural appropriateness, easy-to-understand lay language that does not need high literacy levels, and ease-of-use that does not require any sophisticated technical skills.
Another element that may help reduce disparities in adoption is patient education and support. Comprehensive training materials and continuous technical support may assist some of the most unfavourable patient populations to benefit from these tools. It is worth noting that extending patient education and awareness programs to go beyond mHealth to include health literacy in general and encourage healthy behaviours may foster adoption, as research has shown that these factors may indirectly promote the tools’ uptake.
Ensuring the tools’ fit into the overall patient journey and treatment plan, based on the understanding that mHealth apps are not used in isolation, is crucial for sustainable adoption. Technology providers may opt to co-create the tools with patients (and clinicians) to ensure that they have taken their overall journey into account and establish how their tool relates to other treatments that the patients are receiving, any co-morbidities, and how their specific health condition may influence the way they use the technology.
Data ethics, care teams’ role, and policy implications
Data ethics is one of the most prominent factors in almost all health technology-related discussions, mostly as a barrier to adoption. Fostering patient adoption necessitates addressing their main fears and concerns by ensuring that their health data are stored and managed in a secure and ethical manner, providing higher transparency on data policies, and, whenever possible, enabling users to choose which data they agree to share and with whom.
It’s also important to remember that clinicians’ endorsement is a key facilitator of patient acceptance of the tools. However, lack of knowledge, skills, or active engagement with mHealth from the care team may discourage patient adoption. Therefore, it is central to success to raise clinicians’ awareness of the existing tools and how they can help them and their patients and engage them in digital training to equip them with the necessary skills to administer these tools.
Furthermore, recognizing potential barriers has essential policy implications for mHealth adoption to improve access to health care services and patient support. Encouraging the reimbursement of mHealth tools that contribute to cost efficiency and clinical efficacy may help overcome the cost-related barriers that were often reported in the studies. And enabling the integration of digital tools into the standard of care by supporting system harmonization, interoperability, and infrastructure may play a vital role in overcoming some of the key technical barriers that hinder adoption.
“We recommend a more patient-centred approach by ensuring the tools’ fit into the overall patient journey and treatment plan, emphasizing inclusive design, and warranting comprehensive patient education and support. Moreover, empowering and mobilizing clinicians and care teams, addressing ethical data management issues, and focusing on health care policies may facilitate adoption,” says the first author of the study Christine Jacob, lecturer and health tech researcher at the university of applied sciences Northwestern Switzerland, and founder of Digi-Bridges GmbH.
Source: Jacob C, Sezgin E, Sanchez-Vazquez A, Ivory C. Sociotechnical Factors Affecting Patients’ Adoption of Mobile Health Tools: Systematic Literature Review and Narrative Synthesis. JMIR Mhealth Uhealth 2022;10(5):e36284. doi: 10.2196/36284