Digital health technologies (DHTs) at the intersection of health, medical informatics, and business aim to enhance patient care through personalised digital approaches. Ensuring the efficacy and reliability of these innovations demands rigorous clinical validation. A PubMed literature review (January 2006 to July 2023) identified 1250 papers, highlighting growing academic interest. A focused narrative review (January 2018 to July 2023) delved into challenges, highlighting issues such as diverse regulatory landscapes, adoption issues in complex healthcare systems, and a plethora of evaluation frameworks lacking pragmatic guidance. Existing frameworks often omit crucial criteria, neglect empirical evidence, and clinical effectiveness is rarely included as a criterion for DHT quality. The paper underscores the urgency of addressing challenges in accreditation, adoption, business models, and integration to safeguard the quality, efficacy, and safety of DHTs. A pivotal illustration of collaborative efforts to address these challenges is exemplified by the Digital Health Validation Center, dedicated to generating clinical evidence of innovative healthcare technologies and facilitating seamless technology transfer. In conclusion, it is necessary to harmonise evaluation approaches and frameworks, improve regulatory clarity, and commit to collaboration to integrate rigorous clinical validation and empirical evidence throughout the DHT life cycle.

Non-adherence after heart transplantation (HTx) is a significant problem. The main objective of this study was to evaluate if a mHealth strategy is more effective than standard care in improving adherence and patients’ experience in heart transplant recipients. Methods: This was a single-center, randomized controlled trial (RCT) in adult recipients >1.5 years post-HTx. Participants were randomized to standard care (control group) or to the mHeart Strategy (intervention group). For patients randomized to the mHeart strategy, multifaceted theory-based interventions were provided during the study period to optimize therapy management using the mHeart mobile application. Patient experience regarding their medication regimens were evaluated in a face-to-face interview. Medication adherence was assessed by performing self-reported questionnaires. A composite adherence score that included the SMAQ questionnaire, the coefficient of variation of drug levels and missing visits was also reported. Results: A total of 134 HTx recipients were randomized (intervention N = 71; control N = 63). Mean follow-up was 1.6 (SD 0.6) years. Improvement in adherence from baseline was significantly higher in the intervention group versus the control group according to the SMAQ questionnaire (85% vs. 46%, OR = 6.7 (2.9; 15.8), p-value < 0.001) and the composite score (51% vs. 23%, OR = 0.3 (0.1; 0.6), p-value = 0.001). Patients’ experiences with their drug therapy including knowledge of their medication timing intakes (p-value = 0.019) and the drug indications or uses that they remembered (p-value = 0.003) significantly improved in the intervention versus the control group. Conclusions: In our study, the mHealth-based strategy significantly improved adherence and patient beliefs regarding their medication regimens among the HTx population. The mHeart mobile application was used as a feasible tool for providing long-term, tailor-made interventions to HTx recipients to improve the goals assessed.

Background: Medication nonadherence in heart transplant recipients (HTxR) is related to graft loss and death. mHeart is a mobile app that uses electronic patient-reported outcome measures (ePROMs) to identify and manage medication nonadherence in the outpatient heart transplant (HTx) population. Objective: The study primarily aimed to validate mHeart to measure medication nonadherence in early stage HTxR by assessing the psychometric properties of ePROMs. The secondary aims were to (1) measure patient satisfaction with the mHeart tool and its usability and (2) explore the impact of a theory-based treatment on medication nonadherence rates to determine its scalability to larger research. Methods: A prospective study was conducted in the outpatient clinic of a tertiary hospital. All consecutive early stage HTxR (<1.5 years from HTx) were included. The ePROM psychometric properties assessed were validity, reliability, responsiveness, interpretability, and burden. ePROMs comprised the 4-item Morisky-Green-Levine questionnaire and an adapted version of the Haynes-Sackett questionnaire. The Simplified Medication Adherence Questionnaire (SMAQ) was also applied on-site. Three consecutive medication nonadherence assessments were performed by a transplant pharmacist. To improve medication nonadherence, theory-based interventions were delivered in a 1-month period. Patient satisfaction was assessed by a semiquantitative Web-based survey at the end of the study. Results: We included 31 early stage HTxR (age: mean 54 years, SD 12 years), and 71% (22/31) of them were men. The HTxR were taking a mean 13 (SD 4; range 7-18) drugs per day. A total of 42% (13/31) of patients were unaware of the consequences of medication nonadherence, and 39% (12/31) of patients were nonadherent to immunosuppressive treatment. The content validity measure showed excellent levels of expert panel agreement for the Haynes-Sacket (14/14, 100%) and Morisky-Green-Levine (13/14, 93%) questionnaires. SMAQ and Morisky-Green-Levine ePROMs showed similar measurement domains (convergent validity, phi=0.6, P<.001), which, as expected, differed from Haynes-Sackett ePROMs (divergent validity, phi=0.3, P=.12). Reliability assessment revealed a very strong association between ePROM and on-site PROMs (phi>0.7, P<.001). Reproducibility was moderate (Haynes-Sackett κ=0.6, P<.002) or poor (Morisky-Green-Levine κ=0.3, P=.11) because of unexpected improved medication adherence rates during the test-retest period. According to responsiveness, the theory-based multifaceted intervention program improved medication nonadherence by 16% to 26% (P<.05). A burden analysis showed that ePROMs could potentially overcome traditional on-site limitations (eg, automatic recording of ePROM responses in the hospital information system). The mean score for overall patient satisfaction with the mHeart approach was 9 (SD 2; score range: 0-10). All 100% (29/29) of patients surveyed reported that they would recommend the mHeart platform to other HTxR. Conclusions: ePROMs adhered to the quality standards and successfully identified medication nonadherence in the HTx population, supporting their widespread use. The theory-based intervention program showed a promising improvement in medication adherence rates and produced excellent patient satisfaction and usability scores in HTxR.

Background: Solid organ transplantation could be the only life-saving treatment for end-stage heart failure. Nevertheless, multimorbidity and polypharmacy remain major problems after heart transplant. A technology-based behavioral intervention model was established to improve clinical practice in a heart transplant outpatient setting. To support the new strategy, the mHeart app, a mobile health (mHealth) tool, was developed for use by patients and providers. Objective: The primary objective of this study was to describe the implementation of the mHeart model and to outline the main facilitators identified when conceiving an mHealth approach. The secondary objectives were to evaluate the barriers, benefits, and willingness to use mHealth services reported by heart transplant recipients and cardiology providers. Methods: This was an implementation strategy study directed by a multidisciplinary cardiology team conducted in four stages: design of the model and the software, development of the mHeart tool, interoperability among systems, and quality and security requirements. A mixed methods study design was applied combining a literature review, several surveys, interviews, and focus groups. The approach involved merging engineering and behavioral theory science. Participants were chronic-stage heart transplant recipients, patient associations, health providers, stakeholders, and diverse experts from the legal, data protection, and interoperability fields. Results: An interdisciplinary and patient-centered process was applied to obtain a comprehensive care model. The heart transplant recipients (N=135) included in the study confirmed they had access to smartphones (132/135, 97.7%) and were willing to use the mHeart system (132/135, 97.7%). Based on stakeholder agreement (>75%, N=26), the major priorities identified of the mHealth approach were to improve therapy management, patient empowerment, and patient-provider interactions. Stakeholder agreement on the barriers to implementing the system was weak (<75%). Establishing the new model posed several challenges to the multidisciplinary team in charge. The main factors that needed to be overcome were ensuring data confidentiality, reducing workload, minimizing the digital divide, and increasing interoperability. Experts from various fields, scientific societies, and patient associations were essential to meet the quality requirements and the model scalability. Conclusions: The mHeart model will be applicable in distinct clinical and research contexts, and may inspire other cardiology health providers to create innovative ways to deal with therapeutic complexity and multimorbidity through health care systems. Professionals and patients are willing to use such innovative mHealth programs. The facilitators and key strategies described were needed for success in the implementation of the new holistic theory–based mHealth strategy.

Background: Kidney transplantation (KTx) is a safe procedure that improves the life expectancy and quality of life of patients requiring it. However, despite the known benefits for patients who receive a kidney transplant, non-adherence to immunosuppressive medication is an unsolved problem, reflected mainly by graft rejection. Objective: The aim of this study is to systematically review the existing literature on adherence factors to medication after renal transplantation. Methods: A systematic literature review of studies published since 2010 was conducted in three databases. Records for the search were limited to publications from 2010 to 2024, available in full-text. The search was carried out in July 2024. In total, 2632 abstracts were downloaded from the different databases. Inclusion criteria were papers of any type (quantitative or qualitative) whose objective was the identification of predictors of adherence for patients who were prescribed immunosuppressive medication after kidney transplantation. Results: The predictors of adherence to treatment found in the systematic review were grouped into the following categories of the World Health Organization classification: socio-economic factors, factors related to the treatment/therapy, patient-related factors, disease-related factors, and health care system factors. Most of the studies were excluded, and in the end, 30 were included in the final analysis. According to these studies, a set of strong predictors was identified, but discrepancies among the variables of gender in young patients, pre-emptive transplantation, and the time of the transplantation were detected. Conclusions: In this study, we identified specific predictors and directions for the association of those predictors with adherence to immunosuppressive medication for patients after KTx. Further research should consider conducting reviews for different patient sub-groups on medication adherence and the development and validation of a screening instrument for adherence/non-adherence factors that clinicians could use as a detection tool for subjects at risk of low adherence.