Although increased life expectancy, socioeconomic improvements, and biomedical innovations have led to a reduction in mortality, they have also led to a considerable increase in chronic conditions. A prevalent paradigm of these socio-health phenomena is the multimorbidity of patients, which is one of the leading health problems of the 21st century. Multimorbidity can alter health outcomes and lead to disability that is more significant or can lead to worse quality of life or frailty [1].

Heart failure (HF) has a high and increasing prevalence and incidence, and is one of the leading causes of morbidity and mortality in the Western world [2]. Further, if we add to this the significant impact of chronic diseases, mainly cardiovascular diseases, cancer, diabetes, and chronic lung diseases [3], the situation becomes even more complicated by the fact that patients are responsible for most of the avoidable hospital admissions. In this line, both the World Health Organization (WHO) and the European Union define a patient with chronic disease as a priority and point out the need to develop appropriate strategies with effective measures to meet the challenge of chronicity, reorienting national health systems [4,5].

According to the latest data published by the WHO [4], heart disease continues to be the leading cause of mortality worldwide. Globally, it was estimated that in 2019, 197.2 million people were living with ischemic heart disease (IHD), and it was more prevalent in males than in females (113.7 and 83.6 million people, respectively). North Africa and the Middle East, Central Asia, and Eastern Europe had the highest prevalence rates of IHD in the world. The number of deaths due to heart disease has increased since 2000 by more than 2 million people, from 6-7 million to almost 9 million people, representing 16% of all deaths. In 2019, IHD mortality rates were 118.0 per 100,000. IHD mortality rates were highest in parts of North Africa and the Middle East, Eastern Europe, and Central Asia [6,7].

In Spain, HF remains an enormous health challenge (estimates suggest that there are more than 1,300,000 people with HF). Besides, more than 17,000 people die from this disease every year, making it the fourth leading cause of death [8] and the third leading cause of cardiovascular death after IHD and cerebrovascular disease. It is also the leading cause of hospitalization in people over 65 years of age; furthermore, 50% die within 5 years of diagnosis. The prevalence of HF increases progressively with age, reaching prevalence rates of 1%, 10%, and 17.4% in the population over 40, 70, and 85 years of age, respectively. There are more than 80,000 admissions per year in Spain for HF, and half of the hospitalized patients are readmitted within 1 year due to decompensation. Likewise, the use of pharmacological and nonpharmacological resources (resynchronizers or defibrillators) in people with HF is growing exponentially [9].

Although there is a growing interest in patients with comorbidity and multimorbidity, from an evidence perspective, this population has been excluded from most clinical trials and intervention studies [10,11]. Interdisciplinary teams based on the collaboration of different care settings and sustainable interventions adjusted to the public system are recommended. Similarly, a model centered on the patient’s needs is reinforced, based on primary care and shared action with hospital care, with proactive and planned interventions aimed at promotion and prevention [12].

In the field of public health and health promotion, health literacy (HL) is presented as a means that allows individuals to exercise significant control over their health and over the personal, social, and environmental determinants that determine it, being considered as an individual asset to be built.

Following the theoretical framework proposed by the European Health Literacy Project (European Health Literacy Survey [HLS-EU]) [13], HL is determined by the combination of the 3 dimensions of health (being sick/health care; being at risk/disease prevention, and being healthy/health promotion) and the 4 ways of managing information (finding it, understanding it, evaluating it, and applying it to one’s own life to make informed decisions). Access refers to the ability to seek, find, and obtain health information; understanding refers to the individual’s ability to understand the info accessed; evaluation describes the ability to interpret, filter, judge, and assess the health information accessed and understood; and application refers to the ability to communicate and use the information to decide to maintain and improve health.

Populations that are more likely to experience difficulties in self-managing their diseases are those with low levels of HL [14]. Having poor HL is an independent risk factor for poorer health [15] because of medication errors and a poorer understanding of disease and treatments [16]. Similarly, there is evidence of a relationship between low HL and higher rates of hospital admissions, poorer therapeutic adherence on care plans, and poor use of preventive services [17,18]. In the particular case of people with chronic diseases, HL plays a crucial role in the self-management of their disease [19].

In the last few years, mobile health (mHealth) has emerged prominently as a result of the tremendous sociological and cultural impact of smartphones and tablets.

The Global Observatory for eHealth defines it as “medical and public health practice carried out with the support of mobile devices such as phones, personal digital assistants, Tablets and other wireless communication devices to carry out public health activities and assist in clinical practice” [20].

Despite its expansion in recent years, few studies demonstrate the effectiveness and utility of mHealth in chronic disease self-management beyond diabetes [21,22]. A recent systematic review of clinical trials involving mHealth interventions to improve self-management of patients with chronic diseases, including patients with cancer, HF, fibromyalgia, asthma, and spine bifida, achieved statistically significant effects on health outcomes after the incorporation of mobile apps in disease management [23]. Existing evidence promises an optimistic horizon regarding their effectiveness in chronic diseases [23,24] and, in particular, in patients with cardiac diseases [25,26]. Despite this, we still find contradictory results. For example, the study by Hägglund et al [27] in patients with HF revealed improvements in self-care and quality of life and a reduction in the number of hospitalization days. By contrast, the study by Vuorinen et al [28] found no difference in the number of hospitalization days.

At present, there is a severe deficiency of mHealth tools, which are developed in collaboration with target patients and multidisciplinary teams, are incorporated into daily care practice, and have proven their efficacy in clinical trials [29,30], to improve the self-management of patients with HF having complex health problems. Systematic reviews recently published on this topic [23,25,26] reflect the scarcity of studies aimed at patients with multimorbidity.

Therefore, we aim to develop an app based on the needs of users and deficiencies identified by professionals, which motivates behavior change through gamification strategies, as per scientific evidence and adapted to the user’s level of HL, and to test its effectiveness in terms of self-management and improvement of personal autonomy to perform basic activities of daily living, reduce hospital admissions, promote therapeutic adherence, and increase HL.

A randomized, controlled, multicenter clinical trial evaluated the efficacy of an mHealth intervention with 2 groups: a control group (standard clinical practice) and an experimental group (standard clinical practice together with an ad hoc designed mHealth intervention).

A data matrix will be created, and data will be processed statistically using SPSS, version 22 (IBM). Statistical significance will be set at 95% (α=.05).

A uni-bivariate descriptive analysis will be performed to determine the sample distribution for each of the variables studied, both for the total sample and for each group. The characterization variables will be summarized using descriptive statistics, expressing qualitative variables in terms of frequency and percentages and quantitative variables in terms of mean and SD.

Before the analysis, the normality of the variables will be evaluated using the Kolmogorov-Smirnov and Shapiro-Wilk tests. Baseline differences in the variables related to the sample profile between the intervention and control groups will be compared using the Student t test when these are normally distributed and the nonparametric Mann-Whitney U test in case they are not. Differences between dichotomous qualitative variables will be established using the chi-square test or Fisher statistic when appropriate.

A contrast of means will be applied to test the efficacy of the intervention in terms of the normality distribution of the variables. The results will be analyzed using the nonparametric Wilcoxon signed-rank test, which will allow us to check if there are differences between 2 populations from 2 dependent or related samples, or the Student t test for normally distributed samples. For independent samples, the nonparametric Mann-Whitney U test will be used.

The strength of the relationship between continuous data will be determined from the Pearson or Spearman correlation. The intraclass correlation coefficient will be used if it is necessary to measure concordance between measurements at different times of the study. To identify sociodemographic and clinical characterization variables (independent variables) related to the intervention and the different response variables (dependent variables), multivariate, linear, logistic, or Cox proportional analyses will be performed, as appropriate for each response variable.

This study has been approved by the Cadiz Research Ethics Committee. The aim of the study and the anonymity of participants, as well as the voluntary nature of participation, will be explained before the participants start and their informed consent will be requested. The participants will also be informed that the data obtained would be used for research purposes only. Findings from this study will be disseminated through peer-reviewed scientific journals and at key national and international scientific events. The study was registered in ClinicalTrials.gov (trial registration number: NCT04725526) on January 26, 2021.

Neither the patients nor the general public were involved in the design, or conduct, or reporting, or dissemination plans of the study.

To our knowledge, this is the first study to develop a protocol for a randomized controlled trial of an mHealth intervention to improve HL and self-management in patients with multimorbidity with HF and complex health needs that is based on patients’ perceived needs. The content of the intervention has already been validated.

We will investigate the efficacy, effectiveness, and usability of the proposed intervention in patients with multimorbidity and HF. The experimental group will use the app, hoping that the results of the primary and secondary variables recorded will improve and that significant differences will become evident after use and after comparison with the control group.

It is expected that the intervention developed will be effective and, therefore, improve the level of HL, self-management and therapeutic adherence, reduce the number of admissions per year due to decompensation, and that the incidence rates of HF mortality (adjusted for age, sex, and risk) or 30-day postdischarge mortality of the experimental group under study will be lower than that of the control group.

This study has some limitations that should be acknowledged. The lack of adequate mobile devices or the sensory or cognitive deficits of the target population is taken into account. Likewise, their digital illiteracy could be another limitation. Therefore, and being aware of the population group, digital training is contemplated. As for cognitive impairment, the usual caregiver is included in the intervention.

When evaluating the effectiveness of the intervention, we can find the influence of the variable “time of use of the app” (a record of access to the app will be obtained and monitored).

Finally, we must be aware of the possible loss of participants due to their clinical characteristics. Therefore, when determining the sample size, a loss rate of 30% has been taken into account. The aim is to obtain as many participants as possible to increase the precision of the estimates.

With the aim of overcoming the aforementioned limitations, a qualitative analysis of the risks has been carried out and has been prioritized according to the factor of exposure and urgency.

Randomized controlled trials are considered the “gold standard for assessing efficacy in clinical research and constitute evidence for treatment” [46]. By adopting such a trial and ensuring internal and external validity, we maximized the robustness of our study.

In addition, the multidisciplinary nature of this project is evident in the composition of the research team (psychologist, nurses, physicians, computer engineers), in the incorporation of professionals and patients for its development, and in the association of the 2 public organizations involved in it.

Finally, the cross-cutting nature of this project is reflected in the consideration of the different dimensions of health and the different levels of health care (primary and hospital care). By contrast, the main variable of this project (HL) has a cross-cutting nature as it has been recognized as a basic social determinant for improving health outcomes.

The results of this intervention support the coordinated work between hospital care and primary care, which will have an impact on the improvement of health care and management, thus favoring the continuity of care and the reduction of hospital readmissions in this population.

The incorporation of the mobile app developed will optimize the work performed by professionals while increasing patients’ HL and reducing the number of consultations requested and, ultimately, health care costs.

This study can serve as a starting point for developing other mHealth interventions in other pathologies and for their generalization to other contexts.