https://orcid.org/0000-0002-5378-2304
Correction
9 May 2024: The PLOS One Staff (2024) Correction: mHealth intervention to improve quality of life in patients with chronic diseases during the COVID-19 crisis in Paraguay: A study protocol for a randomized controlled trial. PLOS ONE 19(5): e0303726. https://doi.org/10.1371/journal.pone.0303726 View correction
Figures
Abstract
Background
Patients with chronic disease represent an at-risk group in the face of the COVID-19 crisis as they need to regularly monitor their lifestyle and emotional management. Coping with the illness becomes a challenge due to supply problems and lack of access to health care facilities. It is expected these limitations, along with lockdown and social distancing measures, have affected the routine disease management of these patients, being more pronounced in low- and middle-income countries with a flawed health care system.
Objectives
The purpose of this study is to describe a protocol for a randomized controlled trial to test the efficacy of the Adhera® MejoraCare Digital Program, an mHealth intervention aimed at improving the quality of life of patients with chronic diseases during the COVID-19 outbreak in Paraguay.
Method
A two-arm randomized controlled trial will be carried out, with repeated measures (baseline, 1-month, 3-month, 6-month, and 12-month) under two conditions: Adhera® MejoraCare Digital Program or waiting list. The primary outcome is a change in the quality of life on the EuroQol 5-Dimensions 3-Levels Questionnaire (EQ-5D-3L). Other secondary outcomes, as the effect on anxiety and health empowerment, will be considered. All participants must be 18 years of age or older and meet the criteria for chronic disease. A total of 96 participants will be recruited (48 per arm).
Conclusions
It is expected that the Adhera® MejoraCare Digital Program will show significant improvements in quality of life and emotional distress compared to the waiting list condition. Additionally, it is hypothesized that this intervention will be positively evaluated by the participants in terms of usability and satisfaction. The findings will provide new insights into the viability and efficacy of mHealth solutions for chronic disease management in developing countries and in times of pandemic.
Citation: Escrivá-Martínez T, Vara MD, Czeraniuk N, Denis M, Núñez-Benjumea FJ, Fernández-Luque L, et al. (2022) mHealth intervention to improve quality of life in patients with chronic diseases during the COVID-19 crisis in Paraguay: A study protocol for a randomized controlled trial. PLoS ONE 17(11): e0273290. https://doi.org/10.1371/journal.pone.0273290
Editor: Walid Kamal Abdelbasset, Prince Sattam Bin Abdulaziz University, College of Applied Medical Sciences, SAUDI ARABIA
Received: April 19, 2021; Accepted: July 26, 2022; Published: November 8, 2022
Copyright: © 2022 Escrivá-Martínez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: No datasets were generated or analysed during the current study. All relevant data from this study will be made available upon study completion.
Funding: This work was funded by the National Council of Science and Technology of Paraguay (CONACYT) with support from the Fund for Excellence in Education and Research (FEEI) [project PINV 20-9]. TE-M is the recipient of grant FPU16/01875, funded by MCIN/AEI/ 10.13039/501100011033, and by “ESF Investing in your future.” IF is the recipient of grant PRE2019-089021, funded by MCIN/AEI/ 10.13039/501100011033, and by “ESF Investing in your future.”
Competing interests: The authors have declared that no competing interests exist.
Introduction
The arrival of the COVID-19 has significantly impacted people’s mental health worldwide [1]. Evidence has shown that feelings of fear, uncertainty and loneliness, and symptoms of stress, anxiety and depression, are very prevalent in most individuals after the COVID-19 outbreak [2]. In Paraguay’s context, infection cases increased rapidly as in the rest of the world [3]. The study conducted by Torales et al. [4] showed that most of the Paraguayan population showed a moderate level of self-perceived stress, particularly in women, singles, and those who reported a pre-existing mental disorder such as depression or anxiety.
People with chronic disease (CD) (e.g., hypertension, diabetes, cardiovascular disease, cancer) are one of the groups most vulnerable to COVID-19 infection [5] and associated mental health problems [6]. Moreover, the implementation of the lockdown measures has impacted these people’s lifestyle [7, 8]. The current health crisis has exacerbated difficulties in accessing medical services and treatment, impacting these diseases’ clinical course [9]. Thus, there is a need to develop and disseminate innovative online care tools to engage at-risk populations in effective CD management during the COVID-19 era.
Throughout this pandemic, the integration of digital health solutions (e.g., smartphones) into CD management has improved access to quality and affordable care for patients by facilitating treatment adherence and behavioral changes [10]. Considering the increasing use and access to the Internet and mobile technology by the Latin American population, digital health could help overcome the healthcare gap in this population [11]. However, it remains challenging to address existing barriers to implementing and disseminating effective mHealth initiatives to improve the most vulnerable communities’ physical and mental health in a low-resource context [12].
Scientific literature suggests that mHealth solutions, including mobile applications (apps), help manage a CD [13, 14], including its possible comorbidity with other psychological problems [15]. In particular, the changes in CD patients’ lifestyle through the use of the mHealth apps are remarkable, mainly in adherence to regular exercise, healthy diet and body weight [16]. Besides, a growing body of research examines apps’ possibilities to improve quality of life or well-being in CD patients through different theoretical approaches (e.g., positive psychology), yielding promising results [17, 18].
Despite the wide variety of apps available for the management of CD during the COVID-19 situation, their quality and efficacy are generally unknown [10, 19]. Therefore, more studies are needed to rigorously ensure the efficacy, efficiency and cost-effectiveness of mHealth solutions. In this regard, this paper aims to describe a protocol for a randomized controlled trial (RCT) to test the efficacy of the Adhera® MejoraCare Digital Program to improve the quality of life of CD patients during the COVID-19 situation in Paraguay, comparing to the waiting list (WL) condition. Adhera® MejoraCare Digital Program (Adhera Health, Inc., Palo Alto, CA) is an mHealth intervention focused on tailored education, behavior change and emotional well-being. We hypothesize that a) Adhera® MejoraCare Digital Program will improve quality of life compared to WL condition 1 month after baseline, and this result will be maintained over time (3-, 6- and 12-month follow-ups); b) Adhera® MejoraCare Digital Program will show significant improvements in emotional distress (anxiety, depression and stress) 1 month after baseline compared to the WL condition, and these results will be maintained over time (3-, 6- and 12-month follow-ups); c) participants in this intervention will positively value the mHealth solution in terms of usability and satisfaction; and d) variables such as self-efficacy and health empowerment will show significant relationships with the change in the quality of life outcome, being significant predictors.
Material and methods
Research design
A two-arm RCT will be carried out with repeated measures (baseline, 1-, 3-, 6-, and 12-month follow-ups) and two conditions: Adhera® MejoraCare Digital Program and WL group. The study will be conducted following the CONSORT statement (Consolidated Standards of Reporting Trials, http://www.consort-statement.org) [20] and CONSORT-EHEALTH guidelines [21] and the Recommendations for Interventional Trials (SPIRIT) [22, 23].
Sample size
Sample size calculation was done using G*Power [24], based on the primary endpoint. Specifically, sample size was calculated for a mixed ANOVA design, with the between effects being the experimental condition (2 groups) and the within-effects being the different time points. As we were interested in the interaction between experimental group and time effect, 8 measurements were specified (2 groups x 4 time points). With a power of 80%, and an alpha level = .05, and an expected moderated effect size of d = .50 according to similar studies [25], the required sample size is 74 participants (37 per condition). Taking into account a potential dropout of 20% according to literature [26, 27], this study will recruit a total of 94 participants (47 per condition).
Study population, recruitment, and eligibility criteria
The study will be carried out in the region of Itapúa (Paraguay), in collaboration with the Regional Hospital of Encarnación, where the Autonomous University of Encarnación has its Research and Documentation Center.
The department of Itapúa is located in the extreme south of Paraguay, and its capital is the city of Encarnación. According to the last national census [28], the population of the department of Itapúa is 625.096 inhabitants, which represents 8.5% of the total population of the country. Specifically, 85% of the area of the Encarnación district is rural. Regarding education, illiteracy rate is 7%, and there is a difference between the illiteracy rate between the rural and urban population of 18% and the average year of schooling is 8 years. Regarding the use of digital devices, technology is increasingly being incorporated into the lives of Paraguayans. Specifically, 65% of people use the Internet in Itapúa [29].
The general practitioners and specialists participating in the study will identify patients belonging to the high-risk group for CDs. Once identified, the physicians will make a face-to-face appointment with the potential candidates to explain the conditions of participation in the research study. Those interested in participating will sign the informed consent form and complete a brief questionnaire to verify compliance with the inclusion criteria: men and women; 18 years and older; CD diagnosis (i.e., cardiovascular disease, chronic obstructive pulmonary disease, diabetes, hypertension, and cancer); no severe psychiatric comorbidities (i.e., substance dependence, bipolar affective disorder, obsessive compulsive disorder, psychotic illness); and own an Android smartphone. Physicians participating in the study will have access to the patient’s medical history to check for the presence of a diagnosis of mental illness and the presence of severe psychiatric comorbidity. Subsequently, those who meet the criteria for participation in the study will complete the measures corresponding to the baseline through the LimeSurvey platform via a link that will be sent to the participant’s email address. Afterwards, participants will be randomized to one of the two conditions (intervention or control). An independent researcher from Valencia University will generate a permuted block randomization with fixed and concealed block length (20 participants per block), and with an allocation ratio of 1:1, using a computerized random number generator (Random Allocation Software 2.0 [30]). For methodological reasons, participants will not be blind to the condition. All patients will be assessed at 1-, 3-, 6- and 12-months from the time of randomization via the LimeSurvey platform. Fig 1 shows SPIRIT flow diagram of the study.
CFS, Computer Fluency Scale; EQ-5D-3L, EuroQol 5-Dimensions 3-Levels Questionnaire; GAD-2, Generalized Anxiety Disorder Questionnaire-2; PHQ-2, Patient Health Questionnaire-2; PSS-4, Perceived Stress Scale-4; GSES-12, General Self-Efficacy-12; HES, Health Empowerment Scale; SUS, System Usability Scale; TUQ, Telehealth Usability Questionnaire; MAUQ, mHealth App Usability Questionnaire; and CSQ, Client Satisfaction Questionnaire. *This instrument will be administered in the Adhera MejoraCare condition.
The intervention or follow-up will be discontinued if: a) the participant withdraws consent; b) the RCT is discontinued (e.g., due to poor patient recruitment, technological problems with the app that delivers the intervention); and c) the principal investigator or project administrator determines the need to discontinue the RCT (e.g., due to ethical and privacy issues, funding problems).
Ethics and auditing
The study follows the guidelines of the Helsinki Declaration [31]. All the researchers will follow the guidelines for Good Clinical Practice [32], and the Ethical Principles and Code of Conduct for Psychologists of the American Psychological Association [33]. As noted, all the participants will be volunteers, and they will not receive any compensation for their participation. They will sign informed consent once the study and its conditions have been explained. Participants will be able to withdraw from the study at any time, without giving any reason and with no consequences. This study was approved by the Ethics Committee of Research in Humans of the Ethics Commission in Experimental Research of University of Valencia, (date: December 4, 2020; register number: 1472070) and the Ethics Committee of the Autonomous University of Encarnación. Besides, the study was registered in the United States National Institute of Health Registration System with Clinical Trials Registration Number: NCT04659746 (https://clinicaltrials.gov/ct2/show/NCT04659746). No audit has been planned at this time.
Intervention
Experimental group.
Adhera® MejoraCare Digital Program is an mHealth solution designed to raise awareness, educate and empower patients in the management of CD in the presence of the COVID-19, promoting behavioral changes in their lifestyle and emotional coping. A holistic "empowerment" approach based on cognitive-behavioral techniques (CBT) will be adopted to activate and empower CD management in patients. It will include evidence-based CBT techniques aimed at intervening on 1) cognitive, 2) emotional, and 3) behavioral variables, in three areas: COVID-19 psychoeducation, well-being, and lifestyle, following the guidelines from the World Health Organization for COVID-19 [34], Ryff’s well-being model [35] and health behaviour models for behaviour change [36]. Table 1 shows the contents included in the Adhera® MejoraCare Digital Program.
Adhera® MejoraCare Digital Program includes the following functionalities (Fig 2): a) COVID-19 symptom monitoring; b) patient empowerment components (including educational methods and instructions to promote behavioral changes to establish and maintain a healthy lifestyle that minimizes the risk of contagion/propagation of the disease and improves patients’ quality of life); c) self-assessment tests to check and reinforce acquired knowledge; and d) tailored motivational messages that help to acquire lifestyle habits to prevent the spread of the COVID-19.
Left: Symptom’s monitoring. Center: Patient empowerment categories in the educational section. Right: Educational units included in the ‘‘How to protect from coronavirus” category.
An AI-driven algorithm will be used for selecting and delivering tailored messages based on the user’s profile and scientific evidence, following the integrated behavior change model "I-Change". This algorithm is based on the work of Hors-Fraile et al. [37], and it has been successfully clinically validated for promoting smoking cessation in a randomized clinical trial [38].
The mobile app included in this mHealth solution will be downloaded and installed like any other commercial app from Google Play. The participant will receive an email from the research team with instructions on how to install the application. Specifically, the download link and an individual access code will be provided. The code can only be used once, which ensure that only one device can be use which the specific code. In case that the participant changed the mobile device during the study they can request an additional code, and this change will be track down to prevent data lose.
Measures
Screening related measures.
Sociodemographic variables and medical history. Personal data will be collected, including information such as age, gender, profession, marital status, employment status, level of studies, diagnosis of CD and its duration, clinical course, medication, diagnosis of mental disorder, and pregnancy.
User’s technological profile. The Computer Fluency Scale (CFS; [39]) is a self-report scale that assesses computer comfort and self-report of computing ability through 7 items rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). A high score indicates greater comfort with computers.
Ad hoc questionnaire designed to evaluate the frequency and the perceived ability to use a mobile phone. It is composed of 9 items (e.g., To what extent do you feel capable of using technologies? Have you ever used a computer?).
Primary outcome measure.
Quality of life. The EuroQol 5-Dimensions 3-Levels Questionnaire (EQ-5D-3L; [40, 41]) is a self-report questionnaire that measures health-related quality of life and enables conversion to utility scores to calculate Quality-Adjusted Life-Years (QALYs) [42, 43]. It measures five dimensions of health: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each category consists of 1 item, ranging from 1 (no problems) to 3 (lots of problems). The second part of the EQ-5D-3L is a 20-centimetre vertical Visual Analogue Scale (VAS), ranging from 0 (worst imaginable health) to 100 (best imaginable health). The individual must mark the point on the scale that best reflects the assessment of their global health status today. The VAS’s use provides a complementary score to the descriptive system of the self-assessment of the individual’s health status [44].
Secondary outcome measures.
Anxiety symptoms. The Generalized Anxiety Disorder Questionnaire-2 (GAD-2; [45, 46]) is a self-report measure that contains 2 items that assess anxiety symptoms. The items are scored on a 4-point Likert scale, ranging from 0 (no days) to 3 (almost every day), where a high score indicates greater anxiety symptoms.
Depressive symptoms. The Patient Health Questionnaire-2 (PHQ-2; [47, 48]) is a self-report measure that consists of 2 items that assess depressive symptoms. The items are scored on a 4-point Likert scale, ranging from 0 (never) to 3 (almost every day), where a high score indicates greater levels of depressive symptoms.
Stress. The Perceived Stress Scale-4 (PSS-4; [49, 50]) is a self-report scale that consists of 4 items that measures the degree to which life situations are considered stressful. The items are scored on a 4-point Likert scale, ranging from 0 (never) to 4 (very often), where a high score indicates a high perception of stress.
Self-efficacy. The General Self-Efficacy Scale-12 (GSES-12; [51, 52]) is a self-report questionnaire consisting of 12 items grouped into three factors: initiative (willingness to initiate behaviour), effort (willingness to make an effort to complete the behaviour) and persistence (persevering to complete the task in the face of adversity). The items are scored on a 5-point Likert scale, ranging from 1 (it never happens to me) to 5 (it always happens to me), where a high score indicates higher levels of self-efficacy.
Health empowerment. The Health Empowerment Scale (HES; [53]) is a self-report measure that assesses health-related empowerment (e.g., health-related satisfaction, goal achievement, stress management, social support, self-motivation, decision making). It consists of 8 items scored on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), where high scores are indicators of a higher level of health-related empowerment.
mHealth solution usability and treatment satisfaction.
System usability. The System Usability Scale (SUS; [54, 55]) is a self-reported measure that assesses the intervention’s overall usability. It is a 10-item scale, measured on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Total SUS scores range from 0 to 100. The questionnaire is designed to be answered after the user’s interaction with the system.
Telehealth usability. An adaptation of the original Telehealth Usability Questionnaire (TUQ; [56, 57]) will be used in this study. This self-report questionnaire provides information on the telehealth system’s usability (usefulness, ease of use, effectiveness, reliability and satisfaction). It consists of 16 items on a 7-point Likert scale that range from 1 (strongly disagree) to 7 (strongly agree). The total score ranges from 7 to 112. Higher scores are indicators of higher usability of the telehealth system.
mHealth solution usability. The mHealth App Usability Questionnaire (MAUQ; [58]) is a self-reported instrument that assesses the usability of mHealth solutions. It consists of 26 items with 3 subscales (ease of use, interface and satisfaction, usefulness) on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). The total score ranges from 21 to 147. High scores are indicators of higher usability of mHealth solutions.
Client satisfaction. The Client Satisfaction Questionnaire (CSQ; [59–61]) is a self-reported questionnaire that assesses the general satisfaction of the patient with health and human services. It consists of 8 items on a 4-point Likert scale ranging from 1 (lowest score per item) to 4 (highest score per item). The total score ranges from 8 to 32, with higher values indicating higher satisfaction.
Data management
Online data collected from LimeSurvey will be stored at protected servers of the University of Valencia. Only individuals authorized by the principal investigator will have access to the database. After completing the assessment points, authorized individuals will download the database in a SPSS format that will be automatically saved onto a password-protected cloud server of the University of Valencia (nÚVol, https://nuvol.uv.es). Pseudonymization will be used for the processing of participants’ personal data while the study is active. Once the study has finished, an anonymized copy of the database will be stored at the University of Valencia for up to 5 years after the project ends, following the principles of the European GDPR 2016/679.
Analysis
First, descriptive statistics and normality assumptions will be examined. In case assumptions are met, t-tests for independent samples, ANOVAs, and chi-square analysis will be calculated to observe differences between groups at baseline in quantitative and categorical variables. In case normality assumptions are not met, robust t-test for independent samples and robust ANOVAs will be calculated [62]. Effect size will be quantified using Cohen’s d (d; [63]) or its robust equivalent (dR; [64]) when normality assumptions are not met, and robust estimations are employed. Independent-sample t-tests and chi-squared tests will be also performed to check that there are no significant differences in sociodemographic characteristics of the participants between groups that can act as confounding variables (i.e., age, gender, year and CD diagnosis).
Then, the multivariate normality assumption on the primary and secondary outcome measures will be tested before computing the linear mixed models. If the assumption is met, linear mixed models will be performed for each dependent variable, considering temporal moments as an intra-group factor and condition as an inter-group factor, using the False Discovery Rate (FDR) adjustments for post-hoc multiple comparisons. Otherwise, their mixed robust equivalents will be calculated [62].
In addition, multiple hierarchical regression models will be used to identify possible predictors of evolution in the primary and secondary measures. In particular, it is expected that characteristics as the gender, age, the CD, and user’s technological profile will act as predictor for the level of adherence to Adhera® MejoraCare Digital Program intervention, and the change in quality of life, self-efficacy, health empowerment, and anxiety and depression symptomatology. Finally, moderation analyses will be performed to test whether the gender, age, the CD, user’s technological profile and level of adherence moderate the intervention’s effect on quality of life, self-efficacy, health empowerment, and anxiety and depression symptomatology. These analyses will be performed using the macro PROCESS (version 4.0, https://processmacro.org/). The conditional effects of condition on the dependent variables at medium (the mean), low (−1 SD), and high (+1 SD) levels of the moderator variables will be examined with the “pick-a-point approach”.
Data monitoring
The study will not have a formal data monitoring committee. Any unexpected adverse events or outcomes will be discussed by the trial management committee (identical to the authors of this protocol). In addition, the trial management committee will monitor recruitment, treatment and attrition rates, as well as any study-related concerns.
Access to data
Prior to publication of the main results, the principal investigators will have access to the complete data set. The principal investigators will address data issues and finalize the data set for statistical analysis. After publication, only the principal investigator and persons approved by the principal investigator will have access to the data set.
Dissemination and protocol amendments
The RCT results will be submitted for publication to an international, peer-reviewed journal, regardless of whether the results are positive, negative or inconclusive in relation to the study hypothesis.
Any important protocol amendments will be reported to the Ethics Committee of Research in Humans of the Ethics Commission in Experimental Research of University of Valencia, registered at ClinicalTrials.gov and communicated in the primary RCT report.
Discussion and conclusion
In the present paper, we described the protocol for a RCT to test the efficacy of the Adhera® MejoraCare Digital Program intervention, an mHealth solution designed to provide information about COVID-19 and promote well-being and a healthy lifestyle in CD patients from Paraguay. Mainly, it is expected that the digital intervention group will be more effective than the WL group in increasing the quality of life and decreasing the emotional distress (anxiety, depression, and stress) of CD patients during the COVID-19 outbreak and that these changes will be maintained in the follow-ups. It is also expected that the Adhera® MejoraCare Digital Program will be well rated in terms of usability and satisfaction.
This study stands out for an integrative and holistic approach to addresses the care of chronic conditions, including access to treatment, monitoring of health behaviors, and emotional support through the use of digital interventions, especially in times of social distancing and movement restriction measures during the COVID-19 period [65].
To our knowledge, this is the first RCT in Paraguay, which uses an mHealth solution intervention in a clinical population for the management of CD. In this regard, a study’s strength is the response to the emergence of COVID-19 in a low- middle-income country (LMIC). The global crisis in well-resourced countries (e.g., United States, United Kingdom) in containing this pandemic raises concerns about the impact of COVID-19 in LMICs. In this regard, this work’s results will provide relevant evidence on the use of technological solutions to complement primary care services and reduce inequalities in access to long-term care [66]. So, it is expected that the results will contribute with evidence to transform clinical practice and access to healthcare in countries with fragile health systems.
We would like to mention some possible limitations. First, the possible presence of a high dropout or attrition rate by participants from the mHealth solution intervention condition [67]. Second, the results of this RCT should be interpreted with caution as they are limited to only a few specific groups of CDs. Future research with mHealth solutions should focus on increasing the sample size and performing in-depth analyses on a wide range of CDs to draw more robust and generalized conclusions. Third, implementing mHealth technologies in a LMIC will represent a major challenge for both researchers and users given the characteristics of the context (e.g., Internet connectivity issues, experience with technology). Finally, it is worth mentioning that this study only includes participants with an Android smartphone, thus limiting the recruitment of potential users with smartphones with different operating systems such as iOS.
In conclusion, this study’s results could help understand better how to offer self-care strategies to people with CD and shed evidence on the impact of mHealth solutions on the management of chronic medical conditions in a LMIC during the COVID-19 pandemic.
Acknowledgments
We thank the CIBEROBN, an initiative of the ISCIII (ISC III CB06 03/ 0052) and "INTERSABIAS" Project (PROMETEO/2018/110).
References
- 1. Xiong J, Lipsitz O, Nasri F, Lui LMW, Gill H, Phan L, et al. Impact of COVID-19 pandemic on mental health in the general population: A systematic review. J. Affect. Disord. 2020;277: 55–64. pmid:32799105
- 2. Torales J, O’Higgins M, Castaldelli-Maia JM, Ventriglio A. The outbreak of COVID-19 coronavirus and its impact on global mental health. I Int. J. Soc. Psychiatry. 2020;66: 317–320. pmid:32233719
- 3. de Oliveira-Andrade R. Covid-19: Concerns rise as cases expand rapidly in South America. BMJ. 2020;369: m1926. pmid:32404317
- 4. Torales J, Ríos-González C, Barrios I, O’Higgins M, González I, García O, et al. Self-Perceived Stress During the Quarantine of COVID-19 Pandemic in Paraguay: An exploratory survey. Front Psychiatry. 2020;11: 558691. pmid:33192674
- 5. Wang B, Li R, Lu Z, Huang Y. Does comorbidity increase the risk of patients with covid-19: Evidence from meta-analysis. Aging. 2020;12: 6049–6057. pmid:32267833
- 6. Kang C, Yang S, Yuan J, Xu L, Zhao X, Yang J. Patients with chronic illness urgently need integrated physical and psychological care during the COVID-19 outbreak. Asian J Psychiatr. 2020; 51: 102081. pmid:32289729
- 7. Cransac-Miet A, Zeller M, Chagué F, Faure AS, Bichat F, Danchin N, et al. Impact of COVID-19 lockdown on lifestyle adherence in stay-at-home patients with chronic coronary syndromes: Towards a time bomb. Int J Cardiol. 2021;323: 285–287. pmid:32889019
- 8. Saqib MAN, Siddiqui S, Qasim M, Jamil MA, Rafique I, Awan UA, et al. Effect of COVID-19 lockdown on patients with chronic diseases. Diabetes Metab Syndr Clin Res Rev. 2020;14: 1621–1623. pmid:32889403
- 9. Chudasama Y V., Gillies CL, Zaccardi F, Coles B, Davies MJ, Seidu S, et al. Impact of COVID-19 on routine care for chronic diseases: A global survey of views from healthcare professionals. Diabetes Metab Syndr Clin Res Rev. 2020;14: 965–967. pmid:32604016
- 10. Liu N, Huang R, Baldacchino T, Sud A, Sud K, Khadra M, et al. Telehealth for noncritical patients with chronic diseases during the COVID-19 pandemic. J. Med. Internet Res. 2020;22: e19493. pmid:32721925
- 11. Curioso WH. Building capacity and training for digital health: Challenges and opportunities in Latin America. J. Med. Internet Res. 2019;21: e16513. pmid:31850849
- 12. Kruse C, Betancourt J, Ortiz S, Luna SMV, Bamrah IK, Segovia N. Barriers to the use of mobile health in improving health outcomes in developing countries: Systematic review. J. Med. Internet Res. 2019;21: e13263. pmid:31593543
- 13. Kouroubali A, Koumakis L, Kondylakis H, Katehakis DG. Smart healthcare apps for quality cancer patient support. International Journal of Big Data and Analytics in Healthcare. 2020;5: 28–48.
- 14. Triantafyllidis A, Kondylakis H, Votis K, Tzovaras D, Maglaveras N, Rahimi K. Features, outcomes, and challenges in mobile health interventions for patients living with chronic diseases: A review of systematic reviews. Int. J. Med. Inform. 2019; 132: 103984. pmid:31605884
- 15. Maisto M, Diana B, Di Tella S, Matamala-Gomez M, Montana JI, Rossetto F, et al. Digital interventions for psychological comorbidities in chronic diseases—A systematic review. J Pers Med. 2021;11: 30. pmid:33418971
- 16. Debon R, Coleone JD, Bellei EA, De Marchi ACB. Mobile health applications for chronic diseases: A systematic review of features for lifestyle improvement. Diabetes Metab Syndr. 2019;13: 2507–2512. pmid:31405669
- 17. Kristjansdottir OB, Børøsund E, Westeng M, Ruland C, Stenberg U, Zangi HA, et al. Mobile app to help people with chronic illness reflect on their strengths: Formative evaluation and usability testing. J Med Internet Res. 2020;4: e16831. pmid:32130126
- 18. Parks AC, Williams AL, Kackloudis GM, Stafford JL, Boucher EM, Honomichl RD. The effects of a digital well-being intervention on patients with chronic conditions: Observational study. J Med Internet Res. 2020;22: e16211. pmid:31922491
- 19. Kondylakis H, Katehakis DG, Kouroubali A, Logothetidis F, Triantafyllidis A, Kalamaras I, et al. COVID-19 Mobile apps: A systematic review of the literature. J. Med. Internet Res. 2020; 22; e23170. pmid:33197234
- 20. Moher D, Schulz KF, Simera I, Altman DG. Guidance for developers of health research reporting guidelines. PLoS Med. 2010;7: e1000217. pmid:20169112
- 21. Eysenbach G, Consort-Ehealth Group. Consort-Ehealth: improving and standardising evaluation reports of web-based and mobile health interventions. J J. Med. Internet Res. 2011;13: e126. pmid:22209829
- 22. Chan AW, Tetzlaff JM, Gøtzsche PC, Altman DG, Mann H, Berlin JA, et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. BMJ. 2013; 346: e7586. pmid:23303884
- 23. Chan AW, Tetzlaff JM, Altman DG, Laupacis A, Gøtzsche PC, Krleža-Jerić K, et al. SPIRIT 2013 statement: Defining standard protocol items for clinical trials. Ann. Intern. Med. 2013; 158: 200–207. pmid:23295957
- 24. Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. In: Behavior Research Methods. 2007; 39: 175–191. pmid:17695343
- 25. Liu WT, Huang CD, Wang CH, Lee KY, Lin SM, Kuo HP. A mobile telephone-based interactive self-care system improves asthma control. Eur Respir J. 2011;37: 310–317. pmid:20562122
- 26. Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N. Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: A meta-analysis. PLoS One. 2010;5: e13196. pmid:20967242
- 27. Van Ballegooijen W, Cuijpers P, Van Straten A, Karyotaki E, Andersson G, Smit JH, et al. Adherence to internet-based and face-to-face cognitive behavioural therapy for depression: A meta-analysis. PLoS One. 2014;9: e100674. pmid:25029507
- 28.
Instituto Nacional de Estadística. Proyección de la Población por Sexo y Edad, 2019. Itapúa: INE 2021. Available from: https://www.ine.gov.py/Publicaciones/Biblioteca/documento/be0b_Anuario%20Estadistico%202019_Web.pdf
- 29.
Ministerio de Tecnologías de la Información y Comunicación. Dirección General de Encuestas, Estadísticas y Censos 2019. Paraguay: MITIC 2021. Available from: https://www.mitic.gov.py/noticias/el-972-de-la-poblacion-paraguaya-usa-internet-para-mensajeria-instantanea
- 30. Saghaei M. Random allocation software for parallel group randomised trials. BMC Med Res Methodol. 2004;4: 26. pmid:15535880
- 31. World Medical Association. World medical association declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013; 310: 2191–2194. pmid:24141714
- 32. Food Drug Administration. International conference on harmonization, good clinical practice: consolidated guidelines, Federal Register, USA. 1997. Avaliable at: https://www.federalregister.gov/documents/1997/05/09/97-12138/international-conference-on-harmonisation-good-clinical-practice-consolidated-guideline-availability
- 33. American Psychological Association. Ethical Principles of Psychologists and Code of Conduct. Am. Psychol. 2002;57, 1060–1073. pmid:12613157
- 34.
World Health Organization. Coronavirus disease (COVID-19) advice for the public. World Health Organization. 2020. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public#:~:text=Don%27t%20forget%20the,faucets%20and%20phone%20screens
- 35. Ryff CD. Happiness is everything, or is it? Explorations on the meaning of psychological well-being. J Pers Soc Psychol. 1989;57: 1069–1081.
- 36. Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: Toward an integrative model of change. J Consult Clin Psychol. 1983;51: 390–395. pmid:6863699
- 37. Hors-Fraile S, Schneider F, Fernandez-Luque L, Luna-Perejon F, Civit A, Spachos D, et al. Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: A study protocol. BMC Public Health. 2018;18: 698. pmid:29871595
- 38. Carrasco-Hernandez L, Jódar-Sánchez F, Núñez-Benjumea F, Conde JM, González MM, Civit-Balcells A, et al. A mobile health solution complementing psychopharmacology-supported smoking cessation: Randomized controlled trial. JMIR mHealth uHealth. 2020; 8: e17530. pmid:32338624
- 39. Becker EM. Computer fluency, access to technology, and attitudes towards technologically-based therapeutic tools among practicing clinicians—University of Miami; 2012. Available from: https://scholarship.miami.edu/discovery/fulldisplay/alma991031447859202976/01UOML_INST:ResearchRepository?tags=scholar
- 40. Badia X, Roset M, Montserrat S, Herdman M Segura A. The Spanish version of EuroQol: a description and its applications. European Quality of Life scale. Med Clin. 1999;112: 79–85. Available from: https://pubmed.ncbi.nlm.nih.gov/10618804/
- 41. EuroQol Group. EuroQol—a new facility for the measurement of health-related quality of life. Health Policy. 1990;16: 199–208. Available from: https://pubmed.ncbi.nlm.nih.gov/10109801/ pmid:10109801
- 42. Janssen MF, Pickard AS, Golicki D, Gudex C, Niewada M, Scalone L, et al. Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: A multi-country study. Qual Life Res. 2013;22: 1717–1727. pmid:23184421
- 43. Keeley T, Al-Janabi H, Lorgelly P, Coast J. A qualitative assessment of the content validity of the ICECAP-A and EQ-5D-5L and their appropriateness for use in health research. PLoS One. 2013;8: e85287. pmid:24367708
- 44. Herdman M, Badia X, Berra S. El EuroQol-5D: una alternativa sencilla para la medición de la calidad de vida relacionada con la salud en atención primaria. Aten Primaria. 2001;28: 425–430. pmid:11602124
- 45. Kroenke K, Spitzer RL, Williams JBW, Monahan PO, Löwe B. Anxiety disorders in primary care: Prevalence, impairment, comorbidity, and detection. Ann Intern Med. 2007;146: 317–325. pmid:17339617
- 46. García-Campayo J, Zamorano E, Ruiz MA, Pérez-Páramo M, López-Gómez V, Rejas J. The assessment of generalised anxiety disorder: psychometric validation of the Spanish version of the self-administered GAD-2 scale in daily medical practice. Health Qual Life Outcomes. 2012;10: 114. pmid:22992432
- 47. Kroenke K, Spitzer RL, Williams JBW. The Patient Health Questionnaire-2. Med Care. 2003;41: 1284–1292.
- 48. Rodríguez-Muñoz MF, Castelao PC, Olivares ME, Soto C, Izquierdo N, Ferrer FJ, et al. PHQ-2 como primer instrumento de cribado de la depresión prenatal. Rev Esp Salud Pública. 2017; 91: e201701010. Available from: https://www.mscbs.gob.es/biblioPublic/publicaciones/recursos_propios/resp/revista_cdrom/VOL91/ORIGINALES/RS91C_MFRM.pdf pmid:28134236
- 49. Herrero J, Meneses J. Short web-based versions of the Perceived Stress (PSS) and Center for Epidemiological Studies-Depression (CESD) Scales: A comparison to pencil and paper responses among Internet users. Comput Human Behav. 2006;22: 830–846.
- 50. Vallejo MA, Vallejo-Slocker L, Fernández-Abascal EG, Mañanes G. Determining factors for stress perception assessed with the Perceived Stress Scale (PSS-4) in Spanish and other European samples. Front Psychol. 2018;9: 37. pmid:29434563
- 51. Bosscher R. J., Smit J. H., & Kempen GIJM. Algemene competentieverwachtingen bij ouderen [Global expectations of self-efficacy in the elderly: An investigation of psychometric properties of the General Self-Efficacy Scale]. Ned Tijdschr voor Psychol. 1997;52: 239–248. Available from: https://psycnet.apa.org/record/1997-39086-001
- 52. Herrero R, Espinoza M, Molinari G, Etchemendy E, Garcia-Palacios A, Botella C, et al. Psychometric properties of the General Self Efficacy-12 Scale in Spanish: General and clinical population samples. Compr Psychiatry. 2014;55: 1738–1743. pmid:24973225
- 53. Serrani DJL. Escala de empoderamiento sobre la salud para adultos mayores. Adaptación al español y análisis psicométrico. Colombia Médica. 2014; 45: 179–186. Available from: https://colombiamedica.univalle.edu.co/index.php/comedica/article/download/1518/2576?inline=1 (accessed on 2 February 2021).
- 54. Brooke J. SUS: a quick and dirty usability scale. Usability Eval Ind. 1996;189: 4–7. Available from: https://books.google.es/books?hl=es&lr=&id=ujFRDwAAQBAJ&oi=fnd&pg=PA189&dq=Brooke+J.+SUS:+a+quick+and+dirty+usability+scale.+Usability+Eval+Ind.+1996%3B+189:4–7.+Sevilla-Gonzalez,+M.+D.+R.,+Loaeza,+L.+M.,+Lazaro-Carrera,+L.+S.,+Ramirez,+B.+B.,+Rodríguez,+
- 55. Sevilla-Gonzalez MDR, Moreno Loaeza L, Lazaro-Carrera LS, Bourguet Ramirez B, Vázquez Rodríguez A, Peralta-Pedrero ML, et al. Spanish version of the System Usability Scale for the Assessment of Electronic Tools: Development and Validation. JMIR Hum Factors. 2020;7: e21161. pmid:33325828
- 56. Parmanto B, Lewis AN Jr., Graham KM, Bertolet MH. Development of the Telehealth Usability Questionnaire (TUQ). Int J Telerehabilitation. 2016;8: 3–10. pmid:27563386
- 57. Torre AC, Bibiloni N, Sommer J, Plazzotta F, Angles MV, Terrasa SA, et al. Traducción al español y adaptación transcultural de un cuestionario sobre la usabilidad de la telemedicina. Med (Buenos Aires). 2020;80: 134–137. Available from: https://www.medicinabuenosaires.com/indices-de-2020/volumen-80-ano-2020-no-2-indice/telemedicina/
- 58. Zhou L, Bao J, Setiawan IMA, Saptono A, Parmanto B. The mhealth app usability questionnaire (MAUQ): Development and validation study. JMIR mHealth uHealth. 2019;7: e11500. Available from: https://mhealth.jmir.org/2019/4/e11500 pmid:30973342
- 59. Attkisson C, Greenfield TK. The Client Satisfaction Questionnaire (CSQ) scales and the Service Satisfaction Scale-30. Assess Clin Pr. 1996;30: 120–127. Available from: https://www.researchgate.net/publication/265377848
- 60. Larsen DL, Attkisson CC, Hargreaves WA, Nguyen TD. Assessment of client/patient satisfaction: Development of a general scale. Eval Program Plann. 1979;2: 197–207. pmid:10245370
- 61. Roberts RE, Atrkisson CC, Mendias RM. Assessing the Client Satisfaction Questionnaire in English and Spanish. Hisp J Behav Sci. 1984;6: 385–396.
- 62.
Wilcox R. Introduction to robust estimation and hypothesis testing. 3rd ed. Burlington: Elsevier; 2013.
- 63.
Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdate: LEA; 1988.
- 64. Algina J, Keselman HJ, Penfield RD. An alternative to Cohen’s standardized mean difference effect size: A robust parameter and confidence interval in the two independent groups case. Psychol. Methods. 2005; 10: 317–328. pmid:16221031
- 65. Budd J, Miller BS, Manning EM, Lampos V, Zhuang M, Edelstein M, et al. Digital technologies in the public-health response to COVID-19. Nat. Med. 2020; 26; 1183–1192. pmid:32770165
- 66. Hoffer-Hawlik MA, Moran AE, Burka D, Kaur P, Cai J, Frieden TR, et al. Leveraging telemedicine for chronic disease management in low- and middle-income countries during Covid-19. Glob Heart. 2020; 15: 63. pmid:33150128
- 67. Meyerowitz-Katz G, Ravi S, Arnolda L, Feng X, Maberly G, Astell-Burt T. Rates of attrition and dropout in app-based interventions for chronic disease: Systematic review and meta-analysis. J. Med. Internet Res. 2020; 22: e20283. pmid:32990635