Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25472, first published .
Determinants of the Use of Health and Fitness Mobile Apps by Patients With Asthma: Secondary Analysis of Observational Studies

Determinants of the Use of Health and Fitness Mobile Apps by Patients With Asthma: Secondary Analysis of Observational Studies

Determinants of the Use of Health and Fitness Mobile Apps by Patients With Asthma: Secondary Analysis of Observational Studies

Authors of this article:

Ana Luísa Neves 1, 2, 3 Author Orcid Image ;   Cristina Jácome 1, 3 Author Orcid Image ;   Tiago Taveira-Gomes 1, 3, 4, 5 Author Orcid Image ;   Ana Margarida Pereira 1, 3, 6 Author Orcid Image ;   Rute Almeida 1, 3 Author Orcid Image ;   Rita Amaral 1, 3, 7, 8 Author Orcid Image ;   Magna Alves-Correia 6 Author Orcid Image ;   Sandra Mendes 1 Author Orcid Image ;   Cláudia Chaves-Loureiro 9 Author Orcid Image ;   Margarida Valério 9 Author Orcid Image ;   Cristina Lopes 10, 11 Author Orcid Image ;   Joana Carvalho 12 Author Orcid Image ;   Ana Mendes 13 Author Orcid Image ;   Carmelita Ribeiro 14 Author Orcid Image ;   Sara Prates 15 Author Orcid Image ;   José Alberto Ferreira 16 Author Orcid Image ;   Maria Fernanda Teixeira 17 Author Orcid Image ;   Joana Branco 18 Author Orcid Image ;   Marta Santalha 19 Author Orcid Image ;   Maria João Vasconcelos 20 Author Orcid Image ;   Carlos Lozoya 21 Author Orcid Image ;   Natacha Santos 22 Author Orcid Image ;   Francisca Cardia 23 Author Orcid Image ;   Ana Sofia Moreira 24 Author Orcid Image ;   Luís Taborda-Barata 25, 26 Author Orcid Image ;   Cláudia Sofia Pinto 27 Author Orcid Image ;   Rosário Ferreira 28 Author Orcid Image ;   Pedro Morais Silva 29 Author Orcid Image ;   Tania Monteiro Ferreira 30 Author Orcid Image ;   Raquel Câmara 31 Author Orcid Image ;   Rui Lobo 32 Author Orcid Image ;   Diana Bordalo 33 Author Orcid Image ;   Cristina Guimarães 34 Author Orcid Image ;   Maria Espírito Santo 35 Author Orcid Image ;   José Ferraz de Oliveira 36 Author Orcid Image ;   Maria José Cálix Augusto 37 Author Orcid Image ;   Ricardo Gomes 38 Author Orcid Image ;   Inês Vieira 39 Author Orcid Image ;   Sofia da Silva 40 Author Orcid Image ;   Maria Marques 41 Author Orcid Image ;   João Cardoso 42 Author Orcid Image ;   Ana Morete 6, 43 Author Orcid Image ;   Margarida Aroso 44 Author Orcid Image ;   Ana Margarida Cruz 45 Author Orcid Image ;   Carlos Nunes 46 Author Orcid Image ;   Rita Câmara 47 Author Orcid Image ;   Natalina Rodrigues 48 Author Orcid Image ;   Carmo Abreu 49 Author Orcid Image ;   Ana Luísa Albuquerque 50 Author Orcid Image ;   Claúdia Vieira 51 Author Orcid Image ;   Carlos Santos 52 Author Orcid Image ;   Rosália Páscoa 1, 3, 53 Author Orcid Image ;   Carla Chaves-Loureiro 54 Author Orcid Image ;   Adelaide Alves 55 Author Orcid Image ;   Ângela Neves 56 Author Orcid Image ;   José Varanda Marques 57 Author Orcid Image ;   Bruno Reis 58 Author Orcid Image ;   Manuel Ferreira-Magalhães 1, 17 Author Orcid Image ;   João Almeida Fonseca 3 Author Orcid Image

Original Paper

1Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal

2Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Porto, Portugal

3Imperial NIHR Patient Safety Translational Research Centre, Institute of Global Health Innovation, Imperial College London, London, United Kingdom

4Instituto de Investigação e Formação Avançada em Ciências e Tecnologias da Saúde, Institute of Research and Advanced Training in Health Sciences and Technologies, University Institute of Health Sciences, Cooperativa de Ensino Superior Politécnico e Universitário, CRL, Gandra, Portugal

5Faculdade de Ciências da Saúde, Universidade Fernando Pessoa, Porto, Portugal

6Allergy Unit, Instituto and Hospital CUF, Porto, Portugal

7Department of Cardiovascular and Respiratory Sciences, Porto Health School, Polytechnic Institute of Porto, Porto, Portugal

8Department of Women’s and Children’s Health, Paediatric Research, Uppsala University, Uppsala, Sweden

9Serviço Pneumologia, Hospitais da Universidade de Coimbra, Coimbra, Portugal

10Unidade de Imunoalergologia, Hospital Pedro Hispano, Unidade Local de Saúde de Matosinhos, Matosinhos, Portugal

11Imunologia Básica e Clínica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal

12Serviço de Pediatria, Hospital Pedro Hispano, Unidade Local de Saúde de Matosinhos, Matosinhos, Portugal

13Serviço de Imunoalergologia, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisboa, Portugal

14Serviço de Imunoalergologia, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal

15Serviço de Imunoalergologia, Hospital de Dona Estefânia, Centro Hospitalar Universitário de Lisboa Central, Lisboa, Portugal

16Serviço de Imunoalergologia, Unidade I, Centro Hospitalar Vila Nova de Gaia/Espinho, Vila Nova de Gaia, Portugal

17Serviço de Pediatria, Centro Materno Infantil do Norte, Centro Hospitalar Universitário do Porto, Porto, Portugal

18Serviço de Pneumologia, Hospital Beatriz Ângelo, Loures, Portugal

19Serviço de Pediatria, Hospital da Senhora da Oliveira, Guimarães, Portugal

20Serviço de Imunoalergologia, Centro Hospitalar Universitário de São João, Porto, Portugal

21Serviço de Imunoalergologia, Hospital Amato Lusitano, Unidade Local de Saúde de Castelo Branco, Castelo Branco, Portugal

22Serviço de Imunoalergologia, Centro Hospitalar Universitário do Algarve, Portimão, Portugal

23Unidade de Saúde Familiar Terras de Azurara, Agrupamento de Centros de Saúde Dão Lafões, Mangualde, Portugal

24Unidade de Imunoalergologia, Hospital do Divino Espirito Santo, Ponta Delgada, Portugal

25Department of Allergy & Clinical Immunology, Cova da Beira University Hospital Centre, Covilhã, Portugal

26Centro de Investigação em Ciências da Saúde - Health Sciences Research Centre & NuESA –Environment & Health Study Group, Faculty of Health Sciences, University of Beira Interior, Covilhã, Portugal

27Serviço de Pneumologia, Hospital São Pedro de Vila Real, Centro Hospitalar de Trás-os-Montes e Alto Douro, Vila Real, Portugal

28Departamento de Pediatria, Hospital de Santa Maria, Centro Hospitalar de Lisboa Norte, Lisboa, Portugal

29Imunoalergologia, Grupo HPA Saúde, Portimão, Portugal

30Unidade de Saúde Familiar Progresso e Saúde, Agrupamento de Centros de Saúde Baixo Mondego, Tocha, Portugal

31Serviço de Pneumologia, Hospital Nossa Senhora do Rosário, Centro Hospitalar Barreiro Montijo, Barreiro, Portugal

32Unidade de Saúde Familiar João Semana, Agrupamento de Centros de Saúde Baixo Vouga, Ovar, Portugal

33Serviço de Pediatria, Unidade Hospitalar de Famalicão, Centro Hospitalar do Médio Ave, Vila Nova de Famalicão, Portugal

34Unidade de Saúde Familiar Caminhos do Cértoma, Agrupamento de Centros de Saúde Baixo Mondego, Pampilhosa, Portugal

35Unidade de Saúde Familiar Arte Nova, Agrupamento de Centros de Saúde Baixo Vouga, Oliveirinha, Portugal

36Imunoalergologia, Hospital Privado de Alfena, Trofa Saúde, Alfena, Portugal

37Serviço de Pediatria, Hospital de São Teotónio, Centro Hospitalar Tondela–Viseu, Viseu, Portugal

38Serviço de Pneumologia, Hospital Garcia de Orta, Almada, Portugal

39Unidade de Cuidados Saúde Personalizados Arnaldo Sampaio, Agrupamento de Centros de Saúde Pinhal Litoral, Leiria, Portugal

40Unidade de Saúde Familiar Cuidarte, Unidade Local de Saúde do Alto Minho, Portuzelo, Portugal

41Serviço de Imunoalergologia, Centro Hospitalar Universitário do Porto, Porto, Portugal

42Serviço de Pneumologia, Hospital Santa Marta, Centro Hospitalar Universitário de Lisboa Central, Lisboa, Portugal

43Serviço de Imunoalergologia, Hospital Infante D Pedro, Centro Hospitalar Baixo Vouga, Aveiro, Portugal

44Unidade de Saúde Familiar Pedras Rubras, Agrupamento de Centros de Saúde do Grande Porto III - Maia/Valongo, Maia, Portugal

45Unidade de Saúde Familiar Bom Porto, Agrupamento de Centros de Saúde do Grande Porto V - Porto Ocidental, Porto, Portugal

46Imunoalergologia, Centro de Imunoalergologia do Algarve, Portimão, Portugal

47Serviço de Imunoalergologia, Serviço de Saúde da Região Autónoma da Madeira, Funchal, Portugal

48Unidade de Saúde Familiar Mondego, Agrupamento de Centros de Saúde Baixo Mondego, Coimbra, Portugal

49Serviço de Imunoalergologia, Hospital São Pedro de Vila Real, Centro Hospitalar De Trás-Os-Montes E Alto Douro, Vila Real, Portugal

50Unidade de Saúde Familiar Coimbra Centro, Agrupamento de Centros de Saúde Baixo Mondego, Coimbra, Portugal

51Unidade de Saúde Familiar Corgo, Agrupamentos de Centros de Saúde Douro I - Marão e Douro Norte, Vila Real, Portugal

52Unidade de Saúde Familiar Santo António, Agrupamento de Centros de Saúde do Cávado III - Barcelos/Esposende, Barcelos, Portugal

53Unidade de Saúde Familiar Abel Salazar, Agrupamento de Centros de Saúde do Gaia, Vila Nova de Gaia, Portugal

54Serviço Pediatria Ambulatória, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal

55Serviço de Pneumologia, Unidade I, Centro Hospitalar Vila Nova de Gaia/Espinho, Vila Nova de Gaia, Portugal

56Unidade de Saúde Familiar Araceti, Agrupamento de Centros de Saúde Baixo Mondego, Arazede, Portugal

57Unidade de Saúde Familiar Viseu-Cidade, Agrupamento de Centros de Saúde do Dão Lafões, Viseu, Portugal

58Unidade de Cuidados Saúde Personalizados Sicó, Agrupamento de Centros de Saúde Pinhal Litoral, Leiria, Portugal

*these authors contributed equally

Corresponding Author:

Cristina Jácome, PT, MSc, PhD

Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS)

Faculty of Medicine

University of Porto

Praça de Gomes Teixeira

Porto, 4099-002

Portugal

Phone: 351 225513622

Email: cristinajacome.ft@gmail.com


Background: Health and fitness apps have potential benefits to improve self-management and disease control among patients with asthma. However, inconsistent use rates have been reported across studies, regions, and health systems. A better understanding of the characteristics of users and nonusers is critical to design solutions that are effectively integrated in patients’ daily lives, and to ensure that these equitably reach out to different groups of patients, thus improving rather than entrenching health inequities.

Objective: This study aimed to evaluate the use of general health and fitness apps by patients with asthma and to identify determinants of usage.

Methods: A secondary analysis of the INSPIRERS observational studies was conducted using data from face-to-face visits. Patients with a diagnosis of asthma were included between November 2017 and August 2020. Individual-level data were collected, including age, gender, marital status, educational level, health status, presence of anxiety and depression, postcode, socioeconomic level, digital literacy, use of health services, and use of health and fitness apps. Multivariate logistic regression was used to model the probability of being a health and fitness app user. Statistical analysis was performed in R.

Results: A total of 526 patients attended a face-to-face visit in the 49 recruiting centers and 514 had complete data. Most participants were ≤40 years old (66.4%), had at least 10 years of education (57.4%), and were in the 3 higher quintiles of the socioeconomic deprivation index (70.1%). The majority reported an overall good health status (visual analogue scale [VAS] score>70 in 93.1%) and the prevalence of anxiety and depression was 34.3% and 11.9%, respectively. The proportion of participants who reported using health and fitness mobile apps was 41.1% (n=211). Multivariate models revealed that single individuals and those with more than 10 years of education are more likely to use health and fitness mobile apps (adjusted odds ratio [aOR] 2.22, 95%CI 1.05-4.75 and aOR 1.95, 95%CI 1.12-3.45, respectively). Higher digital literacy scores were also associated with higher odds of being a user of health and fitness apps, with participants in the second, third, and fourth quartiles reporting aORs of 6.74 (95%CI 2.90-17.40), 10.30 (95%CI 4.28-27.56), and 11.52 (95%CI 4.78-30.87), respectively. Participants with depression symptoms had lower odds of using health and fitness apps (aOR 0.32, 95%CI 0.12-0.83).

Conclusions: A better understanding of the barriers and enhancers of app use among patients with lower education, lower digital literacy, or depressive symptoms is key to design tailored interventions to ensure a sustained and equitable use of these technologies. Future studies should also assess users’ general health-seeking behavior and their interest and concerns specifically about digital tools. These factors may impact both initial engagement and sustained use.

J Med Internet Res 2021;23(9):e25472

doi:10.2196/25472

Keywords



Smart mobile technology has revolutionized how we communicate, share, and consume content, seeping into many different sectors of society, including health care [1]. With the democratization of smartphone use, with 3.8 billion smartphone users worldwide [2], the market of specific apps has experienced a boom. Often free, easy to download, and easy to use, mobile apps have an extensive application in social, educational, and entertainment fields and naturally in the fields of self-management and health behavior change [3]. According to the software application industry, around 500 million smartphone users worldwide were using a health and fitness app in 2015; and by 2018, an estimated 50% of the 3.4 billion smartphone and tablet users, including health care professionals, consumers, and patients, would have downloaded one [4]. The total global mHealth market is predicted to reach the US $100 billion mark in 2021, which constitutes a 5-fold increase from 2016 [5]. In this context, it is hypothesized that apps may become ubiquitous solutions impacting a large number of patients, often capitalizing on gamification strategies and social interaction [6]. In particular, health and fitness apps are a promising approach for improving self-management behaviors in patients with asthma, a prevalent long-term condition with potential social and economic impacts [7,8], which requires a range of self-management skills in everyday life [9]. Indeed, around 1500 mobile apps are targeting patients with asthma in both the Apple App Store and the Google Play Store [10]. A systematic review published by Unni et al [11] suggests that the use of mobile apps by patients with asthma may have benefits across a range of outcomes, including medication adherence and asthma control.

However, the current use of smart devices and apps among patients with asthma remains unexplored, as emphasized by a position paper of the European Academy of Allergy and Clinical Immunology, highlighting the lack of published studies on the use of mHealth in allergic diseases [12]. While there are more than 100 papers published over the last 5 years, they either evaluate the characteristics of specific apps (rather than their use) or focus on the impact of asthma-specific apps.

A recent study reported that smart device ownership levels in patients with asthma are similar to those of the general population, that three-quarters of patients had downloaded/used a general app, yet only one-third had ever used a health and fitness app [13]. A significant variability exists in usage among different racial/ethnic and sociodemographic groups. Nonetheless, this evidence comes mainly from studies conducted in the United States and does not address specific disease contexts [14-18]. The use of health and fitness apps in the asthma context can be explored through the lens of the conceptual model developed by Andersen et al [19], which proposes that the use of health services is driven by three dynamics: predisposing factors (eg, age and gender), enabling factors (eg, socioeconomic level, education, and literacy), and need (eg, clinical characteristics and severity of disease).

The purpose of this study is to evaluate the use of health and fitness apps by patients with asthma and to identify determinants of usage. Specifically, we will investigate the following: (1) the proportion of patients with asthma using health and fitness apps and (2) the relationships among predisposing, enabling and need factors, and using mHealth apps.


Study Design

A secondary analysis of INSPIRERS observational studies involving 32 secondary care centers (allergy, pulmonology, and pediatrics departments) and 17 primary care centers in Portugal was performed (Figure 1), as part of the INSPIRERS project. The design of the INSPIRERS observational studies was disseminated through email contacts, social networks, and oral communications at national meetings/conferences, and physicians/centers interested in being part of the study contacted the research team. A convenience sample of adolescents and adults with persistent asthma was recruited for the INSPIRERS studies between November 2017 and August 2020. Depending on the study, each center was asked to recruit a minimum of 2-10 patients. The 3 INSPIRERS observational studies address the topic of adherence to asthma inhalers among adolescents and adults with persistent asthma (Figure 1). Further details on the project setting and methods have been previously published [20,21]. This study is reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines [22].

Figure 1. INSPIRERS studies flowchart.
View this figure

Participants and Data Collection

Data were collected using a questionnaire during a face-to-face visit. The questionnaire had a section to be completed by the physician addressing patients’ asthma characteristics and a set of questions to be self-completed by the patient, as detailed below. Patients were included if they had a previous medical diagnosis of persistent asthma, were at least 13 years old, and had an active prescription for an inhaled controller medication for asthma. Patients were excluded if they had a diagnosis of a chronic lung disease other than asthma or a diagnosis of another significant chronic condition with possible interference with the study aims.

Users and nonusers were defined as individuals who answered “yes” or “no,” respectively, to the question “Have you ever downloaded and used a health and fitness app?” Health and fitness apps were defined as a range of apps related to personal fitness, workout tracking, diet and nutritional tips, health and safety, etc. In accordance with the conceptual model proposed by Andersen et al [19], variables collected included predisposing factors, enabling factors, and need (Figure 2). Predisposing factors included demographic data (ie, age, gender, marital status, parish, and postcode), and enabling factors included education level, use of smart devices, and digital literacy, both collected from patients. Digital literacy was defined as the median of 5 items of the Media and Technology Usage and Attitudes Scale (MTUAS, ie, use of the GPS, browsing the web, taking pictures, gaming, and checking social networks) rated by frequency of use in a 10-point Likert scale (1=never to 10=all the time) [23]. Additionally, socioeconomic level was explored as an enabling factor, which was defined as the Portuguese ecological deprivation index, extracted from the patient residence information (civil parish/postcode), and categorized into 5 quintiles (Q1=least deprived to Q5=most deprived) [24]. Need variables included smoking status, patients’ perceived overall health status (from EQ-5D Visual Analog Scale [VAS], ranging from 0 [worst imaginable health state] to 100 [best imaginable health state]) [25], the presence of anxiety or depression (cut-off≥8 in the Hospital Anxiety and Depression Subscales) [26], and physicians’ input, including asthma control level (uncontrolled, partially controlled, or well controlled according to the classification of the Global Initiative for Asthma [27]), number of exacerbations (episodes of progressive increase in shortness of breath, cough, wheezing, or chest tightness, requiring a change in maintenance therapy) in the past year [28], and the number of unplanned appointments in the past year.

Age was categorized into age bands (13-18,18-30, 30-40, 40-50, 50-65, and ≥65 years). Other continuous variables (socioeconomic level, median digital literacy, and overall health status) were categorized into quartiles.

Figure 2. Variables collected included predisposing factors, enabling factors, and need according to the Andersen et al [19]. MTUAS: Media and Technology Usage and Attitudes Scale; VAS: Visual Analog Scale.
View this figure

Data Analysis

Counts and proportions were calculated for each variable. Multivariate logistic regression with the Enter method was used to ascertain the determinants of being a user of a health and fitness mobile app (dependent variable) in accordance with Andersen et al’s [19] conceptual model. Categorical variables, such as gender, age, marital status, educational level, socioeconomic level, use of smart devices, digital literacy, overall health status, smoking status, presence of anxiety or depression, and asthma status were explored as independent variables, irrespective of significance in preliminary univariate logistic regressions. We assessed model fit using the pseudo-R2 Nagelkerke method and tested for evidence for poor model fit using the Hosmer–Lemeshow test. In addition, to assess the increasing contribution of each covariate to the model, we adopted a stepwise approach to build models starting from 1 covariate until including all covariates in the full models. For each model, we computed the described goodness of fit statistics. The quality of the final model was also assessed using the Aikake Information Criterion. Adjusted odds ratios and 95% CIs were calculated. Statistical analyses were conducted using R and the “glm” package. The map of Portugal was created using Paintmaps [29].

Ethics Approval

The studies were approved by the ethics committees of all participating centers. The studies were conducted in accordance with the ethical standards established in the Declaration of Helsinki. Eligible patients were approached by physicians during medical visits and invited to participate. Written informed consent was obtained before enrollment. Adult patients signed a consent form; adolescents signed an assent form and a parental consent form was also obtained.


A total of 526 patients attended a face-to-face visit between November 2017 and August 2020 at the 49 recruiting centers. Of those, 12 did not answer the question “Have you ever downloaded and used a health and fitness app?” and were excluded (Figure 1). The recruiting centers included 12 of the 18 Portuguese districts, which represented 9,189,723 inhabitants (89% of the total national population) [30]. A detailed overview of the distribution of the participating centers by district is provided in Figure 3.

The majority of the subjects were ≤40 years old (66.4%, n=341) and 63.4% (n=326) were female. Most participants were single (58.0%, n=298) and had at least 10 years of education (57.4%, n=295). Approximately one-third were in the 2 lower quintiles of the socioeconomic deprivation index (29.9%, n=154). Regarding general health status, as assessed by EQ-5D VAS, 69.8% (n=359) of the participants reported a score of ≥70. Most of the subjects were never smokers (75.5%, n=388). The prevalence of anxiety and depression symptoms in the sample was, respectively, 34.4% (n=177) and 11.9% (n=61).

Figure 3. Number of participating centres per district.
View this figure

Asthma was well controlled among 51.0% (n=262) of participants. While 50.8% (n=261) of participants had 1 or more asthma exacerbations during the last year, most of the participants did not have any unplanned appointments (66.1%, n=340) or inpatient admissions (94.2%, n=484). The proportion of participants who reported using health and fitness mobile apps was 41.1% (n=211). A full description of the sample, as well as the characteristics of the nonuser and user groups, is provided in Table 1.

Characteristics of both users and nonusers were explored using multivariate logistic regression. The aORs show that single individuals and those with more than 10 years of education are more likely to use health and fitness mobile apps (aOR 2.22, 95%CI 1.05-4.75, and aOR 1.95 95%CI 1.12-3.45, respectively). Higher digital literacy scores were also associated with higher odds of being a user of health and fitness apps, with participants in the second, third, and fourth quartiles showing, respectively, aORs of 6.74 (95%CI 2.90-17.40), 10.30 (95%CI 4.28-27.56), and 11.52 (95%CI 4.78-30.87). Participants with depression symptoms had lower odds of using health and fitness apps (aOR 0.32, 95%CI 0.12-0.83). No significant associations were found with gender, age, socioeconomic level, general health status, smoking status, anxiety, and asthma control (including level of control, number of inpatient admissions, or number of exacerbations). A detailed overview of the multivariate analysis is provided in Table 2.

Table 1. Characteristics of the participants according to their use of health and fitness mobile apps (N=514).
CharacteristicsNonusers (n=303), n (%)Users (n=211), n (%)Total, n (%)
Sociodemographic

Gender


Female189 (62.4)137 (64.9)326 (63.4)


Male114 (37.6)74 (35.1)188 (36.6)

Age band (years)a


13-1892 (30.4)62 (29.4)154 (30.0)


18-3041 (13.5)74 (35.1)115 (22.4)


30-4040 (13.2)32 (15.2)72 (14.0)


40-5051 (16.8)31 (14.7)82 (16.0)


50-6551 (16.8)7 (3.3)58 (11.3)


≥6523 (7.6)3 (1.4)26 (5.1)

Marital statusb


Married128 (42.2)49 (23.2)177 (34.4)


Separated19 (6.3)11 (5.2)30 (5.8)


Single149 (49.2)149 (70.6)298 (58.0)


Widow6 (2.0)2 (1.0)8 (1.6)

Education level (years)


0-10159 (52.5)60 (28.4)219 (42.6)


>10144 (47.5)151 (71.6)295 (57.4)

Socioeconomic levelc


Q1 (least deprived)32 (10.6)19 (9.0)51 (9.9)


Q255 (18.2)48 (22.7)103 (20.0)


Q372 (23.8)37 (17.5)109 (21.2)


Q472 (23.8)61 (28.9)133 (25.9)


Q5 (most deprived)64 (21.1)40 (19.0)104 (20.2)
Digital use and literacy

Use of smart devices262 (86.5)211 (100)473 (92.0)

Mean digital literacyd


Q1 (0-4.17)82 (27.1)10 (4.7)92 (17.9)


Q2 (4.17-5.67)76 (25.1)66 (31.3)142 (28.6)


Q3 (5.67-6.83)51 (16.8)66 (31.3)117 (22.8)


Q4 (6.83-10.00)54 (17.8)69 (32.7)123 (23.9)
General health status

Overall healthe


Q1 (0-70)92 (30.4)54 (25.6)146 (28.4)


Q2 (70-80)68 (22.4)53 (25.1)121 (23.5)


Q3 (80-90)87 (28.7)64 (30.3)151 (29.4)


Q4 (90-100)49 (16.2)38 (18.0)87 (16.9)

Smoking statusb


Never smokers237 (78.2)151 (71.6)388 (75.5)


Former smokers40 (13.2)46 (21.8)86 (16.7)


Current smokers25 (8.3)14 (6.6)39 (7.6)

Anxiety symptomsf110 (36.3)67 (31.8)177 (34.4)

Depression symptomsf52 (17.2)9 (4.3)61 (11.9)
Asthma status

Asthma controlg


Well-controlled152 (50.2)110 (52.1)262 (51.0)


Partially/uncontrolled150 (49.5)98 (46.4)248 (48.2)

≥1 asthma exacerbation in the past yearh160 (52.8)101 (47.9)261 (50.8)

≥1 unplanned appointment in the past yearc105 (34.7)55 (26.1)160 (31.1)

≥1 inpatient admission in the past yearc11 (3.6)5 (2.4)16 (3.1)

a7 patients with missing data.

b1 patient with missing data.

c14 patients with missing data.

d40 patients with missing data.

e9 patients with missing data.

f2 patients with missing data.

g4 patients with missing data.

h15 patients with missing data.

Table 2. Multivariate analysis to explain the use of health and fitness apps.
PredictorOdds ratio (5%-95% CI)P value
Gender

MaleReferencea

Female1.37 (0.86-2.20).19
Age band (years)

13-18Reference

18-301.93 (0.97-3.89).06

30-401.53 (0.59-4.05).38

40-501.41 (0.53-3.77).49

50-650.63 (0.12-2.86).57

≥651.68 (0.19-11.47).62
Marital status

MarriedReference

Separated1.64 (0.55-4.96).37

Single2.22 (1.05-4.75).04b

Widow1.17 (0.03-33.55).93
Education level (years)

0-10Reference

>101.95 (1.12-3.45).02
Socioeconomic level

Q1 (least deprived)Reference

Q22.02 (0.84-4.92).12

Q31.08 (0.45-2.64).86

Q41.42 (0.61-3.33).41

Q5 (most deprived)1.34 (0.56-3.24).52
Mean digital literacy

Q1 (0-4.17)Reference

Q2 (4.17-5.67)6.74 (2.90-17.40)<.001

Q3 (5.67-6.83)10.30 (4.28-27.56)<.001

Q4 (6.83-10)11.52 (4.78-30.87)<.001
Overall health

Q1 (0-70)Reference

Q2 (70-80)1.05 (0.56-1.99).87

Q3 (80-90)0.87 (0.46-1.63).67

Q4 (90-100)0.86 (0.42-1.76).69
Smoking status

Never smokersReference

Former smokers1.83 (0.97-3.53).07

Current smokers0.84 (0.34-2.05).70
Anxiety symptoms

NoReference

Yes1.12 (0.64-1.95).69
Depression symptoms

NoReference

Yes0.32 (0.12-0.83).02
Asthma control

Well-controlledReference

Partially/uncontrolled0.89 (0.54-1.44).62
Asthma exacerbation in the past year

0Reference

≥10.99 (0.56-1.75).96
Unplanned appointment in the past year

0Reference

≥11.01 (0.52-1.91).99
Inpatient admission in the past year

0Reference

≥11.73 (0.37-8.03).48

aConfidence intervals could not be calculated. Reference means the category used as reference (ie, to which other categories are compared). The Hosmer–Lemeshow Test yielded a P value of .21, χ28=10.9, Aikake Information Criterion of 542, and coefficient of determination (R2) of 48%.

bItalicized P values are significant.


Principal Findings

Use of health and fitness mobile apps was positively associated with a single status, >10 years of education, and higher digital literacy scores, and negatively associated with depressive symptoms. No significant associations were found with other variables, including gender, age, socioeconomic level, general health status, smoking status, anxiety, and asthma control.

Comparison With Previous Studies

According to our results, single participants are more likely to use health and fitness apps. In a recent mixed methods study, Zhou et al [18] explored the barriers to and facilitators of the use of mobile health apps and found that single users had less strong concerns about information security and privacy and less desire to have stringent security protection, which could contribute to higher usage levels in this group.

Consistent with our findings, previous studies have described educational attainment as an important predictor of use of mobile devices and apps [14-16]. However, the relationship may be more complex than initially predicted, as in another study by Carroll et al [14] where both patients with a degree and those with less than high school education were significantly associated with a reduced likelihood of using health apps. The reasons for the educational differences are not fully understood but may reflect the effect of digital skills and confidence, and social norms related to the perceived value of using health and fitness apps [16].

A significant association between digital health literacy and use of health and fitness apps was also found. Digital health literacy has been previously shown to affect the use of health apps [17]. However, comparisons between different studies are limited by the heterogeneity of tools used to evaluate patients’ digital health literacy; therefore, standardization of the assessment methods used is recommended in the future. It is also important to note that although individuals with low general health literacy tend to use less health information technology [31], previous evidence has shown that tailored approaches including apps programmed with computer-animated characters, text, and graphics to provide health communication and education could be a widely accepted option for these patients [32].

Interestingly, our study found a negative association between the use of health and fitness apps and the presence of depressive symptoms. Despite the breadth of research exploring the determinants of use of mental health apps [33-35], there was a lack of evidence specifically exploring the impact of the presence of mental health symptoms on usage rates. However, several studies show that patients with depressive symptoms often exhibit poor engagement with health services, and health care avoidance [36,37], and such behaviors may also contribute to a lower interest and usage of health and fitness apps by these groups of patients. This finding opens a new research avenue, and future work should explore the drivers for this effect and should involve patients with mental health problems in the co-design of digital solutions and interventions that promote both early and sustained use. The other needed variables, such as the ones related to asthma control, were not significant in explaining mHealth behaviors, although they showed a similar trend to the one observed with the variable depression. This is in line with previous asthma mHealth studies showing that patients with worse asthma control engage less with the tested apps [15,38]. Regarding age, odds ratios were higher for patients aged 18-30 years (1.93, 95%CI 0.97-3.89), but no significant differences were detected, despite the P value being close to the significance threshold (P=.06). Increasing the sample size in future studies could help further explore this effect and to confirm or exclude a potential type II error [39]. Previous studies found that younger adults were more likely to engage with health apps [14-16] and suggested that the effect of age likely reflects both social norms and cohort effects, such as the increased exposure to these devices and apps at younger ages [14]. Previous evidence also suggests that younger adults seem to have higher digital health literacy levels, which can contribute to increased use of digital solutions [40]. While it is recognized that the use of digital health solutions by older persons could improve patient engagement and reduce both financial burden and pressure on health systems, usage rates among this group remain low. According to a mixed methods study conducted by Fox et al, this digital health divide is deepening owing to older adults’ perceived inability and unwillingness to use digital technologies, stemming from mistrust, high-risk perceptions, and a strong desire for privacy [41].

Finally, no significant associations were found with gender. Previous literature shows mixed evidence on this subject: while some studies had found a higher use among male subjects [15], others reported the opposite [14,16]. The reasons for gender differences in some samples are unclear but may reflect sample-specific differences in health-seeking behavior, and interest and participation in healthy lifestyle interventions in general.

Strengths and Limitations

This study has several strengths. It is the first national-level study performed in Portugal, evaluating the use of health and fitness apps among patients with asthma, covering the majority of the geographic regions of the country. A comprehensive set of individual-level characteristics was collected and analyzed, which allowed us to explore the impact of a range of sociodemographic factors, health literacy, and cofactors such as general health status and asthma status. Although no power calculation was performed (which was associated with the secondary analysis nature of this study), the overall large sample size contributes to the robustness of these findings.

Other limitations also need to be acknowledged. Intrinsic to the study design using convenience sampling, a potential selection bias cannot be excluded. This can possibly explain the low number of patients above 65 years of age. Nevertheless, this risk was mitigated by sampling patients from different health care settings, centers, and geographic regions. Health and fitness app use was patient-reported; therefore, a potential information bias cannot be excluded either. As an alternative, future studies could use patient log-in as a measure of app use. Furthermore, digital literacy was assessed using selected items of MTUAS scale, not the complete instrument. This choice emerged as a mitigation measure to reduce the data collection burden and allow us to efficiently collect data on an aspect seldom reported in the literature. Future studies should explore the possibility of including complete validated tools, such as MTUAS [23] or the eHealth Literacy Scale [42]. We also need to consider that although the questionnaires were independently answered by the study participants, they were provided to them by their family physicians provided during an in-person visit, which may have influenced them to give desirable responses, namely those related to their general health status and asthma control.

Finally, most subjects included in this study were relatively young, had at least 7 years of education, were in the 3 higher quintiles of the socioeconomic deprivation index, had an overall good health status, and had good asthma control. Consequently, attempts to generalize these findings to other populations that do not share the same characteristics need to be cautious. Future studies should consider involving patients with specific needs (ie, lower education attainment, lower sociodemographic levels, poorer overall health status, or poorer asthma status control) and evaluate the replicability of these findings.

Conclusions

Our results show a negative association with low literacy and the presence of depressive symptoms, highlighting the need for future research to explore the effect of disparities on app use, particularly among those with lower digital health literacy or mental health issues. Importantly, such studies should also assess participants’ general health-seeking behavior (ie, interest and participation in healthy lifestyle interventions in general) and the key constructs of their acceptance the unified theory of acceptance and use of technology) [43,44]. This theory was developed through a consolidation of the constructs of 8 previous models (theory of reasoned action, technology acceptance model, motivational model, theory of planned behavior, a combined theory of planned behavior/technology acceptance model, model of personal computer use, diffusion of innovations theory, and social cognitive theory), and includes four constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. Gender, age, experience, and voluntariness of use have been suggested to moderate the impact of the 4 key constructs on usage intention and behavior [43,44].

A better understanding of which patients with asthma are (and are not) using general health and fitness apps is key to design tailored mHealth interventions to improve sustained use by this specific group of patients. Additionally, this knowledge can also inform high-level delivery strategies to ensure that these solutions reach out comprehensive groups of patients with asthma, thus improving rather than entrenching health inequities within this population.

Acknowledgments

This study was funded by the European Regional Development Fund through the following programs: POCI-01-0145-36 FEDER-029130 (“mINSPIRE—mHealth to measure and improve adherence to medication in chronic obstructive respiratory diseases - generalisation and evaluation of gamification, peer support and advanced image processing technologies”) cofunded by the COMPETE2020 (Programa Operacional Competitividade e Internacionalização), Portugal 2020 and by Portuguese Funds through Fundação para a Ciência e a Tecnologia. ALN is funded by the National Institute for Health Research’s Imperial Patient Safety Translational Research Centre and supported by the National Institute of Health Research’s Imperial Biomedical Research Centre. This study was supported by National Funds through Fundação para a Ciência e a Tecnologia, IP, within the research and development unit of CINTESIS (reference UIDB/4255/2020).

Conflicts of Interest

JAF is the co-founder of the University of Porto Spin-off Medida that is co-responsible for the Inspirermundi app. The other authors have no conflicts to declare.

  1. Bert F, Giacometti M, Gualano MR, Siliquini R. Smartphones and health promotion: a review of the evidence. J Med Syst 2014 Jan;38(1):9995. [CrossRef] [Medline]
  2. Number of smartphone users from 2016 to 2021. Statista. 2021 Jun.   URL: https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/ [accessed 2021-06-29]
  3. Dennison L, Morrison L, Conway G, Yardley L. Opportunities and challenges for smartphone applications in supporting health behavior change: qualitative study. J Med Internet Res 2013 Apr 18;15(4):e86 [FREE Full text] [CrossRef] [Medline]
  4. Bhuyan SS, Lu N, Chandak A, Kim H, Wyant D, Bhatt J, et al. Use of Mobile Health Applications for Health-Seeking Behavior Among US Adults. J Med Syst 2016 Jun;40(6):153. [CrossRef] [Medline]
  5. Total global mHealth market forecast from 2016 to 2025. Statista. 2018 Nov.   URL: https://www.statista.com/statistics/938544/mhealth-market-size-forecast-globally/ [accessed 2021-06-29]
  6. Alvarez-Perea A, Sánchez-García S, Muñoz Cano R, Antolín-Amérigo D, Tsilochristou O, Stukus DR. Impact Of "eHealth" in Allergic Diseases and Allergic Patients. J Investig Allergol Clin Immunol 2019;29(2):94-102 [FREE Full text] [CrossRef] [Medline]
  7. Partridge MR, Dal Negro RW, Olivieri D. Understanding patients with asthma and COPD: insights from a European study. Prim Care Respir J 2011 Sep;20(3):315-323, 17 p following 323 [FREE Full text] [CrossRef] [Medline]
  8. Masoli M, Fabian D, Holt S, Beasley R, Global Initiative for Asthma (GINA) Program. The global burden of asthma: executive summary of the GINA Dissemination Committee report. Allergy 2004 May;59(5):469-478. [CrossRef] [Medline]
  9. British Guideline on the Management of Asthma: Quick Reference Guide. British Thoracic Society, Scottish Intercollegiate Guidelines Network. 2008.   URL: http:/​/globalasthmanetwork.​org/​management/​guides/​bosnia_and_herzegovina/​British%20guideline%20for%20asthma.​pdf [accessed 2021-08-24]
  10. Nikolova S. Why do asthma apps only capture less than 1% of the addressable market? (Top 10 asthma apps). Research2Guidance. 2018.   URL: https:/​/research2guidance.​com/​why-do-asthma-apps-only-capture-less-than-1-of-the-addressable-market-top-10-asthma-apps/​ [accessed 2021-06-29]
  11. Unni E, Gabriel S, Ariely R. A review of the use and effectiveness of digital health technologies in patients with asthma. Ann Allergy Asthma Immunol 2018 Dec;121(6):680-691.e1. [CrossRef] [Medline]
  12. Matricardi PM, Dramburg S, Alvarez-Perea A, Antolín-Amérigo D, Apfelbacher C, Atanaskovic-Markovic M, et al. The role of mobile health technologies in allergy care: An EAACI position paper. Allergy 2020 Feb;75(2):259-272. [CrossRef] [Medline]
  13. Jácome C, Almeida R, Pereira AM, Araújo L, Correia MA, Pereira M, INSPIRERS group. Asthma App Use and Interest Among Patients With Asthma: A Multicenter Study. J Investig Allergol Clin Immunol 2020 Apr;30(2):137-140 [FREE Full text] [CrossRef] [Medline]
  14. Carroll JK, Moorhead A, Bond R, LeBlanc WG, Petrella RJ, Fiscella K. Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach. J Med Internet Res 2017 Apr 19;19(4):e125 [FREE Full text] [CrossRef] [Medline]
  15. Chan YY, Wang P, Rogers L, Tignor N, Zweig M, Hershman SG, et al. The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit. Nat Biotechnol 2017 Apr;35(4):354-362 [FREE Full text] [CrossRef] [Medline]
  16. Kontos E, Blake KD, Chou WS, Prestin A. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012. J Med Internet Res 2014 Jul 16;16(7):e172 [FREE Full text] [CrossRef] [Medline]
  17. Lin TTC, Bautista JR. Understanding the Relationships between mHealth Apps' Characteristics, Trialability, and mHealth Literacy. J Health Commun 2017 Apr;22(4):346-354. [CrossRef] [Medline]
  18. Zhou L, Bao J, Watzlaf V, Parmanto B. Barriers to and Facilitators of the Use of Mobile Health Apps From a Security Perspective: Mixed-Methods Study. JMIR Mhealth Uhealth 2019 Apr 16;7(4):e11223 [FREE Full text] [CrossRef] [Medline]
  19. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav 1995 Mar;36(1):1-10. [CrossRef] [Medline]
  20. Jácome C, Guedes R, Almeida R, Teixeira JF, Pinho B, Vieira-Marques P, pelo grupo INSPIRERS. mINSPIRERS – Estudo da exequibilidade de uma aplicação móvel para medição e melhoria da adesão à medicação inalada de controlo em adolescentes e adultos com asma persistente: Protocolo de um estudo observacional multicêntrico. Rev Port Imunoalergologia 2018;26(1):47-61 [FREE Full text]
  21. Jácome C, Pereira AM, Almeida R, Ferreira-Magalhaes M, Couto M, Araujo L, Inspirers group. Patient-physician discordance in assessment of adherence to inhaled controller medication: a cross-sectional analysis of two cohorts. BMJ Open 2019 Nov 07;9(11):e031732 [FREE Full text] [CrossRef] [Medline]
  22. Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, STROBE Initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Int J Surg 2014 Dec;12(12):1500-1524 [FREE Full text] [CrossRef] [Medline]
  23. Rosen LD, Whaling K, Carrier LM, Cheever NA, Rokkum J. The Media and Technology Usage and Attitudes Scale: An empirical investigation. Comput Human Behav 2013 Nov 01;29(6):2501-2511 [FREE Full text] [CrossRef] [Medline]
  24. Ribeiro AI, Mayer A, Miranda A, Pina MF. The Portuguese Version of the European Deprivation Index: An Instrument to Study Health Inequalities. Acta Med Port 2017 Jan 31;30(1):17-25 [FREE Full text] [CrossRef] [Medline]
  25. Pickard AS, Wilke C, Jung E, Patel S, Stavem K, Lee TA. Use of a preference-based measure of health (EQ-5D) in COPD and asthma. Respir Med 2008 Apr;102(4):519-536 [FREE Full text] [CrossRef] [Medline]
  26. Pais-Ribeiro J, Silva I, Ferreira T, Martins A, Meneses R, Baltar M. Validation study of a Portuguese version of the Hospital Anxiety and Depression Scale. Psychol Health Med 2007 Mar;12(2):225-235; quiz 235. [CrossRef] [Medline]
  27. 2020 Gold Reports. Global Initiative for Chronic Obstructive Lung Disease.   URL: https://goldcopd.org/gold-reports/ [accessed 2021-06-29]
  28. Loymans RJB, Ter Riet G, Sterk PJ. Definitions of asthma exacerbations. Curr Opin Allergy Clin Immunol 2011 Jun;11(3):181-186. [CrossRef] [Medline]
  29. Paintmaps.   URL: https://paintmaps.com/ [accessed 2021-08-24]
  30. População residente: total e por grandes grupos etários. Pordata.: INE   URL: https:/​/www.​pordata.pt/​Municipios/​Popula%C3%A7%C3%A3o+residente+total+e+por+grandes+grupos+et%C3%A1rios-390 [accessed 2020-11-10]
  31. Herndon JB, Chaney M, Carden D. Health literacy and emergency department outcomes: a systematic review. Ann Emerg Med 2011 Apr;57(4):334-345. [CrossRef] [Medline]
  32. Kim H, Xie B. Health literacy and internet‐ and mobile app‐based health services: A systematic review of the literature. Proc Assoc Info Sci Tech 2016 Feb 24;52(1):1-4. [CrossRef]
  33. Lipschitz J, Miller CJ, Hogan TP, Burdick KE, Lippin-Foster R, Simon SR, et al. Adoption of Mobile Apps for Depression and Anxiety: Cross-Sectional Survey Study on Patient Interest and Barriers to Engagement. JMIR Ment Health 2019 Jan 25;6(1):e11334 [FREE Full text] [CrossRef] [Medline]
  34. Huang H, Bashir M. Users' Adoption of Mental Health Apps: Examining the Impact of Information Cues. JMIR Mhealth Uhealth 2017 Jun 28;5(6):e83 [FREE Full text] [CrossRef] [Medline]
  35. Williams MG, Stott R, Bromwich N, Oblak SK, Espie CA, Rose JB. Determinants of and barriers to adoption of digital therapeutics for mental health at scale in the NHS. BMJ Innov 2020 May 07;6(3):92-98. [CrossRef]
  36. Lo C, Calzavara A, Kurdyak P, Barbera L, Shepherd F, Zimmermann C, et al. Depression and use of health care services in patients with advanced cancer. Can Fam Physician 2013 Mar;59(3):e168-a174 [FREE Full text] [Medline]
  37. Ganson KT, Weiser SD, Tsai AC, Nagata JM. Associations between Anxiety and Depression Symptoms and Medical Care Avoidance during COVID-19. J Gen Intern Med 2020 Nov;35(11):3406-3408 [FREE Full text] [CrossRef] [Medline]
  38. Jácome C, Almeida R, Pereira AM, Amaral R, Mendes S, Alves-Correia M, et al. Feasibility and Acceptability of an Asthma App to Monitor Medication Adherence: Mixed Methods Study. JMIR Mhealth Uhealth 2021 May 25;9(5):e26442 [FREE Full text] [CrossRef] [Medline]
  39. Rothman KJ. Curbing type I and type II errors. Eur J Epidemiol 2010 Apr;25(4):223-224 [FREE Full text] [CrossRef] [Medline]
  40. Knitza J, Simon D, Lambrecht A, Raab C, Tascilar K, Hagen M, et al. Mobile Health Usage, Preferences, Barriers, and eHealth Literacy in Rheumatology: Patient Survey Study. JMIR Mhealth Uhealth 2020 Aug 12;8(8):e19661 [FREE Full text] [CrossRef] [Medline]
  41. Fox G, Connolly R. Mobile health technology adoption across generations: Narrowing the digital divide. Info Systems J 2018 Jan 29;28(6):995-1019. [CrossRef]
  42. Norman CD, Skinner HA. eHEALS: The eHealth Literacy Scale. J Med Internet Res 2006 Nov 14;8(4):e27 [FREE Full text] [CrossRef] [Medline]
  43. Venkatesh V, Morris MG, Davis GB, Davis FD. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 2003;27(3):425. [CrossRef]
  44. Venkatesh V, Thong JYL, Xu X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly 2012;36(1):157. [CrossRef]


aOR: adjusted odds ratio
MTUAS: Media and Technology Usage and Attitudes Scale
VAS: Visual Analog Scale


Edited by R Kukafka; submitted 13.11.20; peer-reviewed by C Mueller, O El-Gayar, J Alqahtani, S Rostam Niakan Kalhori; comments to author 29.01.21; revised version received 01.04.21; accepted 14.06.21; published 22.09.21

Copyright

©Ana Luísa Neves, Cristina Jácome, Tiago Taveira-Gomes, Ana Margarida Pereira, Rute Almeida, Rita Amaral, Magna Alves-Correia, Sandra Mendes, Cláudia Chaves-Loureiro, Margarida Valério, Cristina Lopes, Joana Carvalho, Ana Mendes, Carmelita Ribeiro, Sara Prates, José Alberto Ferreira, Maria Fernanda Teixeira, Joana Branco, Marta Santalha, Maria João Vasconcelos, Carlos Lozoya, Natacha Santos, Francisca Cardia, Ana Sofia Moreira, Luís Taborda-Barata, Cláudia Sofia Pinto, Rosário Ferreira, Pedro Morais Silva, Tania Monteiro Ferreira, Raquel Câmara, Rui Lobo, Diana Bordalo, Cristina Guimarães, Maria Espírito Santo, José Ferraz de Oliveira, Maria José Cálix Augusto, Ricardo Gomes, Inês Vieira, Sofia da Silva, Maria Marques, João Cardoso, Ana Morete, Margarida Aroso, Ana Margarida Cruz, Carlos Nunes, Rita Câmara, Natalina Rodrigues, Carmo Abreu, Ana Luísa Albuquerque, Claúdia Vieira, Carlos Santos, Rosália Páscoa, Carla Chaves-Loureiro, Adelaide Alves, Ângela Neves, José Varanda Marques, Bruno Reis, Manuel Ferreira-Magalhães, João Almeida Fonseca. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.09.2021.

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