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

Original Paper Determinants of the Use of Health and Fitness Mobile Apps by Patients With Asthma: Secondary Analysis of Observational Studies Ana Luísa Neves1,2,3*, MD, MSc, PhD; Cristina Jácome1,2*, PT, MSc, PhD; Tiago Taveira-Gomes1,2,4,5, MD, PhD; Ana Margarida Pereira1,2,6, MD; Rute Almeida1,2, PhD; Rita Amaral1,2,7,8, PhD; Magna Alves-Correia6, MD; Sandra Mendes2, MSc; Cláudia Chaves-Loureiro9, MD, PhD; Margarida Valério9, MD; Cristina Lopes10,11, MD, PhD; Joana Carvalho12, MD; Ana Mendes13, MD; Carmelita Ribeiro14, MD; Sara Prates15, MD; José Alberto Ferreira16, MD; Maria Fernanda Teixeira17, MD; Joana Branco18, MD; Marta Santalha19, MD; Maria João Vasconcelos20, MD; Carlos Lozoya21, MD, PhD; Natacha Santos22, MD; Francisca Cardia23, MD; Ana Sofia Moreira24, MD; Luís Taborda-Barata25,26, MD, PhD; Cláudia Sofia Pinto27, MD; Rosário Ferreira28, MD; Pedro Morais Silva29, MD; Tania Monteiro Ferreira30, MD; Raquel Câmara31, MD; Rui Lobo32, MD; Diana Bordalo33, MD; Cristina Guimarães34, MD; Maria Espírito Santo35, MD; José Ferraz de Oliveira36, MD; Maria José Cálix Augusto37, MD; Ricardo Gomes38, MD; Inês Vieira39, MD; Sofia da Silva40, MD; Maria Marques41, MD; João Cardoso42, MD, PhD; Ana Morete6,43, MD; Margarida Aroso44, MD; Ana Margarida Cruz45, MD; Carlos Nunes46, MD; Rita Câmara47, MD; Natalina Rodrigues48, MD; Carmo Abreu49, MD; Ana Luísa Albuquerque50, MD; Claúdia Vieira51, MD; Carlos Santos52, MD; Rosália Páscoa1,2,53, MD; Carla Chaves-Loureiro54, MD, MSc; Adelaide Alves55, MD; Ângela Neves56, MD; José Varanda Marques57, MD; Bruno Reis58, MD; Manuel Ferreira-Magalhães2,17, MD, PhD; João Almeida Fonseca1, MD, PhD 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 J Med Internet Res 2021 | vol. 23 | iss. 9 | e25472 | p. 1https://www.jmir.org/2021/9/e25472 (page number not for citation purposes) Neves et alJOURNAL OF MEDICAL INTERNET RESEARCH XSL • FO RenderX 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: [email protected] Abstract 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, J Med Internet Res 2021 | vol. 23 | iss. 9 | e25472 | p. 2https://www.jmir.org/2021/9/e25472 (page number not for citation purposes) Neves et alJOURNAL OF MEDICAL INTERNET RESEARCH XSL • FO RenderX 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 mobile apps; smartphone; patient participation; self-management; asthma Introduction 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. Methods 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 J Med Internet Res 2021 | vol. 23 | iss. 9 | e25472 | p. 3https://www.jmir.org/2021/9/e25472 (page number not for citation purposes) Neves et alJOURNAL OF MEDICAL INTERNET RESEARCH XSL • FO RenderX 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. 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. J Med Internet Res 2021 | vol. 23 | iss. 9 | e25472 | p. 4https://www.jmir.org/2021/9/e25472 (page number not for citation purposes) Neves et alJOURNAL OF MEDICAL INTERNET RESEARCH XSL • FO RenderX 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. 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. Results 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). J Med Internet Res 2021 | vol. 23 | iss. 9 | e25472 | p. 5https://www.jmir.org/2021/9/e25472 (page number not for citation purposes) Neves et alJOURNAL OF MEDICAL INTERNET RESEARCH XSL • FO RenderX Figure 3. Number of participating centres per district. 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. J Med Internet Res 2021 | vol. 23 | iss. 9 | e25472 | p. 6https://www.jmir.org/2021/9/e25472 (page number not for citation purposes) Neves et alJOURNAL OF MEDICAL INTERNET RESEARCH XSL • FO RenderX Table 1. Characteristics of the participants according to their use of health and fitness mobile apps (N=514). Total, n (%)Users (n=211), n (%)Nonusers (n=303), n (%)Characteristics Sociodemographic Gender 326 (63.4)137 (64.9)189 (62.4)Female 188 (36.6)74 (35.1)114 (37.6)Male Age band (years)a 154 (30.0)62 (29.4)92 (30.4)13-18 115 (22.4)74 (35.1)41 (13.5)18-30 72 (14.0)32 (15.2)40 (13.2)30-40 82 (16.0)31 (14.7)51 (16.8)40-50 58 (11.3)7 (3.3)51 (16.8)50-65 26 (5.1)3 (1.4)23 (7.6)≥65 Marital statusb 177 (34.4)49 (23.2)128 (42.2)Married 30 (5.8)11 (5.2)19 (6.3)Separated 298 (58.0)149 (70.6)149 (49.2)Single 8 (1.6)2 (1.0)6 (2.0)Widow Education level (years) 219 (42.6)60 (28.4)159 (52.5)0-10 295 (57.4)151 (71.6)144 (47.5)>10 Socioeconomic levelc 51 (9.9)19 (9.0)32 (10.6)Q1 (least deprived) 103 (20.0)48 (22.7)55 (18.2)Q2 109 (21.2)37 (17.5)72 (23.8)Q3 133 (25.9)61 (28.9)72 (23.8)Q4 104 (20.2)40 (19.0)64 (21.1)Q5 (most deprived) Digital use and literacy 473 (92.0)211 (100)262 (86.5)Use of smart devices Mean digital literacyd 92 (17.9)10 (4.7)82 (27.1)Q1 (0-4.17) 142 (28.6)66 (31.3)76 (25.1)Q2 (4.17-5.67) 117 (22.8)66 (31.3)51 (16.8)Q3 (5.67-6.83) 123 (23.9)69 (32.7)54 (17.8)Q4 (6.83-10.00) General health status Overall healthe 146 (28.4)54 (25.6)92 (30.4)Q1 (0-70) 121 (23.5)53 (25.1)68 (22.4)Q2 (70-80) 151 (29.4)64 (30.3)87 (28.7)Q3 (80-90) 87 (16.9)38 (18.0)49 (16.2)Q4 (90-100) Smoking statusb 388 (75.5)151 (71.6)237 (78.2)Never smokers 86 (16.7)46 (21.8)40 (13.2)Former smokers J Med Internet Res 2021 | vol. 23 | iss. 9 | e25472 | p. 7https://www.jmir.org/2021/9/e25472 (page number not for citation purposes) Neves et alJOURNAL OF MEDICAL INTERNET RESEARCH XSL • FO RenderX Total, n (%)Users (n=211), n (%)Nonusers (n=303), n (%)Characteristics 39 (7.6)14 (6.6)25 (8.3)Current smokers 177 (34.4)67 (31.8)110 (36.3) Anxiety symptomsf 61 (11.9)9 (4.3)52 (17.2) Depression symptomsf Asthma status Asthma controlg 262 (51.0)110 (52.1)152 (50.2)Well-controlled 248 (48.2)98 (46.4)150 (49.5)Partially/uncontrolled 261 (50.8)101 (47.9)160 (52.8) ≥1 asthma exacerbation in the past yearh 160 (31.1)55 (26.1)105 (34.7) ≥1 unplanned appointment in the past yearc 16 (3.1)5 (2.4)11 (3.6) ≥1 inpatient admission in the past yearc 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. J Med Internet Res 2021 | vol. 23 | iss. 9 | e25472 | p. 8https://www.jmir.org/2021/9/e25472 (page number not for citation purposes) Neves et alJOURNAL OF MEDICAL INTERNET RESEARCH XSL • FO RenderX Table 2. Multivariate analysis to explain the use of health and fitness apps. PvalueOdds ratio (5%-95% CI)Predictor Gender Referencea Male .191.37 (0.86-2.20)Female Age band (years) Reference13-18 .061.93 (0.97-3.89)18-30 .381.53 (0.59-4.05)30-40 .491.41 (0.53-3.77)40-50 .570.63 (0.12-2.86)50-65 .621.68 (0.19-11.47)≥65 Marital status ReferenceMarried .371.64 (0.55-4.96)Separated .04b 2.22 (1.05-4.75)Single .931.17 (0.03-33.55)Widow Education level (years) Reference0-10 .021.95 (1.12-3.45)>10 Socioeconomic level ReferenceQ1 (least deprived) .122.02 (0.84-4.92)Q2 .861.08 (0.45-2.64)Q3 .411.42 (0.61-3.33)Q4 .521.34 (0.56-3.24)Q5 (most deprived) Mean digital literacy ReferenceQ1 (0-4.17) <.0016.74 (2.90-17.40)Q2 (4.17-5.67) <.00110.30 (4.28-27.56)Q3 (5.67-6.83) <.00111.52 (4.78-30.87)Q4 (6.83-10) Overall health ReferenceQ1 (0-70) .871.05 (0.56-1.99)Q2 (70-80) .670.87 (0.46-1.63)Q3 (80-90) .690.86 (0.42-1.76)Q4 (90-100) Smoking status ReferenceNever smokers .071.83 (0.97-3.53)Former smokers .700.84 (0.34-2.05)Current smokers Anxiety symptoms ReferenceNo .691.12 (0.64-1.95)Yes J Med Internet Res 2021 | vol. 23 | iss. 9 | e25472 | p. 9https://www.jmir.org/2021/9/e25472 (page number not for citation purposes) Neves et alJOURNAL OF MEDICAL INTERNET RESEARCH XSL • FO RenderX