Review Article | Open Access
Mobile Apps for the Management of Comorbid Overweight/Obesity and Depression/Anxiety: A Systematic Review
Objective. This review aimed at searching for scientific literature on mobile apps for the management of comorbid overweight/obesity and depression/anxiety and providing a brief and comprehensive summary of their main features, targeted groups, and relevant results. Methods. A bibliographical search was performed in Pubmed, PsycNet, Web of Science, ResearchGate, and Lilacs databases. The terms “obesity” and “overweight” were introduced in combination with “anxiety” and “depression” and “mobile app (application),” “smartphone app (application),” “android app (applicattion),” “iOS app (application),” “mobile health app (application),” and “mHealth app (application).” Results. The initial search eliciting 204 citations was reduced to 7 relevant papers (4 original articles, 1 brief communication, and 2 study protocols). All publications were from the last five years, most were produced by research teams from the United States. All had adult samples, and interventions mostly followed a cognitive behavioral framework. Regarding mobile apps, five studies only used one to monitor weight and physical activity, one study to provide therapy to improve psychological wellness, and one study to monitor cognitions and emotions. No mobile app was found for the simultaneous management of overweight/obesity and depression/anxiety. Conclusions. The prevalence and costs related to overweight/obesity and depression/anxiety are significant and likely to increase. Very often these conditions overlap; thus, it would be recommendable to treat their comorbidity simultaneously. Nevertheless, no mobile app has been designed for this purpose, which would help to reduce service provision costs and make treatment more easily accessible for patients.
During the last decades, technology has driven societies into an accelerated process of change. Users are provided with easy and virtually infinite access to information. Learning and working have gradually become more dependent on the use of electronic tools and computer programs. This has made human tasks more efficient and effective while also promoting a rather unhealthy life style. New technologies and electronic-based activities have produced a decrease in physical activity, more sedentary behavior, and poorer dietary patterns, all acknowledged as major behavioral determinants of obesity . Basically, obesity and overweight are the outcome of an energy imbalance between calories consumed and calories expended , and over the last decades, their prevalence has doubled and even quadrupled . These pathologies increase the risk of chronic diseases, mainly type 2 diabetes, dyslipidemia, hypertension, cardiovascular diseases, cancer, and musculoskeletal disorders, these conditions being responsible for the vast majority of deceases worldwide [2, 4, 5].
Moreover, excess body weight is also known to be associated with increased prevalence of mental disorders [6–9]. This association seems influenced by gender, age [7, 9–11], and even race/ethnicity [8, 11, 12]. For instance, McCrea and colleagues  found that, in young women, having a disorder increased along with Body Mass Index (BMI), whereas in young men, probabilities were higher for both underweight and obese men; but these associations diminished in older age groups. Among various mental disorders, depression and anxiety stand out, not only for their higher prevalence but also for their marked association with overweight/obesity [13–15]. Although a bidirectional link has been repeatedly observed [11, 16–19], there are also some discrepancies in findings that might be attributed to the effects of gender , the criteria to measure obesity , and the selected mental disorder instruments . The link rather than linear seems to follow a U-shaped pattern, with stronger associations in the underweight and obesity groups [7, 12, 22]. Evidence, mainly from cross-sectional studies, supports a significant association between anxiety/depression and increased overweight/obesity, although no causal relationship can be inferred in either direction [19, 23]. To the best of our knowledge, public survey data of the prevalence of mental health disorders and overweight/obesity coexistance are not available; yet, systematic reviews have provided some estimates regarding the strength of relationship between obesity and psychiatric disorders. Luppino and colleagues  concluded that obese people had a 55% increased risk of developing depression over time, whereas people with depression had a 58% increased risk of becoming obese. Rajan and Menon  estimated the odds ratios (ORs) to be similar for developing depression in obesity (OR: 1.21–5.8) and vice versa (OR: 1.18–3.76) with a stronger association observed in women. For anxiety disorders, they found ORs to be less strong (OR: 1.27–1.40). Gariepy and colleagues  estimated a pooled OR of an association between obesity and anxiety of 1.40.
On the other hand, technology can also support the achievement of health objectives. Mobile health (mHealth), defined by the Global Observatory for eHealth as the “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices” , has the potential to influence on a variety of health outcomes and has become a key trend in health service provision during the last years . Psychosocial and health behavior interventions can now reach a bigger number of patients and from more distance locations by using mobile technology during their everyday lives (i.e., in real time) and in natural settings (i.e., real world) . Mobile applications, known as “apps,” facilitate remote access to health services, connecting patients with health professionals around the world in a safe, private, and confident manner, offering results in short terms [27, 28].
A meta-analysis performed by Qudah and Luetsch  found that more than 325 thousand health-related apps for iOS and Android platforms are available. Among the most targeted health conditions were obesity and dietetics, mental health disorders, diabetes, asthma, cancer, chronic kidney disease, chronic obstructive pulmonary disease, Parkinson’s disease, pregnancy-prenatal care, and rehabilitation. The number of apps for weight loss/management has increased rapidly. However, most commercial mobile apps for weight loss/management seem suboptimal in quality: inadequate scientific coverage, inaccurate weight-related information, lack important evidence-based features, do not involve health-care experts in their development process, overlook behavior change techniques, and have not undergone rigorous scientific testing [29–32]. Numerous mobile apps aiming at managing depression and anxiety symptoms are also available. Although most of them have not been rigorously designed and tested, there is some promising evidence of their effectiveness that needs to be validated [33–35].
The epidemic of overweight and obesity is expanding rapidly and to younger ages; thus, it has become a major challenge to healthcare systems due to their high economic and psychosocial burden [3, 4, 36, 37]. According to the 2017 Obesity Update released by the Organization for Economic Cooperation and Development (OECD), Mexico has an obesity rate of 33.3%, only surpassed by the United States and Chile, and it is projected to rise to 39% in 2030. Considering overweight and obesity in people aged 15–74 years, Mexico has the highest rate, 72.5% . The most recent national health survey reported overweight/obesity prevalence of 71.2% in adults, 36.3% in adolescents, and 33.2% in school-aged children .
On the other hand, depression and anxiety are the most common mental disorders, with prevalence rates (in 2015) of 4.4% and 3.6% on global population, respectively . In Mexico, anxiety disorders, followed by mood disorders (including depression), have been reported with the highest prevalence in adult populations, with rates of 14.3% and 9.2%, respectively . It has been estimated that depression accounts for a global total of over 50 million Years Lived with Disability (YLD), and anxiety disorders led to a global total of 24.6 million YLD . Direct and indirect costs of depression and anxiety make these disorders main contributors to the global burden of disease [42, 43].
Given the high prevalence of these disorders reported in Mexico, overweight/obesity and depression/anxiety are public health priorities demanding cost-effective interventions to reduce their burden. Given the extent evidence supporting their association, it might be worth considering the provision of a combined mHealth treatment for both conditions through the use of a mobile app. This review aimed at searching for scientific literature on mobile apps for the management of comorbid overweight/obesity and depression/anxiety and providing a brief and comprehensive summary of their main features, targeted groups, and, if available, relevant results. The focus was on finding available mobile apps built upon scientific foundations, regardless of their features, empirical evidence, target population, or interventional level (i.e., prevention or alleviation of comorbidity). Findings would point the way for the design, development, and testing of an intervention program aiming at attending simultaneously overweight/obesity and depression/anxiety through the use of mobile apps.
For the first time, a bibliographical search was performed on the topic consulting the Pubmed, PsycNet, Web of Science, ResearchGate, and Lilacs databases. The terms “obesity” and “overweight” were introduced in combination with “anxiety” and “depression” and “mobile app (application),” “smartphone app (application),” “android app (applicattion),” “iOS app (application),” “mobile health app (application),” and “mHealth app (application).” Inclusion criteria were (1) research papers, (2) published in peer-reviewed journals, (3) available in English or Spanish, and (4) published during the last decade (2009–2019). Exclusion criteria were (1) reviews and/or meta-analyses, (2) papers exclusively discussing theoretical perspectives, (3) empirical studies not reporting on mobile apps, and (4) empirical studies not reporting on the use of mobile apps for the purpose of overweight/obesity and depression/anxiety management. Papers reporting study protocols were agreed to be included, given that, although they do not provide empirical evidence of the use of the mobile apps, they may offer a detailed description of the development of the application and the study design. Online consultations proceeded from the 13th to the 17th of May, 2019.
Citations to works other than research articles (i.e., responses, conference reports, and books) were first withdrawn. Following this, original research was filtered and (systematic) reviews and/or meta-analysis were omitted, as well as theory-based papers. Through the information provided in the abstracts of the remaining material, research not focusing on the use of mobile apps was excluded. According to the study objective, the relevance of each publication was verified and only publications reporting on the use of mobile apps for the management of comorbid overweight/obesity and depression/anxiety were selected. Finally, the following features were recorded from each article: authors, year of publication, location of research team, type of manuscript, name of the project (if available), design, number of groups, objectives, sampling criteria, number and main features of participants, intervention, use of mobile app, outcome measures, and main results. All four authors worked together through the procedure; discrepancies were minimal.
The initial search from the five selected databases elicited 204 citations with 24 duplicated. Through the review of available abstracts, the list reduced to 7 relevant papers (Figure 1).
Table 1 presents the manuscripts’ basic identification and design features. All were published from 2015 to 2019, and most were original articles. All but one publication (from Finland) is from four different research groups in the United States.
Notes. References are presented alphabetically by the name of authors. USA: United States of America.
Table 2 summarizes the publications’ main features. All studies included adult patients with overweight or obesity. One study focused on pregnant women with no history of psychosis or depression. Another study included people with no clinical diagnosis of mental disorder but with psychological distress. The studies by Williams et al. , Ma et al. , and their colleagues included patients with clinically significant depression, the studies by Naslund and colleagues [46, 47] included patients with diagnoses of severe mental health illness (i.e., schizophrenia, bipolar disorder, major depressive disorder), and the study by Levinson and colleagues  included exclusively patients with eating disorders. Actual sample sizes ranged from 10 (pilot study with patients with severe mental illness recruited from one community mental health centre) to 219 (randomized control trial including people with psychological distress recruited by advertisements in local newspapers from three Finnish cities). The study protocols published by the RAINBOW/ENGAGE group aimed at recruiting 100 to 404 participants. Mean age of participants ranged from 24.9 to 50.2.
Excluding the descriptive study, all six designs relied on behavioural management interventions intended for the target samples. For instance, the SmartMoms intervention for pregnant women, the Acceptance and Commitment Therapy (ACT) for the psychologically distressed, the I-CARE program for comorbid obesity and depression, and the In SHAPE for weight loss adapted to people with severe mental illness.
Regarding the use of mobile apps, the studies by Ma et al. , Naslund et al. [46, 47], and their colleagues included apps to monitor physical activity alone or with dietary intake: MyFitnessPal, Fitbit, and Nike FuelBan; the last two were designed to synchronize with their corresponding physical devices. The study by Levinson and colleagues  described a mobile app aiming at keeping track of not only comportments but also cognitions and emotions in relation to eating disorders, behaviours, and anxiety. The ENGAGE study would use the Mindstrong app to collect data on participants’ use of smartphones (e.g., patterns of keyboard typing, screen swiping and tapping, search terms, and interaction by phone calls); these data will be, through algorithms, converted to specific psychometric tasks capable of assessing neuropsychological capacities. The Expecting Success study did not provide much information regarding the mobile app, and its purpose seems limited to providing remote behavioural weight management counselling through a smartphone, as counterpart to a face-to-face interaction.
Considering those studies where the individuals’ outputs were the focus, internal outcomes relate to various psychosocial variables including quality of life, perceived stress, functioning/disability, and symptoms of depression, anxiety and eating disorders, whereas external outcomes were mainly weight, BMI, and physical activity. One study assessed the feasibility and acceptability of a mobile app among individuals with serious mental illness, while other proposed data collection to develop algorithms for neuropsychological tasks.
Regarding the studies’ main outcomes, Naslund and colleagues  found patients with severe mental illnesses to be highly satisfied and motivated with the use of a mobile app to improve their physical activity and even their social interactions. Levinson and colleagues  identified some cognitions (e.g., worry about gaining weight) that could predict higher anxiety before, during, and after the meal and eating disorder pathology. Regarding interventional designs, Järvelä-Reijonen and colleagues  reported benefits of Acceptance and Commitment therapy on eating behaviours in people with psychological distress; for instance, increment in eating for physical rather than emotional reasons and decrement in uncontrolled eating and using food as a reward. Also, Naslund and colleagues  reported that their lifestyle behavioural intervention on patients with severe mental illness increased physical activity (i.e., step count) and weight loss, although not fitness. Altazan and colleagues  found in the group of pregnant women an association between higher weight gain and worse mood and an increment of depressive symptoms over time; yet, regarding the SmartMoms intervention, no significant effects were found on mood and quality of life.
Although not initially considered, it is worth to mention some features regarding the quality of the designs. The two studies by Naslund and colleagues [46, 47] and the one by Levinson and colleagues  were neither randomized nor blinded. Although the other four studies [44, 45, 49, 50] were randomized controlled trials, only the study by Ma and colleagues  was blind. Although the target populations of the studies were diverse (e.g., pregnant women and people with an eating disorder or a severe mental illness), according to their reports, a common bias is the recruitment by convenience sampling, which limits the generalization of their results. Retention rates were mentioned in all but one  empirical study, ranging from 79.1%  concluding a 6-month intervention to 93.6% and 97.6% assessed at 10 and 36 weeks after the baseline and having concluded a 8-week intervention .
Overweight/obesity and depression/anxiety are physical and mental public health priorities due to their high and increasing prevalence and their substantial direct and indirect costs. Research has repeatedly found a strong association between increased BMI and mental health problems [6, 8, 9, 13], though the direction of influence is still unclear [11, 51]. People with depression or anxiety might present significant weight gain due to irregular eating patterns and sedentary lifestyles triggered by clinical symptoms and/or the medications to treat them. A tendency to overeat in response to negative emotions, known as emotional eating, is often observed in depression and anxiety and highly associated to weight outcomes, both in respect to weight gain over time and difficulties with weight loss and weight loss maintenance . Thus, weight management on emotional eaters should not focus on calorie-restricted diets but rather on emotion regulation skills .
From the last decades the use of smartphones and mobile apps has exponentially expanded. Along, mHealth services have spread making it possible to provide attention in a remote way, to a larger number of patients and at a lower cost. Numerous mobile apps are available on the market to help users with the management of health issues, including weight loss and mental health difficulties [26, 54]; yet, most lack scientific foundation and evidence. For people with overweight/obesity and depression/anxiety and important health issues that are strongly associated, it would be advisable to use a mobile app designed for their simultaneous management. This study aimed at searching for scientific literature on this topic. Publications are still scarce, produced during the last five years and mainly from the United States. No research on the topic has been published from Mexico regardless of the significant prevalence of these conditions. This epidemiology calls for research and clinical efforts to provide efficient services targeting comorbidities, and the use of mobile technology might well serve to this purpose, particularly when working with young populations more familiarized with mobile apps.
Interventions mostly followed a cognitive behavioral framework. This type of therapy focuses on identifying and modifying unhelpful cognitions and behaviors to improve emotional regulation and produce more convenient solutions to problems. The selected publications provide evidence of its feasibility and efficacy to manage weight and mental disturbances through mobile apps, although to the best of our knowledge, no mobile app has been developed and tested as a simultaneous intervention for both aspects. Relying on the vast literature on cognitive behavioral therapy, it would be recommended to design a mobile app intervention for managing comorbidity, considering the targeted age group (i.e., adults, adolescents, or children) and the presence of psychiatric symptoms on a clinical or subclinical level.
It must be underlined that some publications were protocols. This practice is very advantageous for research, as it provides detailed information regarding the design of the intervention and the selected electronic devices, allowing for replication and the development of new studies.
The specific uses of mobile apps through the studies were diverse. The Expecting Success study reported in a previous paper  that the SmartMoms remote intervention group received a Fitbit device accompanied by its app to self-monitor body weight and step counts daily. It must be pointed out that SmartMoms intervention was designed to assist an expectant mother in gaining weight within the recommended guidelines. The study by Altazan and colleagues  measured psychological variables such as mood, depressive symptoms, and quality of life; nevertheless, these were not treated or monitored through the use of a mobile app. The Elixir study provided its mobile group with smartphones with the preinstalled Oiva mobile app. Ahinen and colleagues  had previously described Oiva; based on acceptance and commitment therapy, it is a stand-alone mobile intervention for self-administered active learning skills to prevent stress and improve mental wellness. The study by Järvelä-Reijonen and colleagues  focused on eating behavior and diet, although neither the Oiva app nor the interventions were originally designed for these outcomes. The study by Levinson and colleagues  used a mobile app for recording answers to questionnaires addressing cognitions, emotions, anxiety symptoms, and eating disorder behaviors, not for delivering an intervention. It must be noticed that the target sample was people with an eating disorder, not all of them (probably only a few) with overweight/obesity and/or depression/anxiety. The RAINBOW/ENGAGE studies [44, 45] reported on an intervention designed for the management of both weight and depression. Yet, the use of a mobile app was exclusively for recording physical activity and did not include the recording of any mental dimension. Naslund and colleagues [46, 47] worked with a group of patients with severe mental illnesses from an urban community mental health center enrolled in a group behavioral weight loss program targeting fitness and healthy eating. The mobile app was used exclusively to record physical activity.
Overall, even though overweight/obesity and depression/anxiety are highly extended, increasing and related public health conditions, only a few studies provide scientific foundations relating to their simultaneous follow-up through the use of mobile apps. Results suggest the use of MyFitness, Fitbit, and Nike Fuel Ban apps to record physical activity; yet, they would need to be complemented with an app to monitor psychological wellbeing. Cognitive behavioral therapy seems the most suitable framework for interventions, aiming at long term behavioral change. Yet, as the diversity of the studies suggests, an intervention program needs to be adapted considering if its purpose is the prevention (e.g., the SmartMoms Behavioral program ) or the management of comorbid unhealthy weight and mental conditions (e.g., the I-CARE program ).
The prevalence and costs related to overweight/obesity and depression/anxiety are significant and likely to increase. Very often these conditions overlap; thus, it would be recommendable to treat their comorbidity simultaneously. Nevertheless, no mobile app has been designed for this purpose, which would help to reduce service provision costs and make treatment more easily accessible for patients. This opens an opportunity for mHealth research, particularly in those countries (e.g., Mexico) where these comorbid conditions prevail.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this article.
“Hospital Regional de Alta Especialidad de la Península of Yucatán” supported this work by covering the article processing charges.
Appendix 1. Number of results according to introduced keywords and database. (Supplementary Materials)
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