Research on mHealth App Adoption
Research has shown that mental health illness accounts for about 33% of the world's disability among adults; an aspect that challenges both the wellbeing of a population and burdens the economy. As a result, numerous solutions have been proposed, over the years, in an attempt to alleviate the challenge. mHealth is one such solution that has received significant traction due to its capacity to deliver medical solutions through apps. Nonetheless, despite the popularity of mHealth apps, it is however observed that most individuals suffering from mental health issues do not download such apps due to the stigma associated with their usage and their lack of important features. The current study sought to investigate the causes of low mHealth app adoption and identify the different features sought by end users. Views from users were collected by administering 72 questionnaires and interviewing 10 individuals. Data was further analysed using descriptive statistics. Findings obtained from the study showed that about 63% of users highly valued apps that were easy to use while 75% of them valued patient-centeredness. A similar percentage was interested in security and multiple diagnosis capabilities. Conversely, only 32% of the users were interested in feedback features while a fewer number of users were interested in self-monitoring features. As such, the study proposes an app that incorporates the highly valued aspects.
Keywords: mHealth, mental, disorder, solutions, health, apps.
Study background
A mobile health (mHealth) application provides important functionalities for a diverse set of users in its ecosystem and subsequently, offers immense potential in the provision of healthcare. Hilty et al. (2017) notes that mHealth apps are a disruptive technology in the healthcare industry as they bring together the slow-paced healthcare sector and the fast-paced digital sector. Fairburn and Patel (2017) postulate that the main aim of mHealth is to provide patients with cost effective and efficient services through available technology such as mobile phones, computers and tablets among others. Through mHealth technology, doctors as well as patients, are able to access health information on a real-time basis thereby facilitating monitoring, diagnosis and treatment of health conditions.
According to Lake (2017), about 33% of the world’s disability resulting from adult health is attributed to mental illness. The researcher highlights that the failure of existent health approaches to adequately address mental illness, imposes huge costs on the economy in addition to causing immense personal suffering. Kopinak (2014) further adds that efforts to treat mental health are challenged by the lack of adequate knowledge to treat the condition, high stigma that causes individuals to shy away from seeking help and the inability to access health care, particularly in developing countries. Subsequently, mHealth applications have been adopted as a cost-effective solution to help tackle mental illness among other diseases. Figure 1.1 below summarizes survey findings illustrating the potential of mHealth solutions.
As indicated above in figure 1.1, diabetes is the leading condition in market size in mHealth solutions. While diabetes is reported to be the leading sector in regards to mHealth solutions, depression and other mental health solutions have also been on the rise. Research2Guidance (2017) indicate that mental health care has potential but requires the involvement of health practitioners in its development as adoption of the solution by patients is highly influenced by acceptance and recommendations from health practitioners.
Mohr et al. (2017) report that mental health app adoption is quite low with most users citing prescription by doctors as the main factor motivating app download. Clinicians of mental health care as well as other health care professionals play a role in adoption of app as patients rely on their recommendations before using mental health apps. Hilty et al. (2017) reveals that apps in mental health care that are recommended by an end users’ doctor, have the highest rate of adoption with the users downloading and using them as part of their treatment plan to improve their health. This is when compared to other apps such as medication apps at 55% and fitness apps at 48%. Majority of patients and ends users accept and adopt apps after recommendation from doctors. Naslund et al. (2017) report that majority of the mental health apps have failed in including the interests, needs and views of their end users foregoing a major principle of the provision of health care which is a user/patient centred approach.
Adopting a patient-centred approach is highly important in developing mental health software products. Mohr et al. (2017) highlight that considering the opinions of clinicians and experts in health is also crucial in developing and designing an app. Many mental health apps in the market have failed to incorporate this aspect; a research gap that will be fulfilled by the proposed mental health app. While software development is central in creating the mental health app, the proposed mental health app will include the views of end users and experts in mental health care to ensure that the app developer will be able to meet these two crucial stakeholders. This will result in the development of an app which is all round in its design and will appropriately fill the current gap and meet the needs of the patients as well as the clinicians.
However, a major issue with the current mental health apps is that, majority of the available apps target specific users thus reinforcing stigma about mental health conditions facing such individuals (Bhugra et al., 2017). For instance, there are apps which target individuals faced with depression. Bakker et al., (2016) illustrate that the targeting of specific users such as those who are depressed, results in stigma and also negatively impacts users seeking out and using the available mental health apps. The proposed mental health app will delimit this challenge by taking a design which does not target users but rather empowers all individuals. The proposed mental health app will focus on providing overall emotional wellbeing of individuals thus eliminating the stigma of seeking help or care in mental health. The proposed mental app will also uniquely provide an all round solution for all emotional wellbeing issues which will include mental health issues such as depression, addiction, anxiety, emotional trauma management among others.
As such, any individual seeking help from the app will be able to search any issue they are facing under the various categories made available within the app. As illustrated by Hilty et al., (2017), mental health patients often do not suffer from conditions in isolation but rather mental health care requires managing different issues in ensuring the overall mental health and emotional wellbeing of a patient is secured. As such the proposed mental health app will provide an effective solution for all the mental health needs of a patient. The following is a list of features which the proposed app will provide: All round solution for diverse conditions of mental health such as: addiction, anxiety, eating disorders, depression and schizophrenia among others; Evidence based app features; Patient centred design and involvement of mental health practitioners in design.
Purpose of the Study
Mental health is a major challenge in society that has been linked to enormous health related economic burdens worldwide (Naslund et al., 2017). With advancement in technology, several disruptive technologies have emerged with mHealth being one of them. With mHealth, software applications are adopted in health care in order to improve service as well as patient wellbeing. Bakker et al. (2016) notes that mental health care has benefited from technological advancement with mHealth apps becoming a trend for both clinicians and patients. However, Lui et al. (2017) argue that only a small number of mental health apps have been adopted successfully by end users as most of them don’t download them for mental health care or treatment.
Research questions
i. What are some of the factors causing low adoption of mental health apps?
ii. What attributes and features are end users considering and looking in mental health apps?
iii. What recommendations can be provided to ensure delivery of an innovative mental health app?
Research approach
The business report reviews diverse sources of information and data such as business periodicals, journal articles, newspaper articles and magazines, government and institutional reports as well as websites in order to identify existing research and evidence on mental health apps. A mixed method approach is utilized in collecting data whereby a quantitative data is collected using questionnaires while qualitative data collected using interviews. The researcher utilizes a questionnaire with the focus of understanding the demands and need of end users in regards to the features and attributes of a mental health app.
The target audience for this research is the general population based on the assumption that mental health affects all individuals in society. The researcher utilizes purposive sampling whereby participants are selected on a preference basis. Purposive sampling was chosen as it is simple and it helps the researcher to reach the targeted sample quickly. 100 participants are targeted and questionnaires administered for data collection purposes. The researcher further selects 10 participants from the sample population for the interview process in order to provide further views on the research problem.
The data collected will be analyzed in order to provide insight on the development of a mental health app. The proposed mental health app provides a personal platform for seeking help when faced with mental health issues such as depression and anxiety among others. The proposed app also provides privacy, security, non-judgmental support, as well as confidentiality. The app will be available in Android and iOS and will provide a platform for clients and therapists as well as psychiatrists to interact. The use of questionnaires and interviews enables the researcher to acquire feedback on the proposed ideas and improve the developed app.
The researcher utilizes descriptive statistics to analyze quantitative data and thematic analysis for the qualitative one. Insight from the research findings are incorporated in the proposed mental app in order to identify features demanded by ends users and also deemed important. For example, a feature that is likely to be demanded by app users is the capturing of evidence. Thus, the proposed mental health app needs to incorporate this feature as in order to meet the demands of individuals seeking mental health care. Findings from literature review and primary data allow the researcher to gain insight on innovative ways to develop an mHealth app for the mental health market.
Outline of Business Report
The business report is structured into five sections. In the first, the research is introduced by highlighting the problem background. Specific research questions to be answered through the work are also identified. A description of how the study will be conducted is also provided as well as a Gantt chart illustrating all activities undertaken. The second section summarizes literature findings while the third presents findings from research conducted. The fourth section provides a conclusion to the study while the final section highlights important recommendations.
Demand for Mental Health Apps
Mental health disorders are a major challenge facing the society and are identified as a global economic liability (Naslund et al., 2017). Common mental health disorders include anxiety and depression and they make up the largest proportion of economic liability as about 25% of individuals are at one point in their lives affected by mental health conditions (Mohr et al., 2017). Subsequently, health care systems in the world have been pressured in the last few years, to develop accessible and low cost care for individuals who are suffering from conditions in mental health. In the UK, the demand for care in mental health exceeded the resources available in the National Health Service (NHS). In effect, only about 33% of the individuals facing mental health issues were able to receive care from the system.
In 2017, the Lancet Psychiatry Commission on the Future of Psychiatry, a subset of the World Psychiatric Association, revealed that digital technology had a significant potential to close the gap in mental health care through provision of more accessible and cost effective approaches which are not only flexible but also involve less patient stigmatization in regards to mental health care and treatment (Burgha et al., 2017). Fairburn and Patel (2017) argue that digital technologies, inclusive of internet, wearables as well as smartphones have the ability to connect to services, patients and health data in innovative ways which are not available within the traditional treatment approaches. Naslund et al., (2017) argue that in the UK like most parts of the globe, there is an increased accessibility of smartphones and the internet.
In the UK, 88% of the adult population has access to the internet at home while about 75% have smartphones. Estimates indicate that by 2020, 80% of the adult population globally will own a smartphone and have accessibility to the internet (Bhugra et al., 2017). Firth et al., (2017) argue that with more individuals having a connection to the internet as well as accessibility to digital technology platforms such as tablets and smartphones, there exists an opportunity to close the gap within mental health care systems and provide mental health care services that are easily accessible and cost effective.
Many countries are taking on measures and strategies to include digital technologies in their health systems (Mohr et al., 2017). NHS (2016) reports that the increase of accessibility to digital services is one of the policies that the UK government is undertaking in its mental health Five Year Forward View. In 2017, the government similarly highlighted efforts to invest more in digital technologies to the tune of more than £67 million in the NHS with the focus of improving the accessibility of psychological therapies programs for patients (UK Government, 2017).
Rationale for Mental Health App
Currently, there are numerous mental health apps in existence. However, despite the increasing numbers, only few apps have been successful (Lui et al., 2017). A major challenge affecting the use of app in mental health care stems from the fact that most apps target only one specific mental health disorder at a time and label the target market thus reinforcing end user stigmatization (Bakker et al., 2016). Such labelling of users further leads to social stigma in relation to mental health and consequently, negatively impacts user adoption. The proposed mental health app neither targets specific users nor labels them through diagnosis. Instead, it provides an app which focuses on the overall mental health care for individuals seeking specific help. For instance, an individual seeking help with anxiety management can download the app and seek specific help on the issue in the app. By providing a holistic and one-stop mental health app, any stigma associated with targeting specific users for instance addicts and labels by diagnosis is eliminated completely.
Furthermore, most of the developed apps developed have not been tested clinically despite high expectations for them to perform within clinical practice. In order for an app to be viewed seriously by both mental health practitioners and patients, there is a need for them to be evidence-based (Peiris, Miranda and Mohr, 2018). Reid et al. (2011) state that evidence-based interventions have been identified to be successful as they have shown that they can be effective in treating and managing particular conditions. As such, it is important to ensure that resources are put aside for the clinical studies and trials an app before launching and marketing it in the market. As illustrated by Nikou and Mezei (2013), evidence-based interventions are more successful than interventions which have no scientific basis.
Another major problem facing mental health app users is the proposed framework to choose appropriate apps for their specific mental health disorders. While there are numerous apps available in the store, users have to review all available ones before settling on an appropriate one. Kenny et al. (2016) argue that the only criteria utilized in filtering the apps is referring to their ratings or prices. A user requiring an app to help in anxiety management may have to go through all available apps before selecting an appropriate one. The other alternative is to select an app randomly; an aspect which may not meet user needs. Consequently, the diversity of numerous mental health apps in the market makes it challenging for users to critically identify appropriate apps for their mental health care and treatment. The proposed app will solve this problem by providing an all inclusive platform for individuals seeking mental health treatment and services.
Adoption of Mental Health Apps
Mobile apps have been identified as avenues that facilitate the implementation of mental health interventions and approaches (Donker et al., 2013). However, their effectiveness and success in serving out this role and helping mentally ill patients, is significantly challenged. Research indicates that options of mental health apps and their alignment to the daily lives of end users is still an issue of concern (Lannin et al., 2015). Understanding the manner on which mental health apps are adopted is thus important as it helps in the development of the application as well as in its marketing and release to the market.
Mental health apps can be deployed in diverse capacities ranging from the provision of guidance for individual recovery and mental disorders to encouraging individuals to prevent further deterioration and adopt beneficial behaviour to improve their overall emotional health (Huang & Bashir, 2017). For instance, there are various mental health apps which can provide direction in clinical practice, psychoeducation as well as engaging with real-time communication (Luxton et al., 2011). The adoption of mental health apps is also sensitive in nature thus being unique in comparison to other health apps. According to Bakker et al., 2016), the sensitivity of mental health care arises from the social stigma that has long been linked to mental health disorders that push individuals away from seeking mental health care (Lannin et al., 2015). Kenny et al. (2016) argue that the research gap showed that a key reason why young people adopted mental health apps was to avoid the associated social stigma.
Bakker et al. (2016) however notes that majority of mental health apps in the market target specific users and in effect, end up labelling them through diagnosis thus further aggravating the stigma. Furthermore, such actions adversely affect the adoption of mental health apps. Similarly, the lack of specific guidelines or regulations in the mental health app market further challenges their adoption (Huang & Bashir, 2017). A key concern that users have is that, mental health apps do not have a regulatory framework which makes the decision making process complex. Bellur and Sundar (2014) state that the lack of a framework for selecting an appropriate mental health app from the many available in the market is a critical challenge.
Venkatesh and Davis (2000) propose the technology acceptance model (TAM) to explain how end users adopt and accept apps. TAM argues that the behavioural intention of an individual utilizing a technology arises before its use which then results to uptake and then finally adoption. TAM has further been expanded into unified theory of acceptance and use of technology (UTAUT) in order to highlight the elements which influence and affect the behavioural intention of an individual’s such as social influence, price value, habit, performance, and effort (Venkatesh et al., 2003).
UTAUT provides an explanation on how particular elements influence the manner in which end users utilise apps. However, it is critical to understand each feature of the specific technology being implemented and adopted. For instance, according to Krebs and Duncan (2015), cost is an important element to consider in adopting a health app. However, Kelley et al., (2013) argue that there are individuals willing to pay a premium for health apps that promise true health benefits. Stigma is a serious issue in mental health that affects the willingness of individuals to talk or publicly utilize an app for mental health care.
Dogruel et al. (2015) argue that in selecting an app, users utilize a heuristic approach in processing available information instead of using a systematic one. Such approaches involve the adoption of strategies based on processes whereby individuals make decisions in a faster manner through reduction of cognitive efforts (Gigerenzer and Gaissmaier, 2011). Shah and Oppenheimer (2008) further explain this and argue that when using a heuristic approach, individuals evaluate and use fewer cues of information. Heuristic process involve three stages; searching, stopping and deciding (Bellur and Sundar, 2014). Luxton et al., (2011) and Nikou and Mezei (2013) report that prices, functions, titles, rankings, ratings, titles, functions, reviews and privacy issues of apps are the information cues utilized by users when selecting apps.
Huang and Bashir (2017) argue that when users have identified the type of app they require, they utilize the simple take first heuristics approach whereby informational cues such as titles of apps, rating, and ranking of apps are utilized in making a decision on what to use. However, without sensitizing users and creating awareness on the potential of mobile apps, the adoption of such apps will remain to be impoverished (Kayyali et al., 2017).
Key Success Factors of Mental Health Apps
mHealth application development is an evolving field that is highly dynamic. Many developers and entrepreneurs are investing time and resources to develop new and innovative applications aimed at improving health and wellbeing. Mental health care is undergoing this evolution with the provision of numerous mental health apps in the market. However, the success of these apps is in question with the sensitivity of the mental health being a major challenge. Before starting the software development for the mental health app, it is important for success factors to be considered in order to ensure that they are aligned and used as input for the application (Huang & Bashir, 2017). There are a number of features and attributes which have been identified in existing research as crucial for the success of any application.
Individual tailoring is stated as an important success factor for mental health apps (Reid et al., 2011). According to Palmier-Claus et al., (2013), it is critical to provide fine-grained personalization in smartphone apps whereby an individual is able to add elements which are specifically facing them is crucial. Each individual experience different feelings and emotions and personalization allows an individual to evaluate and be aware of their personal needs. One way of doing this is by customization of questions similar to those they would receive during treatment in a traditional therapists- client session.
Through the personalization of applications to the differing needs and preferences of the client, the outcomes are improved as individuals recognize relevance in their personal care. Reid et al., (2011) argue that customization and personalization enable individuals to see apps as applicable to their personal situation. However, this was criticized as being a potential threat to provision of person-centred care. A feedback system is also cited as another critical success factor for mental health apps. According to Lappalaine et al., (2013), personal feedback, as well as personal perspectives, are considered to be a critical component in an innovation. The use of either automated or clinician-delivered feedback as it can help in acceptance as well as compliance. According to Redi et al., (2011), feedback can involve the use of summary reports, perspectives, discussions as well as recommendations from both users and expert’s enabling effective feedback mechanism. Harrison et al., (2011) argues that adaptive learning can also be utilized in order to provide trends as well as present automated feedback.
According to Lappalaine et al., (2013), while mental health app has arisen as a beneficial form of care, traditional human to human care is still highly valued. Palmier-Claus et al., (2013) posit that apps in mental health care should not be used in isolation but rather complementary to traditional treatment approaches. According to Lappalaine et al., (2013), peer, as well as therapists, support networks are critical in increasing engagement of users as well as motivation. For example, one is likely to download and use a mental health app when a therapist or a peer recommends its use. As such for the success and implementation phase of the mental health app, it is important to consider support from therapists to their clients. Technical support is also critical as a success factor for a mental health app. Harrison et al., (2011) report that insufficient instruction was reported to be an element of discounted use of mental health app for treatment. In the app development, developers need to secure support for the end users to make sure that they find it easy to interact with the app (Huang & Bashir, 2017).
High patient engagement is reported to be crucial for the successful adoption of an app. According to Bakker et al., (2016), patent utilizing health apps do so in their own time and there is no clinical oversight. As a result, it is critical for the users to be motivated to utilize and engage with the mental health app. Fleming et al., (2017) agree with this agreement and state that patient-centred care is required with patient engagement secured through real-time engagement, reminders to use the app for instance through email notifications as well as gamified interactions.
