Healthcare Apps Research Articles

Overcoming the Mental Health Stigma Through m-Health Apps: Results from the Healthy Minds Study.

Background: The advance in digital media technologies has enabled the development of technologically mediated health care solutions. Mobile health app for mental health care is one of such innovative tools to monitor and manage users' mental health and psychological well-being. Objective: This study examines the factors predicting the acceptance of mental health app (MHA) that are: technological (i.e., perceived ease of use and perceived usefulness) and sociocultural (i.e., perceived stigma toward mental health treatment and prior experience of face-to-face mental health service utilization). Methods: Using structural equation modeling, we tested an extended Technology Acceptance Model with data from the Healthy Minds Study, a web-based national survey of U.S. college students (N = 429). Results: In addition to the technological factors, the sociocultural factors significantly influenced MHA acceptance. Notably, college students with higher levels of stigma toward mental health treatment were less likely to have experience of using face-to-face mental health service. This, in turn, led to more openness to accepting MHA that does not involve in-person visits but rather private and mediated communication. Conclusions: Findings of this study provide critical insight into the increasing needs for and use of mobile apps for mental health care. Implications are discussed and future research directions are suggested.

Healthcare apps’ purchase intention: A consumption values perspective

Healthcare services/products, such as medicine, consultation with doctors, lab tests, are growing in popularity, and their benefits are well known. Little is known about the consumption values that impact customers' brand love despite the increased knowledge of healthcare apps. To overcome this hurdle, healthcare apps must improve client engagement by providing superior value propositions. This necessitates a thorough understanding of the healthcare app-specific consumption values. The current study uses the theory of consumption value to explain consumer behavior toward healthcare apps to fill this gap. Data from 878 healthcare app users is used to test the suggested model. The findings indicate that all the consumption values influence brand love toward healthcare apps, except social values, with the emotional value being the most important factor, followed by the conditional value. The mediating role of purchase intention and the moderating role of online risk perceptions and customer involvement in brand love is also confirmed. The study's findings add to the body of knowledge about consumer behavior and practice.

Enhancing Problem-Solving Therapy With Smartphone Technology: A Pilot Randomized Controlled Trial.

Development of smartphone apps for mental health care has outpaced research on their effectiveness. This pilot study tested Moving Forward, an app designed to support problem-solving therapy (PST). Thirty-three veterans seeking mental health care in U.S. Department of Veterans Affairs primary care clinics were randomly assigned to receive six sessions of PST accompanied by either the Moving Forward app (N=17) or a workbook (N=16). Participants completed measures of anxiety, depression, stress, problem-solving style, satisfaction, and between-session practice at baseline and 6- and 12-week follow-ups. Qualitative interviews were used to elicit feedback. Participants in both groups reported high satisfaction and reductions in depression, anxiety, and stress. Veterans who used the app reported skills practice, and qualitative data indicated that patients perceived the app as valuable, with the potential to reduce barriers to care. This study provides preliminary evidence to support the ability of the Moving Forward app to augment brief psychotherapy in primary care clinics.

An appraisal of healthcare mobile applications

Mobile applications (Apps) may become effective aids in health care. Health Apps could reduce barriers such as access and costs and could be used to monitor symptoms, behaviors and even treatments. There is more evidence of their usefulness in nutrition, cardiovascular and mental health. Despite this, its current use is predominantly for information purposes. Healthcare App quality evaluation should consider both clinical and technological aspects since the evidence on its clinical effectiveness is still incipient and they have associated risks. In Chile, the use of mobile technology and Apps is increasing, but there are no regulations for their use. There are few national institutions oriented to the creation and development of Apps for healthcare, highlighting the Digital Transformation Committee, part of the Corporation for the Promotion of Production (CORFO) and the National Center for Health Information Systems (CENS). General recommendations for healthcare App development and use have been established. In this process, it would be beneficial to include actors involved in care. Given the progress of healthcare Apps worldwide and nationally, it is important that health professionals develop digital skills to maximize the potential benefit of these technologies.

Applying an Extended UTAUT2 Model to Explain User Acceptance of Lifestyle and Therapy Mobile Health Apps: Survey Study.

BackgroundMobile health (mHealth) care apps are a promising technology to monitor and control health individually and cost-effectively with a technology that is widely used, affordable, and ubiquitous in many people’s lives. Download statistics show that lifestyle apps are widely used by young and healthy users to improve fitness, nutrition, and more. While this is an important aspect for the prevention of future chronic diseases, the burdened health care systems worldwide may directly profit from the use of therapy apps by those patients already in need of medical treatment and monitoring.ObjectiveWe aimed to compare the factors influencing the acceptance of lifestyle and therapy apps to better understand what drives and hinders the use of mHealth apps.MethodsWe applied the established unified theory of acceptance and use of technology 2 (UTAUT2) technology acceptance model to evaluate mHealth apps via an online questionnaire with 707 German participants. Moreover, trust and privacy concerns were added to the model and, in a between-subject study design, the influence of these predictors on behavioral intention to use apps was compared between lifestyle and therapy apps.ResultsThe results show that the model only weakly predicted the intention to use mHealth apps (R2=0.019). Only hedonic motivation was a significant predictor of behavioral intentions regarding both app types, as determined by path coefficients of the model (lifestyle: 0.196, P=.004; therapy: 0.344, P<.001). Habit influenced the behavioral intention to use lifestyle apps (0.272, P<.001), while social influence (0.185, P<.001) and trust (0.273, P<.001) predicted the intention to use therapy apps. A further exploratory correlation analysis of the relationship between user factors on behavioral intention was calculated. Health app familiarity showed the strongest correlation to the intention to use (r=0.469, P<.001), stressing the importance of experience. Also, age (r=–0.15, P=.004), gender (r=–0.075, P=.048), education level (r=0.088, P=.02), app familiarity (r=0.142, P=.007), digital health literacy (r=0.215, P<.001), privacy disposition (r=–0.194, P>.001), and the propensity to trust apps (r=0.191, P>.001) correlated weakly with behavioral intention to use mHealth apps.ConclusionsThe results indicate that, rather than by utilitarian factors like usefulness, mHealth app acceptance is influenced by emotional factors like hedonic motivation and partly by habit, social influence, and trust. Overall, the findings give evidence that for the health care context, new and extended acceptance models need to be developed with an integration of user diversity, especially individuals’ prior experience with apps and mHealth.

Smartphone-Based Healthcare Apps for Older Adults in the COVID-19 Era: Heuristic Evaluation.

Many national governments have attempted to prevent and combat COVID-19 using mobile health (mHealth) technologies during the epidemic. During this time, governments began developing smartphone-based apps for prevention, call tracking, and monitoring people with COVID-19. An important question is, does everyone benefit from these technologies equally and fairly? To answer this question, we evaluated the user interface of smartphone-based apps developed during the COVID-19 era by considering their design for older adults, in order to determine whether social justice has been considered in the development of these apps.

The Use of M-Government and M-Health Applications during the COVID-19 Pandemic in Saudi Arabia

The automated proficiencies of the Kingdom of Saudi Arabia, in vision 2030, influence a decade of continual expenditure in contemporary automation and unified government platforms. It affords a reliable base for the COVID-19 response’s significant facets, encompassing continued connection to many e-government systems. The worldwide COVID-19 epidemic has reinvigorated the role of e-government. The health crisis has altered SADAIA to lunch Tawakkalna m-government application to execute business continuity strategies, offer important services to people, and assure their fulfilment. The study intends to assess the impact of the COVID-19 pandemic on users’ satisfaction with m-government services and Health Care apps in the Al-Madinah region. It analyses the elements that enable people’s use of online government services, and m-government services are a solution to handle the crisis. The finding shows that trust in e-government, trust in the internet, ease of use and usefulness are the important factors in using the application during the pandemic.

Healthcare Apps, Customer Engagement and the COVID-19 Pandemic Crisis

Healthcare Apps, Customer Engagement and the COVID-19 Pandemic Crisis

Evaluating the sustainability of smart technology applications in healthcare after the COVID-19 pandemic: A hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence.

During the COVID-19 pandemic, some smart technology applications were more effective than had been expected, whereas some others did not achieve satisfactory performance. Consequently, whether smart technology applications in healthcare are sustainable is a question that warrants investigation. To address this question, a hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence was proposed in this study and then used to evaluate the sustainability of smart technology applications in healthcare. The contribution of this research is its subjective evaluation of the sustainability of smart technology applications followed by correction of the evaluation outcome on the basis of the applications' objective performance during the COVID-19 pandemic. To this end, a fuzzy nonlinear programming model was formulated and optimised. In addition, the impact of several major global events that occurred during the pandemic on the sustainability of smart technology applications was considered. The proposed methodology was applied to evaluate the sustainability levels of eight smart technology applications in healthcare. According to the experimental results, three applications-namely healthcare apps, smartwatches, and remote temperature scanners-are expected to be highly sustainable in healthcare, whereas one application, namely smart clothing, is not.

Approaches for implementing digital interventions for alcohol use disorders in primary care: A qualitative, user-centered design study.

Digital interventions, such as smartphone apps, can be effective in treating alcohol use disorders (AUD). However, efforts to integrate digital interventions into primary care have been challenging. To inform successful implementation, we sought to understand how patients and clinicians preferred to use apps in routine primary care. This study combined user-centered design and qualitative research methods, interviewing 18 primary care patients with AUD and nine primary care clinicians on topics such as prior experiences with digital tools, and design preferences regarding approaches for offering apps for AUD in primary care. Interviews were recorded and transcribed for template analysis whereby a priori codes were based on interview topics and refined through iterative coding. New codes and cross-cutting themes emerged from the data. Patient participants with AUD indicated they would be more likely to engage in treatment if primary care team members were involved in their use of apps. They also preferred to see clinicians "invested" and recommended that clinicians ask about app use and progress during follow-up appointments or check-ins. Clinician participants valued the opportunity to offer apps to their patients but noted that workflows would need to be tailored to individual patient needs. Time pressures, implementation complexity, and lack of appropriate staffing were cited as barriers. Clinicians proposed concrete solutions (e.g., education, tools, and staffing models) that could improve their ability to use apps within the constraints of primary care and suggested that some patients could potentially use apps without clinician support. A user-centered approach to engaging patients in digital alcohol interventions in primary care may require personalized support for both initiation and follow-up. Meeting patients' needs likely require increased staffing and efficient workflows in primary care. Health systems should consider offering multiple pathways for enrolling patients in apps to accommodate individual preferences and contextual barriers. Healthcare systems have begun using app-based treatments to help patients manage their health conditions, including alcohol use disorders. Some apps have been tested in research studies and appear to be effective. However, it is difficult for healthcare teams to offer apps to patients. Clinicians must engage in new activities that they have not done before, such as "teaching" patients to use apps and checking in on their use of the apps. Identifying how to use apps in routine healthcare is critical to their successful implementation. This study interviewed 27 people, including healthcare providers and patients in primary care, to uncover the most optimal ways to offer apps to patients with alcohol use disorders. The interviews combined the use of qualitative research methods and user-centered design. Results suggest that to use to address alcohol use disorders, primary care teams should be prepared to offer personalized support to patients. Both patient and clinician interviewees said that the steps required to use apps must be intuitive and simple. Patients could gain more benefits if clinicians introduced the apps and guided patients to use them, as opposed to making apps available for patients to download and use on their own. However, the exact approach to offering apps would depend on a given patient's preferences and the extent that staffing was available in the clinic to support patients. Health systems should be prepared to offer and support patients in their use of apps, which should accommodate patient preferences and the constraints of the clinic.

Targeting the Needs of Self-Determination Theory: An Overview of Mental Health Care Apps

Introduction: Smartphone apps are a highly accessible tool to supplement self-treatment for mental health challenges, such as depression, and are underrepresented in research. While many studies have performed content analyses of health apps, few studies have reviewed their adherence to behavior theory. Aims: The objective of this study is to assess mHealth depression apps through the lens of the Self-Determination Theory and identify if app functions target the three basic needs: autonomy, competence, and relatedness. Methods: All depression apps available from iTunes and Google Play that met inclusion criteria were analyzed (N = 194). Apps were reviewed for price options, store availability, download rates, and how functions targeted the three basic needs for intrinsic and sustained health behavior change outlined in the Self-Determination Theory. Results: Findings showed that most of the apps targeted at least one of the basic needs (158/194, 81.4%). However, only a few of these apps targeted all three basic needs to some degree (15/194, 7.7%), and no single app targeted all three basic needs fully. Furthermore, neither store availability, price option nor download rates were accurate predictors that apps targeted the three basic needs. Conclusions: The results suggest that some depression apps targeted autonomy, competence, and relatedness but this was limited to a small number of apps through few functions available in each app. People who want access to more functions targeting the needs would need to download a suite of apps.

Effects of Mobile Healthcare Applications on the Lifestyle of Patients With Breast Cancer: A Protocol for a Randomized Clinical Trial.

Physical activity (PA) in patients with breast cancer is associated with improved quality of life (QoL); however, many breast cancer survivors do not meet the recommended PA level. This study aims to evaluate the effect of digital health interventions using mobile apps to promote PA and QoL in patients with postoperative breast cancer. This study will also identify effective digital intervention methods and perform an economic analysis. The main hypothesis is that the use of mobile healthcare apps will improve health-related quality of life (HRQOL), promote PA, and reduce healthcare costs. The Promotion of a better lifestyle (PA) with Precise and Practicable digital healthcare in postoperative CANCER patients through a Multi-Disciplinary Network (P4CancerMDnet) study is examined by a prospective 4-group randomized controlled trial with a concurrent cost-utility evaluation. Patients are randomly assigned to 3 different mobile app intervention groups or control groups in a 1:1:1:1 ratio. The intervention group is encouraged to use the assigned mobile app. The targeted outcomes are HRQOL, metabolic health markers, and quality-adjusted life-years. The outcomes will be measured at the 6- and 12-month follow-ups. This study will contribute towards a better lifestyle and HRQOL through digital healthcare for postoperative breast cancer patients. These findings are expected to provide evidence of the effectiveness of mobile apps for breast cancer survivors. Clinical Research Information Service Identifier: KCT0005447.

LAMP: a monitoring framework for mHealth application research

The usage of mobile applications in healthcare has gained popularity in recent years. In 2018, at least, 10,000 apps related to mental health could be downloaded in the app stores. The popularity of healthcare apps, especially in the field of mental health, is based on in their simplicity in large-scale data collection scenarios used for the improvement of health-related services or research. For these apps, instruments to quantify the quality of an app and repositories for app quality ratings have emerged in recent years. What is rarely considered, however, is the degree of functional correctness of an app, which can have a serious impact on the data collection process and thus on data quality. The increasing restrictions of background services are a challenge for app developers, who need to implement recurring tasks reliably in the background, like the collection of longitudinal data based on questionnaires or sensor measurements. In this paper, we present a monitoring framework to investigate the degree of functional correctness regarding the background service implementation of apps based on notification events. With this framework, we want to enable the large-scale collection of app execution data in the wild to gain more insights into the execution of apps in different execution environments and configurations. The gained knowledge shall help to improve existing applications in the field of mental health and eventually to improve the degree of functional correctness of those apps.

Healthcare and IoT devices: role of information technology in the healthcare industry

Today, wearable health products play a crucial role in most locations, such as constant wellness monitoring of people, street traffic management, weather forecasting, along with smart house. These sensor devices constantly generate massive amounts of data and are kept in cloud computing. This particular chapter proposes Internet of Things design to store and system scalable sensor information for healthcare apps. Proposed architecture comprises 2 primary architecture, specifically, MetaFog-Redirection and Choosing and Grouping architecture. Though cloud computing offers scalable data storage, effective computing platforms must process it. There's a requirement for scalable algorithms to process the big sensor information and recognize the helpful patterns. To conquer this problem, this particular chapter proposes a scalable MapReduce based logistic regression to process such massive quantities of sensor information. Apache Mahout includes scalable logistic regression to system BDA in a distributed way. This particular chapter uses Apache Mahout with Hadoop Distributed File System to process the sensor information produced by the wearable health units.

POSA264 Reimbursable Healthcare Apps (DIGA) in Germany: Which Factors Impact the BfArM’s2 Assessment Outcome and a Directory Listing?

Since May 2020, reimbursement can be requested for DIGAs1 in Germany. The prerequisite for reimbursement is a listing in the BfArM’s2 DiGA-directory, granted after successful completion of an assessment process. The purpose of this analysis was to evaluate BfArM’s decision-making and to identify criteria that may positively impact a directory listing.

Differences in Park Walking, Comparing the Physically Inactive and Active Groups: Data from mHealth Monitoring System in Seoul.

Despite the overall increase in physical activities and park uses, the discrepancies between physically inactive and active people have increasing widened in recent times. This paper aims to empirically measure the differences in walking activity in urban parks between the physically inactive and active. As for the dataset, 22,744 peoples’ 550,234 walking bouts were collected from the mHealth system of the Seoul government, using the smartphone healthcare app, WalkOn, from September to November 2019, in Seocho-gu district, Seoul, Korea. We classified the physically inactive and active sample groups, based on their regular walking (≥150 min of moderate-to-vigorous walking activity a week), and analyzed their park walking activities. We found that while there was no significant difference in walking measures of non-park walking between the sample groups, the difference did exist in park walking. The park walking average in the physically active group had more steps (p = 0.021), longer time (p = 0.008), and higher intensity (p < 0.001) of walking than that in the inactive group. Each park also revealed differences in its on-site park walking quantity and quality, based on which we could draw the list of ‘well-walked parks’, which held more bouts and more moderate-to-vigorous physical activities (MVPAs) than other parks in Seocho-gu district. This paper addresses how park walking of physically inactive and active people is associated with multiple differences in everyday urban walking.

Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications.

Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies.

"I" Am Willing to Disclose, but "We" are Unwilling: The Impact of Self-Construal on Individuals' Willingness to Disclose.

PurposeIn the big data era, many institutions (ie, hospitals) and firms use various methods to encourage people to disclose more personal information to gain competitive advantages in many businesses, such as healthcare and the Internet of Things (IoT) devices. Discussions on antecedents of individuals’ willingness to reveal private data from individual differences perspective are limited. Drawing on information boundary theory, we examine how self-construal prompts a different regulatory focus (promotion focus versus prevention focus), thus, affects individuals’ willingness to disclose private data.MethodsA mixed-method approach was used to examine our hypothesis. Study 1 (N = 93, participants in China) manipulated self-construal in lab experiments and examined participants’ actual disclosure behavior in the emerging IoT context of connected cars. Study 2 (an online survey, N = 200, participants in US) measured chronic self-construal in another disclosure context (healthcare app), replicating the preliminary effect and examined the mediating effect of the regulatory focus. Study 3 (an online experiment, N = 284, participants in US) tested the moderating effect of message framing.ResultsStudy 1 showed that participants primed an independent self-construal were more willing to share private information, whether it is real driving data or private identity information. Study 2 showed that independent (interdependent) self-construal individuals tend to have promotion focus (prevention focus), thus leading to higher (lower) willingness to disclose personal health information. Study 3 demonstrated that independent (interdependent) self-construal individuals are more willing to share information when presented with gain-framing (loss-framing) information.ConclusionIndependent (interdependent) self-construal positively (negatively) affects individuals’ willingness to disclose and these effects will be mediated by regulatory focus and moderated by message farming. Our study provides a theoretical paradigm that is new to the willingness to disclose literature, and offers an effective, actionable strategy on how institutions and firms can facilitate individuals’ personal information disclosure.

A Conversational Artificial Intelligence Agent for a Mental Health Care App: Evaluation Study of Its Participatory Design.

BackgroundMobile apps for mental health are available on the market. Although they seem to be promising for improving the accessibility of mental health care, little is known about their acceptance, design methodology, evaluation, and integration into psychotherapy protocols. This makes it difficult for health care professionals to judge whether these apps may help them and their patients.ObjectiveOur aim is to describe and evaluate a protocol for the participatory design of mobile apps for mental health. In this study, participants and psychotherapists are engaged in the early phases of the design and development of the app empowered by conversational artificial intelligence (AI). The app supports interventions for stress management training based on cognitive behavioral theory.MethodsA total of 21 participants aged 33-61 years with mild to moderate levels of stress, anxiety, and depression (assessed by administering the Italian versions of the Symptom Checklist-90-Revised, Occupational Stress Indicator, and Perceived Stress Scale) were assigned randomly to 2 groups, A and B. Both groups received stress management training sessions along with cognitive behavioral treatment, but only participants assigned to group A received support through a mobile personal health care agent, designed for mental care and empowered by AI techniques. Psychopathological outcomes were assessed at baseline (T1), after 8 weeks of treatment (T2), and 3 months after treatment (T3). Focus groups with psychotherapists who administered the therapy were held after treatment to collect their impressions and suggestions.ResultsAlthough the intergroup statistical analysis showed that group B participants could rely on better coping strategies, group A participants reported significant improvements in obsessivity and compulsivity and positive distress symptom assessment. The psychotherapists’ acceptance of the protocol was good. In particular, they were in favor of integrating an AI-based mental health app into their practice because they could appreciate the increased engagement of patients in pursuing their therapy goals.ConclusionsThe integration into practice of an AI-based mobile app for mental health was shown to be acceptable to both mental health professionals and users. Although it was not possible in this experiment to show that the integration of AI-based conversational technologies into traditional remote psychotherapy significantly decreased the participants’ levels of stress and anxiety, the experimental results showed significant trends of reduction of symptoms in group A and their persistence over time. The mental health professionals involved in the experiment reported interest in, and acceptance of, the proposed technology as a promising tool to be included in a blended model of psychotherapy.

"Within the hour" and "wherever you are": Exploring the promises of digital healthcare apps

The use of healthcare apps for medical advice is becoming increasingly common. This paper explores apps that offer interaction with medical experts. Working from the supposition that digital technologies are intimately entangled in their cultural context, we argue that the apps do more than just neutrally mediate contacts and offer medical and psychological advice. The article addresses the cultural dimensions of healthcare apps and answers questions about the ways in which such apps contribute to forming changing notions of what “healthcare” and being a “patient” entail. Three popular Swedish apps and their marketing material is studied using a discursive interface analysis of the apps’ affordances. The results show that the apps significantly contribute to producing a marketable narrative about app health care that includes accessibility, security/safety and personalisation, and which is partly produced as an alternative to what is offered by Swedish public health care. The results further show that this narrative primarily represents and addresses users who are young, busy, urban consumers of care – partly contrasting policy expectations and hopes.