Current Apps Research Articles

The biopsychosocial perspective on designing mobile health apps for self-management of chronic headaches

ABSTRACT This study identifies user-preferred features in apps for self-management of chronic headaches from the biopsychosocial perspective and examines the extent to which such features are represented in the current apps. We first conducted semi-structured interviews to identify user-preferred features that tap into the biopsychosocial domains of health. Then, we conducted a landscape analysis to review existing apps with respect to the identified features. Our findings revealed participants’ preferences for features in apps to self-manage chronic headaches and were categorised based on the biopsychosocial model. Further, our landscape analysis showed that several features (e.g. physical symptom tracker) are highly present in existing apps, while most of the preferred features (e.g. journaling) are scarce. The identified features appear to align with theory-based behaviour change techniques and, therefore, have implications for health behaviour change. In addition, our findings demonstrate that most of the user-preferred biopsychosocial features are not widely present in the existing headache apps. Overall, our study highlights the importance of incorporating user-preferred features that align with the biopsychosocial needs of headache app users. By acknowledging and addressing these needs, we can broaden the existing perspectives concerning app design and evaluation and cater to the holistic health experiences of users.

Status in using wearable devices as surveillance tools in public health research among people with disabilities: preliminary analysis of 2017 BRFSS

Purpose: The study aims to examine the association between disability status and willingness to share wearable devices and mobile apps data for public health research using the 2017 Behavioral risk Factor Surveillance System (BRFSS) through secondary data analysis. Materials and Methods: Current wearable device and mobile apps users at the time of the 2017 BRFSS survey administration from eight states were included in the analysis. Descriptive, chi-square analysis, and multivariable logistic regressions were performed to examine the associations. Subsample analyses were also conducted for individuals with disabilities and different types of disability, including visual impairments, hearing impairments, cognitive disability, independent living disability, self-care disability, and mobility disability on their willingness to share wearable device and mobile app data. Results: Among the 1575 wearable device and mobile app users, 17.8% (95% CI [14.6, 22.0]) were participants with disabilities. Less than half of the participants (43.8%, 95% CI [38.0, 50.0]) were willing to share their data for public health research with no significant differences between those with and without disabilities or among specific disability types. Conclusion: The findings underscore a general reluctance to share information for public health research. To address this reluctance, it is crucial to enhance public awareness of the potential benefits and educate users on how sharing data could contribute to understanding health behaviors and identifying public health needs.

Public Perceptions and Engagement in mHealth Across Eight European Countries

Abstract Background Mobile health technologies have the potential to revolutionize healthcare delivery and patient engagement. This study explores public attitudes toward health app usage and data sharing across eight European countries, addressing a significant gap in understanding the public’s perspective on mHealth adoption. Methods A cross-sectional survey was conducted involving 6,581 participants from Italy, the Netherlands, France, Germany, Spain, Poland, Romania, and Hungary. The survey assessed current health app usage, future interest, willingness to share health data, and concerns about data privacy and security. Results 21.87% of respondents currently use health apps, with 42.71% expressing interest in future use. Regarding data sharing, 52.82% are willing to share health data with healthcare providers, and 25.48% would share data with research institutions. However, 53.18% fear unauthorized use or hacking of their health data. Significant generational and geographical differences in mHealth engagement were observed, with older generations showing higher willingness to adopt health apps OR 1.22 (CI 1.03 - 1.46) for Baby Boomers, OR 1.45 (CI 0.97 - 2.16) for Silent Generation. Education level emerged as a crucial factor, with tertiary-educated individuals more likely to use health apps (OR 1.41 CI 1.24 - 1.58) and demand transparency (OR 1.61 CI 1.45 -1.78). Conclusions The findings underscore the need for targeted strategies to enhance digital literacy, ensure data privacy, and promote equitable access to mHealth technologies across Europe. Policymakers and healthcare stakeholders should prioritize these areas to foster greater public trust and engagement in mHealth, ultimately improving health outcomes and patient care. Key messages • Public attitudes toward mHealth vary significantly across generations, countries, and education levels in Europe. • Addressing data privacy concerns and enhancing digital literacy are crucial for widespread mHealth adoption.

Factors Related to mHealth App Use Among Japanese Workers: Cross-Sectional Survey.

Health care providers can make health guidance more effective by using mobile health technologies such as health apps. Although health care providers need to know who uses health apps, existing studies have yielded inconsistent results. The aim of the study was (1) to clarify the prevalence and patterns of health app use to improve health behaviors for preventing lifestyle-related diseases among Japanese workers and (2) to identify the associations among demographic characteristics, health behavior, and internet use and health app use by gender. Data were collected from a cross-sectional internet survey in 2023. In total, 2200 participants were included, with an even distribution of men and women in each age group aged 20 to 60 years. The participants were workers with smartphones and reported their gender, age, residence area, marital status, education, employment status, occupation, work pattern, diseases under treatment, health checkups, health guidance, health behaviors, internet use duration, and number of devices used. We asked about current and previous health app use for 1 month. A multivariate logistic regression analysis was conducted by gender. Of the participants, 472 (21.5%) and 189 (8.6%) were current and previous health app users, respectively. Most current and previous health app users used features that record and track their physical activity and other health behaviors. Health app users-both men and women-were more likely to have health checkups (odds ratio [OR] 1.53, 95% CI 1.12-2.11 and OR 1.51, 95% CI 1.10-2.07, respectively), receive health guidance (OR 2.01, 95% CI 1.47-2.74 and OR 1.86, 95% CI 1.32-2.62, respectively), engage in regular physical activity (OR 2.57, 95% CI 1.91-3.47 and OR 1.94, 95% CI 1.41-2.67, respectively), use the internet for 120-179 minutes per day (OR 1.76, 95% CI 1.13-2.75 and OR 1.70, 95% CI 1.12-2.57, respectively), and were less likely to be older (50-59 years: OR 0.54, 95% CI 0.33-0.88 and OR 0.40, 95% CI 0.25-0.6, respectively, and 60-69 years: OR 0.37, 95% CI 0.22-0.62 and OR 0.47, 95% CI 0.28-0.77, respectively). According to gender, male health app users were more likely to be married (OR 1.69, 95% CI 1.23-2.33) and less likely to work in the security, agriculture, forestry, fishing, manufacturing, or transportation industries (OR 0.62, 95% CI 0.41-0.95). Female health app users were more likely to have a university education or higher (OR 1.55, 95% CI 1.061-2.26), maintain an appropriate body weight (OR 1.52, 95% CI 1.10-2.11), and use 3 or more devices (OR 2.13, 95% CI 1.41-3.23). Physical activity and health guidance are strong predictors of app use. Health care providers should assess the target populations' preferences for app use based on their characteristics, support their app use, and enhance the effectiveness of health guidance.

Neural Architecture Search: Automating the Design of Convolutional Models for Scalability

Neural Architecture Search (NAS) has changed the way convolutional neural networks (CNNs) are designed and optimized by providing an automatic way to make models that are scalable and perform well. Traditional ways of making CNN designs depend on a lot of expert knowledge and a lot of hand tuning, which can make them less efficient and scalable. NAS simplifies this process by using powerful search algorithms to look through huge architecture areas and find the best models for each job. This method not only makes CNNs more scalable, but it also speeds up the planning process, which means that highly efficient systems can be made with little help from humans. Most of the time, NAS uses gradient-based methods, genetic algorithms, or reinforcement learning to find and analyze possible designs. NAS can quickly look through big search areas using these methods, which make the best use of measures like accuracy, delay, and processing cost. So, NAS has shown that it can perform better than models that were made by hand in a number of situations, such as picture classification, object recognition, and segmentation. When it comes to scaling, NAS adds freedom to model design, so designs can be changed to fit different hardware and application settings, such as cloud-based systems and edge devices. NAS speeds up testing and feedback by automating the design search. This cuts down on the time and work needed for model development. This flexibility is very important in current deep learning apps, which need models that can work on a lot of different systems. NAS has created new design patterns, like brain cell structures and efficient building blocks, that have raised the bar for how well and efficiently models work. Because of these improvements, NAS has become an important part of neural network design, pushing the limits of what is possible in terms of implementation and growth. This essay looks at the ideas behind NAS, how it affects the scaling of CNN, and how future progress could be made in automatically designing neural networks for a wide range of uses.

An empirical study of AI techniques in mobile applications

The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized services through advanced machine learning (ML) and deep learning (DL) technologies. AI-driven mobile apps typically refer to applications that leverage ML/DL technologies to perform key tasks such as image recognition and natural language processing. Despite existing research exploring how mobile apps exploit AI techniques, they have the following main limitations: (1) Most existing studies focus on DL-based apps, with limited research on ML-based apps. (2) Existing research typically focuses on investigating the apps and the technologies utilized in the apps, lacking user-level analysis. (3) The number of apps studied is limited, with only 1,000 to 2,000 ML/DL apps identified after filtering. To fill the gap, in this paper, we conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study encompasses 56,682 real-world AI applications, focusing on three crucial perspectives: (1) Application analysis, where we analyze the popularity of AI apps and investigate the update states of AI apps; (2) Framework and model analysis, where we analyze AI framework usage and AI model protection; (3) User analysis, where we examine user privacy protection and user review attitudes. Our study has strong implications for AI app developers, users, and AI R&D. On one hand, our findings highlight the growing trend of AI integration in mobile applications, demonstrating the widespread adoption of various AI frameworks and models. On the other hand, our findings emphasize the need for robust model protection to enhance app security. Additionally, our study highlights the importance of user privacy and presents user attitudes towards the AI technologies utilized in current AI apps. We provide our AI app dataset (currently the most extensive AI app dataset) as an open-source resource for future research on AI technologies utilized in mobile applications.

A specialized inclusive road dataset with elevation profiles for realistic pedestrian navigation using open geospatial data and deep learning

Built environment characteristics can greatly influence pedestrians' route choices with factors beyond distance, such as accessibility, convenience, safety, and aesthetics, playing crucial roles. Although current navigation apps, such as Google Maps and Waze, have successfully provided driving directions, their navigation services are insufficient and sometimes unrealistic for addressing pedestrians' needs, largely due to the lack of dedicated pedestrian networks and the associated navigation algorithms. To address the research gaps, this paper proposes a novel approach that integrates freely available geospatial data and computer vision technology to create a specialized inclusive network dataset for outdoor pedestrian navigation. Moreover, a pedestrian navigation algorithm is developed to generate more practical “shortest” and “alternative” paths by incorporating various sidewalk attributes. We applied the method to create a pedestrian navigation network in Las Vegas. SpaceNet's open imagery dataset was used to extract Las Vegas's road networks. A virtual audit process assessed the visual and operational properties of the sidewalk networks using Google street-level images, evaluating factors including sidewalk presence, widths, surface types and conditions, missing curb ramps, greenery, protection from weather conditions, and lighting. Google Earth's open elevation data were used to analyze road elevation profiles as meaningful 3D indicators of sidewalk accessibility for wheelchair users. Further, additional geometric properties of the network, including road curviness, proximity to road intersections, and directional changes, were detected and analyzed. A navigation experiment conducted with individuals having varying mobility abilities, including regular pedestrians, older adults, and wheelchair users demonstrated the effectiveness of the newly developed network and algorithm in meeting the diverse needs of pedestrians.

14 - Do current electronic bladder diary Apps provide data suited for clinical use? Evaluation using a Validated Framework Analysis.

14 - Do current electronic bladder diary Apps provide data suited for clinical use? Evaluation using a Validated Framework Analysis.

Mobile mental health application use, and app feature preferences among individuals with mental disorders in Ethiopia: A cross-sectional survey

BackgroundMobile health applications have been shown to assist in the treatment of mental illnesses, yet their potential remains underutilized. As supportive care, mental health applications use may be useful tools in improving mental health literacy and treatment outcomes, but patients’ use of and interest in using these apps have not been studied in low-income countries like Ethiopia. This study explores the determinants of patients’ mental health app use interests and app feature preferences. MethodsA cross-sectional study was conducted with 419 outpatient individuals with mental health disorders at Amanuel Mental Specialized Hospital, Ethiopia, between October and November 2023, using a convenience sampling method. Patients’ self-reported mental health app use, interests, attitudes, and app feature preferences were collected. A multivariate logistic regression model was used to identify the associated factors. ResultsOverall prevalence of not interested, somewhat interested, interested, and extremely interested in mental health app use was 5.3 %, 16.9 %, 27.7 %, and 50.1 % respectively. The proportion of current mental health apps use was low (21.2 %). Current mental health app use is associated with living in an urban area (AOR = 6.69, CI: 3.19–14) and having a higher education level (4.12 (2.02–8.04). Furthermore, app features such as psychoeducation, symptom tracking, and self-management techniques ranked as the most preferred app features. ConclusionsMost study participants were interested in using mental health apps, but only about one in four were using mental health apps. This suggests promise for their utility in the Ethiopian context. App developers and policymakers should consider rural residents’, lower educational levels, and patient app preferences when developing these applications.

Mobile Phone Apps for Pelvic Floor Disorders.

Up to 50% of patients report not readily seeking treatment for pelvic floor disorders (PFDs). The increase in phone applications (apps) for health care information is an opportunity to increase access to care. The aim of the study was to systematically evaluate content and function of apps for patients with PFDs. Apps were screened using PFD-related search terms. Included apps were on the Apple store, in English, and targeted patients with PFDs. The primary outcome was app quality based on the APPLICATIONS scoring system (scored 0-16). Secondary outcomes included professional medical involvement, iTunes rating details, the presence of a voiding/bowel diary, tracking of diet, pain/symptoms, exercise, and medication, graphing or social functions, reminders, disease information, and decision support. Data was reported with descriptive statistics (medians (ranges) and n (percentages). Eight hundred forty apps were identified and 83 were analyzed. The top 3 PFD categories represented were defecatory dysfunction (29), overactive bladder (28), and stress incontinence (27). The median APPLICATIONS score was 7 (3-12). Most apps (78%) were developed without professional medical involvement. Most apps were free, while the remainder ranged from $1.99 to $4.99. No app had all features. Twenty-five apps (30%) included a voiding diary, 33 (40%) had a bowel diary, 27 (33%) included exercise tracking, and 44 (53%) had reminder systems. Most apps had reasonable, but not high, functionality. Current apps provide varying degrees of overall utility, with limited disease information and decision support. Further collaboration with medical providers in app development would support better integration of clinician and patient needs.

MOBILE HEALTH APPS FOR THE SELF-MANAGEMENT OF LOW BACK PAIN: SYSTEMATIC SEARCH IN APP STORES AND CONTENT ANALYSIS

Purpose and BackgroundClinical practice guidelines (CPGs) recommend self-management for low back pain (LBP). Our recent narrative review on self-management needs revealed a consensus with respect to the critical components of self-management interventions. With mobile health advancements, apps offer innovative support for LBP management. This study aims to identify current apps for the self-management of LBP, assessing them for their quality, intervention content, theoretical approaches, and risk management approaches.Methods and ResultsWe identified 69 apps for LBP self-management from a systematic search in the UK iTunes and Google Play stores. The most recommended interventions are muscle stretching (n=51, 73.9%), muscle strengthening (n=42, 60.9%), and core stability exercises (n=32, 46.4%). The average MARS (SD) overall score for the included apps was 2.4 (0.44) out of a possible 5 points, with the engagement and information dimension scoring the lowest at 2.1. In terms of theoretical and risk management approaches, no apps offered a theoretical care model and all failed to specify the age group targeted; only one (1.4%) provided a tailored care approach; 18 (26.1%) included intervention progression; and 11 (15.9%) reported management safety checks.ConclusionThis study shows that app developers generally select interventions endorsed by CPGs. However, the application of a biopsychosocial care model is not being considered. Most of them are of low quality, lacking theoretical approaches to care and consideration of associated risks. It is essential to involve clinicians and patients in developing LBP self-management apps to improve the quality and related approach.Conflicts of interestNoneSources of fundingNo funding obtainedThis study has been published in JMIR mHealth and uHealth.

A comprehensive survey on exploring and analyzing COVID-19 mobile apps: Meta and exploratory analysis

During the current COVID-19 pandemic, many digital solutions around the world have been proposed to cope with the deadly virus but the role of mobile-based applications is dominant one. In Pakistan, during the current COVID-19 pandemic, an array of mobile health applications (apps) and platforms have been launched to grapple with the impacts of the COVID-19 situation. In this survey, our major focus is to explore and analyze the starring role of mobile apps based on the features and functionalities to tackle the COVID-19 disease, particularly in Pakistan. In this study, over fifty (50) mobile apps have been scrapped from the well-known three different sources i.e. Google Play Store, iOS Play Store, and web source. We developed our own data set after searching through the different play stores. We have designed two criteria such that the first criteria are known as eligibility criteria, while the second one is known as assessment criteria. The features and functions of each mobile app are pinpointed and discussed against the parameters of the assessment criteria. The major parameters of assessment criteria are: (i) Home monitoring; (ii) COVID-19 awareness; (iii) contact tracing; (iv) telemedicine; (v) health education; (vi) COVID-19 surveillance; (vii) self-assessment; (viii) security; and (ix) accessibility. This study conducted exploratory analysis and quantitative meta-data analysis by adopting PRISMA guidelines. This survey article is not only discussing the function and features of each COVID-19-centered app in Pakistan, but it also sheds light on the limitations of every mobile app as well. The results of this survey might be helpful for the mobile developers to review the current app products and enhance the existing mobile platforms targeted towards the COVID-19 pandemic. This is the first attempt of its kind to present a state-of-the-art survey of the COVID-19-centered mobile health apps in Pakistan.

Correction: Sign language mobile apps: a systematic review of current app evaluation progress and solution framework

Correction: Sign language mobile apps: a systematic review of current app evaluation progress and solution framework

A Multilevel Triadic-Experience Model of Travel App-Induced Attitude

ABSTRACT The literature on tourist experience commonly puts the current en-route experience at the center stage through single-level analysis. This research steers away from that tradition to improvise a triple-experience model through multiple inquiries. Data were collected from a survey to assess how prior and current travel app usage experiences could impact app-induced site attitude and revisit intention via the app, based on a multilevel design with survey data collected from 21 sites. The cross-level moderation of others’ experiences was also explored. This research draws on the functional theory of attitude and the theory of normative conduct to acknowledge the knowledge and social functions of attitude change. It contributes to the literature by bridging the divide between the online app and offline travel evaluations. It builds a theoretical synthesis between the aforementioned theoretical lens to demonstrate how the experiences of others through travel apps can impact an individual’s experience, ultimately affecting attitude toward app-induced tourism destinations.

Supporting parents with acutely ill children: Environment scan and user evaluation of mobile applications (the SuPa kids project)

BackgroundChildren are one of the biggest users of emergency departments in the UK, sometimes utilising services when experiencing acute illnesses that can be managed at home. mHealth can be an efficacious way for parents/guardians to manage acute illnesses. It has also become a ubiquitous resource to promote public health interests within a resource constrained health system. However, to be effective, co-design is necessary with concepts such as usefulness, ease of use, trustworthiness and security underpinning the utility of mHealth interventions for acute childhood illness. ObjectiveThe objective of this study was to conduct an environmental scan of mHealth apps for parental assessment and management of acute childhood illnesses in 0- to 5-year-olds, review the content and functionality of each identified app against the list of desired attributes identified in previous research and synthesise publicly available reviews from end users to demonstrate the quality of current apps in the marketplace and identify areas for improvement. MethodApp characteristics were mapped to an a priori defined list of desirable attributes. End user reviews were mapped to a modified Technology Acceptance Model framework that included overarching themes of: Perceived Usefulness (PU), Perceived Ease of Use (PEU), Perceived Trustworthiness (PT) and Perceived Security (PS) using reflexive thematic analysis. ResultsOverall, 15 apps were included in the analysis (Table 2). Fourteen of the 15 apps were available on both the iOS and Android platforms. ‘Baby Illness, Prevention, Cure’ was only available on the Android store. All apps were free to download and did not restrict access to information or features via in-app purchases. Baby and child first aid (British Red Cross) was the earliest app to be released (2014). This app was updated annually, with the most recent update being 2022. The most recent apps to be released (2018) were ‘AskFirst’ (formerly Ask NHS), ‘Essex Child Health’ and ‘The Lullaby Trust Baby Check’, updated in 2022, 2020 and 2019, respectively. No individual app met all the desirable attributes highlighted by parents in previous research. Both ‘Healthier Together’, and ‘The Lullaby Trust Baby Check’ included all but one desirable attributes (video), as did the ‘’CATCH (Common Approach to Children’s Health), which did not utilise a traffic light system. Apps that were locality specific were not rated by users (Berkshire Child Health, Cheshire Child Health, Child Health Guide Newham, Child Health HMR, Oldham Child Illness and Walsall Healthy Child). All other apps were rated from 2/5 to 5/5 stars. When considering localities, the ‘CATCH’ app was only supported in Halton, Cheshire, Knowsley, Liverpool, St Helens and Vale Royal. Further investigation evidenced no correlation between inclusion of the desirable attributes and app rating. ConclusionOverall, this research has highlighted several aspects of best practise when developing mHealth apps for the management of acute childhood illnesses, for increasing PU, PEU, PT and PS; and also, places importance on co-design.

Research on Portable Intelligent Terminal and APP Application Analysis and Intelligent Monitoring Method of College Students' Health Status

As a carrier of college students' health status monitoring, portable intelligent terminal APP, the study of its APP application analysis not only provides a new way for college students' extracurricular physical exercise, guides college students to actively participate in extracurricular physical activities using intelligent terminal APP software, but also promotes college students' physical health monitoring and enhancement in various aspects. Aiming at the current portable terminal APP college students' health monitoring application analysis method research exists low precision, real-time poor and other problems, through the analysis of the basic functional framework and functional characteristics of the portable intelligent terminal APP, the establishment of the portable intelligent terminal APP analysis index system applied to college students' health monitoring, combined with the heuristic optimisation algorithm and the improvement of deep learning algorithms, the construction of the marine predator based heuristic optimisation algorithm and the attention mechanism to improve the gating control loop. Combining the heuristic optimisation algorithm and the improved deep learning algorithm, we construct the portable intelligent terminal APP application analysis method for college students' health monitoring based on the marine predator heuristic optimisation algorithm and the attention mechanism improved gated recurrent unit neural network. Through simulation analysis, the results show that the proposed method meets the real-time requirements while improving the prediction accuracy of the portable smart terminal APP application analysis method, and significantly improves the efficiency of portable smart terminal APP analysis.

Patients and Stakeholders' Perspectives Regarding the Privacy, Security, and Confidentiality of Data Collected via Mobile Health Apps in Saudi Arabia: Protocol for a Mixed Method Study.

There is data paucity regarding users' awareness of privacy concerns and the resulting impact on the acceptance of mobile health (mHealth) apps, especially in the Saudi context. Such information is pertinent in addressing users' needs in the Kingdom of Saudi Arabia (KSA). This article presents a study protocol for a mixed method study to assess the perspectives of patients and stakeholders regarding the privacy, security, and confidentiality of data collected via mHealth apps in the KSA and the factors affecting the adoption of mHealth apps. A mixed method study design will be used. In the quantitative phase, patients and end users of mHealth apps will be randomly recruited from various provinces in Saudi Arabia with a high population of mHealth users. The research instrument will be developed based on the emerging themes and findings from the interview conducted among stakeholders, app developers, health care professionals, and users of mHealth apps (n=25). The survey will focus on (1) how to improve patients' awareness of data security, privacy, and confidentiality; (2) feedback on the current mHealth apps in terms of data security, privacy, and confidentiality; and (3) the features that might improve data security, privacy, and confidentiality of mHealth apps. Meanwhile, specific sections of the questionnaire will focus on patients' awareness, privacy concerns, confidentiality concerns, security concerns, perceived usefulness, perceived ease of use, and behavioral intention. Qualitative data will be analyzed thematically using NVivo version 12. Descriptive statistics, regression analysis, and structural equation modeling will be performed using SPSS and partial least squares structural equation modeling. The ethical approval for this research has been obtained from the Biomedical and Scientific Research Ethics Committee, University of Warwick, and the Medical Research and Ethics Committee Ministry of Health in the KSA. The qualitative phase is ongoing and 15 participants have been interviewed. The interviews for the remaining 10 participants will be completed by November 25, 2023. Preliminary thematic analysis is still ongoing. Meanwhile, the quantitative phase will commence by December 10, 2023, with 150 participants providing signed and informed consent to participate in the study. The mixed methods study will elucidate the antecedents of patients' awareness and concerns regarding the privacy, security, and confidentiality of data collected via mHealth apps in the KSA. Furthermore, pertinent findings on the perspectives of stakeholders and health care professionals toward the aforementioned issues will be gleaned. The results will assist policy makers in developing strategies to improve Saudi users'/patients' adoption of mHealth apps and addressing the concerns raised to benefit significantly from these advanced health care modalities. DERR1-10.2196/54933.

Preliminary Survey Data From an App-Delivered Hypnosis Intervention for Smoking Cessation.

Introduction: Hypnosis-based apps geared toward smoking cessation are among the most downloaded by individuals wanting to reduce or stop smoking. However, to date, there are few evaluations regarding the use or efficacy of hypnosis apps for smoking cessation. Finito is an empirically-based mHealth app developed by Mindset Health designed to provide users with a three-week hypnotherapy program to quit smoking. This study aimed to examine demographic and clinical characteristics of Finito app users and conduct a preliminary analysis of survey data from participants of the program. Method: Finito app users were asked to voluntarily complete an online survey regarding their experience with the program, current smoking habits, app usability, relevant improvement, and demographic information. Retrospective data analyses were conducted to provide descriptive and inferential findings from the responses. Results: A total of 120 individuals responded to the survey. Respondents originated from over five different countries and approximately 72.5% reported completing the full Finito program. Approximately 58.3% of participants reported that they accomplished their goal with Finito with 50.8% of all respondents reporting that they quit smoking and an additional 25.8% reporting that their smoking was reduced. Among a variety of secondary outcomes, saving money was the most frequently reported secondary benefit with 46.7% of respondents endorsing that item. Conclusion: Our preliminary survey results suggest that Finito may be a useful, pleasant, and cost-effective tool in a patient's journey to quit smoking. The majority of app users reported that they achieved their goal with Finito and completion of the program was associated with goal achievement. The Finito app may be effective in the dissemination and delivery of a helpful hypnotherapy intervention across a diverse population.

Exploring Maternal and Infant Health App Development and Effectiveness Research: Scoping Review.

Globally, high rates of maternal and infant mortality call for interventions during the perinatal period to engage pregnant people as well as their loved ones in care. Mobile health technologies have become ubiquitous in our lives and in health care settings. However, there is a need to further explore their safety and effectiveness to support and improve health outcomes locally and globally. The aim of this study was to review and synthesize published literature that described the development process or effectiveness evaluations of maternal and infant apps. We applied a methodological framework for scoping reviews as well as the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines; in addition, the systematic review platform Covidence (Veritas Health Innovation Ltd) was used to facilitate the review of included studies. Search terms were developed collaboratively, and health sciences-associated databases were searched for studies conducted between January 1, 2000, and February 4, 2022. We excluded studies about apps that only gathered or tracked data or targeted care providers. A total of 1027 articles were included for title and abstract screening, of which 87 (8.47%) were chosen for full-text screening. Of these 87 articles, 74 (85%) were excluded with reasons, and 19 (22%) were included. Four articles were added at data extraction from hand searching and 2 others were excluded. Thus, we reviewed and synthesized data from 11 unique studies reported in 21 articles published between 2017 and 2021. The included studies represented 8 different countries. Most of the apps (8/11, 73%) were in English, although apps were also developed in Arabic, Bahasa Indonesia, and Nepali. The articles reviewed revealed the early stage of development of the field of maternal and infant health apps, with modest evidence of app use and achievement of study outcomes. Only 1 (9%) of the 11 apps was endorsed by an independent health care provider society. App development and evaluation processes emerged, and specific app features were identified as vital for well-functioning apps. End-user engagement occurred in some, but not all, parts of app research and development. Apps to improve maternal and infant health are being developed and launched in enormous numbers, with many of them not developed with mothers' needs in mind. There are concerns about privacy, safety, and the standardization of current apps as well as a need for professional or institution-specific guidelines or best practices. Despite challenges inherent in currently available apps and their design processes, maternal and infant app technology holds promise for achieving health equity goals and improving maternal and child health outcomes. Finally, we propose recommendations for advancing the knowledge base for maternal and infant apps.

Functionality and Quality of Asthma mHealth Apps and Their Consistency With International Guidelines: Structured Search and Evaluation.

Asthma is a chronic respiratory disorder requiring long-term pharmacotherapy and judicious patient self-management. Few studies have systematically evaluated asthma mobile health (mHealth) apps for quality and functionality; however, none have systematically assessed these apps for their content alignment with international best practice guidelines. This review aims to conduct a systematic search and evaluation of current mHealth apps in the Australian marketplace for their functionality, quality, and consistency with best practice guidelines. The most recent Global Initiative for Asthma (GINA) guidelines were reviewed to identify key recommendations that could be feasibly incorporated into an mHealth app. We developed a checklist based on these recommendations and a modified version of a previously developed framework. App stores were reviewed to identify potential mHealth apps based on predefined criteria. Evaluation of suitable apps included the assessment of technical information, an app quality assessment using the validated Mobile App Rating Scale (MARS) framework, and an app functionality assessment using the Intercontinental Medical Statistics Institute for Health Informatics (IMS) Functionality Scoring System. Finally, the mHealth apps were assessed for their content alignment with the GINA guidelines using the checklist we developed. Of the 422 apps initially identified, 53 were suitable for further analysis based on inclusion and exclusion criteria. The mean number of behavioral change techniques for a single app was 3.26 (SD 2.27). The mean MARS score for all the reviewed apps was 3.05 (SD 0.54). Of 53 apps, 27 (51%) achieved a total MARS score of ≥3. On average, the reviewed apps achieved 5.1 (SD 2.79) functionalities on the 11-point IMS functionality scale. The median number of functionalities identified was 5 (IQR 2-7). Overall, 10 (22%) of the 45 apps with reviewer consensus in this domain provided general knowledge regarding asthma. Of 53 apps, skill training in peak flow meters, inhaler devices, recognizing or responding to exacerbations, and nonpharmacological asthma management were identified in 8 (17%), 12 (25%), 11 (28%), and 14 (31%) apps, respectively; 19 (37%) apps could track or record "asthma symptoms," which was the most commonly recorded metric. The most frequently identified prompt was for taking preventive medications, available in 9 (20%) apps. Five (10%) apps provided an area for patients to store or enter their asthma action plan. This study used a unique checklist developed based on the GINA guidelines to evaluate the content alignment of asthma apps. Good-quality asthma apps aligned with international best practice asthma guidelines are lacking. Future app development should target the currently lacking key features identified in this study, including the use of asthma action plans and the deployment of behavioral change techniques to engage and re-engage with users. This study has implications for clinicians navigating the ever-expanding mHealth app market for chronic diseases. PROSPERO CRD42021269894; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=269894. RR2-10.2196/33103.