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The trend of the number of released mHealth apps in the Apple App Store (AppStore). 2008Q3: third quarter of year 2008. BR: Brazil; CN: China; JP: Japan; RU: Russia; US: United States.
Source publication
The market of mobile health (mHealth) apps has rapidly evolved in the past decade. With more than 100,000 mHealth apps currently available, there is no centralized resource that collects information on these health-related apps for researchers in this field to effectively evaluate the strength and weakness of these apps.
The objective of this study...
Context in source publication
Context 1
... on the release date information of each app included in our repository, we can analyze the trend of mHealth apps available in the AppStore. We plotted the number of apps released in each quarter since the third quarter of 2008 ( Figure 1 shows this). From this figure, we can see that the apps in the Health & Fitness category show a quadratic growth (R 2 = 0.9867), while the apps in the Medical category demonstrate a linear growth (R 2 = 0.9823). ...
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Citations
... 20,21 The use of mobile health (mHealth) technologies has bourgeoned since the emergence of app stores in 2008. 22 mHealth has many advantages, including increasing healthcare capacity, providing patient access to tailored and immediate support, reducing stigma in accessing care, and improving costeffectiveness. 23 Unfortunately, very few apps undergo any type of rigorous evaluation. ...
Objectives
Fetal alcohol spectrum disorders (FASD) affect the health and development of people across the lifespan. Adults with FASD experience significant barriers to care. Accessible and scalable solutions are needed. In partnership with members of the International Adult Leadership Collaborative of FASD Changemakers, an international group of adults with FASD, we developed a mobile health (mHealth) application based on self-determination theory (SDT), called “My Health Coach,” to promote self-management and health advocacy.
Methods
This project follows an established user-centered design approach to app development and evaluation, allowing for feedback loops promoting iterative change. Research staff and ALC members co-led online focus groups (n = 26) and an online follow-up survey (n = 26) with adults with FASD to elicit feedback on completed design prototypes. Focus group transcriptions and surveys underwent systemic thematic and theoretical framework analysis.
Results
Analyses show overall positive impressions of the My Health Coach app. Participants were enthusiastic about the proposed features and tools the app will provide. Discussions and free responses revealed SDT constructs (autonomy, competence, relatedness) are a strong fit with participants’ perceived outcomes shared in their evaluation of the prototype. Interesting recommendations were made for additional features that would further promote SDT constructs.
Conclusions
This project demonstrates advantages of community-engaged partnerships in FASD research. Adults with FASD have a strong interest in scalable mHealth tools and described the acceptability of our initial design. App features and tools promoted SDT constructs.
... To address this challenge, mobile app intelligence has emerged as a promising approach for enhancing customer satisfaction [6]. Mobile app intelligence refers to the use of data analytics, machine learning, and other advanced technologies to gain insights into user behavior, preferences, and needs [7]. By leveraging these insights, app developers and marketers can optimize the user experience, personalize content and recommendations, and deliver targeted interventions to improve customer satisfaction [8]. ...
The widespread adoption of smartphones and mobile apps has revolutionized many industries, including the wellness sector. Wellness apps, which provide users with tools and resources to improve their physical and mental well-being, have gained significant popularity in recent years. However, to ensure long-term success and customer loyalty, these apps must prioritize customer satisfaction. This study explores the role of mobile app intelligence in enhancing customer satisfaction within the context of wellness apps. Through a comprehensive literature review and empirical research involving surveys and user data analysis, we investigate the key factors contributing to customer satisfaction in wellness apps and propose a framework for leveraging mobile app intelligence to optimize the user experience. Our findings highlight the importance of personalization, user engagement, and data-driven insights in driving customer satisfaction. We also discuss the implications of our research for mobile app developers, marketers, and wellness service providers, offering practical recommendations for improving customer satisfaction through mobile app intelligence. This study contributes to the growing body of literature on mobile app marketing and customer satisfaction, providing valuable insights for both academics and practitioners in the wellness industry.
... Data access features in mHealth apps refer to the ability of users to access and manage their health data. It helps in getting comprehensive medical information [56,63], supporting networks and informed decision-making, timely and informed care [25], etc. The inclusion of data access features in a mHealth app has a significant influence on the adoption, usage, and overall satisfaction of its users [38]. ...
The usage of mobile health (mHealth) applications is rising steadily in India in recent years. Examining factors that influence users’ intention to continue using mHealth apps is vital for increasing the adoption rate. This research adopts the basic TAM model and extends it by incorporating user characteristics and app characteristics to predict continuance intention toward mHealth apps. A questionnaire was administered with a sample of 537 respondents. The proposed model was tested with structural equation modeling (SEM). The empirical evidence confirms the influence of mHealth app characteristics (data access, data storage, interactivity, integration of artificial intelligence, and playfulness), individual characteristics of mHealth app users (self-efficacy, technological anxiety, health consciousness, perceived vulnerability, and personal innovativeness), as well as perceived ease of use and perceived usefulness on users’ intention to continue with mHealth apps. The study offers insights for mHealth app developers, policymakers, and healthcare providers seeking to optimize mHealth interventions and ensure their effectiveness in addressing the healthcare needs of developing nations.
... Knight et al. [4] sought to identify existing physical activity applications supported by scientific evidence and to identify technological features that could potentially improve health outcomes. On the other hand, Xu and Liu [5] created a centralized mHealth app repository for analyzing information to provide insights for future mHealth research. ...
... Menaspà [3] x Knight et al. [4] x Xu & Liu [5] x North & Chaudhry [6] x Kym Price [7] x x Reid et al. [8] x Mendoza et al. [9] x x Hamari et al. [10] x Chang et al. [11] x Feehan et al. [12] x Edney et al. [13] x Genes et al. [14] x Bol et al. [15] x Hartman et al. [16] x Beltran-Carrillo et al. [17] x Haghayegh et al. [18] x Owens & Cribb [19] x Jo et al. [20] x x Kim et al. [21] x Polese et al. [22] x Jung et al. [23] x x Haghayegh et al. [24] x Dinh-Le [25] x Sharon [26] x x Giannakosa et al. [27] x Ringeval et al. [28] x Kim et al. [29] x Bai et al. [30] x x Gleiss et al. [31] x x x Balbim et al. [32] x Rolnick et al. [33] x Mustafa et al. [34] x As can be seen, some efforts sought to analyze different approaches corresponding to cloud-based health platforms. This paper intends to analyze the four main and most popular platforms today, which are Apple Health, Google Fit, Samsung Health, and Fitbit. ...
Cloud-based platforms have gained popularity over the years because they can be used for multiple purposes, from synchronizing contact information to storing and managing user fitness data. These platforms are still in constant development and, so far, most of the data they store is entered manually by users. However, more and better wearable devices are being developed that can synchronize with these platforms to feed the information automatically. Another aspect that highlights the link between wearable devices and cloud-based health platforms is the improvement in which the symptomatology and/or physical status information of users can be stored and syn-chronized in real-time, 24 h a day, in health platforms, which in turn enables the possibility of synchronizing these platforms with specialized medical software to promptly detect important variations in user symptoms. This is opening opportunities to use these platforms as support for monitoring disease symptoms and, in general, for monitoring the health of users. In this work, the characteristics and possibilities of use of four popular platforms currently available in the market are explored, which are Apple Health, Google Fit, Samsung Health, and Fitbit.
... Other researchers also used similar scraping methods. In 2015, using web scraping methods, Xu and Liu [44] and Dehling et al. [45] made datasets of 24405 and 60000 mHealth apps, respectively. More recently, in 2020, Tsinaraki et al. [46] analysed COVID-19-related apps from the two app stores using updated scraping methods. ...
... The implications of this issue are particularly pronounced in low-resource settings, where access to more advanced smartphones and reliable internet connectivity for downloading and utilizing these large apps is a persistent concern [44,61]. Addressing these logistical challenges and developing easily usable apps is essential in ensuring equitable access to mHealth apps, especially in Indian settings where resources are constrained. ...
BACKGROUND
Smartphone health apps (mHealth apps) have gained significant popularity recently. mHealth apps can address healthcare disparities and enhance access to healthcare services in low-resource countries. With its vast and diverse population, India presents a unique context for studying the landscape of mHealth apps. However, only a few studies explored the breadth of mHealth apps available in India.
OBJECTIVE
Our study aimed to address this critical gap and aid in comprehensive evaluations by introducing an automated method for collecting data on mHealth apps. We aimed to create a comprehensive dataset initially focusing on heart disease-related apps.
METHODS
We collected individual app data from apps in the "Medical" and "Health & Fitness" categories from the Google Play Store and the Apple App Store in December 2022 and July 2023, respectively. Using Natural Language Processing (NLP) techniques, we selected apps relevant to heart diseases (HD apps), performed statistical analysis, and applied Latent Dirichlet Allocation (LDA) for clustering and topic modelling to categorise the resulting HD apps.
RESULTS
We collected 118.555 apps from the Apple App Store and 108.945 apps from the Google Play Store. Within these datasets, we found that approximately 1.7 percent apps on the Apple App Store and 0.5 percent on the Google Play Store included support for Indian languages. Using monograms and bigrams related to heart disease, we identified 1.681 HD apps from the Apple App Store and 588 HD apps from the Google Play Store. HD apps make only a small fraction of the total number of health apps available in India. About 90 percent of the HD apps are free of cost. However, more than 70 percent of HD apps have no reviews and rating scores, indicating low usage. Exploratory correlation analysis revealed that apps with longer descriptions tend to have higher rating-scores. Clustering analysis of these HD apps revealed three prominent clusters in both the Apple App Store and Google Play Store datasets - Clinical apps, Fitness & Lifestyle apps, and Sleep & Wellbeing apps.
CONCLUSIONS
Our paper contributes to mapping mHealth apps in India. It offers a user-friendly approach for researchers, potentially overcoming barriers to accessing and analysing valuable health app metadata. This approach's adaptability to other low-resource settings and diverse health issues makes it useful for further research and analysis.
... The launch of various applications has made it easier to store and record information, making smartphones a key part of our lives (McClain & Tudor-Locke, 2009). Around 100,000 health-related apps have been developed for the public, and the term "mobile health (mHealth)" was created to describe how smartphones can act as practical advisors to individuals (Bailey et al., 2015;Kahn et al., 2010;Smith et al., 2014;Xu & Liu, 2015). mHealth is defined as "the use of wireless communication devices to support public health and clinical practices", and it can be used to measure and record an individual's physical activity and even to record and analyse sports games (Fiordelli et al., 2013). ...
The present study examines the validity of the SwingVision application by comparing SwingVision's data to criterion data. Also, we investigated the difference in recording angles of SwingVision (optimal and suboptimal). Six college students played four matches, and every match was recorded from two different angles. After the data collection , recorded videos were analysed by SwingVision and human analysts (criterion). A total of 1065 strokes were analysed in the agreement of SwingVision and human analysts. Cross-tabulation with a column proportion test, Cochran's Q test, and Kappa statistics were utilised to demonstrate the association of categorical variables (stroke, hit depth, hit zone, bounce depth, bounce zone, spin, result) between the three data (i.e. optimal, suboptimal, and criterion). Repeated measures analysis of variance (ANOVA) and Pearson correlation were used to compare speed data.
... The launch of various applications has made it easier to store and record information, making smartphones a key part of our lives (McClain & Tudor-Locke, 2009). Around 100,000 health-related apps have been developed for the public, and the term "mobile health (mHealth)" was created to describe how smartphones can act as practical advisors to individuals (Bailey et al., 2015;Kahn et al., 2010;Smith et al., 2014;Xu & Liu, 2015). mHealth is defined as "the use of wireless communication devices to support public health and clinical practices", and it can be used to measure and record an individual's physical activity and even to record and analyse sports games (Fiordelli et al., 2013). ...
The present study examines the validity of the SwingVision application by comparing SwingVision’s data to criterion data. Also, we investigated the difference in recording angles of SwingVision (optimal and suboptimal). Six college students played four matches, and every match was recorded from two different angles. After the data collection, recorded videos were analyzed by SwingVision and human analysts (criterion). A total of 1065 strokes were analyzed in the agreement of SwingVision and human analysts. Cross-tabulation with a column proportion test, Cochran’s Q test, and Kappa statistics were utilized to demonstrate the association of categorical variables (stroke, hit depth, hit zone, bounce depth, bounce zone, spin, result) between the three data (i.e. optimal, suboptimal, and criterion). Repeated measures analysis of variance (ANOVA) and Pearson correlation were used to compare speed data. SwingVision data in most variables showed high proportional similarity and percent agreement with criterion data. Additionally, the optimal angle data had much more similar results to the criterion data than the suboptimal data. Therefore, this present study documented that SwingVision is trustworthy, and users should be aware of possible errors derived from angle differences.
... In our best knowledge, there are no other app repositories or publications but ours [14] to compare our data. In this regard, Xu et al. [23] performed a repository of applications focused on health based on the two main app stores, the Apple App Store and the Google Play Store, extracting detailed information from a total of 60,000 medical applications. On the other hand, there is the European Health Apps Directory [24], which makes a classification by type of app, areas of specialization (excluding neurorehabilitation), and several languages. ...
Background:
New technologies have gained popularity, especially the use of mobile phone applications, in neurorehabilitation. The aim of this paper was (1) to develop a free mobile application (NeurorehAPP) that provides information about and helps to select the appropriate mobile application related to a list of neurological disorders (cognitive impairment, Alzheimer's disease, Parkinson's disease, multiple sclerosis, traumatic brain injury, stroke, cerebral palsy, muscular dystrophy, spina bifida, and facial paralysis), based on different objectives such as healthy habits, information, assessment, and treatment; and (2) to assess the feasibility, acceptability, and degree of satisfaction by physiotherapists after using NeurorehAPP for a minimum of three months.
Methods:
A free application was created to work with the Android® operating system. The degree of satisfaction and acceptance with the application was assessed with an adaptation of the Customer Satisfaction Questionnaire through a survey via email applied to physiotherapists from hospitals and neurological rehabilitation centers in Spain after using the application.
Results:
NeurorehAPP includes a total of 131 apps. A total of 121 physiotherapists completed a satisfaction survey. The total sample showed 85.41% satisfaction with the service provided by the app and 86.41% overall satisfaction with NeurorehAPP.
Conclusions:
NeurorehAPP is a free, intuitive, and friendly app used with the Android® operating system that allows the selection of the most appropriate app according to the type of user, neurological disorder, objective, and FDA criteria. Physiotherapists showed a high degree of satisfaction and acceptance with NeurorehAPP.
... This issue can be solved by developing mobile apps using WHO grows charts [13]. According to approximate estimates hundreds of thousands health apps available worldwide [34,37]. However, it is reported that, baby care apps not always fail to provide adequate information on normal growth and development later in childhood and adolescence [13]. ...
The WHO's standards for analysis and presentation of anthropometric data is widely recognized as the best system. The aim of this study was to assess the agreement of calculation of height-for-age and BMI-for-age Z-scores according to WHO growth charts using of two randomly selected mobile apps in two co-horts of European and Asian subjects. In 1,347 adolescents aged 13 to 17 years, boys and girls, living in St. Petersburg, Russia (European cohort, 663 subjects) and Nukus, Uzbekistan (Asian cohort 684 subjects) measured body weight and stature. Each child's height-for-age Z-score and BMI-for-age Z-scores were cal-culated based on WHO Child Growth Standards PC version WHO AntroPlus software and mobile applications. It was performed a Blend-Altman analysis to assess the consistency of Z-scores calculated using WHO AntroPlus software and two mobile apps.
The results of the evaluation of the consistency of height-for-age and BMI-for-age Z-scores obtained using WHO AntroPlus software and mobile applica-tions showed that Android-based mobile applications systematically overestimate Z-scores. However, this error is much smaller than any clinically significant devi-ations of the physical development evaluation parameters both in the European and Asian cohort volunteers. Thus, it can be claimed that it is possible to use An-droid-based mobile applications for growth monitoring and BMI and WHO An-troPlus software for children from 5 to 19. This is especially relevant to the need for «field» monitoring of children's growth and development performed by gen-eral practitioners visiting patients at home.
... To be eligible for inclusion within the review, publications had to be in English but we did not place any geographic restrictions on the publication. We restricted the time range of publications from 2008 to February 2022, accounting for the rise in mobile applications (apps) popular in digital health technologies in that time period [31]. Included publications had to conduct studies or evaluations with individuals with LTHCs sharing personally generated health data via digital health technologies with healthcare professionals or others. ...
The use of digital technology amongst people living with a range of long-term health conditions to support self-management has increased dramatically. More recently, digital health technologies to share and exchange personal health data with others have been investigated. Sharing personal health data with others is not without its risks: sharing data creates threats to the privacy and security of personal data and plays a role in trust, adoption and continued use of digital health technology. Our work aims to inform the design of these digital health technologies by investigating the reported intentions of sharing health data with others, the associated user experiences when using these digital health technologies and the trust, identity, privacy and security (TIPS) considerations for designing digital health technologies that support the trusted sharing of personal health data to support the self-management of long-term health conditions. To address these aims, we conducted a scoping review, analysing over 12,000 papers in the area of digital health technologies. We conducted a reflexive thematic analysis of 17 papers that described digital health technologies that support sharing of personal health data, and extracted design implications that could enhance the future development of trusted, private and secure digital health technologies.
