Evaluation of mobile applications related to patients with Parkinson’s disease based on their essential features and capabilities

Abstract

Background

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Mobile technologies enable Parkinson's patients to improve their quality of life, manage symptoms, and enhance overall well-being through various applications (apps). There is no integrated list of specific capabilities available to cater to the unique needs of Parkinson's patient-focused mobile apps.

Objective

This study aimed to identify the key features and capabilities prioritized in developing mobile apps for patients with Parkinson's disease (PWP) and rank the related apps in this field.

Methods

We searched iTunes and Google Play for PWP apps with "Parkinson" or "Parkinson's" in their title or description. We evaluated existing mobile apps through a four-step process: identification, screening, eligibility, and feature analysis. We installed apps on Android and iOS devices, categorized their features/capabilities by the “use case model” and other additional identified features. We scored them using a tool called FARM (Feature-based Application Rating Method) and ranked PWP-related apps.

Results

Thirty-three apps related to the PWP were included and evaluated. Almost half of the apps were available on both the Android and iOS platforms. Seventy-five percent of the genres were associated with health and fitness. Although the included apps utilized certain features, none of the capabilities were used simultaneously. According to the experts' opinions, 'large font' was the most important feature and was utilized in 70% of the mobile applications. Additionally, the average score for all Parkinson's disease-related applications was 17.71 (SD = 7.92). The app titled ‘Swiss Parkinson’ had the highest score.

Conclusions

Integrating a relevant list of features used for Parkinson's patients' applications yielded valuable insights for the design of mobile applications tailored to patients’ needs. These features are highly efficient in dealing with the specific obstacles related to this disease.

Introduction

Parkinson's disease (PD) is a progressive neurodegenerative disease that mainly affects people later in life [1]. It is the second most prevalent neurodegenerative disorder worldwide, and its incidence and prevalence are expected to increase with increasing age [2]. The latest theories suggest that genetic predisposition and a range of environmental stimuli are the etiologies of PD. It is estimated that this disease will influence more than 14 million people globally by 2040 [3]. PD symptoms include both motor and nonmotor symptoms. Motor symptoms include slow movement, tremors, rigidity, walking difficulty, and balance problems; nonmotor symptoms encompass neuropsychiatric and autonomic symptoms, sleep and wakefulness disorders, pain, and other sensory disturbances [4]. The progression of these symptoms and complications has a significant impact on daily activities and diminishes the quality of life for patients with Parkinson’s disease (PWP) [5]. Since there is no definite treatment for PD and access to neurologists is limited [6], self-management strategies can help patients not depend on others and increase their life expectancy [7, 8].

Self-management strategies include medication management, physical exercise, self-monitoring techniques, psychological strategies, maintaining independence, encouraging social engagement, and providing knowledge and information [8]. In recent decades, the advancement of technologies has led to the development of mobile health (mHealth) to promote patient self-management remotely instead of through physical presence [9]. Furthermore, using smartphone applications (apps) has improved many self-management behaviors, such as resource utilization, relationships with healthcare providers, and making healthcare decisions [10]. These apps are accessible and have the potential to enhance the quality of healthcare services and decrease expenses [11]. For this reason, there are more than 300,000 health-related applications on the market today, most of which focus on helping patients [12, 13]. Many mobile applications, especially for PWPs, have been developed and implemented [14, 15].

Mobile health applications have shown potential in supporting patients with Parkinson's Disease (PD), but their usability remains critical for successful implementation. Studies have found that ease of use and information quality significantly influence app usefulness for PD patients [16]. Structured strategies to promote adherence and consider user limitations during app development can contribute to increased usability and potentially greater autonomy in care for PD patients [16, 17]. Most of the studies reviewed the applications of mobile apps for PWPs, and there may not be a good assessment of the essential usability capabilities and features [18]. In a systematic review, Lee et al. [14] explored the types, characteristics, and outcomes of smartphone apps for PWP. They found that the most common usage of these apps was symptom monitoring, mainly through the use of sensors or wearable devices and task performance. Focusing on the PWP, Linares-del Rey et al. [19] systematically reviewed apps available in Google Play and App Store to analyze their usability and validity. Moreover, Estévez Martín et al. [20] discovered that no single app caters to all the needs of PWPs and that individuals should choose an app based on their requirements.

Despite the abundant mobile apps that offer various functionalities to empower PWP, little attention has been given to their limitations in design. One practical solution in designing a competent mobile app for a specific group of patients is to incorporate features and facilities that have been used in other apps [21, 22]. Moreover, designing mobile apps that cater to user-required features makes them more accessible and usable [23]. In a study, Yassini and Marchand [24] proposed a use case classification model for mobile health apps, introducing 31 use cases that could serve as a primary framework for selecting features and capabilities in mobile applications. Given the importance of the limitations of PWP, this study aimed to investigate mobile phone app stores (iTunes and Google Play) to examine the usability features and capabilities of popular apps in the area of PD matched with the use case classification model. In doing so, it aims to assist developers in creating more suitable apps for PWP users by recommending important aspects of such apps.

Methods

To ensure a comprehensive and systematic evaluation of mobile applications for patients with Parkinson’s disease (PWP), we conducted an extensive search of both iTunes (App Store) and Google Play stores on February 5, 2023. Our methodology adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [25] protocol, which provided a structured framework for our app search and selection process. Notably, we applied no date restrictions to our search, allowing for the inclusion of all potentially relevant applications regardless of their release date. This approach ensured a thorough review of the entire landscape of available PWP-related apps. The selection of Google Play and iTunes as our primary sources was strategic, given that these platforms represent the two largest and most widely used app marketplaces globally. This choice facilitated access to a diverse and representative sample of mobile applications.

Inclusion criteria

• 1. Keywords in title or description: Apps with “Parkinson” or “Parkinson’s” in their title or description were included.

  Rationale: This ensures that the apps are specifically targeted towards or relevant to Parkinson’s disease, increasing the likelihood of finding apps designed for PWP.

• 2. Designed specifically for PWP

  Rationale: This criterion ensures that the apps are tailored to the unique needs and challenges faced by Parkinson’s disease patients, rather than general health apps that might have limited utility for PWP.

• 3. English interface

  Rationale: Focusing on English-language apps ensures consistency in evaluation and analysis, and caters to a wider international audience. It also facilitates easier comparison across apps.

• 4. Free apps

  Rationale: Including only free apps ensures accessibility for all users without financial barriers. This approach maximizes the potential user base and allows for a more comprehensive analysis of widely available apps.

Exclusion criteria

• 1. Apps targeting patients other than PWP

  Rationale: Excluding apps not specifically designed for PWP ensures that the study focuses on tools directly relevant to Parkinson’s disease management, avoiding dilution of results with non-specific health apps.

• 2. Apps developed for research purposes

  Rationale: Research apps may not be publicly available or may have limited functionality outside of specific studies. Excluding these ensures the focus remains on apps designed for general use by PWP.

• 3. Apps with bugs and crashes

  Rationale: Apps with significant technical issues prevent proper content analysis and would likely provide a poor user experience. Excluding these ensures that the study focuses on functional, usable apps.

To ensure consistency and comprehensiveness in our evaluation process, we implemented specific testing protocols and selection criteria. For device testing, we utilized a Samsung Galaxy A24 running Android 13 for Android applications and an iPhone 6s running iOS 15.7.9 for iOS applications. This standardized testing environment allowed for consistent assessment across all apps. In cases where an application was available on both Android and iOS platforms, we included only one version to avoid redundancy in feature analysis. When selecting between versions, we opted for the more feature-complete iteration to ensure the most comprehensive evaluation. These methodological choices collectively ensured a focused, relevant, and thorough analysis of mobile applications designed for Parkinson’s disease patients while maintaining accessibility and avoiding duplication of data.

The study process consisted of four main phases: identification, screening, eligibility, and analysis of existing apps in the knowledge base, as shown in Fig. 1. To identify various features used in mobile applications, we evaluated their characteristic items (including platform, genres, version, and developer) and constructed a primary ground-truth dataset of features that matched the use case classification model [24] in an Excel file. We then examined each installed mobile app thoroughly to identify any additional features and capabilities beyond those we already listed. If any new features were discovered, we added them to the list of features, as illustrated in Table 2 and Table 3. Then, a medical informatics expert examined each included mobile application separately and documented its specific features in the prepared table list.

Fig. 1

PRISMA process to identify apps for inclusion in this study. PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-analyzes

To ascertain the importance of each feature related to the PWP, we employed a two-pronged approach. First, we conducted a frequency analysis (FA) of each feature's usage in mobile applications. Second, we sought the guidance of six experts (EA), comprising two professionals from each field of medicine, computer or biomedical engineering, and medical informatics. They lent us their expertise in evaluating the significance of each feature. They evaluated each desirable feature in mobile apps related to PD separately, assigning a score of 1 (inappropriate) to 5 (excellent) based on the standard 5-point Likert rating method [26]. To determine the final score (FS) for each feature, we calculated the average score assigned by the experts, as shown in Table 2. Moreover, we ranked the included mobile applications based on the number of features used in each of them and the final score given by experts according to the framework outlined in the Feature-based Application Rating Method (FARM) [27]. FARM is a specialized evaluation tool designed to assess mobile applications based on their specific features and capabilities [27]. Unlike more general app evaluation methods, FARM focuses on identifying and rating the presence and quality of features that are particularly relevant to the target user group—in this case, patients with Parkinson’s disease.

Results

Our search on Google Play and App Store found a total of 424 apps, of which 255 were retrieved from Google Play and 169 from App Store. To provide a visual representation of the research findings, Fig. 1 offers an overview of the application selection process. After screening out duplicates (n = 87) and non-English apps (n = 232), we reviewed the remaining 91 apps according to the inclusion criteria. Finally, we evaluated the characteristics of 33 related PWP apps and documented our findings in Table 1. In total, 35% of the included apps were compatible with only iOS (n = 11), 21% were exclusively designed for Android (n = 7), and 45.5% could be installed on both platforms (n = 15). Accordingly, based on the respective genres of the included apps, 75.5% of the total Apps (n = 25) focused on health and fitness. This was followed by medicine, which constituted 36.5% of the apps (n = 12). Lifestyle Apps constituted 12% of the apps (n = 4), while PWP apps accounted for 9% (n = 3) and 3% (n = 1) of the evaluated apps, respectively. Additionally, 36.5% of the apps (n = 12) were categorized into more than one genre. The included apps were developed by a variety of developers, including Point of Care (n = 3, 9%), Belles Farm (n = 2, 6%), and Vilimed (n = 2, 6%). Moreover, each program included its version number for easier access to its features.

Table 1 Characteristics of the included apps

The list of features/capabilities extracted from mobile apps used for Parkinson's patients and those included in Yasini’s classification model are presented in Table 2. Of the 20 features/capabilities, 10 were based on the use case classification model. The most commonly used features among the 33 included apps were 'help for each section' and 'large font' features (n = 23). The second most commonly used feature was the 'Calculate and/or interpret data*' use case (n = 18). According to the experts, the 'large font' and 'Monitor And/OR collect data*' features are considered to be more important. Accordingly, Based on both frequency (FA) and expert (EA) assessment approaches, the 'Searching a database (for drug, image, nutrition, etc.)*' feature in mobile apps for Parkinson's disease has low significance as its frequency of use was 1% and the expert's score was 1.17. Moreover, Table 2 illustrates that the features we identified in this study are more significant than Yasini's features, according to both assessment approaches. In the frequency analysis, the average scores of features identified by Yasini and in this study are 6.6 (5.64) and 10.4 (8.51), respectively. However, the scores given by experts were 2.65 (1.13) and 3.38 (0.66), respectively.

Table 2 Frequency of features in the included apps

Table 3 presents the mobile applications included in this study, along with their respective features and rankings using the FARM method. Each application employs specific capabilities, and none uses all features simultaneously. The average score (standard deviation) of all apps related to PWP in this study was calculated as 17.71 (SD = 7.92) using the FARM method. Moreover, the applications named "Swiss Parkinson", "PD Me" and "Reach your Peak" ranked first to third among all the programs studied related to Parkinson's disease, receiving the highest scores of 31, 30.17, and 26.67, respectively. In contrast, "Parkinson's property" received the lowest score of 2.67.

Table 3 A. The list of mobile applications and the specific features utilized within them and B. Ranking of mobile applications using the FARM method [27]

Table 4 summarizes the statistical data about mobile app scoring using the FARM method. The range of scores obtained by PWP mobile apps was between 2.67 and 31, with an average score of 17.7. Based on the calculated negative skewness (-0.23), the majority (n = 17) of PWP's app scores were above the average (17.7).

Table 4 Statistical analysis of mobile application scores related to Parkinson's disease using the FARM method

Discussion

The study aimed to identify the key features and capabilities prioritized in developing mobile applications for (PWP) and categorize them by the use case classification model. Combining the features of Yasini's model and the features we found in the review of mobile apps resulted in a set of 20 features for mobile apps related to Parkinson's disease. These features could be incorporated into mobile phone applications to address the needs of individuals diagnosed with Parkinson's disease. Features that we identified in this study are more repeatable than the features presented in Yasini's model [24], and they are more intriguing to experts. Furthermore, we utilized the FARM method to score each identified feature. Based on the number of features used in each mobile application and their determined score, we ranked the applications related to Parkinson's disease. Our finding showed a significant correlation between the number of features employed in mobile applications and their ultimate score.

One of the key findings of the study is that none of the included apps utilized all available features/capabilities simultaneously. In mobile app development, commonly used features vary by context and disease type, leading developers to prioritize certain features over others [28, 29]. For instance, mobile applications designed for individuals with Parkinson's disease who struggle with movement problems commonly utilize 'large font' features, as demonstrated in our study. People with Alzheimer's disease who experience short-term memory loss find 'voice or video lecture/tutorial' features more helpful [28]. In the field of self-management for rare diseases,' monitoring data' features are the most commonly used [30].

Another finding of our study is that a significant proportion of mobile applications are accessible on both the Android and iOS platforms. These findings were also corroborated in a review article examining mobile applications related to rare diseases [30], as well as an article focusing on patients experiencing persistent pain [31]. Offering a mobile application on various platforms increases development costs, but it enhances accessibility for a wider user.

Moreover, the majority of PWP applications can be downloaded free of charge. A study on the usage of health applications among mobile phone users in the United States demonstrated that users are primarily concerned with the cost and are hesitant to pay for such applications [32]. As such, offering these health applications for free download is deemed prudent for providing patients with greater empowerment.

The implications of these findings are significant for the design and development of future mobile applications tailored to the needs of Parkinson's patients. Mobile technologies play an important role in improving the quality of life of Parkinson's patients [19], and there is a need for an integrated list of specific capabilities for Parkinson's patient-focused mobile applications. The findings of this study provide valuable insights for app developers, healthcare professionals, and researchers on how to integrate the identified features to create more effective and efficient mobile applications for Parkinson's patients. By categorizing the features and capabilities, the study contributes to a better understanding of the specific needs of Parkinson's patients and provides a framework for the development of patient-focused mobile apps.

This study had three limitations. First, the exclusive focus on English-language apps available on iTunes and Google Play may not provide a comprehensive representation of the full range of mobile applications and features designed for Parkinson's patients. While it appears that non-English-language mobile applications available in other local stores have minimal impact on the identified features, we recommend that future studies conduct a comprehensive examination of all the applications. Second, we exclusively identified features that are primarily beneficial for developers in the development of mobile applications, but future research could prioritize the impact of these features on the overall well-being and management of symptoms in PWP. Finally, we excluded a small number of mobile applications that required payment to download. Future research could consider including both free and paid applications to provide a more comprehensive analysis of all available features for Parkinson’s disease patients.

Conclusion

Our systematic search of iTunes and Google Play stores to identify relevant apps for Parkinson’s patients has yielded valuable insights into the most effective features and capabilities for these applications. We identified a total of 20 key features, with ‘large font’, ‘help for each section’, and ‘Calculate and/or interpret data’ being the most commonly implemented across the 33 apps we evaluated. Notably, expert assessment highlighted ‘large font’ and ‘Monitor and/or collect data’ as the most important features for PWP apps.

The study revealed that while many apps incorporate multiple features, none utilized all identified capabilities simultaneously, suggesting room for more comprehensive app development. We found that 45.5% of the apps were available on both Android and iOS platforms, potentially increasing accessibility for users. Additionally, our ranking system using the FARM method showed an average score of 17.71 (SD = 7.92) for all evaluated apps, with ‘Swiss Parkinson’, ‘PD Me’, and ‘Reach your Peak’ ranking as the top three applications.

The findings of our study provide a robust foundation for the design and development of mobile applications tailored to the specific needs of Parkinson’s patients, offering valuable guidance for both the creation of new apps and the enhancement of existing ones, thereby enabling developers to craft more impactful solutions that deliver targeted support to individuals living with Parkinson’s disease. We recommend that future app development for Parkinson’s patients prioritize the integration of highly-rated features such as large fonts, data interpretation capabilities, and separate help sections. Moreover, cross-platform development strategies should be considered to enhance user engagement and market penetration.

Our research contributes to the growing body of knowledge on PD and underscores the importance of information technology in enhancing the well-being of patients. Future studies could build on these findings by investigating the impact of these features on patient outcomes and exploring emerging technologies that could further improve app functionality for PWP."