https://mhealth.jmir.org/issue/feedJMIR mHealth and uHealth2023-01-12T10:15:03-05:00JMIR Publicationseditor@jmir.orgOpen Journal Systems Unless stated otherwise, all articles are open-access distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work ("first published in JMIR mHealth and uHealth...") is properly cited with original URL and bibliographic citation information. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included. JMIR mhealth and uhealth is a new journal focussing on mobile and ubiquitous health technologies, including smartphones, augmented reality (Google Glasses), intelligent domestic devices, implantable devices, and other technologies designed to maintain health and improve life. https://mhealth.jmir.org/2024/1/e54866/ Quality Assessment of Smartphone Medication Management Apps in France: Systematic Search2024-03-18T10:45:25-04:00Mickael ToïgoJulie MarcMaurice HayotLionel MoulisFrancois Carbonnel<strong>Background:</strong> Adherence to medication is estimated to be around 50% for chronically ill patients in high-income countries. Improving the effectiveness of adherence interventions could have a far greater impact on population health than any improvement in specific medical treatments. Mobile health (mHealth) is one of the most effective solutions for helping patients improve their medication intake, notably through the use of mobile apps with reminder systems. With more than 327,000 apps available in the mHealth field, it is difficult for health care professionals and patients alike to choose which apps to recommend and use. <strong>Objective:</strong> We aim to carry out a systematic search of medication management smartphone apps available in France that send reminders to patients and assess their quality using a validated scale. <strong>Methods:</strong> Mobile apps were identified in October and November 2022 after a systematic keyword search on the 2 main app download platforms: App Store (Apple Inc) and Google Play Store. Inclusion criteria were free availability, date of last update, and availability in French. Next, 2 health care professionals independently evaluated the included apps using the French version of the Mobile App Rating Scale (MARS-F), an objective scoring system validated for assessing the overall quality of apps in the mHealth field. An intraclass correlation coefficient was calculated to determine interrater reliability. <strong>Results:</strong> In total, 960 apps were identified and 49 were selected (25 from the App Store and 24 from the Google Play Store). Interrater reliability was excellent (intraclass correlation coefficient 0.92; 95% CI 0.87-0.95; <i>P</i><.001). The average MARS-F score was 3.56 (SD 0.49) for apps on the App Store and 3.51 (SD 0.46) for those on the Google Play Store, with 10 apps scoring above 4 out of 5. Further, 2 apps were tested in at least one randomized controlled trial and showed positive results. The 2 apps with the highest ratings were <i>Mediteo rappel de médicaments</i> (Mediteo GmbH) and <i>TOM rappel medicaments, pilule</i> (Innovation6 GmbH), available on both platforms. Each app’s MARS-F score was weakly correlated with user ratings on the App Store and moderately correlated on the Google Play Store. <strong>Conclusions:</strong> To our knowledge, this is the first study that used a validated scoring system to evaluate medication management apps that send medication reminders. The quality of the apps was heterogeneous, with only 2 having been studied in a randomized controlled trial with positive results. The evaluation of apps in real-life conditions by patients is necessary to determine their acceptability and effectiveness. Certification of apps is also essential to help health care professionals and patients identify validated apps. 2024-03-18T10:45:25-04:00 https://mhealth.jmir.org/2024/1/e55177/ Assessing the Quality and Behavior Change Potential of Vaping Cessation Apps: Systematic Search and Assessment2024-03-15T14:45:11-04:00Fiona McKayLilian ChanRebecca CerioSandra RickardsPhillipa HastingsKate ReakesTracey O'BrienMatthew DunnBackground: An increasing number of people are using vapes (e-cigarettes), and with growing evidence of associated harms there is a need for acceptable cessation support and interventions. Smartphone applications (apps) for health and wellbeing have increased in popularity and use. Limited published literature exists that assesses the potential of apps to support vaping cessation. Objective: A systematic search of vaping cessation apps currently available in Australia for both iOS and Android platforms was conducted. Apps were assessed against established health-app assessment tools for quality and behaviour change potential. Methods: A systematic search through the Australian Apple iTunes and Google Play stores was conducted using the search terms of ‘vape’, ‘vaping’, ‘e-cigarette’ and ‘cessation’, ‘quit’ or ‘quitting’ in May 2023. Only apps that encouraged cessation of vaping were included. App descriptions were reviewed to determine if relevant for inclusion in this study, and relevant apps were downloaded onto the appropriate mobile device for review. The Mobile App Rating Scale (MARS) was used to rate the quality (engagement, functionality, aesthetics and information) of the apps using an overall score out of 5. The App Behaviour Change Scale (ABACUS) was used to assess the behaviour change potential of each app using a score out of 21. Results: An initial search of the app stores yielded 220 Android apps and 124 iOS apps. Screening against the inclusion criteria left 20 iOS apps and 10 Android apps for review. Six apps were available on both operating systems, and these were downloaded, reviewed, and reported separately for each operating system. The average MARS score for all apps assessed in this review was 3.1 (SD 0.41) out of 5. The reviewed apps overall performed well for the MARS elements relating to functionality, such as ease of use and navigation, but had lowest scores for information-related elements, such as credibility. The number of ABACUS behaviour-change features per app ranged from 0 to 19 out of 21, with a mean of 8.9 (SD 4.51). The apps commonly included information-related features, such as requesting baseline information. The least common behaviour-change features were those relating to goal-setting, such as asking about the user’s willingness for behaviour change, and providing feedback on current actions in comparison to future goals. Conclusions: The identified vaping cessation apps had moderate levels of quality and some behaviour change components. Future vaping cessation apps could benefit from including more features that are known to support behaviour change, such as goal-setting, to improve the potential benefit of these apps to support people to stop vaping. As guidelines for vaping cessation continue to be established, it is important for future apps to reference these in their development. 2024-03-15T14:45:11-04:00 https://mhealth.jmir.org/2024/1/e45860/ Lessons and Untapped Potential of Smartphone-Based Physical Activity Interventions for Mental Health: Narrative Review2024-03-15T09:45:04-04:00Emily E BernsteinEmma C WolfeBrynn M HuguenelSabine Wilhelm<strong>Background:</strong> Physical activity has well-known and broad health benefits, including antidepressive and anxiolytic effects. However, only approximately half of Americans meet even the minimum exercise recommendations. Individuals with anxiety, depression, or related conditions are even less likely to do so. With the advent of mobile sensors and phones, experts have quickly noted the utility of technology for the enhanced measurement of and intervention for physical activity. In addition to being more accessible than in-person approaches, technology-driven interventions may uniquely engage key mechanisms of behavior change such as self-awareness. <strong>Objective:</strong> This study aims to provide a narrative overview and specific recommendations for future research on smartphone-based physical activity interventions for psychological disorders or concerns. <strong>Methods:</strong> In this paper, we summarized early efforts to adapt and test smartphone-based or smartphone-supported physical activity interventions for mental health. The included articles described or reported smartphone-delivered or smartphone-supported interventions intended to increase physical activity or reduce sedentary behavior and included an emotional disorder, concern, or symptom as an outcome measure. We attempted to extract details regarding the intervention designs, trial designs, study populations, outcome measures, and inclusion of adaptations specifically for mental health. In taking a narrative lens, we drew attention to the type of work that has been done and used these exemplars to discuss key directions to build on. <strong>Results:</strong> To date, most studies have examined mental health outcomes as secondary or exploratory variables largely in the context of managing medical concerns (eg, cancer and diabetes). Few trials have recruited psychiatric populations or explicitly aimed to target psychiatric concerns. Consequently, although there are encouraging signals that smartphone-based physical activity interventions could be feasible, acceptable, and efficacious for individuals with mental illnesses, this remains an underexplored area. <strong>Conclusions:</strong> Promising avenues for tailoring validated smartphone-based interventions include adding psychoeducation (eg, the relationship between depression, physical activity, and inactivity), offering psychosocial treatment in parallel (eg, cognitive restructuring), and adding personalized coaching. To conclude, we offer specific recommendations for future research, treatment development, and implementation in this area, which remains open and promising for flexible, highly scalable support. 2024-03-15T09:45:04-04:00 https://mhealth.jmir.org/2024/1/e49055/ Mobile Apps for Common Noncommunicable Disease Management: Systematic Search in App Stores and Evaluation Using the Mobile App Rating Scale2024-03-12T15:45:10-04:00Khang Jin CheahZahara Abdul ManafArimi Fitri Mat LudinNurul Huda RazalliNorfilza Mohd MokhtarSawal Hamid Md AliBackground: The success of mobile apps in improving the lifestyle of patients with non-communicable diseases through self-management interventions is contingent upon the emerging growth in this field. While users of mobile health apps continue to grow in number, little is known about the quality of the available mobile health (mHealth) apps that provide self-management for common non-communicable diseases such as diabetes, hypertension and obesity. Objective: We aimed to investigate the availability, characteristics and quality of mHealth for common non-communicable diseases health management that include dietary aspects based on the developer’s description and their features for promoting health outcomes and self-monitoring. Methods: A systematic search of apps available in English on Google Play Store (Google LLC) and Apple App Store (Apple Inc) was conducted between August 7th, 2022 and September 13th, 2022. The search terms used were weight loss, obesity, diabetes, hypertension and cardiovascular diseases, stroke, weight management and diet.The selected mHealth apps’ titles and content were screened based on the description that was provided. An app that was not designed with self-management features was excluded. We analysed the mHealth apps category, the involvement of healthcare professionals, scientific testing, and self-monitoring features. The search terms were weight loss, obesity, diabetes, hypertension and cardiovascular diseases, stroke, weight management and diet. A proven and multi-dimensional tool called Mobile App Rating Scale (MARS) was used to evaluate each mHealth apps’ quality. Results: Overall, 42 apps were identified. About 38.1% of the apps were for managing chronic diseases, while 61.9% were for weight management. Diabetes-specific mHealth apps designed account for 4.8% of the market, 11.9% for hypertension apps and 21.4% for general non-communicable diseases (NCDs) management. Self-management features such as weight tracking, BMI calculator, diet tracking and fluid intake were seen in 85.7% of the apps. Most mHealth apps (88.1%) did not indicate whether there is involvement of health professionals in the app development. Additionally, none of the apps reported scientific evidence demonstrating their efficacy in managing health. On the MARS Scale, overall mean score was 3.2 over 5 with the range of 2.0-4.1. Functionality was the best-rated category—3.9 (SD=0.5), followed by aesthetics—3.2 (0.9), information—3.1 (0.7) and engagement—2.9 (0.6). Conclusions: The quality of mHealth apps for managing chronic diseases are heterogeneous with roughly half of them falling short of acceptable standard for both quality and content. The majority of apps contain scant information about scientific evidence and the developer’s history. To increase user confidence and accomplish desired health outcomes, mHealth apps should be optimized with the help of healthcare professional. Future studies on mHealth content analysis should focus on other diseases as well. 2024-03-12T15:45:10-04:00 https://mhealth.jmir.org/2024/1/e50135/ Feasibility Study on Menstrual Cycles With Fitbit Device (FEMFIT): Prospective Observational Cohort Study2024-03-12T10:00:05-04:00Anna-Lena LangRosa-Lotta BruhnMaya FehlingAnouk HeidenreichJonathan ReisdorfIfrah KhanyareeMaike HenningsenCornelius Remschmidt<strong>Background:</strong> Despite its importance to women’s reproductive health and its impact on women’s daily lives, the menstrual cycle, its regulation, and its impact on health remain poorly understood. As conventional clinical trials rely on infrequent in-person assessments, digital studies with wearable devices enable the collection of longitudinal subjective and objective measures. <strong>Objective:</strong> The study aims to explore the technical feasibility of collecting combined wearable and digital questionnaire data and its potential for gaining biological insights into the menstrual cycle. <strong>Methods:</strong> This prospective observational cohort study was conducted online over 12 weeks. A total of 42 cisgender women were recruited by their local gynecologist in Berlin, Germany, and given a Fitbit Inspire 2 device and access to a study app with digital questionnaires. Statistical analysis included descriptive statistics on user behavior and retention, as well as a comparative analysis of symptoms from the digital questionnaires with metrics from the sensor devices at different phases of the menstrual cycle. <strong>Results:</strong> The average time spent in the study was 63.3 (SD 33.0) days with 9 of the 42 individuals dropping out within 2 weeks of the start of the study. We collected partial data from 114 ovulatory cycles, encompassing 33 participants, and obtained complete data from a total of 50 cycles. Participants reported a total of 2468 symptoms in the daily questionnaires administered during the luteal phase and menses. Despite difficulties with data completeness, the combined questionnaire and sensor data collection was technically feasible and provided interesting biological insights. We observed an increased heart rate in the mid and end luteal phase compared with menses and participants with severe premenstrual syndrome walked substantially fewer steps (average daily steps 10,283, SD 6277) during the luteal phase and menses compared with participants with no or low premenstrual syndrome (mean 11,694, SD 6458). <strong>Conclusions:</strong> We demonstrate the feasibility of using an app-based approach to collect combined wearable device and questionnaire data on menstrual cycles. Dropouts in the early weeks of the study indicated that engagement efforts would need to be improved for larger studies. Despite the challenges of collecting wearable data on consecutive days, the data collected provided valuable biological insights, suggesting that the use of questionnaires in conjunction with wearable data may provide a more complete understanding of the menstrual cycle and its impact on daily life. The biological findings should motivate further research into understanding the relationship between the menstrual cycle and objective physiological measurements from sensor devices. 2024-03-12T10:00:05-04:00 https://mhealth.jmir.org/2024/1/e52996/ Quality and Accessibility of Home Assessment mHealth Apps for Community Living: Systematic Review2024-03-11T12:00:05-04:00Jung-hye ShinRachael ShieldsJenny LeeZachary SkroveRoss TredinnickKevin PontoBeth Fields<strong>Background:</strong> Home assessment is a critical component of successful home modifications, enabling individuals with functional limitations to age in place comfortably. A high-quality home assessment tool should facilitate a valid and reliable assessment involving health care and housing professionals, while also engaging and empowering consumers and their caregivers who may be dealing with multiple functional limitations. Unlike traditional paper-and-pencil assessments, which require extensive training and expert knowledge and can be alienating to consumers, mobile health (mHealth) apps have the potential to engage all parties involved, empowering and activating consumers to take action. However, little is known about which apps contain all the necessary functionality, quality appraisal, and accessibility. <strong>Objective:</strong> This study aimed to assess the functionality, overall quality, and accessibility of mHealth home assessment apps. <strong>Methods:</strong> mHealth apps enabling home assessment for aging in place were identified through a comprehensive search of scholarly articles, the Apple (iOS) and Google Play (Android) stores in the United States, and fnd.io. The search was conducted between November 2022 and January 2023 following a method adapted from PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Reviewers performed a content analysis of the mobile app features to evaluate their functionality, overall quality, and accessibility. The functionality assessment used a home assessment component matrix specifically developed for this study. For overall quality, the Mobile Application Rating Scale (MARS) was used to determine the apps’ effectiveness in engaging and activating consumers and their caregivers. Accessibility was assessed using the Web Content Accessibility Guidelines (WCAG) 2.1 (A and AA levels). These 3 assessments were synthesized and visualized to provide a comprehensive evaluation. <strong>Results:</strong> A total of 698 apps were initially identified. After further screening, only 6 apps remained. Our review revealed that none of the apps used thoroughly tested assessment tools, offered all the functionality required for reliable home assessment, achieved the “good” quality threshold as measured by the MARS, or met the accessibility criteria when evaluated against WCAG 2.1. However, DIYModify received the highest scores in both the overall quality and accessibility assessments. The MapIt apps also showed significant potential due to their ability to measure the 3D environment and the inclusion of a desktop version that extends the app’s functionality. <strong>Conclusions:</strong> Our review revealed that there are very few apps available within the United States that possess the necessary functionality, engaging qualities, and accessibility to effectively activate consumers and their caregivers for successful home modification. Future app development should prioritize the integration of reliable and thoroughly tested assessment tools as the foundation of the development process. Furthermore, efforts should be made to enhance the overall quality and accessibility of these apps to better engage and empower consumers to take necessary actions to age in place. 2024-03-11T12:00:05-04:00 https://mhealth.jmir.org/2024/1/e48986/ The Roles of Trust in Government and Sense of Community in the COVID-19 Contact Tracing Privacy Calculus: Mixed Method Study Using a 2-Wave Survey and In-Depth Interviews2024-03-07T10:30:04-05:00Hyunjin KangJeong Kyu LeeEdmund WJ LeeCindy Toh<strong>Background:</strong> Contact tracing technology has been adopted in many countries to aid in identifying, evaluating, and handling individuals who have had contact with those infected with COVID-19. Singapore was among the countries that actively implemented the government-led contact tracing program known as TraceTogether. Despite the benefits the contact tracing program could provide to individuals and the community, privacy issues were a significant barrier to individuals’ acceptance of the program. <strong>Objective:</strong> Building on the privacy calculus model, this study investigates how the perceptions of the 2 key groups (ie, government and community members) involved in the digital contact tracing factor into individuals’ privacy calculus of digital contact tracing. <strong>Methods:</strong> Using a mixed method approach, we conducted (1) a 2-wave survey (n=674) and (2) in-depth interviews (n=12) with TraceTogether users in Singapore. Using structural equation modeling, this study investigated how trust in the government and the sense of community exhibited by individuals during the early stage of implementation (time 1) predicted privacy concerns, perceived benefits, and future use intentions, measured after the program was fully implemented (time 2). Expanding on the survey results, this study conducted one-on-one interviews to gain in-depth insights into the privacy considerations involved in digital contact tracing. <strong>Results:</strong> The results from the survey showed that trust in the government increased perceived benefits while decreasing privacy concerns regarding the use of TraceTogether. Furthermore, individuals who felt a connection to community members by participating in the program (ie, the sense of community) were more inclined to believe in its benefits. The sense of community also played a moderating role in the influence of government trust on perceived benefits. Follow-up in-depth interviews highlighted that having a sense of control over information and transparency in the government’s data management were crucial factors in privacy considerations. The interviews also highlighted surveillance as the most prevalent aspect of privacy concerns regarding TraceTogether use. In addition, our findings revealed that trust in the government, particularly the perceived transparency of government actions, was most strongly associated with concerns regarding the secondary use of data. <strong>Conclusions:</strong> Using a mixed method approach involving a 2-wave survey and in-depth interview data, we expanded our understanding of privacy decisions and the privacy calculus in the context of digital contact tracing. The opposite influences of privacy concerns and perceived benefit on use intention suggest that the privacy calculus in TraceTogether might be viewed as a rational process of weighing between privacy risks and use benefits to make an uptake decision. However, our study demonstrated that existing perceptions toward the provider and the government in the contact tracing context, as well as the perception of the community triggered by TraceTogether use, may bias user appraisals of privacy risks and the benefits of contact tracing. 2024-03-07T10:30:04-05:00 https://mhealth.jmir.org/2024/1/e52968/ The Real-World Impact of App-Based Mindfulness on Headspace Members With Moderate and Severe Perceived Stress: Observational Study2024-03-04T17:15:10-05:00Christine CallahanJustin KimberEmily HuLeah TannerSarah KunkleBackground: Perceived stress in the United States has drastically increased since the COVID-19 pandemic and is associated with negative mental health outcomes such as depression and anxiety. Digital mental health (DMH) interventions are efficacious tools to address negative mental health outcomes and have helped reduce psychological symptom severity such as anxiety, depression, and perceived stress compared to waitlist controls. Although DMH tools have been studied in controlled settings, less is known about the real-world evidence of such interventions. Objective: 1) Characterize patterns in baseline perceived stress and changes in perceived stress among Headspace members with moderate and severe baseline perceived stress; and 2) examine associations between engagement with Headspace content and changes in perceived stress (evaluate if there is a dose-response relationship). Methods: We evaluated two timepoints of real-world perceived stress and engagement data from Headspace app members with baseline moderate and severe perceived stress. Perceived stress was measured using the Perceived Stress Scale (PSS-10) and engagement using active days and active minutes engaged with Headspace as well as number of user sessions. Descriptive statistics were computed for all variables. Correlations examined relationships between PSS-10 baseline and follow-up scores, PSS-10 percent change, days between PSS-10, active days, active days/week, active minutes, active minutes/day, sessions, and sessions/week. T-tests investigated the differences in baseline PSS-10, follow-up PSS-10, PSS-10 percent change, active days, active days/week, active minutes, active minutes/day, sessions, and sessions/week between: 1) those who did and did not improve PSS-10 scores (yes vs. no improvement); and 2) those who improved ≥30% vs. those with <30% improvement. Results: Overall 21,088 Headspace members were included in these analyses. On average, members saw a 23.52% decrease in PSS-10 scores from baseline to follow-up. On average members had 2.42±1.76 active days/week, 25.89±33.40 active minutes/day, and completed 7.11±8.34 sessions/week. T-tests suggest that members who improved PSS-10 scores from baseline to follow-up had significantly higher baseline PSS-10 scores (d=0.56), more active days/week (d=0.33), and more sessions/week (d=0.27) than those who did not improve PSS-10 scores (all P<0.001). Additional t-tests suggest that members with ≥30% PSS-10 improvement had significantly higher baseline PSS-10 scores (d=0.35), more active days/week (d=0.36), and more sessions/week (d=0.31) than those with >30% PSS-10 improvement (all P<0.001). Conclusions: Findings suggest that real-world use of Headspace is associated with decreased perceived stress. Furthermore, data suggest that more engagement, specifically weekly active days and sessions, is associated with greater likelihood of stress reduction. 2024-03-04T17:15:10-05:00 https://mhealth.jmir.org/2024/1/e55003/ Evaluation of Patient-Facing Mobile Apps to Support Physiotherapy Care: Systematic Review2024-03-04T10:15:03-05:00Mark MerolliJill J FrancisPatrick VallanceKim L BennellPeter MalliarasRana S Hinman<strong>Background:</strong> Mobile health interventions delivered through mobile apps are increasingly used in physiotherapy care. This may be because of the potential of apps to facilitate changes in behavior, which is central to the aims of care delivered by physiotherapists. A benefit of using apps is their ability to incorporate behavior change techniques (BCTs) that can optimize the effectiveness of physiotherapeutic interventions. Research continues to suggest that despite their importance, behavior change strategies are often missing in patient management. Evaluating mobile apps that physiotherapists can use to drive behavior change may inform clinical practice and potentially improve patient outcomes. Examining the quality of apps and exploring their key features that can support behavior change and physiotherapy care are important aspects of such an evaluation. <strong>Objective:</strong> The primary aim of this study was to describe the range of mobile apps in app stores that are intended for use by patients to support physiotherapy care. The secondary aims were to assess app quality, BCTs, and their behavior change potential. <strong>Methods:</strong> A systematic review of mobile apps in app stores was undertaken. The Apple App Store and Google Play were searched using a 2-step search strategy, using terms relevant to the physiotherapy discipline. Strict inclusion and exclusion criteria were applied: apps had to be intended for use by patients and be self-contained (or stand-alone) without the requirement to be used in conjunction with a partner wearable device or another plugin. Included apps were coded for BCTs using the Behavior Change Technique Taxonomy version 1. App quality was assessed using the Mobile App Rating Scale, and the App Behavior Change Scale was used to assess the app’s potential to change behavior. <strong>Results:</strong> In total, 1240 apps were screened, and 35 were included. Of these 35 apps, 22 (63%) were available on both the Apple App Store and Google Play platforms. In total, 24 (69%) were general in their focus (eg, not condition-specific), with the remaining 11 (31%) being more specific (eg, knee rehabilitation and pelvic floor training). The mean app quality score (Mobile App Rating Scale) was 3.7 (SD 0.4) of 5 (range 2.8-4.5). The mean number of BCTs identified per app was 8.5 (SD 3.6). BCTs most frequently included in the apps were instruction on how to perform a behavior (n=32), action planning (n=30), and self-monitoring of behavior (n=28). The mean behavior change potential score (App Behavior Change Scale) was 8.5 (SD 3.1) of 21 (range 3-15). <strong>Conclusions:</strong> Mobile apps available to support patient care received from a physiotherapist are of variable quality. Although they contain some BCTs, the potential for behavior change varied widely across apps. 2024-03-04T10:15:03-05:00 https://mhealth.jmir.org/2024/1/e44406/ Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review2024-02-22T10:00:04-05:00Mehdi GheisariMustafa GhaderzadehHuxiong LiTania TaamiChristian Fernández-CampusanoHamidreza SadeghsalehiAaqif Afzaal Abbasi<strong>Background:</strong> In the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap. <strong>Objective:</strong> With the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic. <strong>Methods:</strong> In this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19 were chosen after applying inclusion and exclusion criteria using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. <strong>Results:</strong> Mobile apps were categorized into 6 areas based on the content of these 42 studies: contact tracing, data gathering, data visualization, artificial intelligence (AI)–based diagnosis, rule- and guideline-based diagnosis, and data transformation. Patients with COVID-19 were identified via mobile apps using a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. Most studies concentrated on using AI methods to identify people who might have COVID-19. Additionally, symptoms, cough sounds, and radiological images were used more frequently compared to other data types. Deep learning techniques, such as convolutional neural networks, performed comparatively better in the processing of health care data than other types of AI techniques, which improved the diagnosis of COVID-19. <strong>Conclusions:</strong> Mobile apps could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and the early identification of suspected cases. These technologies can work with the internet of things, cloud storage, 5th-generation technology, and cloud computing. Processing pipelines can be moved to mobile device processing cores using new deep learning methods, such as lightweight neural networks. In the event of future pandemics, mobile apps will play a critical role in rapid diagnosis using various image data and clinical symptoms. Consequently, the rapid diagnosis of these diseases can improve the management of their effects and obtain excellent results in treating patients. 2024-02-22T10:00:04-05:00