Developments in Privacy and Data Ownership in Mobile Health Technologies, 2016-2019

In gynecological care, mHealth (mobile health) technology may play an important role. Medical professionals' willingness to use this technology is the key to its acceptance. Most doctors utilize mobile health technology; however, there is still room for improvement in the use of mHealth. Gynecologists were asked to participate in this research to see how open they were to use mobile health technologies. In this descriptive-analytical investigation, the researchers determined the average scores for each variable. The overall mean for preparedness to embrace mobile medical technology is 1.8 out of 2, as shown in Table 1. When it came to their desire to embrace mobile health technology, doctors' years of experience correlated negatively with their age. According to our findings, the amount of interest in mobile health technology is high. Patients' private information must be protected throughout the usage of this technology though. Mobile health technology may effectively reach patients in remote areas, but it is not a substitute for face-to-face encounters with medical professionals.

Patients with head and neck cancer (HNC) often experience various types and degrees of complications and functional impairment following surgery or radiotherapy. Consequently, these patients require extensive postdischarge rehabilitation, either at home or in the community. Numerous studies have shown the advantages of mobile Health (mHealth) technology in assisting patients with cancer with self-management and rehabilitation during the postdischarge period. However, few reviews have focused on the intervention, management, and evaluation of mHealth technology in postdischarge patients with HNC. This study aimed to conduct a scoping review of mHealth technology apps and interventions currently available to patients discharged from hospitals after receiving treatment for HNC. This study sought to identify and summarize the types and effectiveness of existing mHealth interventions as well as the differences in their outcome assessments. The PubMed, Embase, Web of Science, and CINAHL databases were used to identify studies with no publication time limits. The keywords "mobile health technology" and "head and neck cancer" were combined to address the main concepts of the research questions. Of the 1625 papers identified, 13 (0.8%) met the inclusion and exclusion criteria. Most studies (n=8, 61.5%) were randomized controlled trials (RCTs) and cohort studies. These studies were conducted in 6 countries. The main aims of the mHealth interventions in these studies are as follows: (1) symptom monitoring and assessment, (2) rehabilitation training, (3) access to medical health information, (4) telehealth advisers, (5) peer communication and support, and (6) follow-up/review reminders. The outcome evaluations of the 13 included studies were grouped into 4 categories: (1) technology usability and patient satisfaction, (2) self-management of symptoms and patient-reported outcome-related indicators, (3) adherence, and (4) health-related quality of life. A limited number of studies have investigated the use of mHealth technology in the postdischarge self-management of patients with HNC. The existing literature suggests that mHealth technology can effectively assist patients with HNC in self-management and postdischarge interventions. It plays an important role in addressing patients' health information needs, reducing both their somatic and psychological burdens, and improving their overall quality of life. Future research should prioritize conducting additional high-quality RCTs to evaluate the usability and analyze the cost-effectiveness of mHealth technology.

BackgroundPulse oximeter apps became of interest to consumers during the COVID-19 pandemic, particularly when traditional over-the-counter pulse oximeter devices were in short supply. Yet, no study to date has examined or scoped the state of privacy policies and notices for the top-rated and most downloaded pulse oximeter apps during COVID-19.ObjectiveThe aim of this study was to examine, through a high-level qualitative assessment, the state and nature of privacy policies for the downloaded and top-rated pulse oximeter apps during the COVID-19 pandemic to (1) compare findings against comparable research involving other mobile health (mHealth) apps and (2) begin discussions on opportunities for future research or investigation.MethodsDuring August-October 2020, privacy policies were reviewed for pulse oximeter apps that had either at least 500 downloads (Google Play Store apps only) or a three out of five-star rating (Apple Store apps only). In addition to determining if the apps had an accessible privacy policy, other key privacy policy–related details that were extracted included, but were not limited to, app developer location (country); whether the app was free or required paid use/subscription; whether an ads disclosure was provided on the app’s site; the scope of personal data collected; proportionality, fundamental rights, and data protection and privacy issues; and privacy safeguards.ResultsSix pulse oximeter apps met the inclusion criteria and only 33% (n=2) of the six apps had an accessible privacy policy that was specific to the pulse oximeter app feature (vs the app developer’s website or at all). Variation was found in both the regulatory nature and data privacy protections offered by pulse oximeter apps, with notable privacy protection limitations and gaps, although each app provided at least some information about the scope of personal data collected upon installing the app.ConclusionsPulse oximeter app developers should invest in offering stronger privacy protections for their app users, and should provide more accessible and transparent privacy policies. This is a necessary first step to ensure that the data privacy of mHealth consumers is not exploited during public health emergency situations such as the COVID-19 pandemic, where over-the-counter personal health monitoring devices could be in short supply and patients and consumers may, as a result, turn to mHealth apps to fill such supply gaps. Future research considerations and recommendations are also suggested for mHealth technology and privacy researchers who are interested in examining privacy implications associated with the use of pulse oximeter apps during and after the COVID-19 pandemic.

The Quest for Information Privacy in Africa: A Review Essay

Background: Mobile health has become widely used within the healthcare system, and there is an increasing worldwide trend toward employing this innovation for behavior management, disease monitoring, the control and prevention of various health issues, and rising enrollment in healthcare services. Although mHealth is becoming more widely available, there is no evidence about the attitude of healthcare professionals toward mHealth in southwest Ethiopia. Therefore, this study is aimed at assessing the attitude of healthcare professionals to using mHealth technology and associated factors in Ethiopia. Methods: An institutional cross-sectional study was conducted among 422 healthcare professionals. Data were collected using a pretested interviewer-administered questionnaire, and the study was conducted from January 08 to February 10, 2023. EpiData Version 4.6 for entering the data and STATA Version 14 for analyzing the data were used. A multivariable logistic regression analysis was carried out to identify factors associated with healthcare professionals' attitudes to using mobile health technology. Results: A total of 415 study participants were included in the study. About 180 (43.4%) respondents had a favorable attitude toward mHealth technology in southwest public hospitals. Master's degree and above (adjusted odds ratio [AOR]: 3.67; 95% CI: 1.22, 4.10), good knowledge of mobile health technology (AOR: 4.08; 95% CI: 1.35, 5.31), more than 5 years of work experience (AOR: 3.09; 95% CI: 1.76, 5.60), had ICT infrastructure (AOR: 2.70; 95% CI: 1.38, 5.31), had own smart mobile (AOR: 3.67; 95% CI: 3.20, 4.31), and had taken computer-related training (AOR: 1.96; 95% CI: 1.03, 3.73) were positively associated with healthcare professionals' attitude to using mobile health technologies in southwest Ethiopia. Conclusions: Overall, healthcare professionals' attitude to using mobile health technologies in southwest Ethiopia was relatively low. Education level, good knowledge, years of work experience, ICT infrastructure, having a smart mobile, and having taken computer-related training were significant factors of attitude to using mobile health technologies. Considering these factors could provide insight into developing and adopting mobile health technologies in Ethiopia.

Background/Aims People with rheumatoid arthritis (RA) commonly experience pain and stiffness which reduces engagement in physical activity (PA). The range of health benefits associated with PA are now well established, and include improvements in cardiovascular health and symptoms associated with RA. However, people with RA are more inactive than the general population and face unique barriers to becoming more active. Mobile health (mHealth) technology (i.e. fitness watches and apps) may provide an effective and economical solution to promoting PA in this population. Therefore, the aim of the current study is to assess the effectiveness of mHealth technology to increase PA levels and reduce disease activity in people living with RA. Methods Following ethical approval, 13 females diagnosed with RA were randomised to either an mHealth (n=7, age 47 ± 14 years; BMI 30 ± 5 kg/m2) or a conventional care (CC) intervention group (n=6, age 50 ± 11 years; BMI 25 ± 3 kg/m2). The CC group were provided with online educational material to increase PA whereas the mHealth group were provided with a personalised, progressive walking programme delivered via mHealth technology with ongoing support throughout the 12-week intervention. Prior to (week 0) and following (week 12) the intervention, participants conducted assessments remotely, with guidance from the research team. At each time point all participants wore a GT9X ActiGraph PA monitor for 7 days to quantify PA and completed a RADAI-5 questionnaire to monitor disease activity Change data between weeks 0 and 12 were analysed using independent t-tests and reported as mean difference (95% confidence intervals). Results Intervention groups were well matched in terms of age [3 years (95% CI= -12, 19); P=0.66] but differed for BMI [-5 kg/m2 (95% CI= -10, -0.1); P=0.05]. Preliminary analyses reveal no significant difference in steps per day between the mHealth vs. CC groups [323 steps/day (95% CI= -2273, 1632; P=0.72]. Similarly, no difference in RADAI-5 score was observed between the mHealth and CC groups [0.09 (95% CI= -0.28, 4.6); P=0.61]. Conclusion These preliminary data identify the potential effectiveness of a PA programme to increase PA levels in people with RA delivered via mHealth, though positive changes in mHealth are very small at this early stage. These data, in full, will be important in identifying whether this technology could be used as a cost effective, non-pharmacological management strategy for RA. Disclosure D.J. Bannell: None. M. Cocks: None. N. Goodson: None. C. Taylor: None. T. Peçanha: None. H. Jones: None. D.A. Low: None. V.S. Sprung: None.

Background/Aims People living with rheumatoid arthritis (RA) tend to be less active than the general population which may be due to RA associated musculoskeletal pain, stiffness and fatigue. The health benefits of physical activity (PA) are widely recognised in the general population and studies in RA have also highlighted improvement in RA associated symptoms. However, encouraging increased PA in people living with RA is challenging and adherence to PA is usually low. Mobile health (mHealth) technology (i.e., fitness watches and apps) may provide an effective and economical solution to promoting PA in this high-risk population. The aims of the current study were to assess whether use of mHealth technology in RA i) increases PA levels and ii) reduces RA disease activity. Methods People with RA were randomised to either the MOTIVATE (mHealth intervention) or conventional care (CC) groups. The CC group were provided with online educational materials, as provided in routine care (e.g., PA information leaflets), to increase PA whereas the MOTIVATE group were provided with a personalised, progressive walking programme delivered via mHealth technology with ongoing, individualised support throughout the 12-week intervention. All participants received a self-testing kit, via mail, at baseline (week 0) and post-intervention (12 weeks). Measures included anthropometrics, blood pressure, PA monitoring, health questionnaires and all were requested to self-report RA disease activity using RADAI-5. Change data between weeks 0 and 12 were compared between groups using One-Way ANCOVA, controlling for baseline, and reported as mean difference (95% CI). Results Of 50 participants, 25 were randomised to the MOTIVATE group (age 50±11years; BMI 29±7kg/m2; females n=23), and 25 to CC (n=25, age 50±11years; BMI 26±6 kg/m2; females n=24). An increase in moderate-to-vigorous PA was observed in the MOTIVATE group, compared to a decrease in CC [81min (1, 160) vs. -35min (-123, 53); P=0.09)]. This was accompanied by an increase in steps per day in the MOTIVATE group, and a decrease in CC [763 (-432, 1957) vs. -76 (-1267, 1115); P=0.40]. A reduction in PHQ-9 depression score was evident in the MOTIVATE group whilst CC were unchanged [-2 (-2, -3) vs. 0 (-2, 0); P=0.07]. RADAI-5 decreased in the MOTIVATE group and stayed the same in CC [-2 (-2, 0) vs. 0(-2, 0), P=0.27]. Conclusion These findings identify the potential effectiveness of an mHealth PA programme to increase PA levels in people living with RA. Use of remote PA monitoring, with support from exercise specialists, may be an effective strategy to improve engagement in exercise and PA in the RA population. Data is suggestive this could improve RA related symptoms, although we did not observe statistical significance. Disclosure D.J. Bannell: Grants/research support; Research grant from the Aintree Arthritis Trust. M. France-Ratcliffe: None. K.L. Hesketh: None. M. Cocks: None. N. Goodson: None. C. Taylor: None. T. Pecanha: None. H. Jones: None. D.A. Low: None. V.S. Sprung: None.

The Mobile Health Technology for Improved Screening and Optimized Integrated Care in Atrial Fibrillation (mAFA-II) trial is a prospective cluster randomized trial that found a significant reduction in the composite clinical outcome of stroke or thromboembolism, all-cause death, and rehospitalization among patients with atrial fibrillation (AF) who used a mobile health (mHealth) technology that implemented the Atrial Fibrillation Better Care (ABC) pathway (ie, A, anticoagulation/avoid stroke; B, better symptom control; and C, cardiovascular disease and comorbidity management) compared with those receiving usual care. Multimorbidity (defined as ≥2 chronic long-term conditions) is common in older patients with AF, but the impact of integrated or holistic care (based on the ABC pathway) on clinical outcomes in this population is uncertain. To evaluate whether implementation of the integrated ABC pathway, supported by mHealth technology, would reduce AF-related adverse events in patients with multimorbidity. This prespecified ancillary analysis of data from the extended follow-up of the mAFA II trial was conducted between June 2018 and April 2021. Adult patients with AF were included in the analysis if they had at least 2 comorbidities. Participants were enrolled across 40 centers in China. Integrated care supported by mHealth technology (mAFA intervention) vs usual care. The main outcome was the composite outcome of stroke or thromboembolism, all-cause death, and rehospitalization. Cox proportional hazard modeling was performed for adverse outcomes after adjusting for cluster effect and baseline risk factors. Of 1890 patients, 833 (mean [SD] age, 72.0 [12.0] years; 278 [33.4%] women) with multimorbidity were allocated to the intervention group (ABC pathway), with a mean (SD) follow-up of 419 (257) days, and 1057 patients (mean [SD] age, 72.8 [13.0] years; 443 [41.9%] women) with multimorbidity were allocated to usual care, with a mean (SD) follow-up of 457 (154) days. Compared with usual care, the composite outcome of stroke or thromboembolism, all-cause death, and rehospitalization was significantly reduced in the intervention group (hazard ratio [HR], 0.37; 95% CI, 0.26-0.53; P < .001), as were rehospitalizations alone (HR, 0.42; 95% CI, 0.27-0.64; P < .001). For the C criterion of the ABC pathway, rates of acute coronary syndrome, heart failure, and uncontrolled blood pressure during follow-up were lower in the intervention group than the usual care group (27 patients [3.2%] vs 145 patients [13.7%]; HR, 0.29; 95% CI, 0.19-0.45; P < .001). Subgroup analyses by age, prior stroke, and sex demonstrated consistently lower HRs for the primary composite outcome and rehospitalization for patients with AF allocated to the intervention group compared with patients receiving usual care. In this study, mHealth technology-based integrated care that facilitated the implementation of the ABC pathway reduced meaningful clinical adverse events in older patients with AF and multimorbidity vs usual care. WHO International Clinical Trials Registry Platform (ICTRP) Registration number: ChiCTR-OOC-17014138.

The integration of wearable devices and mobile health (mHealth) technology to facilitate behavior change has the potential to transform the efficacy of interventions and implementation programs for weight maintenance. The purpose of this review was to provide a comprehensive analysis of the overall utility of wearable devices for assessing and promoting weight maintenance in research and clinical settings. Recent intervention trials using wearable devices have been successful in increasing physical activity and decreasing or maintaining body weight, but complex study designs involving multiple behavioral strategies make it difficult to assess whether wearable devices can independently influence weight status. The daily feedback that wearable devices and mHealth technology provide may assist in motivating higher levels of physical activity achievement. However, the integration of wearable devices into the healthcare setting and implementation of mHealth programs still need to be tested. Recent studies add concrete implications for providers and researchers to better assess and promote physical activity in healthcare settings by identifying how wearable devices can be advantageous for physical activity and health promotion.

The new Lancet Commission on water, sanitation, and hygiene (WASH) hopes to reimagine and guide global WASH efforts. This comes at a time when unequal living conditions and global disparities in response and recovery have been highlighted by the COVID-19 pandemic and recent large impact trials have delivered mostly disappointing results suggesting the need for radically more effective interventions to improve global public health. We find ourselves at an inflection point in global WASH with an opportunity to build new approaches with potentially more equitable, cost-effective, and scalable solutions. Mobile health (mHealth) technology is an important and innovative tool for WASH advances. Yet, the use of mHealth has not been equally distributed in terms of its benefits nor is its impact guaranteed. In resource-constrained settings, where technology can increase inequalities, special attention should be paid to structural and systemic hierarchies during the development of mHealth programs along with the acknowledgment and understanding how these systems can reinforce the systematic exclusion of those most vulnerable. The WASH sector needs to adapt to a future that is innovative and inclusive with a commitment to rethinking the resources needed to enhance scope and impact. We highlight urban sanitation in Brazil as a case study to demonstrate that mHealth can support and enhance publicly funded infrastructure and to help reimagine WASH for postpandemic and beyond.

Little is understood about using mobile health (mHealth) technology to improve cardiovascular (CV) health among African-American women in resource-limited communities. We conducted the Washington, D.C. CV Health and Needs Assessment in predominantly African-American churches in city wards 5, 7, and 8 with the lowest socioeconomic status based on community-based participatory research (CBPR) principles. The assessment measured CV health factors: body mass index (BMI), fasting blood glucose and cholesterol, blood pressure, fruit/vegetable (F/V) intake, physical activity (PA), and smoking. Participants were trained to use a PA monitoring wristband to measure 30days of PA, wirelessly upload the PA data to hubs at the participating churches, and access their data from a church/home computer. CV health factors were compared across weight classes. Among females (N = 78; 99% African-American; mean age = 59years), 90% had a BMI categorized as overweight/obese. Across weight classes, PA decreased and self-reported sedentary time (ST) increased (p ≤ 0.05). Diastolic blood pressure and glucose increased across weight classes (p ≤ 0.05); however, cholesterol, glucose, and BP were near intermediate CV health goals. Decreased PA and increased ST are potential community intervention targets for overweight and obese African-American women in resource-limited Washington D.C. areas. mHealth technology can assist in adapting CBPR intervention resources to improve PA for African-American women in resource-limited communities.

To compare and rank the effects of different mobile health (mHealth) technologies on the physical activity of patients with Chronic Obstructive Pulmonary Disease (COPD).Eight databases from January 2010 to March 2025 were searched. Two researchers independently screened the studies and extracted the data. The quality of the literature was evaluated using the Cochrane Risk of Bias tool. Paired Meta-analysis was conducted using Stata 15.1 software, and network Meta-analysis was performed using RStudio software.A total of 25 studies were included, involving 7 intervention measures, with a sample size of 2093 cases. The results showed that in the paired Meta-analysis, the combination of smart wearable devices and telephone support, as well as smart wearable devices alone, could improve the physical activity level of patients; the WeChat platform, App, and smart wearable devices could effectively improve the patients' exercise endurance. Surface under cumulative rank curve (SUCRA) ranking indicated that, compared with other mHealth technologies, the WeChat platform and the combination of telephone support and smart wearable devices were the top two optimal treatment measures.Current evidence shows that mHealth technologies can promote the physical activity of COPD patients and may improve their exercise endurance. When traditional pulmonary rehabilitation is not feasible, the use of mHealth technologies may be a better intervention option. More high-quality studies with large samples, multiple centers, and long-term follow-up are still needed to further verify this conclusion.

In today's rapidly advancing world, mobile health services have become influential in various aspects of our lives. It is anticipated that mobile applications, especially in rural areas, can play a significant role in the delivery of healthcare services. In this context, it is essential to examine the applications of mobile health services in rural areas and the education methods employed in these applications. The main objective of this study is to explore the contributions of mobile health services applications in rural areas to public health and to examine the education methods used in these services in light of current literature. The focus of this study is on how mobile health applications can be utilized in rural areas, the impact of this usage on public health, and the effectiveness of the education methods employed. The research was conducted through searches using key terms such as "mobile health" and "public health nursing" or "mobile health technologies" and "nursing" or "telehealth" on important academic databases like Google Scholar, PubMed free fulltext, Science Direct, Ebscohost, Sage, Scopus, and CINAHL. The information obtained from literature searches was analyzed to understand the impact of mobile health applications on public health in rural areas and the education methods employed. The study results indicate that the use of mobile health applications in rural areas can contribute significantly to public health. Advantages such as rapid diagnosis and treatment, effective monitoring of chronic diseases, and quick access to emergency services stand out among the potential impacts of mobile health applications. Mobile health applications can contribute significantly to public health in rural areas and increase their usage, particularly in primary healthcare services.

This work resolves the crucial problem of secure data transmission in IoT-aided healthcare devices, where confidential patient data is vulnerable to breaches and cyberattacks. To resolve these complexities, this work proposes a novel secure data transmission system that combines blockchain technology, Multiobjective Weighted Restricted Boltzmann Machine (MW-RBM) for feature extraction, Magnified Feeding-based American Zebra Optimization (MFAZO) for weight optimization, and a Multiscale Stacked Residual-Gated Recurrent Unit (MSRes-GRU) for attack detection. The novelty of this work lies in combining residual GRU and blockchain for secure IoT healthcare data transmission, guaranteeing both transparency and attack detection. The improvement is displayed in weight optimization through MFAZO, which refines the feature extraction task and boosts the accuracy of the technique in attack detection. The designed approach involves gathering attack detection data, performing feature extraction utilizing MW-RBM with optimized weights and identifying IoT node attacks via the MSRes-GRU technique’s multiscale layers and the residual connections. The Homomorphic Polynomial Encryption (HPE) is further employed to secure the healthcare data during transmission. Lastly, the performance of the model is determined with conventional models. The accuracy of the designed MSRes-GRU is 96.22%, which is higher than the existing models such as DNN (85.65%), LSTM (80.71%), SVM (89.99%), and GRU (94.14%). The key results demonstrate the technique’s high detection accuracy and robust performance in recognizing the IoT-based attacks while guaranteeing effective, secure and transparent data transmission via blockchain. This research contributes to improving the secure and scalable IoT-enabled healthcare devices, providing a reliable model for trustworthy healthcare applications that preserve data integrity and privacy.

Introduction: The increasing prevalence of Chronic Kidney Disease (CKD) necessitates innovative approaches to enhance patient care and management. Mobile health (mHealth) technologies have emerged as promising tools for improving health outcomes through remote monitoring, patient engagement, and data-driven decision-making. This scoping review aims to explore the utilization of mHealth technologies in the management of CKD. Methods: A comprehensive literature search was conducted across multiple databases including PubMed/Medline, ProQuest, and DOAJ focusing on studies published in the last decade. The inclusion criteria encompassed articles that evaluated mHealth interventions specifically designed for CKD management. Key outcomes were categorized into patient adherence, clinical outcomes, patient satisfaction, and cost-effectiveness. Results: The review identified diverse mHealth applications, including mobile apps, wearable devices, and telehealth platforms, demonstrating potential benefits in improving medication adherence, monitoring disease progression, and enhancing patient-provider communication. Despite promising results, challenges such as data privacy, technological literacy, and integration with existing healthcare systems were noted. Conclusion: This review underscores the need for further research to optimize mHealth interventions and address barriers to widespread implementation in CKD management.