mHealth System Research Articles

A Systematic Review and Qualitative Evidence Synthesis of factors affecting mHealth adoption in India

Abstract mHealth implementations are increasing in low- and middle- income countries (LMICs) to strengthen health systems and improve health outcomes. Following the proliferation of mobile internet use, Indian health systems have deployed mHealth widely. However, there is little evidence that mHealth has improved health outcomes in India across settings and at scale. The aim of this study was to review current evidence on perceptions and experiences of end users of mHealth in India and synthesize qualitative data to determine the factors influencing mHealth use to inform mHealth design, development, and implementation. A systematic review and qualitative synthesis of studies on mHealth in India was conducted by searching the Web of Science, Medline and CINAHL databases for qualitative studies on mHealth users including both health system beneficiaries and healthcare personnel. Findings from the studies were synthesized using thematic synthesis. The synthesis generated the themes of the Environment, the Users and the mHealth system. The data indicate that mHealth use improves when the environment supports its use; when users are motivated and have the ability to use mHealth systems; and when mHealth systems are aligned with the environmental context and fulfill users’ needs and desires. mHealth adoption in India can be improved through human centered design and by addressing the disparities in digital literacy between socio economic strata. These approaches are required to close the design reality gaps facing mHealth systems, to improve mHealth implementation for health system strengthening, and therefore, to improve health outcomes in India.

Implementing and Evaluating the Timed Up and Go Test Automation Using Smartphones and Smartwatches.

Physical performance tests aim to assess the physical abilities and mobility skills of individuals for various healthcare purposes. They are often driven by experts and usually performed at their practice, and therefore they are resource-intensive and time-demanding. For tests based on objective measurements (e.g., duration, repetitions), technology can be used to automate them, allowing the patients to perform the test themselves, more frequently and anywhere, while alleviating the expert from supervising the test. The well-known Timed Up and Go (TUG) test, typically used for mobility assessment, is an ideal candidate for automation, as inertial sensors (among others) can be deployed to detect the various movements constituting the test without expert supervision. To move from expert-led testing to self-administered testing, we present a mHealth system capable of automating the TUG test using a pocket-sized smartphone or a wrist smartwatch paired with a smartphone, where data from inertial sensors are used to detect the activities carried out by the patient while performing the test and compute their results in real time. All processing (i.e., data processing, machine learning-based activity inference, results calculation) takes place on the smartphone. The use of both devices to automate the TUG test was evaluated (w.r.t. accuracy, reliability and battery consumption) and mutually compared, and set off with a reference method, obtaining excellent Bland-Altman agreement results and Intraclass Correlation Coefficient reliability. Results also suggest that the smartwatch-based system performs better than the smartphone-based system.

PERFECT: Personalized Exercise Recommendation Framework and architECTure

Background : The health benefits of regular physical activity (PA) are well-established and widely acknowledged. Through the integration of wearable trackers, the Internet of Things (IoT)—a network of interconnected devices capable of collecting and exchanging data—coupled with mobile health (mHealth), which refers to the use of mobile devices to support medical and public health practices, it is now feasible to systematically gather and present individual exercise behaviors. This advanced approach enables the precise correlation of users’ physiological data and daily activities with their specific fitness needs, offering a personalized pathway to improving health outcomes. Objective : This study aims to enhance PA levels among individuals by developing a personalized exercise recommendation system. Utilizing reinforcement learning, the system proposes tailored exercise plans based on biomarkers and the user’s specific context. Methods : In this study, we developed applications for smartphones and smartwatches designed to gather, monitor, and recommend exercise routines through the application of a contextual multi-arm bandit algorithm. To evaluate the efficacy of this mHealth exercise regimen, we enlisted the participation of twenty female college students. Results : The outcomes of our investigation revealed a significant enhancement in the average daily duration of exercise (P \({\lt}\) . 001). Participants expressed high levels of satisfaction with both the walking program and the recommendation system, achieving average ratings of 4.31 (SD \(=\) 0.60) and 3.69 (SD \(=\) 0.95), respectively, on a 5-point scale. Furthermore, the average scores for participants’ confidence in safely performing the recommended walking exercises, as well as their perception of the study’s effectiveness in meeting their PA needs, were both above 4, indicating a positive reception and confidence in the program’s design and implementation. Conclusions : The evolution of the IoT and wearable technology has marked the beginning of a new era for mHealth systems, particularly in the personalization of health interventions. Such advancements enable the precise personalization of PA recommendations, potentially enhancing user engagement and performance outcomes. This paper introduces a novel exercise recommendation system that utilizes reinforcement learning to personalize walking exercises based on the user’s biomarkers and context, aiming to improve the user’s aerobic capacity significantly.

Automated image transcription for perinatal blood pressure monitoring using mobile health technology.

This paper introduces a novel approach to address the challenges associated with transferring blood pressure (BP) data obtained from oscillometric devices used in self-measured BP monitoring systems to integrate this data into medical health records or a proxy database accessible by clinicians, particularly in low literacy populations. To this end, we developed an automated image transcription technique to effectively transcribe readings from BP devices, ultimately enhancing the accessibility and usability of BP data for monitoring and managing BP during pregnancy and the postpartum period, particularly in low-resource settings and low-literate populations. In the designed study, the photos of the BP devices were captured as part of perinatal mobile health (mHealth) monitoring programs, conducted in four studies across two countries. The Guatemala Set 1 and Guatemala Set 2 datasets include the data captured by a cohort of 49 lay midwives from 1697 and 584 pregnant women carrying singletons in the second and third trimesters in rural Guatemala during routine screening. Additionally, we designed an mHealth system in Georgia for postpartum women to monitor and report their BP at home with 23 and 49 African American participants contributing to the Georgia I3 and Georgia IMPROVE projects, respectively. We developed a deep learning-based model which operates in two steps: LCD localization using the You Only Look Once (YOLO) object detection model and digit recognition using a convolutional neural network-based model capable of recognizing multiple digits. We applied color correction and thresholding techniques to minimize the impact of reflection and artifacts. Three experiments were conducted based on the devices used for training the digit recognition model. Overall, our results demonstrate that the device-specific model with transfer learning and the device independent model outperformed the device-specific model without transfer learning. The mean absolute error (MAE) of image transcription on held-out test datasets using the device-independent digit recognition were 1.2 and 0.8 mmHg for systolic and diastolic BP in the Georgia IMPROVE and 0.9 and 0.5 mmHg in Guatemala Set 2 datasets. The MAE, far below the FDA recommendation of 5 mmHg, makes the proposed automatic image transcription model suitable for general use when used with appropriate low-error BP devices.

How Patient-Generated Data Enhance Patient-Provider Communication in Chronic Care: Field Study in Design Science Research.

Modern approaches such as patient-centered care ask health care providers (eg, nurses, physicians, and dietitians) to activate and include patients to participate in their health care. Mobile health (mHealth) is integral in this endeavor to be more patient centric. However, structural and regulatory barriers have hindered its adoption. Existing mHealth apps often fail to activate and engage patients sufficiently. Moreover, such systems seldom integrate well with health care providers' workflow. This study investigated how patient-provider communication behaviors change when introducing patient-generated data into patient-provider communication. We adopted the design science approach to design PatientHub, an integrated digital health system that engages patients and providers in patient-centered care for weight management. PatientHub was developed in 4 iterations and was evaluated in a 3-week field study with 27 patients and 6 physicians. We analyzed 54 video recordings of PatientHub-supported consultations and interviews with patients and physicians. PatientHub introduces patient-generated data into patient-provider communication. We observed 3 emerging behaviors when introducing patient-generated data into consultations. We named these behaviors emotion labeling, expectation decelerating, and decision ping-pong. Our findings show how these behaviors enhance patient-provider communication and facilitate patient-centered care. Introducing patient-generated data leads to behaviors that make consultations more personal, actionable, trustworthy, and equal. The results of this study indicate that patient-generated data facilitate patient-centered care by activating and engaging patients and providers. We propose 3 design principles for patient-centered communication. Patient-centered communication informs the design of future mHealth systems and offers insights into the inner workings of mHealth-supported patient-provider communication in chronic care.

Predicting Epileptic Seizures Using Biometric Sensors - A Pilot Study Protocol.

Epilepsy is a prevalent neurological disorder characterized by unpredictable seizures despite some known risk factors. Using an mHealth system and Empatica EmbracePlus, we aim to examine if biometric sensor data can detect and predict epileptic seizures. Five participants with epilepsy will be followed for a minimum of 15 seizures. Expected results and possible implications are presented. This study may provide new insights into biological indicators for seizure forecasting.

Validation of an mHealth System for Monitoring Fundamental Physiological Parameters in the Clinical Setting.

The aim of this work was to validate the measurements of three physiological parameters, namely, body temperature, heart rate, and peripheral oxygen saturation, captured with an out-of-the-lab device using measurements taken with clinically proven devices. The out-of-the-lab specialized device was integrated into a customized mHealth application, e-CoVig, developed within the AIM Health project. To perform the analysis, single consecutive measurements of the three vital parameters obtained with e-CoVig and with the standard devices from patients in an intensive care unit were collected, preprocessed, and then analyzed through classical agreement analysis, where we used Lin's concordance coefficient to assess the agreement correlation and Bland-Altman plots with exact confidence intervals for the limits of agreement to analyze the paired data readings. The existence of possible systematic errors was also addressed, where we found the presence of additive errors, which were corrected, and weak proportional biases. We obtained the mean overall agreement between the measurements taken with the novel e-CoVig device and the reference devices for the measured quantities. Although some limitations in this study were encountered, we present more advanced methods for their further assessment.

A proactive approach to DDoS attack recognition and preclusion in securing m-health systems

A proactive approach to DDoS attack recognition and preclusion in securing m-health systems

The Effectiveness of Medical Adherence Mobile Health Solutions for Individuals With Epilepsy: Protocol for a Systematic Review.

Epilepsy requires continuous management and treatment to optimize patient outcomes. The advancement of digital health has led to the development of various mobile health (mHealth) tools designed to enhance treatment adherence among individuals with epilepsy. These solutions offer crucial support through features such as reminders, educational resources, personalized feedback, assistance with managing costs, shared decision-making, and access to supportive communities. To design effective medication adherence mHealth solutions, it is essential to evaluate the effectiveness of existing mHealth tools, understand the unique circumstances of different patients, and identify the roles of health care professionals within the digital care pathway. Existing studies on epilepsy primarily focus on self-management, whereas the effectiveness and usability of medical adherence mHealth solutions often remain overlooked. Furthermore, the involvement of health care professionals in digital care pathways for epilepsy as well as the impact of adherence mHealth solutions on the patient experience have not been adequately explored. This study aims to assess the effectiveness of current mHealth solutions designed to improve medical adherence among patients with epilepsy. Furthermore, the study will examine the experiences of patients using mHealth solutions for maintaining medical adherence in epilepsy care. Finally, this review intends to determine the roles of health care professionals within mHealth systems aimed at supporting adherence to medication among patients with epilepsy. A systematic literature review has been selected as the appropriate method to address the research questions, adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The inclusion and exclusion criteria have been carefully selected, and both qualitative and quantitative analyses will be used to analyze the results. The expected results will mainly focus on the comparison, classification, and analysis of the effectiveness of current medical adherence mHealth tools. Moreover, the patient experiences using available medical adherence mHealth tools for epilepsy will be assessed. Finally, the role of health care professionals in the epilepsy digital care pathway will be explored, with emphasis on medical adherence. The initial search, full-text screening, and data extraction have been carried out. Thirty-three papers were included in the final stage of the review. The study is expected to be completed by October 2024. To enhance the digital care pathway for epilepsy, a medical adherence mHealth solution should be personalized, manage medications, include an alarm system, track seizures, support consultations, and offer updated treatment plans. This study aims to understand how findings from the research questions can improve mHealth solutions for individuals with epilepsy. Insights from this research on the effectiveness of current mHealth adherence solutions will provide guidance for developing future mHealth systems, making them more efficient and effective in managing epilepsy. PROSPERO CRD4202347400; https://tinyurl.com/48mfx22e. DERR1-10.2196/55123.

Evaluation of a Musculoskeletal Digital Assessment Routing Tool (DART): Crossover Noninferiority Randomized Pilot Trial.

Musculoskeletal conditions account for 16% of global disability, resulting in a negative effect on patients and increasing demand for health care use. Triage directing patients to appropriate level intervention improving health outcomes and efficiency has been prioritized. We developed a musculoskeletal digital assessment routing tool (DART) mobile health (mHealth) system, which requires evaluation prior to implementation. Such innovations are rarely rigorously tested in clinical trials-considered the gold standard for evaluating safety and efficacy. This pilot study is a precursor to a trial assessing DART performance with a physiotherapist-led triage assessment. The study aims to evaluate trial design, assess procedures, and collect exploratory data to establish the feasibility of delivering an adequately powered, definitive randomized trial, assessing DART safety and efficacy in an NHS primary care setting. A crossover, noninferiority pilot trial using an integrated knowledge translation approach within a National Health Service England primary care setting. Participants were patients seeking assessment for a musculoskeletal condition, completing a DART assessment and the history-taking element of a face-to-face physiotherapist-led triage in a randomized order. The primary outcome was agreement between DART and physiotherapist triage recommendation. Data allowed analysis of participant recruitment and retention, randomization, blinding, study burden, and potential barriers to intervention delivery. Participant satisfaction was measured using the System Usability Scale. Over 8 weeks, 129 patients were invited to participate. Of these, 92% (119/129) proceeded to eligibility assessment, with 60% (78/129) meeting the inclusion criteria and being randomized into each intervention arm (39/39). There were no dropouts and data were analyzed for all 78 participants. Agreement between physiotherapist and DART across all participants and all primary triage outcomes was 41% (32/78; 95% CI 22-45), intraclass correlation coefficient 0.37 (95% CI 0.16-0.55), indicating that the reliability of DART was poor to moderate. Feedback from the clinical service team led to an adjusted analysis yielding of 78% (61/78; 95% CI 47-78) and an intraclass correlation coefficient of 0.57 (95% CI 0.40-0.70). Participant satisfaction was measured quantitively using amalgamated System Usability Scale scores (n=78; mean score 84.0; 90% CI +2.94 to -2.94), equating to an "excellent" system. There were no study incidents, and the trial burden was acceptable. Physiotherapist-DART agreement of 78%, with no adverse triage decisions and high patient satisfaction, was sufficient to conclude DART had the potential to improve the musculoskeletal pathway. Study validity was enhanced by the recruitment of real-world patients and using an integrated knowledge translation approach. Completion of a context-specific consensus process is recommended to provide definitive definitions of safety criteria, range of appropriateness, noninferiority margin, and sample size. This pilot demonstrated an adequately powered definitive trial is feasible, which would provide evidence of DART safety and efficacy, ultimately informing potential for DART implementation. ClinicalTrials.gov NCT04904029; http://clinicaltrials.gov/ct2/show/NCT04904029. RR2-10.2196/31541.

Finnish Immigrants' Affective Attitude Towards the IMPMS for Loneliness Detection: A Descriptive Qualitative Study.

Loneliness is a global health concern that contributes to morbidity in immigrant populations. However, traditional treatments for loneliness focus on symptom management rather than prevention. Technology-related solutions for preventing and assessing loneliness among immigrants are crucial. This study explored Finnish immigrants' affective attitude towards the IoT-based Multimodal Personalized mHealth System (IMPMS), a system for building predictive models for loneliness detection. In this descriptive qualitative study embedded within the DOMINO feasibility study, immigrants in Finland shared their experiences and perspectives of the IMPMS. Semi-structured interviews were conducted using an interview guide based on the Theoretical Framework of Acceptability (TFA). Data were analyzed using thematic analysis. Finnish immigrants considered the IMPMS acceptable, as evidenced by their positive experiences with the system. Areas for improvement highlighted in the results could be utilized to further refine and enhance the acceptability of the IMPMS for future implementation.

Effects of the ActiveHip+ mHealth intervention on the recovery of older adults with hip fracture and their family caregivers: a multicentre open-label randomised controlled trial

Mobile health (mHealth) systems are a promising alternative for rehabilitation of hip fracture, addressing constrained healthcare resources. Half of older adults fails to recover their pre-fracture routines, which imposes a burden on caregivers. We aimed to test the effectiveness of the 3-month ActiveHip+mHealth intervention on physical and psychological outcomes of older adults with hip fracture and their family caregivers. In a multicentre open-label randomised controlled trial conducted across 3 hospitals in Andalusia (Spain), patients older than 65 with a hip fracture, who were previously independent and lacked cognitive impairment were recruited alongside with their caregivers. Participants were randomly allocated (1:1) to the intervention group (ActiveHip+) or control (usual care) group. The intervention group underwent a 12-week health education and tele-rehabilitation programme through the ActiveHip+mHealth intervention. The primary outcome, physical performance, was assessed using the Short Physical Performance Battery at three time points: at hospital discharge (baseline), 3-month after surgery (post intervention) and 1-year after surgery follow-up. Primary analyses of primary outcomes and safety data followed an intention-to-treat approach. This study is registered at ClinicalTrials.gov, NCT04859309. Between June 1st, 2021 and June 30th, 2022 data from 105 patients and their caregivers were analysed. Patients engaged in the ActiveHip+mHealth intervention (mean 7.11 points, SE 0.33) showed higher physical performance compared with patients allocated in the control group (mean 5.71 points, SE 0.32) at 3 months after surgery (mean difference in change from baseline 1.40 points, SE 0.36; puncorrected=0.00011). These benefits were not maintained at 1-year after surgery follow-up (mean difference in change from baseline 0.19 points, SE 0.47; puncorrected=0.68). No adverse events, including falls and refractures, were reported during the tele-rehabilitation sessions. At 3-months, the intervention group had 2 falls, compared to 4 in the control group, with no observed refractures. At the 1-year follow-up, the intervention group experienced 7 falls and 1 refracture, while the control group had 13 falls and 2 refractures. This study suggests that the ActiveHip+mHealth intervention may be effective for recovering physical performance in older adults with hip fracture. Importantly, the implementation of ActiveHip+into daily clinical practice may be feasible and has already been adopted in 18 hospitals, mostly in Spain but also in Belgium and Portugal. Thus, ActiveHip+could offer a promising solution when rehabilitation resources are limited. However, its dependence on caregiver support and the exclusion of participants with cognitive impairment makes it necessary to be cautious about its applicability. In addition, the non-maintenance of the effectiveness at 1-year follow-up highlights the need of refinement the ActiveHip+intervention to promote long-lasting behavioural changes. EIT Health and the Ramón y Cajal 2021 Excellence Research Grant action from the Spanish Ministry of Science and Innovation.

A Mobile Health Design Process to Improve User Engagement

It has been shown that less than 25 percent of people continue to use health and wellness apps after the first time they use them. Despite the importance of user engagement for the efficacy of such apps and mobile health (mHealth) systems, many of these interventions frequently do not incorporate user-engaging attributes. This is partly because user needs and other sociocultural contexts are overlooked during the mHealth design, development, and implementation stages. These sociocultural contexts influence users' behaviours, and thus it is critical to capture this across the various stages of mHealth design and development. Based on this, a research gap was identified, leading to the development of the following research question targeting community health workers in Global South settings: How can a design framework be developed to improve community health workers (CHWs) engagement in mHealth technologies? The aim is PhD thesis is to develop an mHealth design process (or framework) called the Design Process Engagement Enhancement System (DECENT) which takes account of socio-cultural contexts in the design process of mHealth systems and improves community health workers' engagement with the designed mHealth technology. This framework is intended to be used by mHealth designers and developers in the field of mHealth. There are three contributions from this thesis. First, the expansion of O'Briens and Toms' engagement model. The process of expanding the engagement model resulted in the identification of engagement themes that have an impact on stages of engagement with mHealth that end users encounter in an mHealth setting. Second, this work makes an important contribution to practice. The DECENT framework is intended to be used by NGOs and mHealth practitioners. Finally, the contribution to theory by developing an analytical and methodological framework for analyzing frameworks based on activity the (AT) and communicative ecology framework (CEF).

Understanding Symptom Self-Monitoring Needs Among Postpartum Black Patients: Qualitative Interview Study.

Pregnancy-related death is on the rise in the United States, and there are significant disparities in outcomes for Black patients. Most solutions that address pregnancy-related death are hospital based, which rely on patients recognizing symptoms and seeking care from a health system, an area where many Black patients have reported experiencing bias. There is a need for patient-centered solutions that support and encourage postpartum people to seek care for severe symptoms. We aimed to determine the design needs for a mobile health (mHealth) patient-reported outcomes and decision-support system to assist Black patients in assessing when to seek medical care for severe postpartum symptoms. These findings may also support different perinatal populations and minoritized groups in other clinical settings. We conducted semistructured interviews with 36 participants-15 (42%) obstetric health professionals, 10 (28%) mental health professionals, and 11 (31%) postpartum Black patients. The interview questions included the following: current practices for symptom monitoring, barriers to and facilitators of effective monitoring, and design requirements for an mHealth system that supports monitoring for severe symptoms. Interviews were audio recorded and transcribed. We analyzed transcripts using directed content analysis and the constant comparative process. We adopted a thematic analysis approach, eliciting themes deductively using conceptual frameworks from health behavior and human information processing, while also allowing new themes to inductively arise from the data. Our team involved multiple coders to promote reliability through a consensus process. Our findings revealed considerations related to relevant symptom inputs for postpartum support, the drivers that may affect symptom processing, and the design needs for symptom self-monitoring and patient decision-support interventions. First, participants viewed both somatic and psychological symptom inputs as important to capture. Second, self-perception; previous experience; sociocultural, financial, environmental, and health systems-level factors were all perceived to impact how patients processed, made decisions about, and acted upon their symptoms. Third, participants provided recommendations for system design that involved allowing for user control and freedom. They also stressed the importance of careful wording of decision-support messages, such that messages that recommend them to seek care convey urgency but do not provoke anxiety. Alternatively, messages that recommend they may not need care should make the patient feel heard and reassured. Future solutions for postpartum symptom monitoring should include both somatic and psychological symptoms, which may require combining existing measures to elicit symptoms in a nuanced manner. Solutions should allow for varied, safe interactions to suit individual needs. While mHealth or other apps may not be able to address all the social or financial needs of a person, they may at least provide information, so that patients can easily access other supportive resources.

Weight management personas of breast cancer patients undergoing chemotherapy in China: a multi-method study

BackgroundMobile health (mHealth) may be an ideal solution for breast cancer (BC) patients in China to access weight management interventions. User retention and engagement are the main challenges faced by mHealth applications. A user persona, which is a user-centered design process, can lead to the development of mHealth that is more acceptable to the needs of target users. This study aimed to investigate the variety of experiences in weight management and the behavioral preferences of BC patients receiving chemotherapy to develop users’ personal information and persona development for the design and implementation of mHealth interventions.MethodsSixteen individual semi-structured in-depth interviews were conducted with BC patients receiving chemotherapy. We employed the thematic analysis method to analyze the interview transcripts in NVivo 11 software. The themes obtained from the analysis were used as the subdomains of personas. A proforma was designed to extract each participant’s experience in each subdomain. Patients who exhibited similar experience in subdomains were grouped into a persona using affinity diagrams. The personas were named according to their prominent features. A questionnaire survey was conducted to validate the personas and to test whether the personas that were generated from the qualitative interview data were applicable to the Chinese population with BC.ResultsFour themes were identified as subdomains of weight management personas: the perception of weight management while undergoing chemotherapy, symptoms and emotional disturbance, changes in diet and exercise, and health literacy and information seeking. Five personas were ultimately obtained: (1) positive weight controllers, (2) patients who were inactive due to fatigue, (3) young patients who avoided communication, (4) overweight patients with treatment priority, and (5) patients who engaged in irregular exercise. Finally, the quantitative study showed that 51.58% of patients chose one of these five personas to represent themselves in weight management. None of the patient reported selecting options that were not explicitly outlined in the questionnaire and provided personalized descriptions of their weight management characteristics.ConclusionsThe selected personas were developed from in-depth interviews on biopsychosocial areas. They highlight different weight management patterns in Chinese BC patients and provide implications for both the design of mHealth systems and traditional interventions.

A Smartphone-Based M-Health Monitoring System for Arrhythmia Diagnosis.

Deep learning technology has been widely adopted in the research of automatic arrhythmia detection. However, there are several limitations in existing diagnostic models, e.g., difficulties in extracting temporal information from long-term ECG signals, a plethora of parameters, and sluggish operation speed. Additionally, the diagnosis performance of arrhythmia is prone to mistakes from signal noise. This paper proposes a smartphone-based m-health system for arrhythmia diagnosis. First, we design a cycle-GAN-based ECG denoising model which takes real-world noise signals as input and aims to produce clean ECG signals. In order to train its two generators and two discriminators simultaneously, we explore an unsupervised pre-training strategy to initialize the generator and accelerate the convergence speed during training. Second, we propose an arrhythmia diagnosis model based on the time convolution network (TCN). This model can identify 34 common arrhythmia events using eight-lead ECG signals, and we deploy such a model on the Android platform to develop an at-home ECG monitoring system. Experimental results have demonstrated that our approach outperforms the existing noise reduction methods and arrhythmia diagnosis models in terms of denoising effect, recognition accuracy, model size, and operation speed, making it more suitable for deployment on mobile devices for m-health monitoring services.

Bright light therapy versus physical exercise to prevent co-occurring depression in adolescents and young adults with attention-deficit/hyperactivity disorder: a multicentre, three-arm, randomised controlled, pilot phase-IIa trial

Abstract Depression is common in attention-deficit/hyperactivity disorder (ADHD), but preventive behavioural interventions are lacking. This randomised controlled, pilot phase-IIa trial aimed to study a physical exercise intervention (EI) and bright light therapy (BLT)—both implemented and monitored in an individual, naturalistic setting via a mobile health (m-health) system—for feasibility of trial design and interventions, and to estimate their effects on depressive symptoms in young people with ADHD. Two hundred seven participants aged 14–45 years were randomised to 10-week add-on intervention of either BLT (10,000 lx; daily 30-min sessions) (n = 70), EI (aerobic and muscle-strengthening activities 3 days/ week) (n = 69), or treatment-as-usual (TAU) (n = 68), of whom 165 (80%) were retained (BLT: n = 54; EI: n = 52; TAU: n = 59). Intervention adherence (i.e. ≥ 80% completed sessions) was very low for both BLT (n = 13, 22%) and EI (n = 4, 7%). Usability of the m-health system to conduct interventions was limited as indicated by objective and subjective data. Safety was high and comparable between groups. Changes in depressive symptoms (assessed via observer-blind ratings, Inventory of Depressive Symptomatology) between baseline and end of intervention were small (BLT: −0.124 [95% CI: −2.219, 1.971], EI: −2.646 [95% CI: −4.777, −0.515], TAU: −1.428 [95% CI: −3.381, 0.526]) with no group differences [F(2,153) = 1.45, p = 0.2384]. These findings suggest that the m-health approach did not achieve feasibility of EI and BLT in young people with ADHD. Prior to designing efficacy studies, strategies how to achieve high intervention adherence should be specifically investigated in this patient group. Trial registration ClinicalTrials.gov, NCT03371810, 13 December 2017.

Prospective systematic risk analysis of the digital technology use within pharmaceutical care

BackgroundDigital technologies are present in every phase of a drug lifecycle, from drug design and development to its dispensing and use. However, given the rapid development and implementation of digital solutions, their monitoring, evaluation, and risk assessment are limited and lacking. ObjectiveThis research is aiming to identify potential errors, quantify and prioritize associated risks in the context of certain technologies used in pharmaceutical care, as well as define corrective measures to improve patient safety and the quality of pharmaceutical care. MethodsA 10-member multidisciplinary team conducted Failure Mode and Effect Analysis (FMEA) to identify critical risks, their causes and effects, along with developing corrective measures within the selected digital health components: Telepharmacy, mHealth, Artificial intelligence (AI), and Software infrastructure and systems. Critical risks were determined by calculating risk priority numbers (RPNs) from severity, occurence, and detectability scores. ResultsThis study identified 42 risks regarding the 4 components. After calculating RPNs and the threshold RPN (RPN=30), 8 critical risks were identified. Corrective measures were proposed for these failure modes, after which the risks were re-evaluated (RPN sum was reduced from 414 to 156). The risk with the highest RPN value was Internet/identity fraud, while the rest included inadequate and incomplete data entry and management, flawed implementation, human and technology errors, and lack of transparency, personalization and infrastructure. For the critical risks, 42 different causes were recognized on a system, technological and individual level while their effects were discussed in terms of patient safety and business management in pharmacies. ConclusionDigitalization of pharmaceutical practice promises greater effectiveness of pharmaceutical care, but in order to achieve this, efforts, resources and initiatives must be directed toward timely identification of problems, appropriate monitoring, and building adequate infrastructure that can support safe implementation of digital tools and services despite the swift development of innovations.

Assessment of the Barriers and Enablers of the Use of mHealth Systems in Sub-Saharan Africa According to the Perceptions of Patients, Physicians, and Health Care Executives in Ethiopia: Qualitative Study.

Digital technologies are increasingly being used to deliver health care services and promote public health. Mobile wireless technologies or mobile health (mHealth) technologies are particularly relevant owing to their ease of use, broad reach, and wide acceptance. Unlike developed countries, Sub-Saharan Africa experiences more challenges and obstacles when it comes to deploying, using, and expanding mHealth systems. In addition to barriers, there are enabling factors that could be exploited for the design, implementation, and scaling up of mHealth systems. Sub-Saharan Africa may require tailored solutions that address the specific challenges facing the region. The overall aim of this study was to identify the barriers and enablers for using mHealth systems in Sub-Saharan Africa from the perspectives of patients, physicians, and health care executives. Multi-level and multi-actor in-depth semistructured interviews were employed to qualitatively explore the barriers and enablers of the use of mHealth systems. Data were collected from patients, physicians, and health care executives. The interviews were audio recorded, transcribed verbatim, translated, and coded. Thematic analysis methodology was adopted, and NVivo software was used for the data analysis. Through this rigorous study, a total of 137 determinants were identified. Of these determinants, 68 were identified as barriers and 69 were identified as enablers. Perceived barriers in patients included lack of awareness about mHealth systems and language barriers. Perceived enablers in patients included need for automated tools for health monitoring and an increasing literacy level of the society. According to physicians, barriers included lack of available digital health systems in the local context and concern about patients' mHealth capabilities, while enablers included the perceived usefulness in reducing workload and improving health care service quality, as well as the availability of mobile devices and the internet. As perceived by health care executives, barriers included competing priorities alongside digitalization in the health sector and lack of interoperability and complete digitalization of implemented digital health systems, while enablers included the perceived usefulness of digitalization for the survival of the highly overloaded health care system and the abundance of educated manpower specializing in technology. mHealth systems in Sub-Saharan Africa are hindered and facilitated by various factors. Common barriers and enablers were identified by patients, physicians, and health care executives. To promote uptake, all relevant stakeholders must actively mitigate the barriers. This study identified a promising outlook for mHealth in Sub-Saharan Africa, despite the present barriers. Opportunities exist for successful integration into health care systems, and a user-centered design is crucial for maximum uptake.

Use of a mHealth System to Improve Antenatal Care: A Literature Review

Antenatal care, often known as ANC, is the foundation of programs assisting reproductive health. It is particularly significant in the field of maternal and child health because it offers services to pregnant women from the time of conception until the delivery of the baby. These services are designed to ensure a safe pregnancy and delivery, hence preventing difficulties. The problem of pregnancy difficulties unquestionably has a bad influence on both the mother and her fetus. As a result, it is the reason for the high maternal mortality rate (MMR) in Indonesia. Some of the complications that contribute to this rate include bleeding, eclampsia, complications during abortion, sepsis, and extended partus. By providing routine prenatal care and examinations to medical professionals, it is possible to save the lives of pregnant women who experience problems. Distance is one of the numerous elements that can function as a barrier, and there are many others. During this digitalization era, technology makes it possible to disseminate information to pregnant women to enhance antenatal care. One example of this is using mobile phones in health services, referred to as m-Health. This is a promising option to improve the health outcomes of both mothers and children. This research was conducted to conduct a literature review to assess the advantages of mobile health in enhancing antenatal care. This literature study technique was selected from January 2019 to January 2024. The study approaches chosen are randomized trials, pseudo-experiments, and cluster randomization, designed to adhere to specific inclusion and exclusion criteria. This study utilizes the PRISMA protocol. A thorough electronic search across many databases yielded 683 articles, with 274 from PubMed and 409 from Google Scholar. After independently obtaining and rigorously filtering publications, 6 studies were included in the review. Final Thoughts Based on the findings of the six chosen publications, a critical analysis was carried out, and the conclusion reached was that MHealth is highly significant and beneficial in pregnancy care.