Mobile Health System Research Articles

Person-Centered Web-Based Mobile Health System (Symptoms) for Reporting Symptoms in COVID-19 Vaccinated Individuals: Observational Study of System, Users, and Symptoms.

The full spectrum of side effects from COVID-19 vaccinations and infections, including milder symptoms or health effects that do not lead to health care visits, remains unknown. Person-centered self-reporting of symptoms may offer a solution. Monitoring patient-reported outcomes over time will vary in importance for different patients. Individuals have unique needs and preferences, in terms of both communication methods and how the collected information is used to support care. This study aims to describe how Symptoms, a system for person-centered self-reporting of symptoms and health-related quality of life, was utilized in investigating COVID-19 vaccine side effects. We illustrate this by presenting data from the Symptoms system in newly vaccinated individuals from the RECOVAC (Register-based large-scale national population study to monitor COVID-19 vaccination effectiveness and safety) study. During the COVID-19 pandemic, newly vaccinated individuals were identified as the ideal population to query for milder symptoms related to COVID-19 vaccinations and infections. To this end, we used posters in observation areas at 150 vaccination sites across the Västra Götaland region of Sweden, inviting newly vaccinated individuals to use a novel digital system, Symptoms. In the Symptoms system, users can track their symptoms, functioning, and quality of life as often as they wish, using evidence-based patient-reported outcome measures and short numeric rating scales. These scales cover a prespecified list of symptoms based on common COVID-19 symptoms and previously reported vaccine side effects. Participants could also use numeric rating scales for self-defined symptoms if their symptom was not included on the prespecified list. A total of 731 people created user accounts and consented to share data for research between July 21, 2021, and September 27, 2022. The majority of users were female (444/731, 60.7%), with a median age of 38 (IQR 30-47) years. Most participants (498/702, 70.9%) did not report any of the comorbidities included in the questionnaire. Of the 731 participants, 563 (77.0%) reported experiencing 1 or more symptoms. The most common symptom was pain at the injection site (486/563, 86.3%), followed by fatigue (181/563, 32.1%) and headache (169/563, 30.0%). In total, 143 unique symptoms were reported. Of these, 29 were from the prespecified list, while the remaining 114 (79.7%) were self-defined entries in the symptom field. This suggests that the flexibility of the self-directed system-allowing individuals to decide which symptoms they consider worth tracking-may be an important feature. Self-reported symptoms in the Symptoms system appeared to align with previously observed post-COVID-19 vaccination symptoms. The system was relatively easy to use and successfully captured broad, longitudinal data. Its person-centered and self-directed design seemed crucial in capturing the full burden of symptoms experienced by users.

The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review.

Supporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in providing feedback to patients, and visualization plays an important role in facilitating the interpretation and understanding of data and, thus, influencing patients' behavior. Visual analytics enable efficient analysis and understanding of large datasets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating CVD risk. This review aims to present the most-used visualization techniques to estimate CVD risk. In this scoping review, we followed the Joanna Briggs Institute PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search strategy involved searching databases, including PubMed, CINAHL Ultimate, MEDLINE, and Web of Science, and gray literature from Google Scholar. This review included English-language articles on digital health, mobile health, mobile apps, images, charts, and decision support systems for estimating CVD risk, as well as empirical studies, excluding irrelevant studies and commentaries, editorials, and systematic reviews. We found 774 articles and screened them against the inclusion and exclusion criteria. The final scoping review included 17 studies that used different methodologies, including descriptive, quantitative, and population-based studies. Some prognostic models, such as the Framingham Risk Profile, World Health Organization and International Society of Hypertension risk prediction charts, Cardiovascular Risk Score, and a simplified Persian atherosclerotic CVD risk stratification, were simpler and did not require laboratory tests, whereas others, including the Joint British Societies recommendations on the prevention of CVD, Systematic Coronary Risk Evaluation, and Framingham-Registre Gironí del COR, were more complex and required laboratory testing-related results. The most frequently used prognostic risk factors were age, sex, and blood pressure (16/17, 94% of the studies); smoking status (14/17, 82%); diabetes status (11/17, 65%); family history (10/17, 59%); high-density lipoprotein and total cholesterol (9/17, 53%); and triglycerides and low-density lipoprotein cholesterol (6/17, 35%). The most frequently used visualization techniques in the studies were visual cues (10/17, 59%), followed by bar charts (5/17, 29%) and graphs (4/17, 24%). On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk. Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health's effectiveness in improving CVD outcomes is limited.

Digital Health: The Critical Value of Mobile Technology for the Health Sector, Different Application Examples from the World and Current Trends

The internet technology, which began to integrate into our lives rapidly by the end of the 1990s, underwent significant transformations with the advent of mobile technology in the 2000s. Initially accessible through desktop or notebook computers, the internet has evolved to become an integral part of virtually every aspect of our lives as mobile technology advanced throughout the 2000s. The purpose of this study is to examine the role of mobile technologies within current health technologies, investigate the necessary competencies, evaluate mobile technology developments both in Türkiye and globally, and assess the information technology infrastructure, competencies, and skills required by the sector. Our research details various and specific mobile applications from numerous countries. Findings indicate that mobile technology has established itself much more rapidly and effectively in countries with strong internet infrastructure. Mobile health services are perceived as highly valuable by citizens. The real quality and effectiveness of mobile technology depend on its acceptance and swift implementation by users. Gamification is an important tool in the adoption of mobile health applications. These applications can enhance motivation by enabling both doctors to monitor patient care services and patients to track their own health. A good mobile health system should be accepted by both patients and doctors. In countries like Türkiye, where the health system is largely supported by the state, it is seen as necessary for the Ministry of Health to prioritize mobile services to establish a robust mobile health system. Furthermore, to effectively structure mobile services, it is essential to focus on the right problems and identify issues in order of priority. Literature review reveals that various mobile applications have been implemented in fields such as dermatology, orthopedics, ophthalmology, neurosurgery, and clinical pathology. Mobile technologies offer significant cost advantages in the delivery of health services. As the population ages in European Union countries, national governments are seeking ways to reduce healthcare costs. Mobile health is considered a solution to transform the delivery of health services and reduce costs through viable new care models for both industrialized and developing countries. Moreover, it has been observed that mobile applications provide significant benefits for the elderly, particularly in developed countries such as Germany and Sweden. Our study provides a comprehensive assessment of mobile technology in the healthcare sector, highlights prominent applications from Türkiye and around the world, and offers an extensive evaluation for field readers.

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.

Privacy-preserving edge federated learning for intelligent mobile-health systems

Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or patients’ mobile devices/sensors. At the same time, transferring the data to a central location for learning is certainly not an option, due to privacy concerns and legal issues, and in certain cases, because of the communication and computation overheads. Federated Learning (FL) is the state-of-the-art collaborative ML approach for training an ML model across multiple parties holding local data samples, without sharing them. However, enabling learning from distributed data over such edge Internet of Things (IoT) systems (e.g., mobile-health and wearable technologies, involving sensitive personal/medical data) in a privacy-preserving fashion presents a major challenge mainly due to their stringent resource constraints, i.e., limited computing capacity, communication bandwidth, memory storage, and battery lifetime. In this paper, we propose a privacy-preserving edge FL framework for resource-constrained mobile-health and wearable technologies over the IoT infrastructure. We evaluate our proposed framework extensively and provide the implementation of our technique on Amazon’s AWS cloud platform based on the seizure detection application in epilepsy monitoring using wearable technologies.

Verifiable Outsourced Attribute-Based Encryption Scheme for Cloud-Assisted Mobile E-Health System

Verifiable Outsourced Attribute-Based Encryption Scheme for Cloud-Assisted Mobile E-Health System

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).

Mobile Health System for Meeting Health Information Needs in Patients With Head and Neck Cancer Undergoing Radiotherapy: Development and Feasibility Study.

Patients with head and neck cancer undergoing radiotherapy encounter physical and psychosocial challenges, indicating unmet needs. Mobile health technology can potentially support patients. This single-armed feasibility study included 30 patients with head and neck cancer undergoing radiotherapy. Patients were asked to use the Health Enjoy System, a mobile health support system that provides a disease-related resource for 1 week. We assessed the usability of the system and its limited efficacy in meeting patients' health information needs. The result showed that the system was well received by patients and effectively met their health information needs. They also reported free comments on the system's content, backend maintenance, and user engagement. This study supplies a foundation for further research to explore the potential benefits of the Health Enjoy System in supporting patients with head and neck cancer.

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.

Adherence to Home Swallowing Therapy with and Without the Assistance of a Mobile Health System in Head and Neck Cancer Patients: A Randomized Crossover Trial

Adherence to Home Swallowing Therapy with and Without the Assistance of a Mobile Health System in Head and Neck Cancer Patients: A Randomized Crossover Trial

Acceptance of mobile application-based clinical guidelines among health professionals in Northwestern Ethiopia: A mixed-methods study.

Globally, healthcare providers have faced significant difficulties in adhering to clinical guidelines. Applying mobile health systems is a crucial strategy for enhancing the dissemination and accessibility of clinical guidelines. This study aimed to assess the acceptance of mobile app-based primary healthcare clinical guidelines and associated factors among health professionals in central Gondar health centers. A cross-sectional study supplemented with qualitative data was conducted on 403 health workers. Data were collected using a pre-test structured printed questionnaire and entered into EpiData version 4.6. Analysis was conducted using Stata version 14, which included bivariable and multivariable logistic regression analyses. For qualitative data, thematic analysis was conducted using Open Code v.4.2. Approximately 28% (95% confidence interval (CI): 23%-32%) of health professionals had utilized mobile app-based clinical guidelines. The availability of IT support (adjusted odds ratio (AOR) = 3.51, 95% CI: 1.82-6.78), good knowledge (AOR = 3.46, 95% CI: 1.5-6.78), perceived usefulness (AOR = 2.21, 95% CI: 1.00-4.99), m-Health app exposure (AOR = 2.34, 95% CI: 1.2-4.50), and ease of use (AOR = 5.77, 95% CI: 2.50-13.32) were significantly associated with the acceptance of the mobile app-based clinical guideline. In qualitative data, lack of training and supervision and access to smartphones were barriers to acceptance of the mobile app-based clinical guideline. In summary, acceptance of the app is currently low. However, it can be increased by improving the availability of IT support in the workplace, offering training and supervision, and enhancing access to smartphones.

Moderating Effect of Mobile Technology on the Relationship Between Health Systems Governance and Service Delivery in National Referral Hospitals in Kenya

In recent years, there is an increased attention to build formidable health systems governance to promote the highest attainable standard of health service delivery which has become a fundamental part of our human rights and of our understanding of a life in dignity. The health systems governance through the establishment of health policy has taken on increasing importance in the provision of health care services in the health institutions. The public hospitals in Kenya have weak health systems governance attributed to the existing health policy affecting quality healthcare. The current study sought to examine the moderating effect of mobile technology on the relationship between health systems governance and service delivery in National Referral Hospitals in Kenya. The study was anchored to the Contingency Leadership Theory. The study identified six categories of the target respondents, namely, 5 directors, 43 board members and 88 head of departments. The study used regression analysis to analyze the association between the variables at 0.05 level of significance. Results revealed that mobile technology positively and significantly moderated the relationship between health systems governance and service delivery in national referral hospitals in Kenya. The results support the current theories related to the study. Consequently, this study provides national referral hospitals with insights of how to improve health care service delivery through the adoption of appropriate mobile technology and health systems governance in the national referral hospitals. This could go a long way in ensuring there is improved service delivery in the national referral hospitals in Kenya. The implications of the study are that the moderating effect of mobile technology on the relationship between health systems governance and healthcare service delivery in Kenyan hospitals can lead to positive outcomes such as improved communication, data management, and patient-centered care. However, it also presents challenges that need to be carefully addressed through appropriate governance mechanisms. The successful integration of mobile technology requires a comprehensive and adaptive approach to healthcare governance in the national referral hospitals in the country.

An empirical study on secure usage of mobile health apps: The attack simulation approach

: Mobile applications (apps) have proven their usefulness in enhancing service provisioning across a multitude of domains that range from smart healthcare, to mobile commerce, and areas of context-sensitive computing. In smart healthcare context, mobile health (mHealth) apps - representing a specific genre of mobile apps that manage health information - face some critical challenges relating to security and privacy of device and user data. In recent years, a number of empirically grounded, survey-based studies have been conducted to investigate secure usage of mHealth apps. However, such studies rely on self-reported behaviors documented via interviews or survey questions that lack practical approaches that can simulate attack scenario for monitoring users’ actions and behaviors while using mHealth apps. : Our objective was to conduct an empirical study - engaging participants with attack simulation scenarios and analyze their actions - for investigating the security awareness of mHealth app users. : We simulated some common security attack scenarios in mHealth context and engaged a total of 105 app users to monitor their actions and analyze their behavior. We analyzed users' data with statistical analysis including correlations test, descriptive analysis, and qualitative data analysis (i.e., thematic analysis method). : Our results indicate that whilst the minority of our participants perceived access permissions positively, the majority had negative views. Users provide their consent, granting permissions, without a careful review of privacy policies that leads to undesired or malicious access to health data. Findings also indicated that 73.3% of our participants had denied at least one access permission, and 36% of our participants preferred no authentication method. : The study complements existing research on secure usage of mHealth apps, simulates security threats to monitor users’ actions, and provides empirically grounded guidelines for secure development and usage of mobile health systems.

The Vinyasa Tool for mHealth solutions: supporting human-centered design in nascent digital health ecosystems.

mHealth systems have been deployed widely in Lower- and Middle-Income countries (LMICs) for health system strengthening, requiring considerable resource allocation. However, most solutions have not achieved scale or sustainability. Poor usability and failure to address perceived needs are among the principal reasons mHealth systems fail to achieve acceptance and adoption by healthcare workers. A human-centered design approach to improve mHealth system use requires an exploration of users' perceptions of mHealth systems including the environmental, user-related, and technological aspects of a system. At present, there is a dearth of contextually intelligent tools available to mHealth developers that can guide such exploration prior to full-scale development and deployment. To develop a tool to aid optimization of mHealth solutions in LMICs to facilitate human-centered design, and consequently, successful adoption. We collated findings and themes from key qualitative studies on mHealth deployment in LMICs. We then used the Informatics Stack framework by Lehmann (2017) to label, sort and collate findings and themes into a list of questions which explore the environment, users, artifacts, information governance, and interoperability of mHealth systems deployed in LMICs. We developed the Vinyasa Tool - to aid qualitative research about the need and usability of mHealth solutions in LMICs. The tool is a guide for focus group discussions and key informant interviews with community-based healthcare workers and primary care medical personnel who use or are expected to use proposed mHealth solutions. The tool consists of 71 questions organized in 11 sections which unpack and explore multiple aspects of mHealth systems from the perspectives of its users. These include the wider world and organization in which an mHealth solution is deployed; the roles, functions, workflow and adoption behavior of a system's users; the security, privacy and interoperability afforded by a system; and the artifacts of an information system - the data, information, knowledge, algorithms and technology which constitute the system. The tool can be deployed in whole or in part, depending on the context of study. The Vinyasa Tool is the first such comprehensive qualitative research instrument incorporating questions contextualized to the LMIC setting. We expect it will find wide application among mHealth developers, health system administrators and researchers developing and deploying mHealth tools for use by patients, providers, and administrators. The tool is expected to guide users toward human centered design with the goal of improving relevance and usability, and therefore, adoption.

Me, My Health, and My Watch: How Children with ADHD Understand Smartwatch Health Data

Children with ADHD can experience a wide variety of challenges related to self-regulation, which can lead to poor educational, health, and wellness outcomes. Technological interventions, such as mobile and wearable health systems, can support data collection and reflection about health status. However, little is known about how ADHD children interpret such data. We conducted a deployment study with 10 children, aged 10 to 15, for six weeks, during which they used a smartwatch in their homes. Results from observations and interviews during this study indicate that children with ADHD can interpret their own health data, particularly at the moment. However, as ADHD children develop more autonomy, smartwatch systems may require alternatives for data reflection that are interpretable and actionable for them. This work contributes to the scholarly discourse around health data visualization, particularly in considering implications for the design of health technologies for children with ADHD.

Chatbot-assisted therapy for patients with methamphetamine use disorder: a preliminary randomized controlled trial.

Methamphetamine (MA) use disorder is associated with a large public health burden. Despite the therapeutic effects of psychosocial interventions based on current evidence, finding an approach to retain patients in treatment remains a real-world challenge. The rapid development of mobile health (mHealth) systems suggests the potential to provide real-time personalized care at any time and from any location, minimize barriers to treatment, maximize use, and promote the dissemination of accessible therapeutic tools in at-risk populations. Our study aimed to investigate the feasibility and effectiveness of chatbots for the treatment of MA use disorder. The inclusion criteria were (a) a diagnosis of MA use disorder as defined by the DSM-5, (b) age between 18 and 65 years, (c) no acute exacerbation of severe mental illness during the initial assessment, such as schizophrenia or bipolar I disorder, (d) willingness to participate in standard outpatient treatment for ≥ 6 months, and (e) an Android phone. Participants were randomly allocated to either a chatbot-assisted therapy via smartphone (CAT) group or a control group following simple randomization procedures (computerized random numbers) without blinding. All participants were followed up for 6 months. Treatment retention and monthly urine test results were analyzed as outcome measures. Participants' satisfaction with CAT was also assessed. In total, 50 and 49 participants were allocated to the CAT and control groups, respectively. There were no significant differences in retention time between the two treatment groups (df = 1, p = 0.099). The CAT group had fewer MA-positive urine samples than the control group (19.5% vs. 29.6%, F = 9.116, p = 0.003). The proportion of MA-positive urine samples was positively correlated with the frequency of MA use (r = 0.323, p = 0.001), severity of MA use disorder (r = 0.364, p < 0.001), and polysubstance use (r = 0.212, p = 0.035), and negatively correlated with readiness to change (r = -0.330, p = 0.001). Totally 55 participants completed the study at the 6-month follow-up and 60% reported relative satisfaction. Participants in this study had favorable acceptance and generally positive outcomes, which indicates that chatbot is feasible for treating people who use MA.

The iMHere 2.0 System for Family Caregivers of Older Adults: A Focus Group.

Family caregivers with continuous caregiving responsibilities are at increased risk for adverse physical and mental health outcomes. In response to the challenges of caregiving, a mobile health system (iMHere 2.0) was developed to support caregivers. The study's objective was to gather feedback from family caregivers of older adults on the current features of iMHere 2.0 and to formulate design criteria for future iterations of the system. An exploratory qualitative study with thematic analyses of focus group feedback. A total of 10 caregivers of older adults participated in a focus group. Five themes emerged: (1) Monitoring health data, (2) Setting up customized reminders, (3) Supporting care coordination, (4) Balancing security and multiple user access, and (5) Disseminating iMHere 2.0 into the community, along with some potential barriers to implementation. Design criteria were developed to provide a framework for iterative design and development of the iMHere system to support caregivers of older adults.

Artificial intelligence‐assisted point‐of‐care testing system for ultrafast and quantitative detection of drug‐resistant bacteria

AbstractAs one of the major causes of antimicrobial resistance, β‐lactamase develops rapidly among bacteria. Detection of β‐lactamase in an efficient and low‐cost point‐of‐care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)‐assisted mobile health system that consists of a paper‐based β‐lactamase fluorogenic probe analytical device and a smartphone‐based AI cloud. An ultrafast broad‐spectrum fluorogenic probe (B1) that could respond to β‐lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three‐dimensional microfluidic paper‐based analytical device was fabricated for integration of B1. Also, a smartphone‐based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the β‐lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem‐solving ability in interdisciplinary research, and demonstrates potential clinical benefits.

Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks.

Although evidence supporting the feasibility of large-scale mobile health (mHealth) systems continues to grow, privacy protection remains an important implementation challenge. The potential scale of publicly available mHealth applications and the sensitive nature of the data involved will inevitably attract unwanted attention from adversarial actors seeking to compromise user privacy. Although privacy-preserving technologies such as federated learning (FL) and differential privacy (DP) offer strong theoretical guarantees, it is not clear how such technologies actually perform under real-world conditions. Using data from the University of Michigan Intern Health Study (IHS), we assessed the privacy protection capabilities of FL and DP against the trade-offs in the associated model's accuracy and training time. Using a simulated external attack on a target mHealth system, we aimed to measure the effectiveness of such an attack under various levels of privacy protection on the target system and measure the costs to the target system's performance associated with the chosen levels of privacy protection. A neural network classifier that attempts to predict IHS participant daily mood ecological momentary assessment score from sensor data served as our target system. An external attacker attempted to identify participants whose average mood ecological momentary assessment score is lower than the global average. The attack followed techniques in the literature, given the relevant assumptions about the abilities of the attacker. For measuring attack effectiveness, we collected attack success metrics (area under the curve [AUC], positive predictive value, and sensitivity), and for measuring privacy costs, we calculated the target model training time and measured the model utility metrics. Both sets of metrics are reported under varying degrees of privacy protection on the target. We found that FL alone does not provide adequate protection against the privacy attack proposed above, where the attacker's AUC in determining which participants exhibit lower than average mood is over 0.90 in the worst-case scenario. However, under the highest level of DP tested in this study, the attacker's AUC fell to approximately 0.59 with only a 10% point decrease in the target's R2 and a 43% increase in model training time. Attack positive predictive value and sensitivity followed similar trends. Finally, we showed that participants in the IHS most likely to require strong privacy protection are also most at risk from this particular privacy attack and subsequently stand to benefit the most from these privacy-preserving technologies. Our results demonstrated both the necessity of proactive privacy protection research and the feasibility of the current FL and DP methods implemented in a real mHealth scenario. Our simulation methods characterized the privacy-utility trade-off in our mHealth setup using highly interpretable metrics, providing a framework for future research into privacy-preserving technologies in data-driven health and medical applications.