Mobile App System Research Articles

Investigating work-related distraction’s impact on male taxi driver safety: A hazard-based duration model

With the increasing use of phone-based ride-hailing apps, concerns have arisen regarding road safety and driver distraction. Despite the recognized safety risks of driver distraction, limited research has explored how distractions from various ride-hailing systems affect drivers in the taxi industry. To close this gap, the current research utilized a driving simulator experiment involving 51 male taxi drivers in two road environments (urban street and motorway) and three distracted driving conditions (no distraction, auditory distraction via radio dispatching system, and visual-manual distraction via mobile application). A car-following scenario with sudden brake events was incorporated into the experiments because this is a typical safety–critical situation where attention will determine the outcome. The collected performance indicators include brake reaction time, time headway, and car-following distance. The grouped random parameters Weibull accelerated failure time model was applied to model the duration data under different road conditions. The brake reaction time and time headway are dependent variables, while the car-following distance is a covariate in the models. The results indicate that although taxi drivers show longer brake reaction time when distracted by mobile app and radio system, this does not necessarily equate with greater risk or reduced safety since they compensate for the risk of rear-end crashes by maintaining a longer time headway. In general, taxi drivers’ brake reaction time and time headway are more profoundly affected by mobile apps when distracted in both urban and motorway scenarios. This highlights the elevated risks associated with such technologies. In addition, significant interaction effects revealed the observed heterogeneity, which suggests that drivers’ personal characteristics influence the relationship between distraction type and driving performance. This research provides valuable insights for designing safer ride-hailing operations and systems.

A deep learning-based mobile app system for visual identification of tomato plant disease

<span lang="EN-US">Tomato is one of many horticulture crops in Indonesia which plays a vital role in supplying public food needs. However, tomato is a very susceptible plant to pests and diseases caused by bacteria and fungus. The infected diseases should be isolated as soon as it was detected. Therefore, developing a reliable and fast system is essential for controlling tomato pests and diseases. The deep learning-based application can help to speed up the identification of tomato disease as it can perform direct identification from the image. In this research, EfficientNetB0 was implemented to perform multi-class tomato plant disease classification. The model was then deployed to an android-based application using machine learning (ML) kit library. The proposed system obtained satisfactory results, reaching an average accuracy of 91.4%.</span>

Active Tuberculosis Screening via a Mobile Health App in Myanmar: Incremental Cost-Effectiveness Evaluation.

A mobile app that calculates a tuberculosis (TB) risk score based on individual social and pathological characteristics has been shown to be a better predictor of the risk of contracting TB than conventionally used TB signs and symptoms (TBSS) in Myanmar, where the TB burden is high. Its cost-effectiveness, however, has not yet been assessed. This study aimed to determine the incremental costs of this mobile app and of chest x-rays (CXRs) in averting disability-adjusted life years (DALYs) among missed cases of active TB in the population being screened. Elements of incremental costs and effectiveness of 3 initial TB screening strategies were examined, including TBSS followed by CXR, the mobile app followed by CXR, and universal CXR. The incremental cost-effectiveness ratio (ICER; ie, the additional cost for each additional DALY averted) was compared to TBSS screening. Based on the latest 2020 gross domestic product (GDP) per capita of Myanmar (US $1477.50), the ICER was compared to willingness-to-pay (WTP) thresholds of 1, 2, and 3 times the GDP per capita. Probabilistic sensitivity analysis was conducted with a Monte Carlo simulation to compute the levels of probability that the ICER for each strategy was below each WTP threshold. For each 100,000 population, the incremental cost compared to TBSS of active TB screening was US $345,942 for the mobile app and US $1,810,712 for universal CXR. The incremental effectiveness was 325 DALYs averted for the mobile app and 576 DALYs averted for universal CXR. For the mobile app, the estimated ICER was US $1064 (72% of GDP per capita) per 1 DALY averted. Furthermore, 100% of the simulated values were below an additional cost of 1 times the GDP per capita for 1 additional DALY averted. The universal CXR strategy has an estimated ICER of US $3143 (2.1 times the GDP per capita) per 1 DALY averted and an additional 77.2% DALYs averted compared to the app (ie, 576 - 325 / 325 DALYs); however, 0.5% of the simulated values were higher than an additional expenditure of 3 times the GDP per capita. Based on the status of the economy in 2020, the mobile app strategy is affordable for Myanmar. The universal CXR strategy, although it could prevent an additional 77% of DALYs, is probably unaffordable. Compared to the TBSS strategy, the mobile app system based on social and pathological characteristics of TB has potential as a TB screening tool to identify missing TB cases and to reduce TB morbidity and mortality, thereby helping to achieve the global goal of "End TB" in resource-limited settings with a high TB burden.

Validity and Reliability of the Acceleration-Speed Profile for Assessing Running Kinematics' Variables Derived From the Force-Velocity Profile in Professional Soccer Players.

Alonso-Callejo, A, García-Unanue, J, Guitart-Trench, M, Majano, C, Gallardo, L, and Felipe, J. Validity and reliability of the acceleration-speed profile for assessing running kinematics' variables derived from the force-velocity profile in professional soccer players. J Strength Cond Res 38(3): 563-570, 2024-The aim of this research is to assess the validity and reliability of the acceleration-speed profile (ASP) for measuring the mechanical variables of running kinematics when compared with the force-velocity profile (FVP) obtained by reference systems. The ASP and FVP of 14 male players of an elite football club were assessed during a competitive microcycle. Three ASPs were tested according to the number and type of sessions included in its plotting (ASP1: 5 training sessions and competitive match; ASP2: 5 training sessions; ASP3: competitive match). Force-velocity profile was tested 4 days before match (MD-4) with a 30-m linear sprint using 3 previously validated devices (encoder, mobile App, and global positioning system). Level of significance was p < 0.05. Acceptable reliability (intraclass correlation coefficient > 0.5) was found between the ASP1 and the encoder for all variables (F 0 -A 0 , V 0 -S 0 , and V max ). The more reliable ASP method was the ASP1 showing a lower bias than the ASP2 and ASP3 methods for almost all variables and reference systems. For ASP1, lower mean absolute error (MAE: 0.3-0.5) and higher correlation (P-M corr: 0.57-0.92) were found on variables related to the velocity in comparison with variables related to the early acceleration phase (F 0 -A 0 ; MAE: 0.49-0.63; P-M corr: 0.13-0.41). Acceleration-speed profile, when computed with data from a complete competitive week, is a reliable method for analyzing variables derived from velocity and acceleration kinematics. From these results, practitioners could implement ASP and the applications of the FVP previously studied, such as resistance training prescription, performance assessment, and return-to-play management.

EPRO Use in Men Receiving Radiotherapy for Localized Prostate Cancer

Describe real-time reporting of localized prostate cancer and patient-reported cancer/radiation treatment symptoms via mobile app or computer-based system. Patients (n = 177) who received standard-of-care treatment at an academic specialty care hospital in Finland from Jan. 2015 through Dec. 2020 and used a proprietary ePRO tool at least once during their course of radiotherapy were included in this study. The Clinical Care Team (CCT) utilized bidirectional software to administer an electronic questionnaire that included CTCAE (v5) items to report disease and/or treatment symptoms at least once during treatment and at 1 month and 3 months posttreatment. Patients could also complete the questionnaire ad hoc. The questionnaire is enhanced with programmed "emergency" and "urgent" clinical alerts which are used for two purposes: sending pre-programmed recommendations to patients and triaging patients by symptom severity on the CCT's dashboard in order to facilitate patient follow-up. Data were analyzed descriptively. Patients tended to be older (M = 73.16 years, SD = 6.86) and lived outside the city (n = 119, 67.23%). The average course of radiation treatment was over one month in duration (M = 39.21 days, SD = 16.96); relatively few patients received surgery (n = 21, 6.78%) and/or hormone therapy (n = 20, 11.30%). Almost all patients completed the module during treatment (response rate = 92.09%) and at posttreatment (response rate = 94.92%). Ad hoc reports were submitted by 18.07% of patients during treatment (78 submissions) and 28.24% of patients posttreatment (182 submissions). The most frequently reported symptoms during and after treatment were urination-related (during treatment = 73.44%; post treatment = 60.45%), diarrhea/constipation (during treatment = 50.84%; post treatment = 31.63%), and sexual function (during treatment = 44.63%; post treatment = 57.06%). Systematizing real-time collection of patient-reported symptoms is increasingly acknowledged as an adjunct to improving patient experience. The high usage observed in this study is a behavioral indicator that ePRO use is accepted and valued by patients. Perhaps the most interesting finding is the use of the ad hoc reporting function. During radiotherapy, patients have almost daily access to the CCT and can easily communicate symptoms; nevertheless, they still used the ePRO platform to report cancer/treatment effects. Understandably, in the posttreatment period, where scheduled contact with the CCT is less frequent, ad hoc usage was higher. These findings suggest that ePRO use (scheduled and ad hoc) helps address an unmet need and has the potential to empower patients throughout their cancer journey.

Comprehensive Use of a Multifunction Software Tool to Facilitate Education and Communication of Treatment-Related Distress in Patients with Cancer Receiving Radiation Therapy

Real-time reporting of patient-reported cancer radiation treatment symptoms via mobile app or computer-based system can improve communication with the Clinical Care Team (CCT) however single-purpose solutions can be impractical. This study describes use of multifunction software and associated patient satisfaction. Patients receiving thoracic, pelvic, or bone radiation therapy at a provincial cancer agency were asked to enroll and receive training on app use. Clinical teams can send tailored educational materials (12 PDFs, 1 file linking patients to facility resources pertinent to treatment). During active treatment, the CCT also uses the software to administer the Edmonton Symptom Assessment System (ESAS) questionnaire once weekly for 4 consecutive weeks. Patients can also complete it ad hoc. The ESAS asks patients to report function across 10 domains using an 11-point scale (range 0-10); the version within the app is enhanced by programmed clinical alerts correlated to the response. When patients report pain, nausea, shortness of breath, or depression as a 7 or 8, then the response is flagged as "urgent"; if patients report a 9 or 10 then it is an "emergency". All the other items are considered "urgent" if the patient reports a 9 or 10. Upon completing the questionnaire, patients with flagged responses receive programmed recommendations, and a CCT member follows up with further instructions. Additionally, the software triages patient responses in order of urgency on the CCT's dashboard. Patients also have ad hoc use of a diary, to record any personal information, and the ability to securely communicate medical and non-medical information with the CCT. Patient satisfaction with the software was assessed by randomly asking patients with active accounts >30 days, "How likely are you to recommend (software name) to another patient" using an 11-point scale. Data for 180 days of software use were downloaded (Jan. 17) and analyzed descriptively. Patients (n = 1,647) were sent educational information and 69.94% opened the files in less than 1 day; the median time to open was 2 hours. The ESAS was completed by 561 patients a total of 2,452 times (mean = 4.37). The CCT received 39 (1.59%) emergency alerts and 299 (12.19%) urgent alerts. The diary and secure communication features were used by 5.32% of patients. The mean satisfaction score was 7.87 (n = 176); 88 patients (50.00%) rated the app a 9 (n = 17; 9.66%) or 10 (n = 71; 40.34%). The implementation of patient-facing multifunction software to those receiving radiation has, so far, been a success because it complements standard treatment care, facilitates CCT-patient partnership, enhances continuity of care, and is well-liked by patients.

Mobile App and Digital System for Patients after Myocardial Infarction (afterAMI): Results from a Randomized Trial.

Cardiac rehabilitation after acute myocardial infarction is crucial and improves patients' prognosis. It aims to optimize cardiovascular risk factors' control. Providing additional support via mobile applications has been previously suggested. However, data from prospective, randomized trials evaluating digital solutions are scarce. In this study, we aimed to evaluate a mobile application-afterAMI-in the clinical setting and to investigate the impact of a digitally-supported model of care in comparison with standard rehabilitation. A total of 100 patients after myocardial infarction were enrolled. Patients were randomized into groups with either a rehabilitation program and access to afterAMI or standard rehabilitation alone. The primary endpoint was rehospitalizations and/or urgent outpatient visits after 6 months. Cardiovascular risk factors' control was also analyzed. Median age was 61 years; 65% of the participants were male. This study failed to limit the number of primary endpoint events (8% with app vs. 27% without app; p = 0.064). However, patients in the interventional group had lower NT-proBNP levels (p = 0.0231) and better knowledge regarding cardiovascular disease risk factors (p = 0.0009), despite no differences at baseline. This study showcases how a telemedical tool can be used in the clinical setting.

Development and validation of mobile app and data management system for intelligent control of indoor thermal environment

In engineering practice, air conditioning systems are not regulated according to the accurate real-time thermal sensation of occupants, which not only hinders improving the thermal comfort of the indoor environment but also wastes the energy of air conditioning systems. To introduce human thermal comfort in real-time into the intelligent control of air conditioning systems, this paper develops a mobile app and data management system for the intelligent control of the indoor thermal environment with three key components: the phone app, server and database. Function tests of the six modules are carried out to verify the stability and usability of system. A comparative experiment between wearable sensing of thermal sensation control based on this system and traditional temperature set value control under summer conditions is then conducted to verify the feasibility of the system. Under wearable sensing of thermal sensation control, the average indoor temperature setting value is 27.5 °C with an average satisfaction score of 5.50, which is higher than the average indoor temperature setting values of 26.3 °C and average satisfaction score of 4.65 under traditional temperature set point-based control. It means that the subject's thermal preference is met while achieving energy savings. The system proposed herein offers a new way to solve the problem of the disconnection between the intelligent control of air conditioning and the actual thermal sensation of occupants.

Distractions by work-related activities: The impact of ride-hailing app and radio system on male taxi drivers

Use of ride-hailing mobile apps has surged and reshaped the taxi industry. These apps allow real-time taxi-customer matching of taxi dispatch system. However, there are also increasing concerns for driver distractions as a result of these ride-hailing systems. This study aims to investigate the effects of distractions by different ride-hailing systems on the driving performance of taxi drivers using the driving simulator experiment. In this investigation, fifty-one male taxi drivers were recruited. During the experiment, the road environment (urban street versus motorway), driving task (free-flow driving versus car-following), and distraction type (no distraction, auditory distraction by radio system, and visual-manual distraction by mobile app) were varied. Repeated measures ANOVA and random parameter generalized linear models were adopted to evaluate the distracted driving performance accounting for correlations among different observations of a same driver. Results indicate that distraction by mobile app impairs driving performance to a larger extent than traditional radio systems, in terms of the lateral control in the free-flow motorway condition and the speed control in the free-flow urban condition. In addition, for car-following task on urban street, compensatory behaviour (speed reduction) is more prevalent when distracted by mobile app while driving, compared to that of radio system. Additionally, no significant difference in subjective workload between distractions by mobile app and radio system were found. Several driver characteristics such as experience, driving records, and perception variables also influence driving performances. The findings are expected to facilitate the development of safer ride-hailing systems, as well as driver training and road safety policy.

Participatory user requirements elicitation, validation, and prioritization for tele-otology app in deprived populations

BackgroundThe timely and accurate diagnosis of ENT (Ear, Nose, and throat) diseases in deprived areas is very important due to the increased burden of the disease in high-risk populations. Therefore, the purpose of this study is to identify, validate and prioritize the information needs of stakeholders of a tele-otology system for diagnosing ear diseases and to provide a solution. Material and methodsIn this study, to identify the information need of the stakeholders and prioritize them, the spiral-iterative model was used. Data were obtained through direct observation, interviews with 23 stakeholders (6 ENT specialist doctors, 3 health center managers, 2 patients, and 12 health facilitators), and a survey among all ENT specialists and residents (n = 49), working in Kerman province, Iran. Finally, based on the study findings, the important information needs of the stakeholders were considered as the input for developing the tele-otology software. ResultsIn the observation phase, the workflow of the tele-otology system was developed. In the interview phase, 4 main themes including patient information; Technology and communication; Policy, guidelines, and requirements; Facilitator skills and knowledge were determined. In total, the four categories of the iterative-spiral model including strategic, patient information, commercial and technical with 8, 9, 3, and 9 sub-categories, respectively, were prioritized by specialists which were considered as input for system development. ConclusionBy applying the spiral-iterative model, the prototype included a mobile app and server system was developed. In the final solution, the possibility of capturing patient history and video images was provided for facilitators to help physicians in effective diagnosis. The results of this study, while identifying the workflow of service providers and determining their information needs can help telemedicine system developers to have an optimal design when designing systems for the diagnosis of ear disease.

Using the PMAQ-AB Mobile App and Management System to Evaluate the Quality of Primary Health Care in Brazil: Qualitative Case Study.

BackgroundThe application of cell phones, similar portable devices (ie, tablets), apps, the internet, and GPS in evaluation have established new ways of collecting, storing, retrieving, transmitting, and processing data or information. However, evidence is incipient as to which technological resources remain at the center of assessment practice and the factors that promote their use by the assessment community.ObjectiveThis study aimed to analyze the relationship between the use of the National Program for Improving Primary Healthcare Access and Quality’s (PMAQ-AB; Programa Nacional de Melhoria do Acesso e da Qualidade da Atenção Básica) mobile app and management system and the external evaluation quality of Brazil’s PMAQ-AB.MethodsWe conducted a qualitative case study during the external evaluation of Brazil’s PMAQ-AB. Data collection consisted of interviews, focus groups, and document analysis. A total of 7 members from the Department of Primary Care of the Ministry of Health and 47 researchers from various higher education and research institutions across the country participated in the study. Data were categorized using the ATLAS.ti software program, according to the quality standards of the Joint Committee on Standards for Educational Evaluation, following the content analysis approach by Bardin.ResultsThe results related to feasibility, thematic scope, field activity management, standardized data collection, data consistency, and transparency. They demonstrated improvements and opportunities for advancements in evaluation mediated by the use of information technology (IT), favored the emergence of new practices and remodeling of existing ones, and took into account the multiple components required by the complex assessment of access and quality in primary health care. Difficulties in technology operation, inoperative systems, and lack of investment in equipment and human resources posed challenges to increasing the effectiveness of IT in evaluation.ConclusionsThe use of technology-based tools—the app and the management system—during the external evaluation offered evaluators a greater opportunity for stakeholder engagement. This also allowed the insertion of different organizational, operational, and methodological components that are capable of triggering influences and confluences. In addition, this allowed connections in collaborative and synergistic networks to increase the quality and allow the development of a more consistent and efficient evaluation process with greater possibility of incorporating the results into public health policies.

AgroAId: A Mobile App System for Visual Classification of Plant Species and Diseases Using Deep Learning and TensorFlow Lite

This paper aims to assist novice gardeners in identifying plant diseases to circumvent misdiagnosing their plants and to increase general horticultural knowledge for better plant growth. In this paper, we develop a mobile plant care support system (“AgroAId”), which incorporates computer vision technology to classify a plant’s [species–disease] combination from an input plant leaf image, recognizing 39 [species-and-disease] classes. Our method comprises a comparative analysis to maximize our multi-label classification model’s performance and determine the effects of varying the convolutional neural network (CNN) architectures, transfer learning approach, and hyperparameter optimizations. We tested four lightweight, mobile-optimized CNNs—MobileNet, MobileNetV2, NasNetMobile, and EfficientNetB0—and tested four transfer learning scenarios (percentage of frozen-vs.-retrained base layers): (1) freezing all convolutional layers; (2) freezing 80% of layers; (3) freezing 50% only; and (4) retraining all layers. A total of 32 model variations are built and assessed using standard metrics (accuracy, F1-score, confusion matrices). The most lightweight, high-accuracy model is concluded to be an EfficientNetB0 model using a fully retrained base network with optimized hyperparameters, achieving 99% accuracy and demonstrating the efficacy of the proposed approach; it is integrated into our plant care support system in a TensorFlow Lite format alongside the front-end mobile application and centralized cloud database. Finally, our system also uses the collective user classification data to generate spatiotemporal analytics about regional and seasonal disease trends, making these analytics accessible to all system users to increase awareness of global agricultural trends.

Mobile app and digital system for patients after myocardial infarction (afterAMI): study protocol for a randomized controlled trial

BackgroundTreatment of acute myocardial infarction has been the subject of studies over the past years. However, the initial months after myocardial infarction are crucial from the perspective of the patient’s prognosis. It is extremely important to take care of all cardiovascular risk factors and undergo a full rehabilitation program. Telemedical solutions are becoming more and more relevant in everyday practice. We describe a protocol of a study evaluating the use of the mobile application “afterAMI” in patients after myocardial infarction. The app offers an educational mode, calendar, vital signs diary, medication reminders, medical history card, and healthcare professional contact panel. It offers several solutions, which individually proved to be effective and improve a patient’s prognosis. Despite general promising results from previous studies regarding telemedical tools, there is a paucity of evidence when it comes to prospective randomized trials. Our aim was to perform a comprehensive evaluation of a newly developed mobile application in the clinical setting.MethodsA group of 100 patients with myocardial infarction on admission at the 1st Chair and Department of Cardiology, Medical University of Warsaw, will be recruited into the study. The project aims to assess the impact of the application-supported model of care in comparison with standard rehabilitation. At the end of the study, cardiovascular risk factors will be analyzed, along with rehospitalizations, the patients’ knowledge regarding cardiovascular risk factors, returning to work, and quality of life. In this prospective, open-label, randomized, single-center study, all 100 patients will be observed for 6 months after discharge from the hospital. Endpoints will be assessed during control visits 1 and 6 months after inclusion into the study.DiscussionThis project is an example of a telemedical solution application embracing everyday clinical practices, conforming with multiple international cardiac societies’ guidelines. Cardiac rehabilitation process enhancements are required to improve patients’ prognosis. The evidence regarding the use of the mobile application in the described group of patients is limited and usually covers a small number of participants. The described study aims to discuss whether telemedicine use in this context is beneficial for the patients.Trial registrationClinicalTrials.govNCT04793425. Registered on 11 March 2021.

A Smartphone App for Parental Management of Adolescent Conduct Problems: Randomized Clinical Trial of iKinnect

ABSTRACT Objective This study evaluates iKinnect, a linked caregiver-teen mobile app system designed to address serious adolescent conduct problems through a focus on key targets of evidence-based treatments for juvenile offending, such as parent expectation setting, monitoring, consistency, and positive reinforcement. Additional gamification and autonomy-supporting features are designed to maximize youth engagement. Digital therapeutics such as mobile apps have great potential to expand access to effective interventions, particularly for youth who engage in serious conduct problems and substance abuse, since most never receive an evidence-based treatment and few apps exist for these concerns. Methods This randomized clinical trial used a short-term (12 week) longitudinal design with four time points. Recruited was a U.S. national sample of teens (n = 72, age 13–17, 59.7% male, 68.1% White) receiving services for a serious conduct problem and their primary caregiver. The efficacy of iKinnect, used by parent and teen dyads, was measured against an active control condition, Life360, an app that provided mutual GPS-based location tracking to dyads. Results Across 12 weeks of app use, youth who used iKinnect showed significantly greater reductions in alcohol use, marijuana use, school delinquency, status offenses, and general delinquency than did controls. Parents who used iKinnect Reported greater improvements in structure/rule clarity and discipline consistency relative to control parents. Teen and parent iKinnect app use and acceptability ratings were high. Conclusions Real-world use of iKinnect in future applications can, like other emerging digital health technologies, help to expand the reach of evidence-based interventions to children, youth, and families. Registered at clinicaltrials.gov (NCT03065517).

A novel game theory based reliable proof‐of‐stake consensus mechanism for blockchain

AbstractThe prominent achievement of blockchain technology stimulates exceptional innovation. The major component of blockchain is the consensus mechanism. The standard consensus mechanisms specifically Proof‐of‐Work (PoW) rely on mining procedures and stake‐based mechanisms such as Proof‐of‐Stake (PoS) rely on massive stake investment as the sole criteria for selection of leader nodes. However, PoW impose huge computational power requirements and latter may incorporate malicious nodes as leader nodes in anonymous blockchain. These issues might fuel the way for distrust among the participants in blockchain. Henceforth, a novel game theory based reliable PoS mechanism for blockchain has been proposed. Federated learning has been used to compute trust_score for each node. The nodes are trained on locally generated dataset. Further, a game theoretic approach has been proposed that uses a reward and punishment scheme to ensure threshold level of trust_score maintenance by each node. Finally, a crop insurance use case has been developed with the consensus mechanism and blockchain coded in python. The insurance claims are made to operate through smart contract based mobile app system to impart more authenticity. The system is tested and results show an intrusion accomplishment rate reduced by approximate 40% when compared to the standard PoS mechanism and by approximately 33% for algorand, 29% for ouroboros and 20% for tendermint. The mean absolute error also decreases by 30% within specific time. Furthermore, the proposed federated learning‐based system is compared with basic neural network‐based machine learning model and the results reveal that a significant reduction in average training time amounting to 8.35 second is achieved. Test accuracy has also been analyzed for various learning mechanisms.

Physician-Authored Feedback in a Type 2 Diabetes Self-management App: Acceptability Study.

BackgroundType 2 diabetes (T2D) is increasingly prevalent in society, in part because of behavioral issues, with sedentary behavior, reduced exercise, and the consumption of foods with a high glycemic index being major contributors. There is evidence for the efficacy of mobile apps in promoting behavior change and lifestyle improvements in people with T2D. Many mobile phone apps help to monitor the condition of people with T2D and inform them about their health. Some of these digital interventions involve patients using apps on their own or in conjunction with health care professionals.ObjectiveThis study aimed to test the acceptability of receiving app-based, daily physician feedback for patients with T2D that is informed by the continuous monitoring of their activity, food choices, and glucose profiles, with the aim of encouraging healthier behavior. The GLOOK! app was designed and developed by an academic research team and pilot-tested at an Australian public hospital.MethodsA total of 15 patients diagnosed with T2D wore a glucose monitor and an Apple Watch for 12 days. The uploaded data were integrated into the GLOOK! app on the patients’ smartphones, which also enabled the recording of activity and consumed food. A physician provided daily feedback to each individual through the app based on their data from each of the 12 days. At the beginning and end of the study, data were collected on vital signs, anthropometry, hemoglobin A1c level, fructosamine level, and fasting lipids level. Participants were also interviewed at the beginning and end of the study to assess the acceptability of the intervention and its potential impact on promoting positive behavior change.ResultsOver the 12 days of the study, there was a significant reduction of 0.22% (P=.004) in hemoglobin A1c level. There were favorable changes in fructosamine and lipid fractions; however, none reached significance. There was also a fall of 0.65 kg in body weight and falls in blood pressure and pulse rate that did not reach significance. Patient feedback on the GLOOK! system was positive. Of the 15 participants, 13 (87%) were enthusiastic about continuing to use the app system if some usability and reliability aspects were improved. All participants regarded the personalized physician feedback as supportive and helpful in understanding their own health behavior. Of the 15 participants, 4 (27%) felt that using the system encouraged long-term behavior changes.ConclusionsA mobile app system that provides people with T2D daily, physician-generated, personalized feedback can produce favorable changes in glycemic and cardiovascular risk parameters—even in the short term—and encourage better self-management of their condition. Study participants found the experience of using the mobile app system acceptable and were motivated to establish longer-term lifestyle improvements through behavior changes.

An Electronic Patient-Reported Outcomes Tool for Older Adults With Complex Chronic Conditions: Cost-Utility Analysis.

BackgroundeHealth technologies for self-management can improve quality of life, but little is known about whether the benefits gained outweigh their costs. The electronic patient-reported outcome (ePRO) mobile app and portal system supports patients with multiple chronic conditions to collaborate with primary health care providers to set and monitor health-related goals.ObjectiveThis study aims to estimate the cost of ePRO and the cost utility of the ePRO intervention compared with usual care provided to patients with multiple chronic conditions and complex needs living in the community, from the perspective of the publicly funded health care payer in Ontario, Canada.MethodsWe developed a decision tree model to estimate the incremental cost per quality-adjusted life year (QALY) gained for the ePRO tool versus usual care over a time horizon of 15 months. Resource utilization and effectiveness of the ePRO tool were drawn from a randomized clinical trial with 6 family health teams involving 45 participants. Unit costs associated with health care utilization (adjusted to 2020 Canadian dollars) were drawn from literature and publicly available sources. A series of sensitivity analyses were conducted to assess the robustness of the findings.ResultsThe total cost of the ePRO tool was CAD $79,467 (~US $ 63,581; CAD $1733 [~US $1386] per person). Compared with standard care, the ePRO intervention was associated with higher costs (CAD $1710 [~US $1368]) and fewer QALYs (–0.03). The findings were consistent with the clinical evidence, suggesting no statistical difference in health-related quality of life between ePRO and usual care groups. However, the tool would be considered a cost-effective option if it could improve by at least 0.03 QALYs. The probability that the ePRO is cost-effective was 17.3% at a willingness-to-pay (WTP) threshold of CAD $50,000 (~US $40,000)/QALY.ConclusionsThe ePRO tool is not a cost-effective technology at the commonly used WTP value of CAD $50,000 (~US $40,000)/QALY, but long-term and the societal impacts of ePRO were not included in this analysis. Further research is needed to better understand its impact on long-term outcomes and in real-world settings. The present findings add to the growing evidence about eHealth interventions’ capacity to respond to complex aging populations within finite-resourced health systems.Trial RegistrationClinicalTrials.gov NCT02917954; https://clinicaltrials.gov/ct2/show/NCT02917954

Assessing the Implementation and Effectiveness of the Electronic Patient-Reported Outcome Tool for Older Adults With Complex Care Needs: Mixed Methods Study.

BackgroundGoal-oriented care is being adopted to deliver person-centered primary care to older adults with multimorbidity and complex care needs. Although this model holds promise, its implementation remains a challenge. Digital health solutions may enable processes to improve adoption; however, they require evaluation to determine feasibility and impact.ObjectiveThis study aims to evaluate the implementation and effectiveness of the electronic Patient-Reported Outcome (ePRO) mobile app and portal system, designed to enable goal-oriented care delivery in interprofessional primary care practices. The research questions driving this study are as follows: Does ePRO improve quality of life and self-management in older adults with complex needs? What mechanisms are likely driving observed outcomes?MethodsA multimethod, pragmatic randomized controlled trial using a stepped-wedge design and ethnographic case studies was conducted over a 15-month period in 6 comprehensive primary care practices across Ontario with a target enrollment of 176 patients. The 6 practices were randomized into either early (3-month control period; 12-month intervention) or late (6-month control period; 9-month intervention) groups. The primary outcome measure of interest was the Assessment of Quality of Life-4D (AQoL-4D). Data were collected at baseline and at 3 monthly intervals for the duration of the trial. Ethnographic data included observations and interviews with patients and providers at the midpoint and end of the intervention. Outcome data were analyzed using linear models conducted at the individual level, accounting for cluster effects at the practice level, and ethnographic data were analyzed using qualitative description and framework analysis methods.ResultsRecruitment challenges resulted in fewer sites and participants than expected; of the 176 target, only 142 (80.6%) patients were identified as eligible to participate because of lower-than-expected provider participation and fewer-than-expected patients willing to participate or perceived as ready to engage in goal-setting. Of the 142 patients approached, 45 (32%) participated. Patients set a variety of goals related to self-management, mental health, social health, and overall well-being. Owing to underpowering, the impact of ePRO on quality of life could not be definitively assessed; however, the intervention group, ePRO plus usual care (mean 15.28, SD 18.60) demonstrated a nonsignificant decrease in quality of life (t24=−1.20; P=.24) when compared with usual care only (mean 21.76, SD 2.17). The ethnographic data reveal a complex implementation process in which the meaningfulness (or coherence) of the technology to individuals’ lives and work acted as a key driver of adoption and tool appraisal.ConclusionsThis trial experienced many unexpected and significant implementation challenges related to recruitment and engagement. Future studies could be improved through better alignment of the research methods and intervention to the complex and diverse clinical settings, dynamic goal-oriented care process, and readiness of provider and patient participants.Trial RegistrationClinicalTrials.gov NCT02917954; https://clinicaltrials.gov/ct2/show/NCT02917954

Acceptability, Validity, and Engagement With a Mobile App for Frequent, Continuous Multiyear Assessment of Youth Health Behaviors (mNCANDA): Mixed Methods Study.

BackgroundLongitudinal studies of many health behaviors often rely on infrequent self-report assessments. The measurement of psychoactive substance use among youth is expected to improve with more frequent mobile assessments, which can reduce recall bias. Researchers have used mobile devices for longitudinal research, but studies that last years and assess youth continuously at a fine-grained, temporal level (eg, weekly) are rare. A tailored mobile app (mNCANDA [mobile National Consortium on Alcohol and Neurodevelopment in Adolescence]) and a brief assessment protocol were designed specifically to provide a feasible platform to elicit responses to health behavior assessments in longitudinal studies, including NCANDA (National Consortium on Alcohol and Neurodevelopment in Adolescence).ObjectiveThis study aimed to determine whether an acceptable mobile app system could provide repeatable and valid assessment of youth’s health behaviors in different developmental stages over extended follow-up.MethodsParticipants were recruited (n=534; aged 17-28 years) from a larger longitudinal study of neurodevelopment. Participants used mNCANDA to register reports of their behaviors for up to 18 months. Response rates as a function of time measured using mNCANDA and participant age were modeled using generalized estimating equations to evaluate response rate stability and age effects. Substance use reports captured using mNCANDA were compared with responses from standardized interviews to assess concurrent validity. Reactivity was assessed by evaluating patterns of change in substance use after participants initiated weekly reports via mNCANDA. Quantitative feedback about the app was obtained from the participants. Qualitative interviews were conducted with a subset of participants who used the app for at least one month to obtain feedback on user experience, user-derived explanations of some quantitative results, and suggestions for system improvements.ResultsThe mNCANDA protocol adherence was high (mean response rate 82%, SD 27%) and stable over time across all age groups. The median time to complete each assessment was 51 s (mean response time 1.14, SD 1.03 min). Comparisons between mNCANDA and interview self-reports on recent (previous 30 days) alcohol and cannabis use days demonstrate close agreement (eg, within 1 day of reported use) for most observations. Models used to identify reactivity failed to detect changes in substance use patterns subsequent to enrolling in mNCANDA app assessments (P>.39). Most participants (64/76, 84%) across the age range reported finding the mNCANDA system acceptable. Participants provided recommendations for improving the system (eg, tailoring signaling times).ConclusionsmNCANDA provides a feasible, multi-year, continuous, fine-grained (eg, weekly) assessment of health behaviors designed to minimize respondent burden and provides acceptable regimes for long-term self-reporting of health behaviors. Fine-grained characterization of variability in behaviors over relatively long periods (eg, up to 18 months) may, through the use of mNCANDA, improve our understanding of the relationship between substance use exposure and neurocognitive development.

An intelligent learning framework for Industry 4.0 through automated planning

AbstractThe fourth industrial revolution, also known as Industry 4.0, has already begun enabling technological innovations in various domains, especially in the manufacturing and service industry. The conditions of the disruptive new product‐service emergence and quickly changing work environments require cultivating a competitive workforce that is equipped with essential knowledge and skills in Industry 4.0. The work presented herein identifies key competencies required for engineers in Industry 4.0 from the extant literature and offers a mechanism to facilitate knowledge and skills acquisition. Specifically, an automated planning software tool, iCoursePlanner, is proposed to recommend an optimized pathway to attain the identified competencies through not only offline courses in a college but also Massive Open Online Courses. This tool is based on a mobile app system in a cloud environment integrated with an optimization model for automated planning. iCoursePlanner uses the initial knowledge and goal knowledge of a learner to suggest a customized course plan. Case studies are conducted for industrial and manufacturing engineering students at Iowa State University and Bradley University to demonstrate how it can be used to help a student to acquire key competencies for Industry 4.0. The originality of the proposed automated course planning tool is rooted in the personalized course plan for each student that considers individual educational background and goal knowledge.