Real-time Data Capture Research Articles

Development of LoRaWAN-based IoT system for water quality monitoring in rural areas

This article delineates the design and deployment of an innovative real-time water quality monitoring system tailored for rural regions, focusing on monitoring the water resource quality parameters. We propose a solar-powered, waterproof, portable, and Internet of Things (IoT)-enabled solution that leverages Long Range Wide Area Network (LoRaWAN) technology. Central to this system is a sophisticated LoRa node outfitted with an array of sensors for capturing key water parameters, such as pH, total dissolved solids, turbidity and temperature. A conjunction of an Arduino microcontroller-based board and a LoRa shield facilitates real-time data capture and transmission to a LoRaWAN gateway. The acquired data is transmitted to The Things Network server, which is seamlessly integrated with a ThingSpeak web-based IoT server and ThingView mobile applications. We incorporate a solar cell with a solar shield to ensure sustainable energy provision for powering the entire system through a rechargeable battery. This allows users to access vital water quality information online simultaneously and continuously in real-time. As a testament to its robustness, the system was empirically tested at Gambang Lake to demonstrate its effectiveness, functionality, buoyancy, and waterproof capabilities. We further validated the results by comparing them with laboratory sample analysis findings. Experimental evaluations confirmed the system's reliability, as evidenced by the strong agreement between the water conditions measured using our solution and those obtained from laboratory instruments. Moreover, our system efficiently and remotely updated data across multiple IoT platforms using the LoRa radio interface over the LoRaWAN gateway.

Digital twin for indoor condition monitoring in living labs: University library case study

Digital twin, a technology bridging the physical and digital domains, has found extensive application in digitally advanced industries. However, its adoption in the construction sector remains limited, hindered by challenges related to construction, integration with real-time data capture, and visualisation platforms. This paper presents a construction industry digital twin that combines Building Information Modelling-based data visualisation with an Internet of Things-driven live data capture platform, outlining a methodology for its development. The digital twin was implemented in a university library embodying the ‘Living Lab’ concept to account for the nuances associated with live environments. It integrates sensors with Building Information Modelling to offer a semiotic representation of the library's internal conditions in digital twin, empowering facility managers to proactively optimize thermal, lighting, and air quality.

LGBTQ+ new and expectant parents' experiences of perinatal services during the UK's first COVID-19 lockdown.

COVID-19 created specific challenges for new and expectant parents and perinatal services. Services changed rapidly in the United Kingdom (UK), including the withdrawal of home birth services, birth center closures, and restrictions on the number of birth partners allowed in the birth room. The purpose of this study was to examine how these changes affected the experiences of LGBTQ+ parents in the UK. An online survey was conducted in April 2020 to provide real-time data capture of new and expectant families' experiences. It was open to those in the third trimester, or to those who had given birth since the beginning of the first UK lockdown period, and their partners. The survey asked open-ended questions about perinatal experiences. Demographic data were also collected, including sexual orientation and gender. Responses were collected from 1754 participants, including 76 who self-identified as LGBTQ+. Thematic analysis identified that LGBTQ+ new and expectant parents faced similar issues to cisgendered, heterosexual expectant parents, though additional concerns were also noted relating to support and recognition. Heterocentric policies negatively affect lesbian families. Non-birthing co-mothers feared invalidation as parents. Sexual minority pregnant women were more likely than heterosexual pregnant women to consider additional birth supporters and to consider freebirthing. Service changes introduced in the pandemic were cisheteronormative, creating additional challenges for LGBTQ+ new and expectant parents and compounding existing inequalities. When planning, changing, or evaluating perinatal services, specific consideration is needed to include birthing parents who are not mothers and mothers who did not give birth. If appropriate care is not available, consequences may include impaired perinatal wellbeing and restricted birth choices. Including sexual orientation and gender in data collection enables different perspectives to be considered.

New Highly Portable Simulator (SECMA) Based on Virtual Reality for Teaching Essential Skills in Minimally Invasive Surgeries

This study presents a new minimal access surgery training system, SECMA, and its constructive validation to determine its usefulness for training basic laparoscopic skills. SECMA is an affordable, highly portable, mobile virtual reality training tool for laparoscopic techniques that integrates the Oculus Quest with a mechanical interface for surgeon simulation of forceps using the hand controllers of these devices. It allows the execution of structured activities (supported by virtual scenarios simulating operating rooms developed in Unity), performance evaluation, and real-time data capture. Two experiments were carried out: 1) coordination; and 2) capture and transport, with a total of 21 individuals divided into two groups: a novice group (inexperienced) of 10 participants and an expert group (>100 endoscopic procedures) of 11 participants. Total task time score, right-hand speed, path length, and other metrics from several consecutive runs on the simulator were compared between experts and novices. Data automatically recorded by SECMA during the experiments were analyzed using hypothesis tests, linear regressions, analysis of variance, principal component analysis, and machine learning-supervised classifiers. In the experiments, the experts scored significantly better than the novices in all the parameters used. The tasks evaluated discriminated between the skills of experienced and novice surgeons, giving the first indication of construct validity for SECMA.

Personalized driving safety: Using telematics to reduce risky driving behavior among young drivers

Introduction: The role of real-time data capture (via telematics technology) is gathering prominence as a strategy to provide feedback to young drivers about important road safety issues. Method: A naturalistic driving study was undertaken to determine whether providing personalized feedback (via a smartphone app) to young provisional drivers aged 17–20 years living in metropolitan and regional Western Australia (WA) reduced their risky driving behavior compared to a control group who did not receive feedback. Speeding over the posted speed limit, harsh decelerations (braking), harsh accelerations and overall driving performance, were recorded continuously using the smartphone app during the 11-week study. Four separate Generalised Estimating Equations (GEE) linear regression models were undertaken after accounting for relevant confounders including driving exposure to determine the difference between the intervention and control group for the 4 driving outcomes obtained from the smartphone app. Results: The study found that there was no significant change in overall driving scores between the intervention and control groups (p = 0.35). However, the overall driving score significantly improved by 0.19 points for young provisional drivers who lived in regional areas compared to those in the metropolitan area (p = 0.05) after adjusting for potential confounders. There was also no significant change in harsh braking scores (p = 0.46) and harsh acceleration scores between the intervention and control groups (p = 0.26) However, harsh acceleration scores improved by 0.37 points for females compared to males (p = 0.04). Lastly, there was no significant change in speed scores between the control and intervention groups (p = 0.72). However, the speed scores of participants who lived in regional WA improved by 0.22 points compared to those in the metropolitan area (p = 0.02). Furthermore, for every 1,000 km travelled, speed scores worsened by −0.08 points (p < 0.01) regardless of group. Conclusions: The study did not find any statistical difference in the driving outcomes examined; however the treatment effects for feedback were consistently in the expected positive direction. Young drivers in regional WA also improved their speeding scores and overall driving performance scores compared to young drivers in the metropolitan area. Females, also significantly improved their harsh deceleration scores compared to males, regardless of group allocation. These results highlight the use of smartphone telematics as an opportunity to not only enhance the safety of provisional young drivers but also provide data-informed decision making and policy development.

Capture of real-time data from electronic health records: scenarios and solutions.

The integration of real-time data (RTD) in the electronic health records (EHRs) is transforming the healthcare of tomorrow. In this work, the common scenarios of capturing RTD in the healthcare from EHRs are studied and the approaches and tools to implement real-time solutions are investigated. Delivering RTD by representational state transfer (REST) application programming interfaces (APIs) is usually accomplished through a Publish-Subscribe approach. Common technologies and protocols used for implementing subscriptions are REST hooks and WebSockets. Polling is a straightforward mechanism for obtaining updates; nevertheless, it may not be the most efficient or scalable solution. In such cases, other approaches are often preferred. Database triggers and reverse proxies can be useful in RTD scenarios; however, they should be designed carefully to avoid performance bottlenecks and potential issues. The implementation of subscriptions through REST hooks and WebSocket notifications using a Fast Healthcare Interoperability Resources (FHIR) REST API, as well as the design of a reverse proxy and database triggers is described. Reference implementations of the solutions are provided in a GitHub repository. For the reverse proxy implementation, the Go language (Golang) was used, which is specialized for the development of server-side networking applications. For FHIR servers a python script is provided to create a sample Subscription resource to send RTD when a new Observation resource for specific patient id is created. The sample WebSocket client is written using the "websocket-client" python library. The sample RTD endpoint is created using the "Flask" framework. For database triggers a sample structured query language (SQL) query for Postgres to create a trigger when an INSERT or UPDATE operation is executed on the FHIR resource table is available. Furthermore, a use case clinical example, where the main actors are the healthcare providers (hospitals, physician private practices, general practitioners and medical laboratories), health information networks and the patient are drawn. The RTD flow and exchange is shown in detail and how it could improve healthcare. Capturing RTD is undoubtedly vital for health professionals and successful digital healthcare. The topic remains unexplored especially in the context of EHRs. In our work for the first time the common scenarios and problems are investigated. Furthermore, solutions and reference implementations are provided which could support and contribute to the development of real-time applications.

Software development process of Neotree - a data capture and decision support system to improve newborn healthcare in low-resource settings.

The global priority of improving neonatal survival could be tackled through the universal implementation of cost-effective maternal and newborn health interventions. Despite 90% of neonatal deaths occurring in low-resource settings, very few evidence-based digital health interventions exist to assist healthcare professionals in clinical decision-making in these settings. To bridge this gap, Neotree was co-developed through an iterative, user-centered design approach in collaboration with healthcare professionals in the UK, Bangladesh, Malawi, and Zimbabwe. It addresses a broad range of neonatal clinical diagnoses and healthcare indicators as opposed to being limited to specific conditions and follows national and international guidelines for newborn care. This digital health intervention includes a mobile application (app) which is designed to be used by healthcare professionals at the bedside. The app enables real-time data capture and provides education in newborn care and clinical decision support via integrated clinical management algorithms. Comprehensive routine patient data are prospectively collected regarding each newborn, as well as maternal data and blood test results, which are used to inform clinical decision making at the bedside. Data dashboards provide healthcare professionals and hospital management a near real-time overview of patient statistics that can be used for healthcare quality improvement purposes. To enable this workflow, the Neotree web editor allows fine-grained customization of the mobile app. The data pipeline manages data flow from the app to secure databases and then to the dashboard. Implemented in three hospitals in two countries so far, Neotree has captured routine data and supported the care of over 21,000 babies and has been used by over 450 healthcare professionals. All code and documentation are open source, allowing adoption and adaptation by clinicians, researchers, and developers.

Embedding electronic patient-reported outcome measures into routine care for patients with stage III MELanoma (ePROMs-MEL): protocol for a prospective, longitudinal, mixed-methods pilot study

IntroductionThe benefits of patient-reported feedback, using questionnaires that allow patients to report how they feel and function without any interpretation from healthcare professionals, are well established. However, patient-reported outcomes measures...

Reorganizing after the pandemic: A chance to energize physical activity promotion – comment on Hohberg et al.

In this commentary on “What is needed to promote physical activity? – Current trends and new perspectives in theory, intervention, and implementation” I discuss my support for the many health, social, and economic benefits of moving more and sitting less as detailed by the authors. I discuss my agreement with the challenges of physical inactivity and sedentary behavior during the COVID-19 pandemic, and that while effective promotion initiatives founded on socioecological whole system approaches seem most logical, the role of individual is still essential for downstream uptake of physical activity. Like the authors, I include my support for the testing, development, and assumptions underlying dual-process theories using real time data-capture, in addition to more sophisticated longitudinal dynamic modeling to translate findings into just-in-time intervention approaches. In addition, however, I highlight it is still important for researchers and practitioners to focus on the role of reflective factors, such as building strong intentions to engage in physical activity, and subsequent self-regulation skills to translate these intentions into action. Furthering our understanding on the distinctions between initiation and maintenance of movement behaviors is important to advance theory and practice and the role of apex-system variables such as self- and social identity may hold considerable utility in physical activity science. I suggest that finding meaning in movement behaviors beyond exercise is critical to reorganizing and reenergizing after the pandemic to promote physical activity.

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network.

Malaria-VisAnalytics: a tool for visual exploratory analysis of Brazilian public malaria data

BackgroundData integration and visualisation techniques have been widely used in scientific research to allow the exploitation of large volumes of data and support highly complex or long-lasting research questions. Integration allows data from different sources to be aggregated into a single database comprising variables of interest for different types of studies. Visualisation allows large and complex data sets to be manipulated and interpreted in a more intuitive way.MethodsIntegration and visualisation techniques were applied in a malaria surveillance ecosystem to build an integrated database comprising notifications, deaths, vector control and climate data. This database is accessed through Malaria-VisAnalytics, a visual mining platform for descriptive and predictive analysis supporting decision and policy-making by governmental and health agents.ResultsExperimental and validation results have proved that the visual exploration and interaction mechanisms allow effective surveillance for rapid action in suspected outbreaks, as well as support a set of different research questions over integrated malaria electronic health records.ConclusionThe integrated database and the visual mining platform (Malaria-VisAnalytics) allow different types of users to explore malaria-related data in a user-friendly interface. Summary data and key insights can be obtained through different techniques and dimensions. The case study on Manaus can serve as a reference for future replication in other municipalities. Finally, both the database and the visual mining platform can be extended with new data sources and functionalities to accommodate more complex scenarios (such as real-time data capture and analysis).

In-situ Field Gamma Spectrometry in a Radionuclide Air Sampler.

The work being presented is on the development of a system to measure the speciation of airborne radionuclide emissions from the environment during a nuclear emergency. On-site air sampling measurements that were conducted during the Fukushima Daiichi accident were limited because field teams had to be sent out to run the sampling systems and retrieve the filters for gamma spectrometry analysis in a separate laboratory. The start of air sampling was delayed, and it was impossible for emergency responders to use the information about the airborne radionuclide composition in a timely way. The goal of the current study is to develop a system that could provide live, near real-time information about the concentrations of different radionuclides in the air without having to rely on human intervention. The development of the prototype in the current work is largely being enabled by Cd-Zn-Te spectrometers, which provide reasonably high-resolution spectrometry given that it is a room temperature sensor, and allow the measurements to be conducted in the field. A custom filter cartridge has been designed to hold a pair of aerosol and iodine filters in place while keeping the gamma spectrometers as close as possible in order to obtain high count rate efficiencies. A single cartridge holds both filters and has an internal flow channel directing the air flow between them. The cartridge design also facilitates replacing the filters as the accumulated radioactivity on the filters becomes too high. An automation system can move a filter cartridge from the fresh cartridge storage bank to the sampling location (filtration and gamma spectrometry) and return the used filter cartridge to the used cartridge storage bank. The radionuclide air sampling system prototype has been designed and constructed. It has been tested with fixed sources located on the respective aerosol and iodine filters. The real-time data capture aspects of the system were also demonstrated with a live 131I capture experiment. The projected performance of the system during a reactor accident was also simulated, emulating the characteristic detector efficiencies and projecting how the airborne concentrations could be reconstructed. The study has designed and constructed a radionuclide air sampler that could be used for measuring airborne radioactivity in emissions from a nuclear accident. Because the gamma spectrometry measurements are done in situ with good resolution and the system is automated, it would allow data to be transmitted back to an emergency operations center immediately rather than having to wait for additional laboratory analysis.

Real-time sensing and low-cost experimental setup for water quantity investigation in Nature-based Solutions

Abstract Nature-based Solutions (NbS) Regulatory Ecosystem Services are less tangible and more complex to quantify. Among these, research to quantify water regulation services provided by Blue-Green Infrastructure is especially relevant for a range of different applications. However, to reach this quantification, experimentalists usually need to measure the flow rate, which can be costly if using high-end industry standard sensors. The present article brings, thus, a low-cost experimental setup for real-time data capture and logging using the US-025 ultrasonic sensor. The proposed setup measures a weir tanks’ water level in order to indirectly estimate the flow rate. A successful pilot experiment is described, estimating the water quantity performance of a vegetated roof in comparison to a ceramic tiled roof. For a same event, flow rate measures taken by the proposed setup are compared to readings from a rain gauge and results show a close trend. The performance of the vegetated roof has shown substantial rainfall retention and detention when compared to the ceramic roof. It is concluded that the setup is a cost-effective tool that can be attached to inlets and outlets of different NbS for characterising a range of water flow rates capable of supporting laboratory and field data capture.

History, Classification, and Clinical Utility of Connected Insulin Pens

Effective management of diabetes relies heavily on insulin administration, often requiring multiple daily injections. The emergence of connected insulin pens has brought about a paradigm shift in diabetes care, empowering patients and healthcare providers with enhanced tools and capabilities. Connected insulin pens are innovative devices that integrate wireless connectivity and advanced technology with traditional insulin delivery systems. These pens facilitate real-time data capture, analysis, and transmission, enabling individuals with diabetes to monitor and manage their condition more efficiently. The pens typically incorporate features such as dose tracking, glucose monitoring integration, and automated data logging, thereby streamlining diabetes self-care. In this review, we discuss the history, classification, and clinical utility of connected insulin pens.

Impact of Precision Nutrition Counseling for Active Duty Service Members at Risk for Metabolic Syndrome

ObjectivesMetabolic Syndrome (MetS) is characterized by abdominal obesity, dyslipidemia, elevated fasting blood glucose (FBG), and hypertension; personalized nutrition counseling and wellness applications have demonstrated positive results for weight management when coupled with high levels of participant engagement and motivation.MethodsIn a prospective RCT RDs conducted personalized nutrition counseling using results of targeted sequencing, biomarkers, and expert recommendations to reduce risk for MetS. Treatment Group (TG) received six weekly sessions; Control Group (CG) received a pamphlet of expert recommendations upon randomization. A digital application provided real-time health data capture. Anthropometrics and BP were evaluated at baseline, 6, & 12 wks; biomarkers at baseline & 12 wks. Primary outcome was change in weight at 12 wks. Statistical analyses included descriptives and t-test or ANOVA; significance set at P < .05.Results138 subjects enrolled from Nov 2019 - Feb 2021 between 2 sites [Northwest (NW) in WA; Southwest (SW) in TX]; 107 completed the study with n = 70 in the TG. Demographics: 66% male, mean age 31 yrs, 66% married, 49% Caucasian, non-Hispanic. There were no differences between TG and CG at baseline. High deleterious variant prevalence found for genes/SNPs associated with obesity (40%), cholesterol (38%), and BP (58%). 65% of subjects had 25(OH) D less than 30 ng/mL upon enrollment. In NW cohort primary outcome of change in weight at 12 wks was not significant p = .34. Significant difference at 6 wks noted for TG change in weight p = 0.02; fat mass p = .01; BMI p = 0.02; and % body fat p = 0.01. BP significant at 12 wks, both systolic (p = .04) and diastolic (p = .04). Change in 25(OH)D favored TG, p = 0.01. SW TG had greater reduction in waist circ p = .04 at 6 wks. Digital app had low adherence and poor correlation with ASA24 reference.ConclusionsSignificant progress was achieved in the TG at 6 weeks although not sustained at 12 weeks. The concept of nutrigenomics was well-received in this cohort.Funding SourcesTriService Nursing Research Program.

The UpSMART Accelerator: driving digital innovation to change the conduct of early phase cancer medicine trials

Digitalizing clinical trials provide an opportunity to address challenges faced in the Phase I trial settings, where near real-time data capture and data interpretation are prerequisites for iterative decision-making to rapidly adapt trial designs based on emerging insights. Although digital technologies have driven significant improvements in many businesses and organizations, the adoption of digital technologies in clinical trials has been slow. In recognition of this lag, the UpSMART consortium, a 5-year funded program (2020-2024), has been established in Europe between the UK, Spain, and Italy to embrace digital technologies and drive benefits to patients. The consortium, led by the Cancer Research UK Manchester Institute Cancer Biomarker Centre, aims to ’digitalize’ Experimental Cancer Medicine Centres in the UK and Early Drug Development Units in Spain and Italy by open-sourcing and sharing digital healthcare products between participating centers across the consortium. The goal is to optimize data capture and interpretation thus accelerating Phase I clinical research to ultimately benefit patients by allowing faster access to tomorrow’s medicines.

Radar Cross Section Analysis of Two Wind Turbines via a Novel Millimeter-Wave Technique and Scale Model Measurements

A novel, low cost, highly accurate, millimeter-wave RCS characterization method is developed and presented in this paper. In order to develop and verify the validity of the proposed method, full scale models and scale models of the horizontal-axis wind turbine (HAWT) and Crossflow turbines have been simulated and compared for a case study. The RCS of a scaled Crossflow turbine model was then experimentally verified using the novel method presented at frequencies of 76-81GHz. The proposed method utilizes the AWR1843BOOST evaluation board and DCA1000EVM real-time high-speed data capture card from Texas Instruments. To the best of the authors’ knowledge, this is the first RCS analysis of a scaled model performed at the mm-wave frequencies of 76-81GHz. This novel method is quick, simple, and fully automated, while maintaining high accuracy. Additionally, this has been achieved at a low cost using commercially available off the shelf parts. Good agreement was observed between the simulated and experimental results. Comparing the RCS data of the two turbines, it appears that the Crossflow turbine geometry offers a lower RCS and Doppler spectrum contamination as compared with a traditional horizontal axis wind turbine structure. These results are necessary and useful in allaying the increasing concerns regarding wind turbine radar interference, which have appeared as a result of the widespread adoption of wind power generation in recent years.

Advancing understanding of dietary and movement behaviours in an Asian population through real-time monitoring: Protocol of the Continuous Observations of Behavioural Risk Factors in Asia study (COBRA).

BackgroundModifiable risk factors for non-communicable diseases, including eating an unhealthy diet and being physically inactive, are influenced by complex and dynamic interactions between people and their social and physical environment. Therefore, understanding patterns and determinants of these risk factors as they occur in real life is essential to enable the design of precision public health interventions.ObjectiveThis paper describes the protocol for the Continuous Observations of Behavioural Risk Factors in Asia study (COBRA). The study uses real-time data capture methods to gain a comprehensive understanding of eating and movement behaviours, including how these differ by socio-demographic characteristics and are shaped by people's interaction with their social and physical environment.MethodsCOBRA is an observational study in free-living conditions. We will recruit 1500 adults aged 21–69 years from a large prospective cohort study. Real-time data capture methods will be used for nine consecutive days: an ecological momentary assessment app with a global positioning system enabled to collect location data, accelerometers to measure movement, and wearable sensors to monitor blood glucose levels. Participants receive six EMA surveys per day between 8 a.m. and 9.30 p.m. to capture information on behavioural risk factors including eating behaviours and diet composition movement behaviours (physical activity, sedentary behaviour, sleep), and related contextual factors. The second wave of ecological momentary assessment surveys with a global positioning system enabled will be sent 6 months later. Data will be analysed using generalised linear models to examine associations between behavioural risk factors and contextual determinants.DiscussionFindings from this study will advance our understanding of dietary and movement behaviours as they occur in real-life and inform the development of personalised interventions to prevent chronic diseases.

The Effects of Remote Activity Monitoring on Family Caregivers of People Living With Dementia Over an 18-Month Period

Technology interventions for older persons and long-term care are generally utilized as real-time data capture tools to complement clinical or family care for older persons or as interventions themselves designed to improve important dementia care outcomes. Although research on novel technological interventions for people with Alzheimer's disease and related dementias (ADRD) and their family caregivers has grown considerably in the past two decades, much of this work continues to focus on design, feasibility, and acceptability (with a need for conceptual refinement in these areas) and less on controlled outcome studies. The objective of this experimental mixed methods demonstration was to determine the 18-month effectiveness of remote activity monitoring (RAM) technology in improving outcomes among family caregivers of community-dwelling persons with dementia. We used an embedded experimental mixed methods design, collecting qualitative data within the structure of a traditional randomized controlled trial ([QUAN+qual]→QUAN) over an 18-month period for 171 dementia caregivers. Change in caregiver self-efficacy, sense of competence, and caregiver distress served as the main quantitative outcomes of interest. Individual growth curve models indicated that the RAM technology did not have direct effects on caregiving outcomes, and although the qualitative findings indicated several potential moderators of RAM effectiveness on caregiving outcomes, the inclusion of these qualitatively-identified moderators did not result in statistically significant (p < .05) effects. Ensuring effective human care management alongside RAM technology may help to overcome the barriers reported by dementia caregivers in this demonstration study.

Product development methodology: non-quality caused in production by mistakes in product development, its measurement and improvement integration in the product development process

A new quality focused product development (NPD) methodology, applying lean manufacturing principles and Industry 4.0 paradigms is described in this paper. This methodology has been tested in a tailor-made equipment manufacturer for the retail sector, where tight product development times are needed. This requires a high agility in the processes, meaning the overlap of phases, the integration of processes, cooperation, and automation of tasks. We have generated a system concept that collects all the manufacturing incidences related to NPD, integrated in workshops stations, creating a system that enables real time data capture and a valuable feedback loop to NPD processes. Its implementation showed an improvement over 10% in the quality of the NPD process in a period of 3 months, an increase of the productivity in both product development and manufacturing areas, and a boost of the internal customer satisfaction in manufacturing area with the product development area.