Reliable Data Collection Research Articles

AUV-aided isolated sub-network prevention for reliable data collection by underwater wireless sensor networks

AUV-aided isolated sub-network prevention for reliable data collection by underwater wireless sensor networks

Mitigating Dead Node Impact on Coverage and Connectivity in Wireless Sensor Networks Using a Hybrid Approach

Wireless sensor networks’ coverage and efficient connectivity are pivotal for reliable data collection and communication. However, dead nodes, resulting from hardware failure or power depletion, can affect coverage and connectivity, leading to information loss and degraded performance. Previous research in the same context indicates the need for further investigation to achieve optimal trade-offs in network resource allocation. This research introduces a hybrid Artificial Bee Colony-Sequential Re-connectivity and Coverage Algorithm (ABC-SRCA) approach, combining the ABC algorithm with a developed SRCA. The ABC algorithm adjusts sensor node placement to maximize the coverage and minimize holes, while the SRCA algorithm restores connectivity by reconnecting the network when nodes fail. The approach uses probabilistic selection to explore various solutions, making the approach adaptive to diverse scenarios. The simulation outcomes indicate that the ABC-SRCA method enhances coverage accuracy by up to 30% compared to ABC and SRCA when they are used separately. In addition, the rate of connectivity error detection decreases by about 25%, highlighting the method’s effectiveness in dynamic network conditions. The approach also surpasses existing methods, including Genetic Algorithms and Sensing Radius Adaptive Coverage Control (SRACC), by achieving coverage level up to 98% while conserving resources. The ABC-SRCA achieves better energy consumption than Particle Swarm Optimization (PSO) and PSO Voronoi Diagram and achieves competent energy when compared with SRACC. The hybrid approach provides an effective solution for ensuring efficient and reliable network operations, supporting the successful deployment of WSNs in diverse applications.

Confirmation of the occurrence of three fish species in Maltese waters based on specimens collected through the EC’s Data Collection Framework

Based on the examination of recently available actual specimens, obtained through the reporting required by Member States for the European Commission’s Data Collection Framework, three fishes whose presence in Maltese waters was uncertain, are now confirmed as occurring: Dentex maroccanus Valenciennes, 1830, Sphyraena viridensis Cuvier, 1829, and Sphyraena sphyraena Linnaeus, 1758. Accurate species identifica-tion is essential for reliable data collection during routine monitoring activities. It is suggested that in local fisheries records, D. maroccanus was previously misidentified as D. macrophthalmus, while despite past re-ports of alien sphyraenids, current findings confirm only native Mediterranean species of Sphyraena to be present in Maltese waters.

Wireless Potentiometric Monitoring of Microbial Biofilm Formation: In Vitro and Ex Vivo Studies of Gram‐Positive and Gram‐Negative Bacteria

A wireless potentiometric sensor offers a robust platform for detecting microbial growth, which is crucial for managing infected wounds that can lead to serious complications such as tissue spread, systemic infection, or sepsis, potentially resulting in life‐threatening conditions. Herein, a solid‐state potentiometric working/reference electrode system with a Bluetooth‐enabled system on a chip, supporting continuous wireless monitoring of microbial growth is shown. The sensor monitors open circuit potentials (OCPs) in culture media, which significantly decrease due to bacterial growth after inoculation with Gram‐positive Staphylococcus aureus, Gram‐negative Pseudomonas aeruginosa, and Escherichia coli. Notably, Staphylococcus aureus demonstrates lower electrogenic activity compared with the Gram‐negative bacteria, likely owing to its reduced viability. Following thorough in vitro testing, the sensor is also evaluated ex vivo. Stable connections between the sensor and a smartphone receiver ensure reliable data collection and processing, facilitating remote monitoring. A slight decrease in OCP is observed in rat wounds inoculated with Staphylococcus aureus and significant decrease with Pseudomonas aeruginosa. Incorporation of the wireless sensing module for continuous measurement and data collection can greatly enhance early detection capabilities regarding bacterial infections in wounds. This setup offers a convenient and effective method for point‐of‐care sensing, significantly advancing the management and treatment of wound infections.

Quality indicators in laboratory medicine: a 2020-2023 experience in a Chinese province.

The identification of reliable quality indicators (QIs) in the total testing process (TTP) is a pivotal step in quantifying laboratory service quality. This study comprehensively evaluated the performance quality and explored the factors affecting laboratory quality in Guangdong Province, China, by analyzing the results of QIs. The Guangdong Clinical Laboratory Center organized an external quality assessment program for QIs, and the Clinet-EQA system was used to distribute questionnaires and collect data. The results of the QIs are expressed as percentages, sigma, or minutes. The optimum, desirable, and minimum quality specifications (QSs) were defined based on the percentiles of the QIs. Furthermore, the QIs were evaluated in different disciplines and hospital grades. A total of 335 laboratories in Guangdong Province reported complete data from 2020 to 2023, and QI performance progressively improved over the years. The performance of 11 QIs attained the minimum acceptable standard (sigma value≥3), and most QIs across the diverse disciplines and hospital grades exhibited statistically significant differences. Compared to the QSs published by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Working Group on Laboratory Errors and Patient Safety (WG-LEPS), the QSs for the 15 QIs in Guangdong Province in 2023 were stricter or roughly equivalent, except for the percentage of intra-laboratory turnaround time for emergency potassium tests. From 2020 to 2023, the QIs in the post-analytical phase achieved the best performance. It is essential for laboratories to reinforce the construction of their information infrastructure, thereby guaranteeing the accurate collection of reliable data and enabling effective long-term monitoring.

Gamified Engagement for Data Crowdsourcing and AI Literacy: An Investigation in Affective Communication Through Speech Emotion Recognition

This research investigates the utilization of entertainment approaches, such as serious games and gamification technologies, to address various challenges and implement targeted tasks. Specifically, it details the design and development of an innovative gamified application named “J-Plus”, aimed at both professionals and non-professionals in journalism. This application facilitates the enjoyable, efficient, and high-quality collection of emotionally tagged speech samples, enhancing the performance and robustness of speech emotion recognition (SER) systems. Additionally, these approaches offer significant educational benefits, providing users with knowledge about emotional speech and artificial intelligence (AI) mechanisms while promoting digital skills. This project was evaluated by 48 participants, with 44 engaging in quantitative assessments and 4 forming an expert group for qualitative methodologies. This evaluation validated the research questions and hypotheses, demonstrating the application’s diverse benefits. Key findings indicate that gamified features can effectively support learning and attract users, with approximately 70% of participants agreeing that serious games and gamification could enhance their motivation to practice and improve their emotional speech. Additionally, 50% of participants identified social interaction features, such as collaboration, as most beneficial for fostering motivation and commitment. The integration of these elements supports reliable and extensive data collection and the advancement of AI algorithms while concurrently developing various skills, such as emotional speech articulation and digital literacy. This paper advocates for the creation of collaborative environments and digital communities through crowdsourcing, balancing technological innovation in the SER sector.

WHO survey tool for behavioral insights on COVID-19: A psychometric study for translation, adaptation and content validity in Persian

Introduction: Different instruments are employed to assess public behavior and understanding of COVID-19. The World Health Organization Survey Tool for Behavioral Insights on COVID-19 (WHO-STBIC) is an appealing and adaptable instrument for evaluating population behavioral insights. This research sought to translate, adapt, and content validate the Persian version of WHO-STBIC. Methods: Forward-backward translation of the English WHO-STBIC to Persian was done by four English language experts. The back-translated version was compared with the original version and disagreements were resolved by a team encompassing four translators and two experts in the field. To investigate the content validity of the preliminary Persian version, nine experts were invited to assess the tool through a content validity form. The internal consistency was evaluated using Cronbach’s alpha. Results: A shortened through adaptation version Persian version of WHO-STBIC (90 items in 17 subscales) was derived with minor adaptations from the original version. In terms of content validity, the modified kappa (mK) index was calculated as excellent for 86 percent (n=78) of items (mK>0.9). The mean Content validity Index (CVI) of the whole scale was 0.87 ranging from 0.46 to 0.86 for 17 subscales. Cronbach’s alpha was calculated to be 0.86. Conclusion: The Persian version of WHO-STBIC was adapted and validated with minimum modifications to be used in Iran. It was hard to say that this tool is a fast one. So, the shortened through adaptation Persian version was developed. Researchers can use these tools to ensure culturally appropriate, relevant, valid, and reliable data collection.

Development and Validation of Data Acquisition System for Real-Time Thermal Environment Monitoring in Animal Facilities

In animal facilities, monitoring and controlling the thermal environment are essential in ensuring productivity and sustainability. However, many production units face challenges in implementing and maintaining effective thermal monitoring and control systems. Given the need for Smart Livestock Farming systems, this study aimed to develop and validate an easy-to-use, low-cost embedded system (ESLC) for the real-time monitoring of dry-bulb air temperature (Tdb, in °C) and relative humidity (RH, in %) in animal production facilities. The ESLC consists of data collection/transmission modules and a server for Internet of Things (IoT) data storage. ESLC modules and standard recording sensors (SRS) were installed in prototype animal facilities. Over 21 days, their performance was evaluated based on the Data Transmission Success Rate (DTSR, in %) and Data Transmission Interval (DTI, in minutes). Additionally, agreement between the ESLC modules and the SRS was assessed using the daily mean root mean square error (RMSE) and mean relative error (RE) across different Tdb and RH ranges. The ESLC successfully collected and transmitted data to the cloud server, achieving an average DTSR of 94.04% and a predominant DTI of one minute. Regarding measurement agreement, distinct daily mean RMSE values were obtained for Tdb (0.26–2.46 °C) and RH (4.37–16.20%). Furthermore, four sensor modules exhibited mean RE values below 3.00% across all Tdb ranges, while all sensor modules showed progressively increasing mean RE values as RH levels rose. Consequently, calibration curves were established for each sensor module, achieving a high correlation between raw and corrected values (determination coefficient above 0.98). It was concluded that the ESLC is a promising solution for thermal monitoring in animal facilities, enabling continuous and reliable data collection and transmission.

Unmanned-Aerial-Vehicle Trajectory Planning for Reliable Edge Data Collection in Complex Environments.

With the rapid advancement of edge-computing technology, more computing tasks are moving from traditional cloud platforms to edge nodes. This shift imposes challenges on efficiently handling the substantial data generated at the edge, especially in extreme scenarios, where conventional data collection methods face limitations. UAVs have emerged as a promising solution for overcoming these challenges by facilitating data collection and transmission in various environments. However, existing UAV trajectory optimization algorithms often overlook the critical factor of the battery capacity, leading to potential mission failures or safety risks. In this paper, we propose a trajectory planning approach Hyperion that incorporates charging considerations and employs a greedy strategy for decision-making to optimize the trajectory length and energy consumption. By ensuring the UAV's ability to return to the charging station after data collection, our method enhances task reliability and UAV adaptability in complex environments.

A79 EAT, SLEEP, WORK, REPEAT! WEARABLES AND APPS TO TRACK IBD PATIENTS’ SYMPTOMS, DIET, SLEEP, AND PHYSICAL ACTIVITY

Abstract Background Lifestyle factors like diet, physical activity, and sleep patterns are known to influence symptoms and progression in Inflammatory Bowel Disease (IBD). However, accurately determining temporal relationships between these factors and symptoms has been hampered by a lack of reliable data collection methods. New technologies, such as wearables and smartphone apps, offer real-time, remote monitoring and allow for better understanding of their relationship with IBD symptoms. Aims To assess the feasibility of using remote monitoring tools (wearables and mobile apps) to explore the temporal relationships between symptoms, diet, and sleep in IBD patients. Methods Adult IBD patients participated in a 3-month pilot study (Track-IBD), during which they wore a fitness monitor (Oura Ring) and tracked their diet and symptoms in real-time using Keenoa (diet tracking) and Zamplo (symptom tracking) smartphone apps. Results Fifteen IBD patients (9 - Crohn’s disease, 6 - ulcerative colitis; 8 female; 12 Caucasian; mean age 41.2 years) were enrolled. The usage rates were 69.8% for Zamplo, 77.0% for Keenoa, and 77.0% for the Oura Ring. The most commonly reported symptoms were abdominal pain, diarrhea, and fatigue, which together accounted for 67% of all recorded symptoms. Symptoms were predominantly reported in the afternoon (30.1%) and late-night (49.2%), while morning and early-night periods saw fewer reports (20.7%). Most symptoms occurred within 5 hours of a meal, with peaks in meal-to-symptom latency (MTSL) at 0.5–1 hour and 4–5 hours post-meal, demonstrating an association between meal timing and symptoms. No correlation was found between MTSL and the nature of the symptoms (upper, lower, or extra-intestinal) or macronutrient intake. Wearable data analysis revealed decreased sleep quality and increased average heart rate on symptomatic days (p<0.04). Most participants found the apps easy to use (Oura: 100%, Zamplo: 93%, Keenoa: 73%) and helpful in managing their IBD (Oura: 100%, Zamplo: 87%, Keenoa: 60%). Conclusions The Track-IBD study demonstrates high adherence to monitoring diet, symptoms, and physiological measures by IBD patients over 3 months. It also shows the feasibility of combining multiple tracking technologies to assess lifestyle patterns, physiological parameters, and symptoms in real-time. Preliminary analyses identified relationships between symptoms, diet and sleep quality, highlighting the potential to identify lifestyle triggers of disease and symptom flares and provide personalized therapeutic advice to IBD patients. Funding Agencies Balsam Foundation

Effective autism spectrum disorder sensory and behavior data collection using internet of things

Wireless body area networks (WBANs) connected with wearable internet of things (WIoT) offer useful features including sensory information collection, analysis, and transmission for continuous behavior monitoring of autism spectrum disorder (ASD) patients. Due to users’ mobility and time-driven sensed data, data collection becomes very difficult. The current approach employs cluster-based multi-objective path-optimized data collection mechanisms that have experienced hotspot issues leading to loss of energy and coverage problems near the base stations. This work presents the high energy and reliable sensory and behavior data collection (HERSBDC) mechanism to address the research difficulties. To ensure network coverage, the HERSBDC initially provides a new uneven clustering mechanism. Next, multi-objective-based cluster head (CH) selection metrics are proposed. The final step is the creation of a multi-objective routing path to gather vital ASD data more reliably and energy-efficiently. Comparing the proposed HERSBDC algorithm to the low energy adaptive cluster-hierarchy (LEACH)-based, and distributed energy-efficient clustering and routing (DECR) methods, the simulation results demonstrate that the HERSBDC mechanism achieves a much better lifetime by 62.28% and 11.89%, the delivery ratio by 15.04% and 9.51%, with minimal delay by 52.65%, and 9.65%, and routing overhead by 32.05%, and 42.65%, respectively.

Widespread yet Unreliable: A Systematic Analysis of the Use of Presence Questionnaires

Widespread yet Unreliable: A Systematic Analysis of the Use of Presence Questionnaires

P-158. Knowledge of Rabies Prevention Measures for Dog Bite Incidents in the Peruvian Population: Findings from a National Survey in 2022

Abstract Background Rabies continues to be a significant global zoonotic threat. In the last two decades, Peru has reported nine dog-transmitted rabies cases, including a preventable death in 2023. This study aimed to assess public knowledge of rabies prevention post-dog bites, focusing on sociodemographic disparities to better tailor future interventions.Figure 1.Factors associated with knowledge of the Rabies Preventive Triad of the Peruvian population, ENAPRESS 2022. Methods Utilizing data from the 2022 National Survey of Budget Programs, targeting Peruvians aged 14 and older through a stratified two-stage sampling across both urban and rural areas, this cross-sectional study included 89,655 adults who completed interviews on the rabies prevention triad (wound washing, biting animal identification, and medical consultation). The "Survey" package in R facilitated data analysis, acknowledging the complex design. Poisson regression was employed to explore sociodemographic disparities in knowledge. Results Out of 89,655 participants, the majority were women (53.7%), aged 30-59 (52.2%), and lived in urban areas (81.6%). The 6.5% displayed adequate knowledge of the rabies preventive triad, 45.7% indicated they would clean their wounds, 20.9% would identify the dog involved, and 86.7% would seek medical attention at a health facility. Regression models revealed better knowledge among older adults and the elderly (adjusted prevalence ratio (aPR): 1.04; 95% confidence interval (95%CI): 1.03-1.05), females (aPR: 1.01; 95% CI: 1.00-1.02), and those with higher educational levels. Highland residents showed greater knowledge than coastal ones (aPR: 1.02; 95% CI: 1.01-1.03). Conversely, Quechua ethnicity and rural residency were negatively associated with knowledge (aPR: 0.97; 95% CI: 0.96-0.98 and aPR: 0.98; 95% CI: 0.97-0.99, respectively) (Figure 1). Conclusion Despite the high frequency of dog bites in Peru, knowledge of rabies prevention is very low. Although limited by potential recall bias and its non-causal inference capacity, the study's strengths lie in its national scope and reliable data collection. The results underscore the urgency of targeted educational campaigns and policy enhancement in high-risk areas to boost community health and rabies prevention. Disclosures All Authors: No reported disclosures

Development of a digital twin prototype of a robotic device for motion reproduction in space

Purpose. To develop a prototype of a digital twin for a robotic device capable of reproducing an object's motion in real time with high accuracy. Methodology. To achieve the objective, the following methods were employed: integration of ESP8266 NodeMCU v3 and Arduino Uno R4 Wi-Fi microcontrollers with an MPU6050 sensor, firmware development using the C++ programming language in the Arduino IDE environment, creation of server-side software using PHP scripts and a MySQL database, and the development of an interactive data visualization system in Unity. Additionally, methods for data filtering and calibration were applied to ensure accuracy. Findings. A digital twin prototype was developed, which accurately reproduces the movements of a physical object based on data from MPU6050 sensors. The system ensures reliable data collection and transmission from the microcontroller to the server without significant losses or distortions. The data is successfully stored in the MySQL database and is available for further analysis and visualization via interactive 3D models in Unity. Implemented mechanisms for data integrity verification and connection stability provide high reliability of the system over extended periods. Originality. A novel approach to developing a digital twin has been proposed, integrating ESP8266 and Arduino Uno R4 Wi-Fi microcontrollers with an MPU6050 sensor for data collection and processing, employing real-time filtering and calibration algorithms to enhance data accuracy. Practical value. The developed prototype of the digital twin for a robotic device expands the possibilities for studying and researching digital twin technologies in robotics.

Lay-interviewer training protocol for the WHO Flexible Interview for ICD-11 for the National Mental Health Survey – 2 (India)

Background: The National Mental Health Survey-2 (NMHS-2) of India is to be conducted in 2024–2026, across the country with over 2,25,000 individual assessments. The survey is to use the Flexible Interview for ICD-11 (FLII-11), a structured diagnostic interview (SDI) consistent with ICD-11, newly developed by an international collaboration under the aegis of the World Health Organization (WHO), as the primary assessment instrument for the mental health morbidity. Lay-interviewers are to administer the FLII-11, and to compensate for their limited competency a 3-week training program has been developed to ensure reliable data collection. Aim: This article serves as a formal documentation of the FLII-11 training protocol for the NMHS-2 including its development to help in its broader applicability and effective implementation across India beyond the NMHS-2. Methods and Results: The comprehensive training involves the lectures, video-demonstration, live-interviews, role plays and competency assessments. This training schema was piloted in the FLII-11 validation exercise at the National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, with lay-interviewers achieving at least 80% score when compared to the psychiatrist’s ratings of test videos. Discussion: Assessment with SDIs ensures standardization of data collection and diagnostic precision. Training further minimizes human errors by ensuring uniformity in administration of the instrument. The FLII-11, being free to use when eventually published by the WHO, has the potential to be the SDI of choice with wide applications in clinical practice, research, and training in our country. The FLII-11 has been translated to 22 Indian languages for the NMHS-2, further widening its utility. The interviewer training resources (power-point presentations, training/rating videos, and scoring sheets) will be made available on request (https://www.elearn.nimhans.ac.in.), for those who have received permission of usage of FLII-11 from the WHO.

Ensuring Data Accuracy and Compliance in Remote Healthcare Monitoring

Remote healthcare delivery and clinical trials have undergone a dramatic transformation, necessitating robust frameworks for ensuring data accuracy and regulatory compliance in virtual settings. This comprehensive article explores the intricate challenges healthcare organizations face when implementing remote monitoring systems while maintaining data integrity and adhering to stringent regulatory requirements. Through examination of current technological infrastructure, quality control methodologies, and regulatory frameworks, this article presents a structured approach to achieving reliable data collection in remote care environments. The article addresses critical aspects, including device validation protocols, HIPAA compliance in virtual settings, and standardized procedures for remote patient monitoring, while offering practical solutions for common challenges in technology adoption and data discrepancy management. This article indicates that the successful implementation of remote healthcare data systems requires a three-pronged approach: robust technical infrastructure, comprehensive staff training programs, and patient-centric education initiatives. Furthermore, this article identifies emerging trends in artificial intelligence and predictive analytics that promise to enhance data validation processes and compliance monitoring in remote healthcare settings. This article provides healthcare professionals and organizations with actionable insights for establishing and maintaining reliable remote data collection systems while ensuring regulatory compliance and optimal patient care outcomes.

The importance and challenges of data collection in risk assessment

Reliable data collection is paramount for assessing and mitigating risks posed by natural hazards and climate change to urban sustainability and resilience. This study underscores the significance of diverse data collection methods, including field surveys, artificial intelligence (AI) analysis, and satellite imagery, each essential for developing robust risk assessment frameworks. Field surveys offer detailed insights into structural and non-structural building components, critical for localized risk assessments. AI enhances data processing efficiency, crucial for handling extensive datasets and enabling rapid response capabilities. Satellite imagery provides comprehensive area coverage, invaluable for strategic planning and emergency management in large or inaccessible areas. However, these methods also present unique challenges. Field surveys are resource-intensive and susceptible to environmental conditions, which can affect data accuracy. AI, while efficient, requires high-quality data and may struggle with complex scenarios that deviate from its training data. Satellite imagery, although broad-reaching, may lack the necessary resolution for detailed assessments and is dependent on weather conditions. Addressing these challenges is crucial to ensuring the integrity and reliability of risk assessments. By continuously refining these methods and maintaining high standards for data quality and ethical considerations, we can better prepare for and mitigate the impacts of natural hazards and climate change. This commitment to advancing data collection techniques is essential for protecting our built environments and ensuring community resilience.

The Technological Assumptions for Teaching Innovation

Background: The scientific knowledge was improving because is the base doubles every 5 to 6 years, and in some domains of medicine even faster. So, it is simply no longer possible to „inject“ all medical knowledge into students, regardless of their previous educational level. Educating medical students is process which depands of assessing appropriate changes in medical education. Objective: The purpose of this article was to present the role of the learning process of "acquiring knowledge or abilities or modifying behavior through to dayly practice“, because the traditional or classic way of studying medicine implies the oral and practical transfer of knowledge and skills from educators to students. Methods: The author used the most influential index databases as a source for collecting of relevant facts about important Information-Communication Technologies (ICTs) which today commonly and actualy used in practice for educational process in the current fields of biomedicine worldwide. Results and Discussion: Modern information technologies (IT) have enabled faster, more reliable and comprehensive data collection. These technologies have started to create a large number of irrelevant information, which represents a limiting factor and a real growing gap, between the medical knowledge on one hand, and the ability of doctors to follow its growth on the other. The term technology is generally reserved for its technical component. Education means, learning, teaching, or the process of acquiring skills or behavior modification through various exercises. Traditionally, medical education meant the oral, practical and more passive transferring of knowledge and skills from the educators to students and health professionals. For the clinical disciplines, of special importance are the principles, such as "learning at bedside", aided by the medical literature. In doing so, these techniques enable students to contact with their teachers, and to refer to the appropriate literature. The disadvantage of these educational methods is in the fact, that teachers often do not have enough time. Additionally they are not very convenient to the horizontal and vertical integration of teaching, create weak or almost no self education, as well as, low skill levels and poor integration of education with a real social environment. Conclusion: In this paper authors described application of modern ICTs in medical education and their advantages and disadvantages comparing with traditional ways of education. In clinical medicine, „learning at the patient's bedside“ is especially important, whereby knowledge is expanded and supplemented with appropriate medical literature. In the all fields of biomedicine in recent decades are in significant correlation with the advances in the information technologies. Current biomedicine studies must be given a solid foundation in the field of using computer's technologies to improve process information, support decision-making, select the right treatments, and develop their abilities to the students as "lifelong learners".

Reliable and Efficient Data Collection in UAV based IoT Networks

Reliable and Efficient Data Collection in UAV based IoT Networks

WEARABLES IN LONG-TERM DEMENTIA RESEARCH: A MIXED METHOD STUDY OF USER EXPERIENCES AND SUPPORT NEEDS

Abstract Passive wearables data collection may be specifically beneficial to aging research featuring dementia populations, who have caregiving and cognitive burdens that can make study participation and reliable data collection more difficult, especially as dementia progresses. This three-phase project aims to inform best practice recommendations to enhance recruitment and adherence in long-term wearables research featuring this population. Based on our systematic review and preliminary in-house data testing, we selected three wearables offering different capabilities and form (from Garmin, Pulse HR, and AngelSense) to test real world usability, data quality, and support needs. This is the first study to recruit persons living with dementia and their caregivers to evaluate multiple devices outside of a laboratory or focus group setting (N=12 dyads). The person living with dementia assented to wearing each wearable for two weeks. Their caregiver rated many facets of each device following its use with the Quebec User Evaluation of Satisfaction with Assistive Technology measure. Open-ended questions and a cumulative semi-structured interview provided context and in-depth comparative perspectives of their experiences in the study. Wearable durability, simplicity, and data availability/monitoring capacity were important to participant favorability and adherence. Technical help and check-ins were also deemed highly valuable. Data indicate how the devices suited the dyads’ needs or caused issues, as well as how study staff could better support ongoing use. Collectively, our findings suggest ideal criteria to guide wearable selection and key protocol factors that can enhance participant recruitment and adherence in long-term dementia research.