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BMC Medical Education Jun 2024Most Japanese medical schools likely continue to rely on peer physical examination (PPE) as a tool to for teaching physical examination skills to students. However, the...
BACKGROUND
Most Japanese medical schools likely continue to rely on peer physical examination (PPE) as a tool to for teaching physical examination skills to students. However, the attitudes of medical students in Japan toward PPEs have not be identified. Therefore, we evaluated students' attitudes toward PPE in a Japanese medical school as a preparation for developing a PPE policy tailored to the context of Japanese culture.
METHODS
We conducted a mixed-methods study with an explanatory sequential approach, in which qualitative data were used to interpret the quantitative findings. Surveys and interviews were conducted with medical students and junior residents at a Japanese university. A total of 63 medical students and 50 junior residents responded to the questionnaire. We interviewed 16 participants to reach theoretical saturation and investigated the attitudes of medical students toward PPE and the themes emerging from the interview data, providing detailed descriptions of the quantitative findings.
RESULTS
Female participants were significantly more likely than male participants to report varying degrees of resistance to being a model patient during PPE (male: 59.7%, female: 87%, p < 0.001). Most of the participants who took on the role of patients that involved undressing were males. The participants expected improvements in issues related to the guarantee of freedom to refuse to be a model patient and measures to protect confidentiality. Approximately 22% of the participants reported that they witnessed incidental findings (including variations within the normal range) in front of other students during PPE.
CONCLUSIONS
The findings imply that medical students expect high levels of autonomy and confidentiality when volunteering as model patients during PPE. Thus, developing a PPE policy suitable for Japanese culture may be effective in establishing a student-centered PPE environment.
Topics: Humans; Students, Medical; Japan; Physical Examination; Female; Male; Peer Group; Attitude of Health Personnel; Surveys and Questionnaires; Adult; Young Adult; Education, Medical, Undergraduate
PubMed: 38902752
DOI: 10.1186/s12909-024-05635-4 -
JMIR Human Factors Jun 2024Young adults in the United States exhibit some of the highest rates of substance use compared to other age groups. Heavy and frequent substance use can be associated...
BACKGROUND
Young adults in the United States exhibit some of the highest rates of substance use compared to other age groups. Heavy and frequent substance use can be associated with a host of acute and chronic health and mental health concerns. Recent advances in ubiquitous technologies have prompted interest and innovation in using technology-based data collection instruments to understand substance use and associated harms. Existing methods for collecting granular, real-world data primarily rely on the use of smartphones to study and understand substance use in young adults. Wearable devices, such as smartwatches, show significant potential as platforms for data collection in this domain but remain underused.
OBJECTIVE
This study aims to describe the design and user evaluation of a smartwatch-based data collection app, which uses ecological momentary assessments to examine young adult substance use in daily life.
METHODS
This study used a 2-phase iterative design and acceptability evaluation process with young adults (aged 18-25 y) reporting recent alcohol or cannabis use. In phase 1, participants (8/15, 53%) used the data collection app for 14 days on their Apple Watches to report their substance use patterns, social contexts of substance use, and psychosocial risk factors (eg, affect). After this 14-day deployment, the participants completed a user experience survey and a semistructured interview to record their perspectives and experiences of using the app. Formative feedback from this phase informed feature modification and refinement of the app. In phase 2, an additional cohort (7/15, 47%) used the modified app for 14 days and provided feedback through surveys and interviews conducted after the app use period.
RESULTS
Analyses of overall app use patterns indicated high, consistent use of the app, with participants using the app for an average of 11.73 (SD 2.60) days out of 14 days of data collection. Participants reported 67 instances of substance use throughout the study, and our analysis indicates that participants were able to respond to ecological momentary assessment prompts in diverse temporal and situational contexts. Our findings from the user experience survey indicate that participants found the app usable and functional. Comparisons of app use metrics and user evaluation scores indicate that the iterative app design had a measurable and positive impact on users' experience. Qualitative data from the participant interviews highlighted the value of recording substance use patterns, low disruption to daily life, minimal overall burden, preference of platforms (smartphones vs smartwatches), and perspectives relating to privacy and app use in social contexts.
CONCLUSIONS
This study demonstrated the acceptability of using a smartwatch-based app to collect intensive, longitudinal substance use data among young adults. The findings document the utility of smartwatches as a novel platform to understand sensitive and often-stigmatized behaviors such as substance use with minimal burden.
Topics: Humans; Male; Feasibility Studies; Mobile Applications; Female; Adult; Young Adult; Adolescent; Substance-Related Disorders; Ecological Momentary Assessment; Smartphone; United States
PubMed: 38901024
DOI: 10.2196/50795 -
Health Informatics Journal 2024Wearables have the potential to transform healthcare by enabling early detection and monitoring of chronic diseases. This study aimed to assess wearables' acceptance,...
Wearables have the potential to transform healthcare by enabling early detection and monitoring of chronic diseases. This study aimed to assess wearables' acceptance, usage, and reasons for non-use. Anonymous questionnaires were used to collect data in Germany on wearable ownership, usage behaviour, acceptance of health monitoring, and willingness to share data. Out of 643 respondents, 550 participants provided wearable acceptance data. The average age was 36.6 years, with 51.3% female and 39.6% residing in rural areas. Overall, 33.8% reported wearing a wearable, primarily smartwatches or fitness wristbands. Men (63.3%) and women (57.8%) expressed willingness to wear a sensor for health monitoring, and 61.5% were open to sharing data with healthcare providers. Concerns included data security, privacy, and perceived lack of need. The study highlights the acceptance and potential of wearables, particularly for health monitoring and data sharing with healthcare providers. Addressing data security and privacy concerns could enhance the adoption of innovative wearables, such as implants, for early detection and monitoring of chronic diseases.
Topics: Humans; Germany; Female; Male; Adult; Cross-Sectional Studies; Wearable Electronic Devices; Surveys and Questionnaires; Middle Aged; Monitoring, Physiologic; Monitoring, Ambulatory
PubMed: 38900846
DOI: 10.1177/14604582241260607 -
Human Brain Mapping Jun 2024With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has...
With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de-identify imaging data using blurring, defacing or refacing. Completely removing facial structures provides the best re-identification protection but can significantly impact post-processing steps, like brain morphometry. As an alternative, refacing methods that replace individual facial structures with generic templates have a lower effect on the geometry and intensity distribution of original scans, and are able to provide more consistent post-processing results by the price of higher re-identification risk and computational complexity. In the current study, we propose a novel method for anonymized face generation for defaced 3D T1-weighted scans based on a 3D conditional generative adversarial network. To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox). The aim was to evaluate (i) impact on brain morphometry reproducibility, (ii) re-identification risk, (iii) balance between (i) and (ii), and (iv) the processing time. The proposed method takes 9 s for face generation and is suitable for recovering consistent post-processing results after defacing.
Topics: Humans; Magnetic Resonance Imaging; Adult; Brain; Male; Female; Neural Networks, Computer; Imaging, Three-Dimensional; Neuroimaging; Data Anonymization; Young Adult; Image Processing, Computer-Assisted; Algorithms
PubMed: 38899549
DOI: 10.1002/hbm.26721 -
BMC Public Health Jun 2024Technology improves accessibility of psychological interventions for youth. An ecological momentary intervention (EMI) is a digital intervention geared toward...
Working mechanisms of the use and acceptability of ecological momentary interventions: a realist evaluation of a guided self-help ecological momentary intervention targeting self-esteem.
BACKGROUND
Technology improves accessibility of psychological interventions for youth. An ecological momentary intervention (EMI) is a digital intervention geared toward intervening in daily life to enhance the generalizability and ecological validity, and to be able to intervene in moments most needed. Identifying working mechanisms of the use of ecological momentary interventions might generate insights to improve interventions.
METHODS
The present study investigates the working mechanisms of the use and acceptability of an ecological momentary intervention, named SELFIE, targeting self-esteem in youth exposed to childhood trauma, and evaluates under what circumstances these mechanisms of use and acceptability do or do not come into play. A realist evaluation approach was used for developing initial program theories (data: expert interviews and a stakeholders focus group), and subsequently testing (data: 15 interviews with participants, a focus group with therapists, debriefing questionnaire), and refining them.
RESULTS
The SELFIE intervention is offered through a smartphone application enabling constant availability of the intervention and thereby increasing accessibility and feasibility. When the intervention was offered on their personal smartphone, this enhanced a sense of privacy and less hesitance in engaging with the app, leading to increased disclosure and active participation. Further, the smartphone application facilitates the practice of skills in daily life, supporting the repeated practice of exercises in different situations leading to the generalizability of the effect. Buffering against technical malfunction seemed important to decrease its possible negative effects.
CONCLUSIONS
This study enhanced our understanding of possible working mechanisms in EMIs, such as the constant availability supporting increased accessibility and feasibility, for which the use of the personal smartphone was experienced as a facilitating context. Hereby, the current study contributes to relatively limited research in this field. For the field to move forward, mechanisms of use, and acceptability of EMIs need to be understood. It is strongly recommended that alongside efficacy trials of an EMI on specific target mechanisms, a process evaluation is conducted investigating the working mechanisms of use.
TRIAL REGISTRATION
The current paper reports on a realist evaluation within the SELFIE trial (Netherlands Trial Register NL7129 (NTR7475)).
Topics: Humans; Self Concept; Female; Male; Adolescent; Ecological Momentary Assessment; Focus Groups; Mobile Applications; Patient Acceptance of Health Care; Smartphone
PubMed: 38898412
DOI: 10.1186/s12889-024-19143-z -
Digital Health 2024Millions of people in the UK have asthma, yet 70% do not access basic care, leading to the largest number of asthma-related deaths in Europe. Chatbots may extend the...
OBJECTIVE
Millions of people in the UK have asthma, yet 70% do not access basic care, leading to the largest number of asthma-related deaths in Europe. Chatbots may extend the reach of asthma support and provide a bridge to traditional healthcare. This study evaluates 'Brisa', a chatbot designed to improve asthma patients' self-assessment and self-management.
METHODS
We recruited 150 adults with an asthma diagnosis to test our chatbot. Participants were recruited over three waves through social media and a research recruitment platform. Eligible participants had access to 'Brisa' via a WhatsApp or website version for 28 days and completed entry and exit questionnaires to evaluate user experience and asthma control. Weekly symptom tracking, user interaction metrics, satisfaction measures, and qualitative feedback were utilised to evaluate the chatbot's usability and potential effectiveness, focusing on changes in asthma control and self-reported behavioural improvements.
RESULTS
74% of participants engaged with 'Brisa' at least once. High task completion rates were observed: asthma attack risk assessment (86%), voice recording submission (83%) and asthma control tracking (95.5%). Post use, an 8% improvement in asthma control was reported. User satisfaction surveys indicated positive feedback on helpfulness (80%), privacy (87%), trustworthiness (80%) and functionality (84%) but highlighted a need for improved conversational depth and personalisation.
CONCLUSIONS
The study indicates that chatbots are effective for asthma support, demonstrated by the high usage of features like risk assessment and control tracking, as well as a statistically significant improvement in asthma control. However, lower satisfaction in conversational flexibility highlights rising expectations for chatbot fluency, influenced by advanced models like ChatGPT. Future health-focused chatbots must balance conversational capability with accuracy and safety to maintain engagement and effectiveness.
PubMed: 38894942
DOI: 10.1177/20552076241258276 -
Sensors (Basel, Switzerland) Jun 2024Monitoring the lifestyles of older adults helps promote independent living and ensure their well-being. The common technologies for home monitoring include wearables,...
BACKGROUND
Monitoring the lifestyles of older adults helps promote independent living and ensure their well-being. The common technologies for home monitoring include wearables, ambient sensors, and smart household meters. While wearables can be intrusive, ambient sensors require extra installation, and smart meters are becoming integral to smart city infrastructure. Research Gap: The previous studies primarily utilized high-resolution smart meter data by applying Non-Intrusive Appliance Load Monitoring (NIALM) techniques, leading to significant privacy concerns. Meanwhile, some Japanese power companies have successfully employed low-resolution data to monitor lifestyle patterns discreetly.
SCOPE AND METHODOLOGY
This study develops a lifestyle monitoring system for older adults using low-resolution smart meter data, mapping electricity consumption to appliance usage. The power consumption data are collected at 15-min intervals, and the background power threshold distinguishes between the active and inactive periods (0/1). The system quantifies activity through an active score and assesses daily routines by comparing these scores against the long-term norms. Key Outcomes/Contributions: The findings reveal that low-resolution data can effectively monitor lifestyle patterns without compromising privacy. The active scores and regularity assessments calculated using correlation coefficients offer a comprehensive view of residents' daily activities and any deviations from the established patterns. This study contributes to the literature by validating the efficacy of low-resolution data in lifestyle monitoring systems and underscores the potential of smart meters in enhancing elderly people's care.
Topics: Humans; Aged; Independent Living; Life Style; Female; Male; Activities of Daily Living; Monitoring, Physiologic; Monitoring, Ambulatory; Aged, 80 and over; Wearable Electronic Devices
PubMed: 38894452
DOI: 10.3390/s24113662 -
Sensors (Basel, Switzerland) Jun 2024This study explored an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments. Compared to...
This study explored an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments. Compared to wearables and camera methods, Millimeter-Wave radar is not plagued by mobility inconveniences, lighting conditions, or privacy issues. We conducted an initial evaluation of radar characteristics, covering aspects such as interference between radars and coverage area. Then, we established a real-time framework to integrate signals received from these radars, allowing us to track the position and body status of human targets non-intrusively. Additionally, we introduced innovative strategies, including dynamic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering based on signal SNR levels, a probability matrix for enhanced target tracking, target status prediction for fall detection, and a feedback loop for noise reduction. We conducted an extensive evaluation using over 300 min of data, which equated to approximately 360,000 frames. Our prototype system exhibited a remarkable performance, achieving a precision of 98.9% for tracking a single target and 96.5% and 94.0% for tracking two and three targets in human-tracking scenarios, respectively. Moreover, in the field of human fall detection, the system demonstrates a high accuracy rate of 96.3%, underscoring its effectiveness in distinguishing falls from other statuses.
PubMed: 38894451
DOI: 10.3390/s24113660 -
Sensors (Basel, Switzerland) Jun 2024The growth of IoT healthcare is aimed at providing efficient services to patients by utilizing data from local hospitals. However, privacy concerns can impede data...
The growth of IoT healthcare is aimed at providing efficient services to patients by utilizing data from local hospitals. However, privacy concerns can impede data sharing among third parties. Federated learning offers a solution by enabling the training of neural networks while maintaining the privacy of the data. To integrate federated learning into IoT healthcare, hospitals must be part of the network to jointly train a global central model on the server. Local hospitals can train the global model using their patient datasets and send the trained localized models to the server. These localized models are then aggregated to enhance the global model training process. The aggregation of local models dramatically influences the performance of global training, mainly due to the heterogeneous nature of patient data. Existing solutions to address this issue are iterative, slow, and susceptible to convergence. We propose two novel approaches that form groups efficiently and assign the aggregation weightage considering essential parameters vital for global training. Specifically, our method utilizes an autoencoder to extract features and learn the divergence between the latent representations of patient data to form groups, facilitating more efficient handling of heterogeneity. Additionally, we propose another novel aggregation process that utilizes several factors, including extracted features of patient data, to maximize performance further. Our proposed approaches for group formation and aggregation weighting outperform existing conventional methods. Notably, significant results are obtained, one of which shows that our proposed method achieves 20.8% higher accuracy and 7% lower loss reduction compared to the conventional methods.
Topics: Humans; Neural Networks, Computer; Internet of Things; Delivery of Health Care; Algorithms; Machine Learning
PubMed: 38894422
DOI: 10.3390/s24113632 -
Sensors (Basel, Switzerland) May 2024To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and...
To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and backscatter communication, are preferred due to their minimal privacy intrusion, reduced physical burden, and low maintenance costs. However, these methods face challenges, including environmental dependence, proximity limitations between the device and the user, and untested accuracy amidst various radio obstacles such as furniture, appliances, walls, and other radio waves. In this paper, we propose a frequency-shift backscatter tag-based in-home activity recognition method and test its feasibility in a near-real residential setting. Consisting of simple components such as antennas and switches, these tags facilitate ultra-low power consumption and demonstrate robustness against environmental noise because a context corresponding to a tag can be obtained by only observing frequency shifts. We implemented a sensing system consisting of SD-WiFi, a software-defined WiFi AP, and physical switches on backscatter tags tailored for detecting the movements of daily objects. Our experiments demonstrate that frequency shifts by tags can be detected within a 2 m range with 72% accuracy under the line of sight (LoS) conditions and achieve a 96.0% accuracy (F-score) in recognizing seven typical daily living activities with an appropriate receiver/transmitter layout. Furthermore, in an additional experiment, we confirmed that increasing the number of overlaying packets enables frequency shift-detection even without LoS at distances of 3-5 m.
Topics: Activities of Daily Living; Humans; Wireless Technology; Radio Waves; Radio Frequency Identification Device
PubMed: 38894070
DOI: 10.3390/s24113277