BACKGROUND: Digital technology and gamified apps can be useful in the health care context. Gamification uses technology to influence users\' actions and motivations through experiences that resemble games. Patient adherence to the enhanced recovery after surgery (ERAS) program is crucial for achieving early recovery after surgery and continuous monitoring is essential for obtaining good results.
OBJECTIVE: This study aimed to describe the development and validation of a mobile app for enhanced recovery after surgery (MobERAS), a gamified mobile health app for telemonitoring patients in the postoperative period based on the ERAS program, and to evaluate its functionality and usability and the experience of patients, health care professionals, and computer professionals with its use.
METHODS: We developed MobERAS for postoperative telemonitoring, with active participation of patients in the process, and offering availability of real-time information for the health team. The app development process included idealization, interdisciplinary team formation, potential needs assessment, and product deployment. Usability tests were conducted throughout the development process with improvements, technical adjustments, and updates. After finalization, comprehensive verification tests were performed. The parameters evaluated are those that can influence the length of hospital stay, such as nausea, vomiting, pain scales, return to normal gastrointestinal function, and thromboembolic events. MobERAS was designed to be downloaded by users on their phones, tablets, or other mobile devices and to provide postoperative data. The app has a GPS that monitors the patient\'s walking time and distance and is connected to a virtual database that stores the collected data.
RESULTS: Women undergoing medium and major gynecologic oncologic surgeries were included. We included 65 patients with an average age of 53.2 (SD 7.4, range 18-85) years. The time of use ranged from 23.4 to 70 hours (mean 45.1, SD 19.2 hours). Regarding adherence to the use of MobERAS, the mean fill rate was 56.3% (SD 12.1%, range 41.7%-100%), and ambulation data were obtained for 60 (92.3%) of the 65 patients. The researcher had access to the data filled out by the patients in real time. There was good acceptance of the use of MobERAS by the patients, with good evaluation of the app\'s usability. MobERAS was easy to use and considered attractive because of its gamified design. The app was rated as good or very good in all items by health care professionals (n=20) and professionals specializing in technological innovation (n=10).
CONCLUSIONS: MobERAS is easy to use, safe, well accepted by patients, and well evaluated by experts. It can be of great use in clinical surgical practice and an important tool for greater engagement of patients and health care professionals with the ERAS program.

OBJECTIVE: Developing a severity assessment scale for critically ill patients\' thirst and conducting reliability and validity tests, aiming to provide healthcare professionals with a scientific and objective tool for assessing the level of thirst.
METHODS: Based on literature review and qualitative interviews, a pool of items was generated, and a preliminary scale was formed through two rounds of Delphi expert consultation. Convenience sampling was employed to select 178 ICU patients in a top-three hospital from May 2023 to October 2023 as the study subjects to examine the reliability and validity of the severity assessment scale for critically ill patients\' thirst.
RESULTS: The developed severity assessment scale for critically ill patients\' thirst consists of 8 evaluation items and 26 evaluation indicators. The agreement coefficients for two rounds of expert consultation were 100% and 92.6% for the positive coefficient, and the authority coefficients were .900 and .906. Kendall\'s concordance coefficients were .101 and .120 (all p < .001). The overall Cronbach\'s α coefficient for the scale was .827. The inter-rater reliability coefficient was .910. The Item-Content Validity Index (I-CVI) ranged from .800 to 1.000, and the Scale-Content Validity Index/Average (S-CVI/Ave) was .950.
CONCLUSIONS: The critically ill patients\' thirst assessment scale is reliable and valid and can be widely used in clinical practice.
UNASSIGNED: The AiMi Academic Services (www.aimieditor.com) for English language editing and review services.
CONCLUSIONS: The scale developed in this study is a simple and ICU-specific scale that can be used to assess the severity of thirst in critically ill patients. As such, the severity of thirst in critically ill patients can be evaluated quickly so that targeted interventions can be implemented according to the patient\'s specific disease and treatment conditions. Therefore, patient comfort can be improved, and thirst-related health problems can be prevented.

BACKGROUND: Blood glucose management around exercise is challenging for youth with type 1 diabetes (T1D). Previous research has indicated interventions including decision-support aids to better support youth to effectively contextualize blood glucose results and take appropriate action to optimize glucose levels during and after exercise. Mobile health (mHealth) apps help deliver health behavior interventions to youth with T1D, given the use of technology for glucose monitoring, insulin dosing, and carbohydrate counting.
OBJECTIVE: We aimed to develop a novel prototype mHealth app to support exercise management among youth with T1D, detail the application of a co-design process and design thinking principles to inform app design and development, and identify app content and functionality that youth with T1D need to meet their physical activity goals.
METHODS: A co-design approach with a user-centered design thinking framework was used to develop a prototype mHealth app \"acT1ve\" during the 18-month design process (March 2018 to September 2019). To better understand and respond to the challenges among youth with diabetes when physically active, 10 focus groups were conducted with youth aged 13-25 years with T1D and parents of youth with T1D. Thereafter, we conducted participatory design workshops with youth to identify key app features that would support individual needs when physically active. These features were incorporated into a wireframe, which was critically reviewed by participants. A beta version of \"acT1ve\" was built in iOS and android operating systems, which underwent critical review by end users, clinicians, researchers, experts in exercise and T1D, and app designers.
RESULTS: Sixty youth with T1D, 14 parents, 6 researchers, and 10 clinicians were engaged in the development of \"acT1ve.\" acT1ve included key features identified by youth, which would support their individual needs when physically active. It provided advice on carbohydrates and insulin during exercise, information on hypoglycemia treatment, pre- and postexercise advice, and an educational food guide regarding exercise management. \"acT1ve\" contained an exercise advisor algorithm comprising 240 pathways developed by experts in diabetes and exercise research. Based on participant input during exercise, acT1ve provided personalized insulin and carbohydrate advice for exercise lasting up to 60 minutes. It also contains other features including an activity log, which displays a complete record of the end users\' activities and associated exercise advice provided by the app\'s algorithm for later reference, and regular reminder notifications for end users to check or monitor their glucose levels.
CONCLUSIONS: The co-design approach and the practical application of the user-centered design thinking framework were successfully applied in developing \"acT1ve.\" The design thinking processes allowed youth with T1D to identify app features that would support them to be physically active, and particularly enabled the delivery of individualized advice. Furthermore, app development has been described in detail to help guide others embarking on a similar project.
BACKGROUND: Australian New Zealand Clinical Trials Registry ACTRN12619001414101; https://tinyurl.com/mu9jvn2d.

BACKGROUND: The COVID-19 pandemic underscored the necessity for innovative educational methods in nursing. Our study takes a unique approach using a multidisciplinary simulation design, which offers a systematic and comprehensive strategy for developing virtual reality (VR) simulations in nursing education.
OBJECTIVE: The aim of this study is to develop VR simulation content for a pediatric nursing module based on a multidisciplinary simulation design and to evaluate its feasibility for nursing education.
METHODS: This study used a 1-group, posttest-only design. VR content for pediatric nursing practice was developed by integrating the technological characteristics of a multimodal VR system with the learning elements of traditional nursing simulation, combining various disciplines, including education, engineering, and nursing. A user test was conducted with 12 nursing graduates (preservice nurses) followed by post hoc surveys (assessing presence, VR systems, VR sickness, and simulation satisfaction) and in-depth, one-on-one interviews.
RESULTS: User tests showed mean scores of 4.01 (SD 1.43) for presence, 4.91 (SD 0.81) for the VR system, 0.64 (SD 0.35) for VR sickness, and 5.00 (SD 1.00) for simulation satisfaction. In-depth interviews revealed that the main strengths of the immersive VR simulation for pediatric pneumonia nursing were effective visualization and direct experience through hands-on manipulation; the drawback was keyword-based voice interaction. To improve VR simulation quality, participants suggested increasing the number of nursing techniques and refining them in more detail.
CONCLUSIONS: This VR simulation content for a pediatric nursing practice using a multidisciplinary educational design model was confirmed to have positive educational potential. Further research is needed to confirm the specific learning effects of immersive nursing content based on multidisciplinary design models.

The office-based laboratory (OBL) industry has proliferated over the past decade as surgical cases have increasingly migrated from inpatient to outpatient surgical settings, including OBLs, ambulatory surgery centers and infusion centers. Although many physicians and patients prefer to provide and receive care in an OBL setting because it provides a high quality, lower cost and convenient alternative to receiving care in a hospital, the OBL industry is nonetheless under attack on a variety of fronts. Governmental and commercial payor reimbursement for OBL procedures has declined substantially over time, and there have been lawsuits, governmental investigations and news articles that have been critical of care provided in OBLs. These issues have generated headwinds for this young but growing industry. It is therefore important for physicians and investors alike interested in developing an OBL to be aware of the complex landscape of laws and regulations that apply to OBLs. This article provides an overview of key legal, corporate, tax, financial and structural considerations for operators to be aware of before opening an OBL.

BACKGROUND: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations.
OBJECTIVE: The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles.
METHODS: The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles.
RESULTS: The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001).
CONCLUSIONS: The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.

BACKGROUND: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items.
OBJECTIVE: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app.
METHODS: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants\' perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses.
RESULTS: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants\' perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory.
CONCLUSIONS: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains.

BACKGROUND: Conversational chatbots are an emerging digital intervention for smoking cessation. No studies have reported on the entire development process of a cessation chatbot.
OBJECTIVE: We aim to report results of the user-centered design development process and randomized controlled trial for a novel and comprehensive quit smoking conversational chatbot called QuitBot.
METHODS: The 4 years of formative research for developing QuitBot followed an 11-step process: (1) specifying a conceptual model; (2) conducting content analysis of existing interventions (63 hours of intervention transcripts); (3) assessing user needs; (4) developing the chat\'s persona (\"personality\"); (5) prototyping content and persona; (6) developing full functionality; (7) programming the QuitBot; (8) conducting a diary study; (9) conducting a pilot randomized controlled trial (RCT); (10) reviewing results of the RCT; and (11) adding a free-form question and answer (QnA) function, based on user feedback from pilot RCT results. The process of adding a QnA function itself involved a three-step process: (1) generating QnA pairs, (2) fine-tuning large language models (LLMs) on QnA pairs, and (3) evaluating the LLM outputs.
RESULTS: We developed a quit smoking program spanning 42 days of 2- to 3-minute conversations covering topics ranging from motivations to quit, setting a quit date, choosing Food and Drug Administration-approved cessation medications, coping with triggers, and recovering from lapses and relapses. In a pilot RCT with 96% three-month outcome data retention, QuitBot demonstrated high user engagement and promising cessation rates compared to the National Cancer Institute\'s SmokefreeTXT text messaging program, particularly among those who viewed all 42 days of program content: 30-day, complete-case, point prevalence abstinence rates at 3-month follow-up were 63% (39/62) for QuitBot versus 38.5% (45/117) for SmokefreeTXT (odds ratio 2.58, 95% CI 1.34-4.99; P=.005). However, Facebook Messenger intermittently blocked participants\' access to QuitBot, so we transitioned from Facebook Messenger to a stand-alone smartphone app as the communication channel. Participants\' frustration with QuitBot\'s inability to answer their open-ended questions led to us develop a core conversational feature, enabling users to ask open-ended questions about quitting cigarette smoking and for the QuitBot to respond with accurate and professional answers. To support this functionality, we developed a library of 11,000 QnA pairs on topics associated with quitting cigarette smoking. Model testing results showed that Microsoft\'s Azure-based QnA maker effectively handled questions that matched our library of 11,000 QnA pairs. A fine-tuned, contextualized GPT-3.5 (OpenAI) responds to questions that are not within our library of QnA pairs.
CONCLUSIONS: The development process yielded the first LLM-based quit smoking program delivered as a conversational chatbot. Iterative testing led to significant enhancements, including improvements to the delivery channel. A pivotal addition was the inclusion of a core LLM-supported conversational feature allowing users to ask open-ended questions.
BACKGROUND: ClinicalTrials.gov NCT03585231; https://clinicaltrials.gov/study/NCT03585231.

BACKGROUND: Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea.
OBJECTIVE: The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard.
METHODS: We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device\'s efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant\'s data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods.
RESULTS: The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of -0.25 and 0.33. The RR bias was 0.018, and the LoAs were -1.89 and 1.89.
CONCLUSIONS: Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals.

BACKGROUND: Overdose Fatality Review (OFR) is an important public health tool for shaping overdose prevention strategies in communities. However, OFR teams review only a few cases at a time, which typically represent a small fraction of the total fatalities in their jurisdiction. Such limited review could result in a partial understanding of local overdose patterns, leading to policy recommendations that do not fully address the broader community needs.
OBJECTIVE: This study explored the potential to enhance conventional OFRs with a data dashboard, incorporating visualizations of touchpoints-events that precede overdoses-to highlight prevention opportunities.
METHODS: We conducted 2 focus groups and a survey of OFR experts to characterize their information needs and design a real-time dashboard that tracks and measures decedents\' past interactions with services in Indiana. Experts (N=27) were engaged, yielding insights on essential data features to incorporate and providing feedback to guide the development of visualizations.
RESULTS: The findings highlighted the importance of showing decedents\' interactions with health services (emergency medical services) and the justice system (incarcerations). Emphasis was also placed on maintaining decedent anonymity, particularly in small communities, and the need for training OFR members in data interpretation. The developed dashboard summarizes key touchpoint metrics, including prevalence, interaction frequency, and time intervals between touchpoints and overdoses, with data viewable at the county and state levels. In an initial evaluation, the dashboard was well received for its comprehensive data coverage and its potential for enhancing OFR recommendations and case selection.
CONCLUSIONS: The Indiana touchpoints dashboard is the first to display real-time visualizations that link administrative and overdose mortality data across the state. This resource equips local health officials and OFRs with timely, quantitative, and spatiotemporal insights into overdose risk factors in their communities, facilitating data-driven interventions and policy changes. However, fully integrating the dashboard into OFR practices will likely require training teams in data interpretation and decision-making.