Currently, over 4000 bariatric procedures are performed annually in Switzerland. To improve outcomes, patients need to have good knowledge regarding postoperative nutrition. To potentially provide them with knowledge between dietetic consultations, a health bot (HB) was created. The HB can answer bariatric nutrition questions in writing based on artificial intelligence.This study aims to evaluate the usability and perception of the HB among patients receiving bariatric care.Patients before or after bariatric surgery tested the HB. A mixed methods approach was used, which consisted of a questionnaire and qualitative interviews before and after testing the HB. The dimensions usability of, usefulness of, satisfaction with, and ease of use of the HB, among others, were measured. Data were analyzed using R Studio (R Studio Inc) and Excel (Microsoft Corp). The interviews were transcribed and a summary inductive content analysis was performed.A total of 12 patients (female: n=8, 67%; male: n=4, 33%) were included. The results showed excellent usability with a mean usability score of 87 (SD 12.5; range 57.5-100) out of 100. Other dimensions of acceptability included usefulness (mean 5.28, SD 2.02 out of 7), satisfaction (mean 5.75, SD 1.68 out of 7), and learnability (mean 6.26, SD 1.5 out of 7). The concept of the HB and availability of reliable nutrition information were perceived as desirable (mean 5.5, SD 1.64 out of 7). Weaknesses were identified in the response accuracy, limited knowledge, and design of the HB.The HB's ease of use and usability were evaluated to be positive; response accuracy, topic selection, and design should be optimized in a next step. The perceptions of nutrition professionals and the impact on patient care and the nutrition knowledge of participants need to be examined in further studies. 

Usability and Overall Perception of a Health Bot for Nutrition-Related Questions for Patients Receiving Bariatric Care: Mixed Methods Study

BACKGROUND
Patients with prostate cancer undergoing radiation therapy (RT) need comfortably full bladders to reduce toxicities during treatment. Poor compliance is common with standard of care written or verbal instructions, leading to wasted patient value (PV) and clinic resources via poor throughput efficiency (TE).


OBJECTIVE
Herein, we assessed the feasibility and acceptability of a smartphone-based behavioral intervention (SBI) to improve bladder-filling compliance and methods for quantifying PV and TE.


METHODS
In total, 36 patients with prostate cancer were enrolled in a single-institution, closed-access, nonrandomized feasibility trial. The SBI consists of a fully automated smart water bottle and smartphone app. Both pieces alert the patient to empty his bladder and drink a personalized volume goal, based on simulation bladder volume, 1.25 hours before his scheduled RT. Patients were trained to adjust their volume goal and notification times to achieve comfortably full bladders. The primary end point was met if qualitative (QLC) and quantitative compliance (QNC) were >80%. For QLC, patients were asked if they prepared their bladders before daily RT. QNC was met if bladder volumes on daily cone-beam tomography were >75% of the simulation's volume. The Service User Technology Acceptability Questionnaire (SUTAQ) was given in person pre- and post-SBI. Additional acceptability and engagement end points were met if >3 out of 5 across 4 domains on the SUTAQ and >80% (15/18) of patients used the device >50% of the time, respectively. Finally, the impact of SBI on PV and TE was measured by time spent in a clinic and on the linear accelerator (linac), respectively, and contrasted with matched controls.


RESULTS
QLC was 100% in 375 out of 398 (94.2%) total treatments, while QNC was 88.9% in 341 out of 398 (85.7%) total treatments. Of a total score of 5, patients scored 4.33 on privacy concerns, 4 on belief in benefits, 4.56 on satisfaction, and 4.24 on usability via SUTAQ. Further, 83% (15/18) of patients used the SBI on >50% of treatments. Patients in the intervention arm spent less time in a clinic (53.24, SEM 1.71 minutes) compared to the control (75.01, SEM 2.26 minutes) group (P<.001). Similarly, the intervention arm spent less time on the linac (10.67, SEM 0.40 minutes) compared to the control (14.19, SEM 0.32 minutes) group (P<.001).


CONCLUSIONS
This digital intervention trial showed high rates of bladder-filling compliance and engagement. High patient value and TE were feasibly quantified by shortened clinic times and linac usage, respectively. Future studies are needed to evaluate clinical outcomes, patient experience, and cost-benefit.


TRIAL REGISTRATION
ClinicalTrials.gov NCT04946214; https://www.clinicaltrials.gov/study/NCT04946214.

HidrateSpark™ 3, A Smart Water Bottle and Companion App to Improve Bladder-Filling Compliance in Prostate Cancer Patients Receiving Radiotherapy: A Non-Randomized Trial of Feasibility and Acceptability (Preprint)

<sec> <title>BACKGROUND</title> Many pregnant and lactating women are concerned about medication. Most women of reproductive age use medicines before, during, and after pregnancy, and they are aware that not all medicines are safe for them and their babies. While women face such concerns daily, they usually have few opportunities to communicate with healthcare professionals about safe medication use because of the limited availability of services and difficulty in making appointments. </sec> <sec> <title>OBJECTIVE</title> This study evaluated the user experience of a medical chatbot service that provides medicine use information to pregnant and lactating women by investigating users’ consultation categories, satisfaction levels, and post-use health problems. </sec> <sec> <title>METHODS</title> We collected the user data of a medical chatbot service providing medicine use information for pregnant and lactating women in Japan (Kusuribo) between December 13, 2021, and August 10, 2022. The satisfaction-related question was placed below the recommendation statements of the chatbot. An additional survey was distributed to collect post-health problem data from residents within one of the municipalities. The user status of this service (number of weeks of pregnancy, number of postpartum months, breastfeeding method, consultation categories, and preexisting conditions/complications) was presented, and the average level of satisfaction was calculated for all users and for each consultation category. The proportion of post-use problems was evaluated using the survey responses. χ2 and Fisher’s exact tests were conducted to assess the differences in user status and satisfaction levels. </sec> <sec> <title>RESULTS</title> The study included 1,000 records comprising 419 (41.9%) cases by pregnant women and 581 (58.1%) cases by lactating women. The response rate for the satisfaction question was 32.7%. The most frequently consulted categories in both groups were headache, cold symptoms, and hay fever symptoms. Of all users, 11.8% reported having preexisting medical or chronic complications, with bronchial asthma being the most prevalent (35.6%). The overall satisfaction rate was 93.9%, and the satisfaction levels of the two user groups showed no significant difference (P = .866). High satisfaction levels were observed across the consultation categories. An additional questionnaire was sent to 70 users, of whom 28 (40%) responded. None of the users indicated that they experienced health problems after using the service. </sec> <sec> <title>CONCLUSIONS</title> A medical chatbot service that provides medicine use information to women during pregnancy and lactation can achieve high satisfaction and safety. </sec> 

Usability, Satisfaction, and Post-use Health Problems of a Medical Chatbot Service Providing Medicine Use Recommendations for Women during Pregnancy and Lactation in Japan (Preprint)

Evaluation of satisfaction with a secure, connected mobile application for women in assisted reproductive technology programmes: a prospective, observational study of patients and medical team members. (Preprint)

Generative artificial intelligence (AI) chatbots, powered by large language models, are emerging as transformative tools with diverse applications in healthcare. This narrative review aims to explore their unique potential for addressing significant gaps in headache education and research, with a main focus on primary headache disorders, a substantial global health burden. In headache education, chatbots can provide tailored, individual information to patients. This improved accessibility could increase the adherence to treatment, reducing the risk of chronification, resulting in a better quality of life. Similarly, clinicians, particularly non-headache specialists, can access a wealth of up-to-date information on headache disorders, including clinical training simulations, which would facilitate reaching a correct diagnosis and optimize treatment. In headache research, generative chatbots can assist by streamlining data collection and analysis, aiding complex experimental setups, and supporting clinical trials, thus accelerating the discovery pipeline. While generative chatbots have demonstrated significant promise for revolutionizing the headache field, challenges persist, with the most important being ensuring data accuracy and privacy. Future developments should focus on pre-training with headache-specific curated databases, multimodal integration, and establishing robust regulatory and ethical frameworks among users (patients, researchers, clinicians), and AI developers to address its limitations. With responsible development, generative chatbots hold the potential to bridge current gaps in headache education and meaningfully advance medical research from bench to bedside, and beyond. 

Generative chatbots in headache education and research: A narrative review

Background The adoption of health technologies is key to empower research participants and collect quality data. However, the acceptance of health technologies is usually evaluated in patients or healthcare practitioners, but not in clinical research participants. Methods A 27-item online questionnaire was provided to the 11,695 members of a nutrition clinical research participant database from the Nantes area (France), to assess (1) participants' social and demography parameters, (2) equipment and usage of health apps and devices, (3) expectations in research setting and (4) opinion about the future of clinical research. Each item was described using frequency and percentage overall and by age classes. A global proportion comparison was performed using chi-square or Fisher-exact tests. Results A total of 1529 respondents (81.0% women, 19.0% men) completed the survey. Main uses of health apps included physical activity tracking (54.7%, age-related group difference, p < 0.001) and food quality assessment (45.7%, unrelated to age groups). Overall, 20.4% of respondents declared owning a connected wristband or watch. Most participants (93.8%) expected the use of connected devices in research. However, protection of personal data (37.5%), reliability (35.5%) and skilled use of devices (28.5%) were perceived as the main barriers. Most participants (93.3%) would agree to track their food intake using a mobile app, and 80.5% would complete it for at least a week while taking part in a clinical study. Only 13.2% would devote more than 10 min per meal to such record. A majority (60.4%) of respondents would accept to share their social media posts in an anonymous way and most (82.2%) of them would accept to interact with a chatbot for research purposes. Conclusions Our cross-sectional study suggests that clinical study participants are enthusiastic about all forms of digital health technologies and participant-centered studies but remain concerned about the use of personal data. Repeated assessments are suggested to evaluate the research participant's interest in technologies following the increase in use and demand for innovative health services during the pandemic of COVID-19. Graphical Abstract Attitudes and expectations of clinical research participants towards digital health and mobile dietary assessment tools: Main results of a cross-sectional survey study.

/pdf/attitudes-and-expectations-of-clinical-research-participants-28j47bxp.pdf

Attitudes and Expectations of Clinical Research Participants Toward Digital Health and Mobile Dietary Assessment Tools: Cross-Sectional Survey Study

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Attitudes and Expectations of Clinical Research Participants Toward Digital Health and Mobile Dietary Assessment Tools: Cross-Sectional Survey Study