JMIR Formative Research最新文献

Background: The Tobacco Heating System (THS; commercialized as IQOS) is a smoke-free heated tobacco product introduced in the United States in 2019 and authorized by the US Food and Drug Administration as a modified risk tobacco product (MRTP) in 2020. THS consists of a holder and specially designed tobacco sticks that are heated instead of burned to produce a nicotine-containing aerosol. THS was available in Atlanta, Georgia; Richmond, Virginia; Charlotte, North Carolina; the Northern Virginia region; and South Carolina before its market removal in November 2021.

Objective: This study aims to describe selected sociodemographic characteristics and self-reported health history of adults who used IQOS (AUIs), their tobacco use patterns (eg, tobacco use history, exclusive and dual-use, and switching from cigarette smoking), their risk perceptions of the product, and their understanding of MRTP messages.

Methods: The IQOS Cross-Sectional Postmarket Adult Consumer Study was a study of AUIs aged 21 years or older who were recruited from a consumer database via direct postal mail and emails. Participants completed the online survey between September and November 2021.

Results: The survey was completed by 645 current and 43 former AUIs who had used at least 100 tobacco sticks (considered established THS use) before the assessment. Of the 688 participants, 424 (61.6%) were male, 502 (73.0%) were non-Hispanic White, and the mean age was 45 years. The vast majority (680/688, 98.8%) of AUIs had ever smoked combusted cigarettes before first trying THS and 628 (91.3%) had smoked cigarettes in the 30 days before first using THS. At the time of assessment, 161 (23.4%) reported using e-cigarettes (vs 229, 33.3%, before THS use), 92 (13.4%) reported smoking cigars (vs 114, 16.6%, before THS use), and 338 (49.1%) were still smoking after an average of 1 year of THS use. Among those currently using THS who were still smoking (n=298), 249 (83.6%) smoked fewer cigarettes compared with before first trying THS; 362 of 688 (52.6%) AUIs reported having no physical health conditions evaluated in this study and almost three-quarters reported having no mental health conditions. Among all AUIs, over 563 (81.8%) had never used a cessation treatment or had not used it in the past 12 months, and 555 (80.7%) AUIs demonstrated a correct understanding of the MRTP message and AUIs perceived THS as having a lower risk than cigarettes (43.8 vs 64.4 on a 100-point composite score scale).

Conclusions: This study provides evidence that THS can help adult smokers in the United States completely switch away from cigarettes or reduce smoking.

Background: Coproduction with users of new digital technology, such as passive mood monitoring, is likely to improve its utility, safety, and successful implementation via improved design and consideration of how such technology fits with their daily lives. Mood-monitoring interventions are commonly used by people with bipolar disorder (BD) and have promising potential for digitization using novel technological methods.

Objective: This study aims to explore how a passive behavioral monitoring platform, Remote Assessment of Disease and Relapse, would meet the needs of people with BD by specifically considering purpose and function, diversity of need, personal preference, essential components and potential risks, and harms and mitigation strategies through an iterative coproduction process.

Methods: A total of 17 people with BD were recruited via national charities. We conducted 3 web-based focus groups as a part of an iterative coproduction process in line with responsible research and innovation principles and with consideration of clinical challenges associated with BD. Data were analyzed thematically. Results were cross-checked by someone with lived experience of BD.

Results: Focus groups were transcribed and analyzed using thematic analysis. Six themes were identified as follows: (1) the purpose of using the app, (2) desired features, (3) when to use the app, (4) risks of using the app, (5) sharing with family and friends, and (6) sharing with health care professionals.

Conclusions: People with BD who are interested in using passive technology to monitor their mood wish to do so for a wide variety of purposes, identifying several preferences and potential risks. Principally, people with BD wished to use this novel technology to aid them in self-managing their BD with greater insight and a better understanding of potential triggers. We discuss key features that may aid this functionality and purpose, including crisis plans and sharing with others. Future development of passive mood-monitoring technologies should not assume that the involvement of formal mental health services is desired.

Background: As Korea rapidly transforms into a super-aged society, research indicates that digital literacy among older adults enhances their life satisfaction. Digital literacy refers to the ability to efficiently use digital technologies, encompassing access, competency, and utilization. It reflects the capacity to navigate and benefit from digital environments effectively. Furthermore, social capital positively influences the quality of life, and digital literacy facilitates social capital formation. However, since most studies have only focused on the direct relationship between digital literacy and life satisfaction, research on the mediating role of social capital remains limited.

Objective: To analyze the effect of digital literacy on the life satisfaction of older adults in South Korea and to verify whether social capital acts as a mediating factor in this process.

Methods: This descriptive cross-sectional study used data from the 2023 Report on the Digital Divide-an annual survey conducted by the Korean Ministry of Science and Information and Communications Technology. The study targeted individuals aged 65 years or older. Descriptive statistics, the Pearson correlation analyses, and the 3-step multiple regression analysis proposed by Baron and Kenny were performed. The bootstrap method was employed, and all analyses were conducted using R, version 4.4.1.

Results: The study included 869 participants. Digital literacy had a significant positive effect on their life satisfaction (β=0.103; P=.008). Social capital was also positively associated with life satisfaction (β=0.337; P<.001). Mediation analysis showed that digital literacy influenced life satisfaction both directly (β=0.103; P=.006) and indirectly through social capital (β=0.037; P=.03). Bootstrapping confirmed the significance of the indirect effect (β=0.037, 95% CI 0.005-0.070; P=.03). The total effect of digital literacy on life satisfaction was also significant (β=0.140, 95% CI 0.058-0.230; P=.002).

Conclusions: This study analyzed the association between digital literacy, social capital, and life satisfaction among older adults in Korea. We identified that social capital mediates the association between digital literacy and life satisfaction among older adults. These findings indicate that tailored digital literacy programs and support policies that promote social capital formation could help bridge the digital divide and foster social inclusion. These measures would enable older adults to access essential services, reduce social isolation, and enhance health and well-being, ultimately improving the overall quality of life.

Background: Technology that detects early when a patient at risk of falling leaves the bed can support nurses in acute care hospitals.

Objective: To develop a better understanding of nurses' perspectives and experiences with a bed-exit information system (BES) in an acute care hospital setting.

Methods: BES was implemented on 3 wards of a university medical center. Nurses completed 2 online surveys at each time point (P0 and P1) and participated in focus groups before (P0) and after (P1) implementation. Additional patient data were collected. Descriptive statistics summarized the survey results, while content analysis was applied to focus group data. Patient rates and adverse events in both phases were compared using negative binomial models. Reporting of this study adhered to the GRAMMS checklist.

Results: A total of 30 questionnaires were completed at P0 (30/72, 42%) and 24 at P1 (24/71, 33%). Of the participants, 15 completed both questionnaires (complete cases). At P1, 64% (9/14) of participants agreed that their perceived workload and strain in caring for patients with cognitive impairment was reduced by the use of the BES. The adverse event rate per patient per day was reduced by a factor of 0.61 (95% CI 0.393-0.955; P=.03). In addition, 11 nurses participated in 4 focus groups before and after the intervention. Participants found it challenging to operationalize the use of the BES due to the heterogeneity of care settings, but certain behaviors of patients with cognitive impairment were recognized as indicating a need for intervention. Negative experiences included information overload and alarm fatigue, leading to occasional removal of the system.

Conclusions: While BES provides some support in managing patients with cognitive impairment, its impact remains limited to specific scenarios and does not significantly reduce nurses' workload or strain. Our findings highlight the need to manage expectations of BES performance to ensure alignment between expected and actual benefits. To improve BES effectiveness and long-term implementation, future research should consider both objective measures of patient care and subjective factors such as nurse experience, structural conditions, and technical specifications. Improving information mechanisms within call systems could help reduce alarm fatigue and increase perceived usefulness. Overall, successful integration of BES in acute care settings will require close collaboration with nursing staff to drive meaningful healthcare innovation and ensure that the technology meets the needs of both patients and nurses.

Trial registration: German Register for Clinical Studies DRKS00021720; https://drks.de/search/de/trial/DRKS00021720.

Background: The incidence of delirium in patients with burns receiving treatment in the intensive care unit (ICU) is high, reaching up to 77%, and has been associated with increased mortality rates. Therefore, early identification of patients at high risk of delirium onset is essential for improving treatment strategies.

Objective: This study aimed to create a machine learning model for predicting delirium in patients with burns during their ICU stay using patient data from the first day of ICU admission and to identify predictive factors for ICU delirium in patients with burns.

Methods: This study focused on 82 patients with burns aged ≥18 years who were admitted to the ICU at Mie University Hospital for 24 h or more between January 2015 and June 2023. Seventy variables were measured in patients upon ICU admission and used as explanatory variables in the ICU delirium prediction model. Delirium was assessed using the Intensive Care Delirium Screening Checklist every 8 h after ICU admission. Ten different machine-learning methods were employed to predict ICU delirium. Multiple receiver operating characteristic curves were plotted for various machine learning models, and the area under each curve (AUC) was compared. Additionally, the top 15 risk factors contributing to delirium onset were identified using Shapley Additive exPlanations analysis.

Results: Among the ten machine learning models tested, logistic regression (AUC: 0.906 ± 0.073), support vector machine (AUC: 0.897 ± 0.056), k-nearest neighbors (AUC: 0.894 ± 0.060), neural network (AUC: 0.857 ± 0.058), random forest (AUC: 0.850 ± 0.074), AdaBoost (AUC: 0.832 ± 0.094), gradient boosting machine (AUC: 0.821 ± 0.074), and Naive Bayes (AUC: 0.827 ± 0.095) demonstrated the highest accuracy in predicting ICU delirium. Specifically, 24-h urine output (from ICU admission to 24 h), oxygen saturation, burn area, total bilirubin level, and intubation upon ICU admission were identified as the major risk factors for delirium onset. Additionally, variables, such as the proportion of white blood cell fractions, including monocytes, methemoglobin concentration, and respiratory rate, were identified as important risk factors for ICU delirium.

Conclusions: This study demonstrated the ability of machine learning models trained with vital signs and blood data upon ICU admission to predict delirium in patients with burns during their ICU stay.

Clinicaltrial: