Frontiers in digital health最新文献

{"title":"Editorial: Current status of and future directions for assessing technology acceptance for digital (mental) health interventions.","authors":"Jennifer Apolinário-Hagen, Giulia Paganin, Silvia Simbula","doi":"10.3389/fdgth.2024.1467297","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1467297","url":null,"abstract":"","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1467297"},"PeriodicalIF":3.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142127558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning.","authors":"Andreas Trojan, Emanuele Laurenzi, Stephan Jüngling, Sven Roth, Michael Kiessling, Ziad Atassi, Yannick Kadvany, Meinrad Mannhart, Christian Jackisch, Gerd Kullak-Ublick, Hans Friedrich Witschel","doi":"10.3389/fdgth.2024.1443987","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1443987","url":null,"abstract":"<p><strong>Background: </strong>The use of smartphone apps in cancer patients undergoing systemic treatment can promote the early detection of symptoms and therapy side effects and may be supported by machine learning (ML) for timely adaptation of therapies and reduction of adverse events and unplanned admissions.</p><p><strong>Objective: </strong>We aimed to create an Early Warning System (EWS) to predict situations where supportive interventions become necessary to prevent unplanned visits. For this, dynamically collected standardized electronic patient reported outcome (ePRO) data were analyzed in context with the patient's individual journey. Information on well-being, vital parameters, medication, and free text were also considered for establishing a hybrid ML model. The goal was to integrate both the strengths of ML in sifting through large amounts of data and the long-standing experience of human experts. Given the limitations of highly imbalanced datasets (where only very few adverse events are present) and the limitations of humans in overseeing all possible cause of such events, we hypothesize that it should be possible to combine both in order to partially overcome these limitations.</p><p><strong>Methods: </strong>The prediction of unplanned visits was achieved by employing a white-box ML algorithm (i.e., rule learner), which learned rules from patient data (i.e., ePROs, vital parameters, free text) that were captured via a medical device smartphone app. Those rules indicated situations where patients experienced unplanned visits and, hence, were captured as alert triggers in the EWS. Each rule was evaluated based on a cost matrix, where false negatives (FNs) have higher costs than false positives (FPs, i.e., false alarms). Rules were then ranked according to the costs and priority was given to the least expensive ones. Finally, the rules with higher priority were reviewed by two oncological experts for plausibility check and for extending them with additional conditions. This hybrid approach comprised the application of a sensitive ML algorithm producing several potentially unreliable, but fully human-interpretable and -modifiable rules, which could then be adjusted by human experts.</p><p><strong>Results: </strong>From a cohort of 214 patients and more than 16'000 available data entries, the machine-learned rule set achieved a recall of 19% on the entire dataset and a precision of 5%. We compared this performance to a set of conditions that a human expert had defined to predict adverse events. This \"human baseline\" did not discover any of the adverse events recorded in our dataset, i.e., it came with a recall and precision of 0%. Despite more plentiful results were expected by our machine learning approach, the involved medical experts a) had understood and were able to make sense of the rules and b) felt capable to suggest modification to the rules, some of which could potentially increase their precision. Suggested modifications o","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1443987"},"PeriodicalIF":3.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous remote monitoring of neurophysiologic Immersion accurately predicts mood.","authors":"Sean H Merritt, Paul J Zak","doi":"10.3389/fdgth.2024.1397557","DOIUrl":"10.3389/fdgth.2024.1397557","url":null,"abstract":"<p><p>Mental health professionals have relied primarily on clinical evaluations to identify <i>in vivo</i> pathology. As a result, mental health is largely reactive rather than proactive. In an effort to proactively assess mood, we collected continuous neurophysiologic data for ambulatory individuals 8-10 h a day at 1 Hz for 3 weeks (<i>N</i> = 24). Data were obtained using a commercial neuroscience platform (Immersion Neuroscience) that quantifies the neural value of social-emotional experiences. These data were related to self-reported mood and energy to assess their predictive accuracy. Statistical analyses quantified neurophysiologic troughs by the length and depth of social-emotional events with low values and neurophysiologic peaks as the complement. Participants in the study had an average of 2.25 (SD = 3.70, Min = 0, Max = 25) neurophysiologic troughs per day and 3.28 (SD = 3.97, Min = 0, Max = 25) peaks. The number of troughs and peaks predicted daily mood with 90% accuracy using least squares regressions and machine learning models. The analysis also showed that women were more prone to low mood compared to men. Our approach demonstrates that a simple count variable derived from a commercially-available platform is a viable way to assess low mood and low energy in populations vulnerable to mood disorders. In addition, peak Immersion events, which are mood-enhancing, may be an effective measure of thriving in adults.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1397557"},"PeriodicalIF":3.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11327156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel affordable user interface for robotic surgery training: design, development and usability study.","authors":"Alberto Neri, Mara Coduri, Veronica Penza, Andrea Santangelo, Alessandra Oliveri, Enrico Turco, Mattia Pizzirani, Elisa Trinceri, Domenico Soriero, Federico Boero, Serena Ricci, Leonardo S Mattos","doi":"10.3389/fdgth.2024.1428534","DOIUrl":"10.3389/fdgth.2024.1428534","url":null,"abstract":"<p><strong>Introduction: </strong>The use of robotic systems in the surgical domain has become groundbreaking for patients and surgeons in the last decades. While the annual number of robotic surgical procedures continues to increase rapidly, it is essential to provide the surgeon with innovative training courses along with the standard specialization path. To this end, simulators play a fundamental role. Currently, the high cost of the leading VR simulators limits their accessibility to educational institutions. The challenge lies in balancing high-fidelity simulation with cost-effectiveness; however, few cost-effective options exist for robotic surgery training.</p><p><strong>Methods: </strong>This paper proposes the design, development and user-centered usability study of an affordable user interface to control a surgical robot simulator. It consists of a cart equipped with two haptic interfaces, a VR visor and two pedals. The simulations were created using Unity, which offers versatility for expanding the simulator to more complex scenes. An intuitive teleoperation control of the simulated robotic instruments is achieved through a high-level control strategy.</p><p><strong>Results and discussion: </strong>Its affordability and resemblance to real surgeon consoles make it ideal for implementing robotic surgery training programs in medical schools, enhancing accessibility to a broader audience. This is demonstrated by the results of an usability study involving expert surgeons who use surgical robots regularly, expert surgeons without robotic surgery experience, and a control group. The results of the study, which was based on a traditional Peg-board exercise and Camera Control task, demonstrate the simulator's high usability and intuitive control across diverse user groups, including those with limited experience. This offers evidence that this affordable system is a promising solution for expanding robotic surgery training.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1428534"},"PeriodicalIF":3.2,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11319275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141977400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Provision of digital devices and internet connectivity to improve synchronous telemedicine access in the U.S.: a systematic scoping review.","authors":"Joshua Bell, Laura M Gottlieb, Courtney R Lyles, Oanh Kieu Nguyen, Sara L Ackerman, Emilia H De Marchis","doi":"10.3389/fdgth.2024.1408170","DOIUrl":"10.3389/fdgth.2024.1408170","url":null,"abstract":"<p><strong>Introduction: </strong>The COVID-19 pandemic led to a dramatic increase in telemedicine use for direct patient care. Inequities in device/internet access can limit the extent to which patients can engage with telemedicine care and exacerbate health disparities. In this review, we examined existing literature on interventions designed to improve patient telemedicine access by providing digital devices including tablets, smartphones, and computers and/or internet connectivity.</p><p><strong>Methods: </strong>In this systematic scoping review, we searched four databases for peer-reviewed studies published 1/1/2000-10/19/2021 that described healthcare interventions that provided patients with devices and/or internet connectivity and reported outcomes related to telemedicine access and/or usage. Data extraction elements included: study population, setting, intervention design, details on device/connectivity provision, and outcomes evaluated.</p><p><strong>Results: </strong>Twelve articles reflecting seven unique interventions met inclusion criteria. Ten articles examined telemedicine utilization (83%) and reported improved patient show rates/utilization. Seven articles examined patient satisfaction with the interventions (58%) and reported positive experiences. Fewer articles examined health outcomes (17%; 2/12) though these also demonstrated positive results. Across included studies, study quality was low. There were no controlled trials, and the most rigorously designed studies (<i>n</i> = 4) involved pre/post-intervention assessments.</p><p><strong>Discussion: </strong>Findings from this review indicate that providing material technology supports to patients can facilitate telemedicine access, is acceptable to patients and clinicians, and can contribute to improved health outcomes. The low number and quality of existing studies limits the strength of this evidence. Future research should explore interventions that can increase equitable access to telemedicine services.</p><p><strong>Systematic review registration: </strong>https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=183442, identifier, PROSPERO: CRD42020183442.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1408170"},"PeriodicalIF":3.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11317370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141972402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing fall risk in osteoporosis patients: a comparative study of age-matched fallers and nonfallers.","authors":"Seong Hyun Moon, Krupa B Doshi, Thurmon Lockhart","doi":"10.3389/fdgth.2024.1387193","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1387193","url":null,"abstract":"<p><p>This study aimed to investigate sway parameters and physical activity level of the age/gender-matched older adults with osteoporosis faller and nonfaller patients. By examining these factors, our objective was to understand how these faller and nonfaller groups with osteoporosis differed particularly in terms of balance capabilities and their impact on physical activity levels. We recruited 24 patients with osteoporosis: 12 who reported a fall within a year before recruitment (fallers) and 12 without falls (nonfallers). Given the close association between biochemical markers of musculoskeletal health such as serum calcium, parathyroid hormone (PTH), Vitamin D, and renal function, we compared these markers in both groups. As a result, elderly individuals with osteoporosis and with a history of falls within the preceding year indicated significantly higher sway velocity (<i>P</i> = 0.012*), sway area (<i>P</i> < 0.001*), and sway path length (<i>P</i> = 0.012*). Furthermore, fallers had significantly lower calcium (<i>P</i> = 0.02*) and Parathyroid hormone (PTH) (<i>P</i> = 0.02*), as well as higher Alkaline Phosphatase (ALP) (<i>P</i> = 0.02*) as compared to nonfallers despite similar vitamin D and creatinine levels. In conclusion, diminished biochemical factors in the osteoporosis faller group could possibly cause postural instability resulting in lower physical activity levels in the osteoporosis fall group and increasing the risk of falls.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1387193"},"PeriodicalIF":3.2,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11266042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Admission prioritization of heart failure patients with multiple comorbidities.","authors":"Rahul Awasthy, Meetu Malhotra, Michael L Seavers, Mark Newman","doi":"10.3389/fdgth.2024.1379336","DOIUrl":"10.3389/fdgth.2024.1379336","url":null,"abstract":"<p><p>The primary objective of this study was to enhance the operational efficiency of the current healthcare system by proposing a quicker and more effective approach for healthcare providers to deliver services to individuals facing acute heart failure (HF) and concurrent medical conditions. The aim was to support healthcare staff in providing urgent services more efficiently by developing an automated decision-support Patient Prioritization (PP) Tool that utilizes a tailored machine learning (ML) model to prioritize HF patients with chronic heart conditions and concurrent comorbidities during Urgent Care Unit admission. The study applies key ML models to the PhysioNet dataset, encompassing hospital admissions and mortality records of heart failure patients at Zigong Fourth People's Hospital in Sichuan, China, between 2016 and 2019. In addition, the model outcomes for the PhysioNet dataset are compared with the Healthcare Cost and Utilization Project (HCUP) Maryland (MD) State Inpatient Data (SID) for 2014, a secondary dataset containing heart failure patients, to assess the generalizability of results across diverse healthcare settings and patient demographics. The ML models in this project demonstrate efficiencies surpassing 97.8% and specificities exceeding 95% in identifying HF patients at a higher risk and ranking them based on their mortality risk level. Utilizing this machine learning for the PP approach underscores risk assessment, supporting healthcare professionals in managing HF patients more effectively and allocating resources to those in immediate need, whether in hospital or telehealth settings.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1379336"},"PeriodicalIF":3.2,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11250659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of a digital behavior change intervention (eCHANGE) for weight loss maintenance support: a service design and technology transfer approach.","authors":"R A Asbjørnsen, J Hjelmesæth, M L Smedsrød, J Wentzel, M M Clark, S M Kelders, J E W C van Gemert-Pijnen, L Solberg Nes","doi":"10.3389/fdgth.2024.1394599","DOIUrl":"10.3389/fdgth.2024.1394599","url":null,"abstract":"<p><p>Obesity is a chronic disease, and while weight loss is achievable, long-term weight loss maintenance is difficult and relapse common for people living with obesity. Aiming to meet the need for innovative approaches, digital behavior change interventions show promise in supporting health behavior change to maintain weight after initial weight loss. Implementation of such interventions should however be part of the design and development processes from project initiation to facilitate uptake and impact. Based on the development and implementation process of eCHANGE, an evidence-informed application-based self-management intervention for weight loss maintenance, this manuscript provides suggestions and guidance into; (1) How a <i>service design approach</i> can be used from initiation to implementation of digital interventions, and (2) How a <i>technology transfer process</i> can accelerate implementation of research-based innovation from idea to market.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1394599"},"PeriodicalIF":3.2,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11249862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of electronic records on mortality among patients in hospital and primary healthcare settings: a systematic review and meta-analyses.","authors":"Tariku Nigatu Bogale, Lemma Derseh, Loko Abraham, Herman Willems, Jonathan Metzger, Biruhtesfa Abere, Mesfin Tilaye, Tewodros Hailegeberel, Tadesse Alemu Bekele","doi":"10.3389/fdgth.2024.1377826","DOIUrl":"10.3389/fdgth.2024.1377826","url":null,"abstract":"<p><strong>Background: </strong>Electronic medical records or electronic health records, collectively called electronic records, have significantly transformed the healthcare system and service provision in our world. Despite a number of primary studies on the subject, reports are inconsistent and contradictory about the effects of electronic records on mortality. Therefore, this review examined the effect of electronic records on mortality.</p><p><strong>Methods: </strong>The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guideline. Six databases: PubMed, EMBASE, Scopus, CINAHL, Cochrane Library, and Google Scholar, were searched from February 20 to October 25, 2023. Studies that assessed the effect of electronic records on mortality and were published between 1998 and 2022 were included. Joanna Briggs Institute quality appraisal tool was used to assess the methodological quality of the studies. Narrative synthesis was performed to identify patterns across studies. Meta-analysis was conducted using fixed effect and random-effects models to estimate the pooled effect of electronic records on mortality. Funnel plot and Egger's regression test were used to assess for publication bias.</p><p><strong>Results: </strong>Fifty-four papers were found eligible for the systematic review, of which 42 were included in the meta-analyses. Of the 32 studies that assessed the effect of electronic health record on mortality, eight (25.00%) reported a statistically significant reduction in mortality, 22 (68.75%) did not show a statistically significant difference, and two (6.25%) studies reported an increased risk of mortality. Similarly, among the 22 studies that determined the effect of electronic medical record on mortality, 12 (54.55%) reported a statistically significant reduction in mortality, and ten (45.45%) studies didn't show a statistically significant difference. The fixed effect and random effects on mortality were OR = 0.95 (95% CI: 0.93-0.97) and OR = 0.94 (95% CI: 0.89-0.99), respectively. The associated I-squared was 61.5%. Statistical tests indicated that there was no significant publication bias among the studies included in the meta-analysis.</p><p><strong>Conclusion: </strong>Despite some heterogeneity among the studies, the review indicated that the implementation of electronic records in inpatient, specialized and intensive care units, and primary healthcare facilities seems to result in a statistically significant reduction in mortality. Maturity level and specific features may have played important roles.</p><p><strong>Systematic review registration: </strong>PROSPERO (CRD42023437257).</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1377826"},"PeriodicalIF":3.2,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11233798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Asking questions that are \"close to the bone\": integrating thematic analysis and natural language processing to explore the experiences of people with traumatic brain injuries engaging with patient-reported outcome measures.","authors":"Daniela Di Basilio, Lorraine King, Sarah Lloyd, Panayiotis Michael, Matthew Shardlow","doi":"10.3389/fdgth.2024.1387139","DOIUrl":"10.3389/fdgth.2024.1387139","url":null,"abstract":"<p><strong>Introduction: </strong>Patient-reported outcomes measures (PROMs) are valuable tools for assessing health-related quality of life and treatment effectiveness in individuals with traumatic brain injuries (TBIs). Understanding the experiences of individuals with TBIs in completing PROMs is crucial for improving their utility and relevance in clinical practice.</p><p><strong>Methods: </strong>Sixteen semi-structured interviews were conducted with a sample of individuals with TBIs. The interviews were transcribed verbatim and analysed using Thematic Analysis (TA) and Natural Language Processing (NLP) techniques to identify themes and emotional connotations related to the experiences of completing PROMs.</p><p><strong>Results: </strong>The TA of the data revealed six key themes regarding the experiences of individuals with TBIs in completing PROMs. Participants expressed varying levels of understanding and engagement with PROMs, with factors such as cognitive impairments and communication difficulties influencing their experiences. Additionally, insightful suggestions emerged on the barriers to the completion of PROMs, the factors facilitating it, and the suggestions for improving their contents and delivery methods. The sentiment analyses performed using NLP techniques allowed for the retrieval of the general sentimental and emotional \"tones\" in the participants' narratives of their experiences with PROMs, which were mainly characterised by low positive sentiment connotations. Although mostly neutral, participants' narratives also revealed the presence of emotions such as fear and, to a lesser extent, anger. The combination of a semantic and sentiment analysis of the experiences of people with TBIs rendered valuable information on the views and emotional responses to different aspects of the PROMs.</p><p><strong>Discussion: </strong>The findings highlighted the complexities involved in administering PROMs to individuals with TBIs and underscored the need for tailored approaches to accommodate their unique challenges. Integrating TA-based and NLP techniques can offer valuable insights into the experiences of individuals with TBIs and enhance the interpretation of qualitative data in this population.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1387139"},"PeriodicalIF":3.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11231399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141565312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}