TY - JOUR AU - Smedslund, Geir AU - Østerås, Nina AU - Hestevik, Hillestad Christine PY - 2025/10/1 TI - Effects of Remote Patient Monitoring on Health Care Utilization in Patients With Noncommunicable Diseases: Systematic Review and Meta-Analysis JO - JMIR Mhealth Uhealth SP - e68464 VL - 13 KW - remote patient monitoring KW - health care resource utilization KW - noncommunicable diseases KW - systematic review KW - hospitalizations KW - outpatient visits KW - remote consultation KW - chronic disease KW - patient monitoring KW - length of stay KW - emergency service KW - hospital N2 - Background: Management of noncommunicable diseases (NCDs) is an increasing challenge for health care systems. Although remote patient monitoring presents a promising solution by utilizing technology to monitor patients outside clinical settings, there is a lack of knowledge about the effect on resource utilization. Objective: This systematic review aimed to review the effects of remote patient monitoring on health care resource utilization by patients with NCDs. Methods: Eligible randomized controlled trials (RCTs) involved digital transmission of health data from patients to health care personnel. Outcomes included hospitalizations, length of stay, outpatient visits, and emergency visits. A systematic literature search was performed in Medline, Embase, and Cochrane Central Register of Controlled Trials in June 2024. Titles, abstracts, and full texts were screened individually by 2 authors. Risk of bias was assessed, and data were extracted, analyzed, and pooled in meta-analysis when possible. Confidence in the estimates was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: We included 40 RCTs published between 2017 and 2024. The largest group of NCDs was cardiovascular disease (16 studies). Remote patient monitoring may slightly decrease the proportion of hospitalizations compared with usual care (risk ratio [RR] 0.86, 95% CI 0.77 to 0.95; low certainty). Compared with usual care, remote patient monitoring had fewer or an equal number of hospitalizations (mean difference ?0.13, 95% CI ?0.29 to 0.03; low certainty). Hospital length of stay may be slightly reduced with remote patient monitoring compared with usual care (mean difference ?0.84, 95% CI ?1.61 to ?0.06 days; low certainty). The proportion of outpatient visits showed probably little to no difference between remote patient monitoring and usual care (RR 0.94, 95% CI 0.87 to 1.02; moderate certainty). Compared with usual care, remote patient monitoring had slightly more outpatient visits, but the CI was wide (mean difference 0.41, 95% CI ?0.22 to 1.03; low certainty). The results indicate a small or no difference between remote patient monitoring and usual care regarding proportion of emergency visits (RR 0.91, 95% CI 0.79 to 1.05; low certainty). We are uncertain whether remote patient monitoring increases or decreases the number of emergency visits, as the evidence was of very low certainty. Conclusions: This systematic review showed that remote patient monitoring possibly led to lower proportions of patients being hospitalized, fewer hospitalizations, and shorter hospital length of stay compared with usual care. Patients undergoing remote monitoring had possibly more outpatient visits compared with usual care. The proportions of patients with outpatient visits or emergency visits were probably similar. Finally, we had very low certainty in the number of emergency visits. The results should be considered with caution as the certainty of evidence was moderate to very low. We did not find results regarding institutional stay. UR - https://mhealth.jmir.org/2025/1/e68464 UR - http://dx.doi.org/10.2196/68464 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/68464 ER - TY - JOUR AU - Katz, S. Dmitri AU - Gooch, Daniel AU - Price, Linda AU - Rauf, Irum AU - Pearce, Oliver AU - Price, Blaine PY - 2025/8/29 TI - Exploring Clinician Experiences With a Digital Platform Supporting Orthopedic Care That Integrates Patient-Generated Health Data: Qualitative Study of Early Users JO - JMIR Hum Factors SP - e65216 VL - 12 KW - patient-generated health data KW - PGHD KW - orthopedic care KW - digital health KW - remote monitoring KW - patient-centered care KW - clinician workload KW - patient engagement KW - health informatics KW - orthopedics KW - quality of life KW - physiotherapists KW - surgeons N2 - Background: Digital care platforms that integrate patient-generated health data (PGHD) alongside education and communication tools have been recognized as potential instruments in transforming health care from clinician-centered to a more patient-centered approach. This transformation is driven by the potential of PGHD to provide deeper insights into patients? conditions, facilitate personalized care, improve patient quality of life, reduce inefficiencies in data collection, and empower patients. Yet, actual implementation within clinical settings is still at early stages; therefore, impacts on clinical care remain limited. Objective: This study sought to explore the benefits, challenges, and opportunities of integrating PGHD into orthopedic care by analyzing the reflections of early adopter surgeons and physiotherapists who have used a digital care management platform. Methods: This qualitative study used thematic analysis of interviews conducted with surgeons and physiotherapists (n=9) from a clinical unit that was among the first to trial ?mymobility,? an industry-produced software platform (Zimmer Biomet). The participants were recruited using snowball sampling, and interviews were conducted from June to July 2022. The interviews focused on work practices, use of digital tools, experiences with PGHD, and experiences with the mymobility software. Thematic analysis was conducted using NVivo software (QSR International Pty Ltd), focusing on identifying key themes and insights. Results: The study identified several benefits of integrating PGHD into orthopedic care, including improved patient education, enhanced communication and assessment, and increased patient motivation and adherence. However, several challenges were also noted, such as increased clinician workload, questionable data utility, lack of patient centricity, and inability to tailor software to clinical contexts. Suggested opportunities included improving dashboard design, personalizing physiotherapy, and using collected data for improving clinical care. Conclusions: The integration of PGHD into orthopedic care shows promise, largely in areas suggested by the literature. However, significant challenges remain. Future research should focus on addressing solvable challenges, such as improving software user interface design and functionality, while embracing the possibility that some challenges lack clear solutions and will likely require careful balancing of design tensions. The findings highlight the need for ongoing development and refinement of PGHD-inclusive systems to better support clinical practice and patient outcomes. UR - https://humanfactors.jmir.org/2025/1/e65216 UR - http://dx.doi.org/10.2196/65216 ID - info:doi/10.2196/65216 ER - TY - JOUR AU - Hämäläinen, Päivi AU - Lämsä, Elina AU - Viitala, Matias AU - Kuusisto, Hanna AU - Niiranen, Marja AU - Avikainen, Sari AU - Puustinen, Juha AU - Ryytty, Mervi AU - Ruutiainen, Juhani AU - Soilu-Hänninen, Merja PY - 2025/8/22 TI - Finnish Registry-Based Protocol for Screening and Management of Fatigue and Cognitive Problems in Multiple Sclerosis: Observational Study JO - JMIR Hum Factors SP - e67990 VL - 12 KW - multiple sclerosis KW - electronic health records KW - digital health KW - patient-generated data KW - cognition KW - fatigue N2 - Background: Digital patient registries are actively used to monitor long-term diseases. However, their potential in symptom management remains underused. Objective: This study aimed to report on the Finnish registry-based protocol to screen and manage cognitive symptoms and fatigue in multiple sclerosis (MS). Data on a sample collected during the first 2 years are presented. Methods: At the beginning of 2021, a Finnish protocol to screen and manage patient-perceived concerns related to cognition and fatigue, together with self-assessment of disease severity, symptoms, and quality of life (QoL) annually, was introduced. The Symbol Digit Modalities Test (SDMT), the Multiple Sclerosis Neuropsychological Questionnaire (MSNQ), the Fatigue Scale for Motor and Cognitive Functions (FSMC), as well as the Patient-Reported Expanded Disability Status Scale, the Visual Analog Scales, and the Euro QoL-5 Dimension were implemented into the Finnish MS registry. To support symptom management, patients were offered feedback reports based on the results of the FSMC and the MSNQ. The implementation of the protocol was evaluated in 5 Finnish wellbeing services counties. Results: Our sample from the beginning of 2021 to the end of 2022 includes data on 430 patients. A total of 86 (20%) patients have been assessed with the SDMT, whereas 329 (76.5%) patients have filled out the FSMC, and 172 (40.0%) patients have completed the MSNQ. The mean SDMT score is 49.0 (SD 13.56), MSNQ score is 35.3 (SD 9.39), total FSMC score is 63.0 (SD 22.49), and subscores for motor and cognitive fatigue are 31.6 (SD 11.43) and 31.5 (SD 11.68), respectively. The SDMT did not correlate with the MSNQ or the FSMC. Instead, the SDMT, MSNQ, and the FSMC correlated significantly with QoL. Conclusions: Fatigue and cognitive problems have an effect on QoL. In our preliminary sample, patient reports of cognitive problems and especially fatigue were conducted more frequently than the objective evaluation of processing speed. Although the Finnish MS registry offers a digital platform for the systematic screening of fatigue and cognitive problems, further education is needed to support the implementation of the protocol. UR - https://humanfactors.jmir.org/2025/1/e67990 UR - http://dx.doi.org/10.2196/67990 ID - info:doi/10.2196/67990 ER - TY - JOUR AU - Murphy, Alex Darcy AU - Ali, Mustafa Syed AU - Boudreau, Ann Shellie AU - Dixon, William AU - Wong, David AU - van der Veer, N. Sabine PY - 2025/8/22 TI - Summary and Analysis of Digital Pain Manikin Data in Adults With Pain Experience: Scoping Review JO - J Med Internet Res SP - e69360 VL - 27 KW - digital pain manikin KW - digital pain drawing KW - digital pain body map KW - digital pain chart KW - pain measurement KW - patient-generated health data KW - artificial intelligence KW - AI N2 - Background: A digital pain manikin is a measurement tool that presents a diagram of the human body where people mark the location of their pain to produce a pain drawing. Digital pain manikins facilitate collection of more detailed spatial pain data compared to questionnaire-based methods and are an increasingly common method for self-reporting and communicating pain. An overview of how digital pain drawings, collected through digital pain manikins, are analyzed and summarized is currently missing. Objective: This study aimed to map the ways in which digital pain drawings were summarized and analyzed and which pain constructs these summaries attempted to measure. The objectives were to (1) identify and characterize studies that used digital pain manikins for data collection, (2) identify which individual drawing?level summary measures they reported and the methods by which these summaries were calculated, and (3) identify if and how multidrawing (eg, time series) summary and analysis methods were applied. Methods: We conducted a scoping review to systematically identify studies that used digital pain manikins for data collection and reported summary measures or analysis of the resulting digital pain drawings. We searched multiple databases using search terms related to pain and manikin. Two authors independently performed title, abstract, and full-text screening. We extracted and synthesized data on how studies summarized and analyzed digital manikin pain data at the individual pain?drawing level as well as across multiple pain drawings. Results: Our search yielded 6189 studies, of which we included 92. The majority were clinical studies (n=51) and cross-sectional (n=64). Eighty-seven studies reported at least 1 individual drawing?level summary measure. We identified individual drawing?level manikin summary measures related to 10 distinct pain constructs, with the most common being pain extent (n=53), physical location (n=28), and widespreadness (n=21), with substantial methodological variation within constructs. Forty-two studies reported at least 1 multidrawing summary method. Heat maps were most common (n=35), followed by the number or proportion of participants reporting pain in a specific location (n=14). Sixteen studies reported multidrawing analysis methods, the most common being an assessment of the similarity between pairs of pain drawings representing the same individual at the same moment in time (n=6). Conclusions: We found a substantial number of studies that reported manikin summary and analysis methods, with the majority being cross-sectional clinical studies. Studies commonly reported pain extent at the individual?drawing level and used heat maps to summarize data across multiple drawings. Analysis methods that went beyond summarizing pain drawings were much rarer, and methodological variation within pain constructs meant a lack of comparability between studies and across manikins. This highlights a need for development of standardized methods that are applicable across manikins and more advanced methods that harness the spatial nature of pain drawings. UR - https://www.jmir.org/2025/1/e69360 UR - http://dx.doi.org/10.2196/69360 UR - http://www.ncbi.nlm.nih.gov/pubmed/40844827 ID - info:doi/10.2196/69360 ER - TY - JOUR AU - Washington, Peter PY - 2025/8/21 TI - Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics JO - JMIR AI SP - e55530 VL - 4 KW - precision health KW - deep learning KW - self-supervised learning KW - patient generated health data KW - digital therapeutics KW - therapeutic KW - digital health solution KW - machine learning KW - artificial intelligence KW - model KW - patient data KW - health outcome KW - deep learning model KW - perspective UR - https://ai.jmir.org/2025/1/e55530 UR - http://dx.doi.org/10.2196/55530 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55530 ER - TY - JOUR AU - Goodings, James Anthony AU - Fadahunsi, Philip Kayode AU - Tarn, M. Derjung AU - Lutomski, Jennifer AU - Chhor, Allison AU - Shiely, Frances AU - Henn, Patrick AU - O'Donoghue, John PY - 2025/8/18 TI - Determinants of Continuous Smartwatch Use and Data-Sharing Preferences With Physicians, Public Health Authorities, and Private Companies: Cross-Sectional Survey of Smartwatch Users JO - J Med Internet Res SP - e67414 VL - 27 KW - smartwatches KW - wearable electronic devices KW - health behavior KW - privacy KW - confidentiality KW - user engagement KW - digital health KW - perceived enjoyment KW - user satisfaction KW - data anonymization KW - continuous use KW - telemedicine KW - smartwatch KW - smartwatch use KW - preferences KW - physicians KW - public health authorities KW - private company KW - surveys KW - users KW - cross-sectional KW - online survey KW - expectation-confirmation model KW - structural equation modeling KW - wearable technology KW - wearables KW - data sharing N2 - Background: Smartwatches are widely adopted globally for tracking health metrics, offering potential for enhancing individual health care and public health efforts. Continuous use of the devices and users? willingness to share the data collected are critical to realizing their full benefits. Objective: This study aimed to identify key factors that determine continuous smartwatch use and users? comfort levels in sharing health data with health care providers and public health authorities. Methods: A cross-sectional online survey of current and past smartwatch users (aged >18 years) was conducted to assess determinants of continuous use based on the Expectation-Confirmation Model (ECM) and user comfort levels with different data-sharing methods. Structural equation modeling was used to evaluate relationships between habit formation, satisfaction, perceived enjoyment, and perceived usefulness with continuous use. Wilcoxon signed-rank tests were used to analyze user comfort in sharing data, comparing noninternet- versus internet-based sharing methods and fully versus partially anonymized data. Results: A total of 273 responses were analyzed, with participants aged 18?65 (mean 35.6, SD 11.7) years. The results indicate that continuous use of smartwatches is explained by habit (?=.35; P<.001) and satisfaction (?=.38; P<.001), which is in turn explained by perceived usefulness (?=.38; P<.001), perceived enjoyment (?=.32; P<.001), confirmation (?=.24; P<.001), and perceived usability (?=.10; P=.03). Smartwatch users preferred noninternet-based sharing options (z=?5.793; P<.001) when sharing data with their physician. Similarly, users were more comfortable sharing fully anonymized data with public health authorities than partially anonymized data (z=?3.592; P<.001). Conclusions: Habit formation and satisfaction emerged as pivotal drivers of continuous intention to use smartwatches, emphasizing the need for features that foster integration into daily routine and a rewarding user experience. Preferences for noninternet-based data sharing with physicians highlight privacy concerns that must be addressed to build users? trust. By aligning device features and data-sharing protocols with user preferences, manufacturers, health care providers, and policy makers can enhance user engagement and maximize the potential of smartwatches to support individual health management and public health initiatives. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2023-081228 UR - https://www.jmir.org/2025/1/e67414 UR - http://dx.doi.org/10.2196/67414 ID - info:doi/10.2196/67414 ER - TY - JOUR AU - Newton, J. Terry AU - Shah, Nilesh AU - Lewis, Katherine AU - Zocchi, S. Mark AU - Bixler, R. Felicia AU - Etingen, Bella AU - Lipschitz, M. Jessica AU - Robinson, A. Stephanie AU - Hogan, P. Timothy AU - Shimada, L. Stephanie PY - 2025/6/6 TI - Developing Infrastructure to Realize the Value of Patient-Generated Health Data in a Large Integrated Health Care System: The Veterans Health Administration Experience JO - J Med Internet Res SP - e70755 VL - 27 KW - patient-generated health data KW - Veterans Health Administration KW - mobile health apps KW - health data interoperability KW - clinical decision-making UR - https://www.jmir.org/2025/1/e70755 UR - http://dx.doi.org/10.2196/70755 ID - info:doi/10.2196/70755 ER - TY - JOUR AU - Nagino, Ken AU - Akasaki, Yasutsugu AU - Fuse, Nobuo AU - Ogishima, Soichi AU - Shimizu, Atsushi AU - Uruno, Akira AU - Sutoh, Yoichi AU - Otsuka-Yamasaki, Yayoi AU - Nagami, Fuji AU - Seita, Jun AU - Nakamura, Tomohiro AU - Nagaie, Satoshi AU - Taira, Makiko AU - Kobayashi, Tomoko AU - Shimizu, Ritsuko AU - Hozawa, Atsushi AU - Kuriyama, Shinichi AU - Eguchi, Atsuko AU - Midorikawa-Inomata, Akie AU - Nakamura, Masahiro AU - Murakami, Akira AU - Nakao, Shintaro AU - Inomata, Takenori PY - 2025/5/12 TI - Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study JO - JMIR Res Protoc SP - e67862 VL - 14 KW - dry eye syndrome KW - dry eye disease KW - mobile health KW - smartphone KW - biobank KW - ocular surface KW - digital health KW - genome-wide association study N2 - Background: Dry eye disease (DED) is a common ocular condition with diverse and heterogeneous symptoms. Current treatment standards of DED include the post facto management of associated symptoms through topical eye drops. However, there is a need for predictive, preventive, personalized, and participatory medicine. The DryEyeRhythm mobile health app enables real-time data collection on environmental, lifestyle, host, and digital factors in a patient?s daily environment. Combining these data with genetic information from biobanks could enhance our understanding of individual variations and facilitate the development of personalized treatment strategies for DED. Objective: This study aims to integrate digital data from the DryEyeRhythm smartphone app with the Tohoku Medical Megabank database to create a comprehensive database that elucidates the interplay between multifactorial factors and the onset and progression of DED. Methods: This prospective observational cohort study will include 1200 participants for the discovery stage and 1000 participants for the replication stage, all of whom have data available in the Tohoku Medical Megabank database. Participants will be recruited from the Community Support Center of Sendai, Miyagi Prefecture, Japan. Participant enrollment for the discovery stage was conducted from August 1, 2021, to June 30, 2022, and the replication stage will be conducted from August 31, 2024, to March 31, 2026. Participants will provide demographic data, medical history, lifestyle information, DED symptoms, and maximum blink interval measurements at baseline and after 30 days using the DryEyeRhythm smartphone app. Upon scanning a registration code, each participant?s cohort ID from the Tohoku Medical Megabank database will be linked to their smartphone app, enabling data integration between the Tohoku Medical Megabank and DryEyeRhythm database. The primary outcome will assess the association between genetic polymorphisms and DED using a genome-wide association study. Secondary outcomes will explore associations between DED and various factors, including sociodemographic characteristics, lifestyle habits, medical history, biospecimen analyses (eg, blood and urine), and physiological measurements (eg, height, weight, and eye examination results). Associations will be evaluated using logistic regression analysis, adjusting for potential confounding factors. Results: The discovery stage of participant enrollment was conducted from August 1, 2021, to June 30, 2022. The replication stage will take place from August 31, 2024, to March 31, 2026. Data analysis is expected to be completed by September 2026, with results reported by March 2027. Conclusions: This study highlights the potential of smartphone apps in advancing biobank research and deepening the understanding of multifactorial DED, paving the way for personalized treatment strategies in the future. International Registered Report Identifier (IRRID): DERR1-10.2196/67862 UR - https://www.researchprotocols.org/2025/1/e67862 UR - http://dx.doi.org/10.2196/67862 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67862 ER - TY - JOUR AU - Nair, Subjagouri Rakhi Asokkumar AU - Hartung, Matthias AU - Heinisch, Philipp AU - Jaskolski, Janik AU - Starke-Knäusel, Cornelius AU - Veríssimo, Susana AU - Schmidt, Maria David AU - Cimiano, Philipp PY - 2025/4/14 TI - Summarizing Online Patient Conversations Using Generative Language Models: Experimental and Comparative Study JO - JMIR Med Inform SP - e62909 VL - 13 KW - patient experience KW - online communities KW - summarizing KW - large language models N2 - Background: Social media is acknowledged by regulatory bodies (eg, the Food and Drug Administration) as an important source of patient experience data to learn about patients? unmet needs, priorities, and preferences. However, current methods rely either on manual analysis and do not scale, or on automatic processing, yielding mainly quantitative insights. Methods that can automatically summarize texts and yield qualitative insights at scale are missing. Objective: The objective of this study was to evaluate to what extent state-of-the-art large language models can appropriately summarize posts shared by patients in web-based forums and health communities. Specifically, the goal was to compare the performance of different language models and prompting strategies on the task of summarizing documents reflecting the experiences of individual patients. Methods: In our experimental and comparative study, we applied 3 different language models (Flan-T5, Generative Pretrained Transformer [GPT], GPT-3, and GPT-3.5) in combination with various prompting strategies to the task of summarizing posts from patients in online communities. The generated summaries were evaluated with respect to 124 manually created summaries as a ground-truth reference. As evaluation metrics, we used 2 standard metrics from the field of text generation, namely, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and BERTScore, to compare the automatically generated summaries to the manually created reference summaries. Results: Among the zero-shot prompting?based large language models investigated, GPT-3.5 performed better than the other models with respect to the ROUGE metrics, as well as with respect to BERTScore. While zero-shot prompting seems to be a good prompting strategy, overall GPT-3.5 in combination with directional stimulus prompting in a 3-shot setting had the best results with respect to the aforementioned metrics. A manual investigation of the summarization of the best-performing method showed that the generated summaries were accurate and plausible compared to the manual summaries. Conclusions: Taken together, our results suggest that state-of-the-art pretrained language models are a valuable tool to provide qualitative insights about the patient experience to better understand unmet needs, patient priorities, and how a disease impacts daily functioning and quality of life to inform processes aimed at improving health care delivery and ensure that drug development focuses more on the actual priorities and unmet needs of patients. The key limitations of our work are the small data sample as well as the fact that the manual summaries were created by 1 annotator only. Furthermore, the results hold only for the examined models and prompting strategies, potentially not generalizing to other models and strategies. UR - https://medinform.jmir.org/2025/1/e62909 UR - http://dx.doi.org/10.2196/62909 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/62909 ER - TY - JOUR AU - Zhang, Chunyan AU - Wang, Ting AU - Dong, Caixia AU - Dai, Duwei AU - Zhou, Linyun AU - Li, Zongfang AU - Xu, Songhua PY - 2025/3/5 TI - Exploring Psychological Trends in Populations With Chronic Obstructive Pulmonary Disease During COVID-19 and Beyond: Large-Scale Longitudinal Twitter Mining Study JO - J Med Internet Res SP - e54543 VL - 27 KW - COVID-19 KW - chronic obstructive pulmonary disease (COPD) KW - psychological trends KW - Twitter KW - data mining KW - deep learning N2 - Background: Chronic obstructive pulmonary disease (COPD) ranks among the leading causes of global mortality, and COVID-19 has intensified its challenges. Beyond the evident physical effects, the long-term psychological effects of COVID-19 are not fully understood. Objective: This study aims to unveil the long-term psychological trends and patterns in populations with COPD throughout the COVID-19 pandemic and beyond via large-scale Twitter mining. Methods: A 2-stage deep learning framework was designed in this study. The first stage involved a data retrieval procedure to identify COPD and non-COPD users and to collect their daily tweets. In the second stage, a data mining procedure leveraged various deep learning algorithms to extract demographic characteristics, hashtags, topics, and sentiments from the collected tweets. Based on these data, multiple analytical methods, namely, odds ratio (OR), difference-in-difference, and emotion pattern methods, were used to examine the psychological effects. Results: A cohort of 15,347 COPD users was identified from the data that we collected in the Twitter database, comprising over 2.5 billion tweets, spanning from January 2020 to June 2023. The attentiveness toward COPD was significantly affected by gender, age, and occupation; it was lower in females (OR 0.91, 95% CI 0.87-0.94; P<.001) than in males, higher in adults aged 40 years and older (OR 7.23, 95% CI 6.95-7.52; P<.001) than in those younger than 40 years, and higher in individuals with lower socioeconomic status (OR 1.66, 95% CI 1.60-1.72; P<.001) than in those with higher socioeconomic status. Across the study duration, COPD users showed decreasing concerns for COVID-19 and increasing health-related concerns. After the middle phase of COVID-19 (July 2021), a distinct decrease in sentiments among COPD users contrasted sharply with the upward trend among non-COPD users. Notably, in the post-COVID era (June 2023), COPD users showed reduced levels of joy and trust and increased levels of fear compared to their levels of joy and trust in the middle phase of COVID-19. Moreover, males, older adults, and individuals with lower socioeconomic status showed heightened fear compared to their counterparts. Conclusions: Our data analysis results suggest that populations with COPD experienced heightened mental stress in the post-COVID era. This underscores the importance of developing tailored interventions and support systems that account for diverse population characteristics. UR - https://www.jmir.org/2025/1/e54543 UR - http://dx.doi.org/10.2196/54543 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053739 ID - info:doi/10.2196/54543 ER - TY - JOUR AU - Southwick, Lauren AU - Sharma, Meghana AU - Rai, Sunny AU - Beidas, S. Rinad AU - Mandell, S. David AU - Asch, A. David AU - Curtis, Brenda AU - Guntuku, Chandra Sharath AU - Merchant, M. Raina PY - 2024/12/16 TI - Integrating Patient-Generated Digital Data Into Mental Health Therapy: Mixed Methods Analysis of User Experience JO - JMIR Ment Health SP - e59785 VL - 11 KW - digital data KW - social media KW - psychotherapy KW - latent Dirichlet allocation KW - LDA KW - mobile phone N2 - Background: Therapists and their patients increasingly discuss digital data from social media, smartphone sensors, and other online engagements within the context of psychotherapy. Objective: We examined patients? and mental health therapists? experiences and perceptions following a randomized controlled trial in which they both received regular summaries of patients? digital data (eg, dashboard) to review and discuss in session. The dashboard included data that patients consented to share from their social media posts, phone usage, and online searches. Methods: Following the randomized controlled trial, patient (n=56) and therapist (n=44) participants completed a debriefing survey after their study completion (from December 2021 to January 2022). Participants were asked about their experience receiving a digital data dashboard in psychotherapy via closed- and open-ended questions. We calculated descriptive statistics for closed-ended questions and conducted qualitative coding via NVivo (version 10; Lumivero) and natural language processing using the machine learning tool latent Dirichlet allocation to analyze open-ended questions. Results: Of 100 participants, nearly half (n=48, 49%) described their experience with the dashboard as ?positive,? while the other half noted a ?neutral? experience. Responses to the open-ended questions resulted in three thematic areas (nine subcategories): (1) dashboard experience (positive, neutral or negative, and comfortable); (2) perception of the dashboard?s impact on enhancing therapy (accountability, increased awareness over time, and objectivity); and (3) dashboard refinements (additional sources, tailored content, and privacy). Conclusions: Patients reported that receiving their digital data helped them stay ?accountable,? while therapists indicated that the dashboard helped ?tailor treatment plans.? Patient and therapist surveys provided important feedback on their experience regularly discussing dashboards in psychotherapy. Trial Registration: ClinicalTrials.gov NCT04011540; https://clinicaltrials.gov/study/NCT04011540 UR - https://mental.jmir.org/2024/1/e59785 UR - http://dx.doi.org/10.2196/59785 ID - info:doi/10.2196/59785 ER - TY - JOUR AU - Degen, Isabella AU - Robson Brown, Kate AU - Reeve, J. Henry W. AU - Abdallah, S. Zahraa PY - 2024/11/27 TI - Beyond Expected Patterns in Insulin Needs of People With Type 1 Diabetes: Temporal Analysis of Automated Insulin Delivery Data JO - JMIRx Med SP - e44384 VL - 5 KW - multivariate time series KW - k-means KW - clustering KW - machine learning KW - temporal patterns KW - data-driven KW - OpenAPS KW - open dataset KW - type 1 diabetes KW - insulin needs N2 - Background: Type 1 diabetes (T1D) is a chronic condition in which the body produces too little insulin, a hormone needed to regulate blood glucose. Various factors such as carbohydrates, exercise, and hormones impact insulin needs. Beyond carbohydrates, most factors remain underexplored. Regulating insulin is a complex control task that can go wrong and cause blood glucose levels to fall outside a range that protects people from adverse health effects. Automated insulin delivery (AID) has been shown to maintain blood glucose levels within a narrow range. Beyond clinical outcomes, data from AID systems are little researched; such systems can provide data-driven insights to improve the understanding and treatment of T1D. Objective: The aim is to discover unexpected temporal patterns in insulin needs and to analyze how frequently these occur. Unexpected patterns are situations where increased insulin does not result in lower glucose or where increased carbohydrate intake does not raise glucose levels. Such situations suggest that factors beyond carbohydrates influence insulin needs. Methods: We analyzed time series data on insulin on board (IOB), carbohydrates on board (COB), and interstitial glucose (IG) from 29 participants using the OpenAPS AID system. Pattern frequency in hours, days (grouped via k-means clustering), weekdays, and months were determined by comparing the 95% CI of the mean differences between temporal units. Associations between pattern frequency and demographic variables were examined. Significant differences in IOB, COB, and IG across temporal dichotomies were assessed using Mann-Whitney U tests. Effect sizes and Euclidean distances between variables were calculated. Finally, the forecastability of IOB, COB, and IG for the clustered days was analyzed using Granger causality. Results: On average, 13.5 participants had unexpected patterns and 9.9 had expected patterns. The patterns were more pronounced (d>0.94) when comparing hours of the day and similar days than when comparing days of the week or months (0.3