首页 > 最新文献

JMIR Cancer最新文献

英文 中文
Exploring the Impact of the Multimodal CAPABLE eHealth Intervention on Health-Related Quality of Life in Patients With Melanoma Undergoing Immune-Checkpoint Inhibition: Prospective Pilot Study.
IF 3.3 Q2 ONCOLOGY Pub Date : 2025-01-30 DOI: 10.2196/58938
Itske Fraterman, Lucia Sacchi, Henk Mallo, Valentina Tibollo, Savannah Lucia Catherina Glaser, Stephanie Medlock, Ronald Cornet, Matteo Gabetta, Vitali Hisko, Vadzim Khadakou, Ella Barkan, Laura Del Campo, David Glasspool, Alexandra Kogan, Giordano Lanzola, Roy Leizer, Manuel Ottaviano, Mor Peleg, Konrad Śniatała, Aneta Lisowska, Szymon Wilk, Enea Parimbelli, Silvana Quaglini, Mimma Rizzo, Laura Deborah Locati, Annelies Boekhout, Lonneke V van de Poll-Franse, Sofie Wilgenhof

Background: Patients with melanoma receiving immunotherapy with immune-checkpoint inhibitors often experience immune-related adverse events, cancer-related fatigue, and emotional distress, affecting health-related quality of life (HRQoL) and clinical outcome to immunotherapy. eHealth tools can aid patients with cancer in addressing issues, such as adverse events and psychosocial well-being, from various perspectives.

Objective: This study aimed to explore the effect of the Cancer Patients Better Life Experience (CAPABLE) system, accessed through a mobile app, on HRQoL compared with a matched historical control group receiving standard care. CAPABLE is an extensively tested eHealth app, including educational material, remote symptom monitoring, and well-being interventions.

Methods: This prospective pilot study compared an exploratory cohort that received the CAPABLE smartphone app and a multisensory smartwatch for 6 months (intervention) to a 2:1 individually matched historical prospective control group. HRQoL data were measured with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 at baseline (T0), 3 months (T1), and 6 months (T2) after start of treatment. Mixed effects linear regression models were used to compare HRQoL between the 2 groups over time.

Results: From the 59 eligible patients for the CAPABLE intervention, 31 (53%) signed informed consent to participate. Baseline HRQoL was on average 10 points higher in the intervention group compared with controls, although equally matched on baseline and clinical characteristics. When correcting for sex, age, disease stage, and baseline scores, an adjusted difference in fatigue of -5.09 (95% CI -15.20 to 5.02, P=.32) at month 3 was found. No significant nor clinically relevant adjusted differences on other HRQoL domains over time were found. However, information satisfaction was significantly higher in the CAPABLE group (β=8.71, 95% CI 1.54-15.88, P=.02).

Conclusions: The intervention showed a limited effect on HRQoL, although there was a small improvement in fatigue at 3 months, as well as information satisfaction. When aiming at personalized patient and survivorship care, further optimization and prospective investigation of eHealth tools is warranted.

{"title":"Exploring the Impact of the Multimodal CAPABLE eHealth Intervention on Health-Related Quality of Life in Patients With Melanoma Undergoing Immune-Checkpoint Inhibition: Prospective Pilot Study.","authors":"Itske Fraterman, Lucia Sacchi, Henk Mallo, Valentina Tibollo, Savannah Lucia Catherina Glaser, Stephanie Medlock, Ronald Cornet, Matteo Gabetta, Vitali Hisko, Vadzim Khadakou, Ella Barkan, Laura Del Campo, David Glasspool, Alexandra Kogan, Giordano Lanzola, Roy Leizer, Manuel Ottaviano, Mor Peleg, Konrad Śniatała, Aneta Lisowska, Szymon Wilk, Enea Parimbelli, Silvana Quaglini, Mimma Rizzo, Laura Deborah Locati, Annelies Boekhout, Lonneke V van de Poll-Franse, Sofie Wilgenhof","doi":"10.2196/58938","DOIUrl":"https://doi.org/10.2196/58938","url":null,"abstract":"<p><strong>Background: </strong>Patients with melanoma receiving immunotherapy with immune-checkpoint inhibitors often experience immune-related adverse events, cancer-related fatigue, and emotional distress, affecting health-related quality of life (HRQoL) and clinical outcome to immunotherapy. eHealth tools can aid patients with cancer in addressing issues, such as adverse events and psychosocial well-being, from various perspectives.</p><p><strong>Objective: </strong>This study aimed to explore the effect of the Cancer Patients Better Life Experience (CAPABLE) system, accessed through a mobile app, on HRQoL compared with a matched historical control group receiving standard care. CAPABLE is an extensively tested eHealth app, including educational material, remote symptom monitoring, and well-being interventions.</p><p><strong>Methods: </strong>This prospective pilot study compared an exploratory cohort that received the CAPABLE smartphone app and a multisensory smartwatch for 6 months (intervention) to a 2:1 individually matched historical prospective control group. HRQoL data were measured with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 at baseline (T0), 3 months (T1), and 6 months (T2) after start of treatment. Mixed effects linear regression models were used to compare HRQoL between the 2 groups over time.</p><p><strong>Results: </strong>From the 59 eligible patients for the CAPABLE intervention, 31 (53%) signed informed consent to participate. Baseline HRQoL was on average 10 points higher in the intervention group compared with controls, although equally matched on baseline and clinical characteristics. When correcting for sex, age, disease stage, and baseline scores, an adjusted difference in fatigue of -5.09 (95% CI -15.20 to 5.02, P=.32) at month 3 was found. No significant nor clinically relevant adjusted differences on other HRQoL domains over time were found. However, information satisfaction was significantly higher in the CAPABLE group (β=8.71, 95% CI 1.54-15.88, P=.02).</p><p><strong>Conclusions: </strong>The intervention showed a limited effect on HRQoL, although there was a small improvement in fatigue at 3 months, as well as information satisfaction. When aiming at personalized patient and survivorship care, further optimization and prospective investigation of eHealth tools is warranted.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e58938"},"PeriodicalIF":3.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Spatiotemporal Correlation Analysis for the Incidence of Esophageal and Gastric Cancer From 2010 to 2019: Ecological Study.
IF 3.3 Q2 ONCOLOGY Pub Date : 2025-01-29 DOI: 10.2196/66655
Zixuan Cui, Chen Suo, Yidan Zhao, Shuo Wang, Ming Zhao, Ruilin Chen, Linyao Lu, Tiejun Zhang, Xingdong Chen

Background: Esophageal and gastric cancer were among the top 10 most common cancers worldwide. In addition, sex-specific differences were observed in the incidence. Due to their anatomic proximity, the 2 cancers have both different but also shared risk factors and epidemiological features. Exploring the potential correlated incidence pattern of them, holds significant importance in providing clues in the etiology and preventive strategies.

Objective: This study aims to explore the spatiotemporal correlation between the incidence patterns of esophageal and gastric cancer in 204 countries and territories from 2010 to 2019 so that prevention and control strategies can be more effective.

Methods: The data of esophageal and gastric cancer were sourced from the Global Burden of Disease (GBD). Spatial autocorrelation analysis using Moran I in ArcGIS 10.8 (Esri) was performed to determine spatial clustering of each cancer incidence. We classified different risk areas based on the risk ratio (RR) of the 2 cancers in various countries to the global, and the correlation between their RR was evaluated using Pearson correlation coefficient. Temporal trends were quantified by calculating the average annual percent change (AAPC), and the correlation between the temporal trends of both cancers was evaluated using Pearson correlation coefficients.

Results: In 2019, among 204 countries and territories, the age-standardized incidence rates (ASIR) of esophageal cancer ranged from 0.91 (95% CI 0.65-1.58) to 24.53 (95% CI 18.74-32.51), and the ASIR of gastric cancer ranged from 3.28 (95% CI 2.67-3.91) to 43.70 (95% CI 34.29-55.10). Malawi was identified as the highest risk for esophageal cancer (male RR=3.27; female RR=5.19) and low risk for gastric cancer (male RR=0.21; female RR=0.23) in both sexes. Spatial autocorrelation analysis revealed significant spatial clustering of the incidence for both cancers (Moran I>0.20 and P<.001). A positive correlation between the risk of esophageal and gastric cancer was observed in males (r=0.25, P<.001). The ASIR of both cancers showed a decreasing trend globally. The ASIR for esophageal and gastric cancer showed an AAPC of -1.43 (95% CI -1.58 to -1.27) and -1.76 (95% CI -2.08 to -1.43) in males, and -1.93 (95% CI -2.11 to -1.75) and -1.79 (95% CI -2.13 to -1.46) in females. In addition, a positive correlation between the temporal trends in ASIR for both cancers was observed at the global level across sexes (male r=0.98; female r=0.98).

Conclusions: Our study shows that there was a significant spatial clustering of the incidence for esophageal and gastric cancer and a positive correlation between the risk of both cancers across countries was observed in males. In addition, a codescending incidence trend between both cancers was observed at the global level.

{"title":"Spatiotemporal Correlation Analysis for the Incidence of Esophageal and Gastric Cancer From 2010 to 2019: Ecological Study.","authors":"Zixuan Cui, Chen Suo, Yidan Zhao, Shuo Wang, Ming Zhao, Ruilin Chen, Linyao Lu, Tiejun Zhang, Xingdong Chen","doi":"10.2196/66655","DOIUrl":"https://doi.org/10.2196/66655","url":null,"abstract":"<p><strong>Background: </strong>Esophageal and gastric cancer were among the top 10 most common cancers worldwide. In addition, sex-specific differences were observed in the incidence. Due to their anatomic proximity, the 2 cancers have both different but also shared risk factors and epidemiological features. Exploring the potential correlated incidence pattern of them, holds significant importance in providing clues in the etiology and preventive strategies.</p><p><strong>Objective: </strong>This study aims to explore the spatiotemporal correlation between the incidence patterns of esophageal and gastric cancer in 204 countries and territories from 2010 to 2019 so that prevention and control strategies can be more effective.</p><p><strong>Methods: </strong>The data of esophageal and gastric cancer were sourced from the Global Burden of Disease (GBD). Spatial autocorrelation analysis using Moran I in ArcGIS 10.8 (Esri) was performed to determine spatial clustering of each cancer incidence. We classified different risk areas based on the risk ratio (RR) of the 2 cancers in various countries to the global, and the correlation between their RR was evaluated using Pearson correlation coefficient. Temporal trends were quantified by calculating the average annual percent change (AAPC), and the correlation between the temporal trends of both cancers was evaluated using Pearson correlation coefficients.</p><p><strong>Results: </strong>In 2019, among 204 countries and territories, the age-standardized incidence rates (ASIR) of esophageal cancer ranged from 0.91 (95% CI 0.65-1.58) to 24.53 (95% CI 18.74-32.51), and the ASIR of gastric cancer ranged from 3.28 (95% CI 2.67-3.91) to 43.70 (95% CI 34.29-55.10). Malawi was identified as the highest risk for esophageal cancer (male RR=3.27; female RR=5.19) and low risk for gastric cancer (male RR=0.21; female RR=0.23) in both sexes. Spatial autocorrelation analysis revealed significant spatial clustering of the incidence for both cancers (Moran I>0.20 and P<.001). A positive correlation between the risk of esophageal and gastric cancer was observed in males (r=0.25, P<.001). The ASIR of both cancers showed a decreasing trend globally. The ASIR for esophageal and gastric cancer showed an AAPC of -1.43 (95% CI -1.58 to -1.27) and -1.76 (95% CI -2.08 to -1.43) in males, and -1.93 (95% CI -2.11 to -1.75) and -1.79 (95% CI -2.13 to -1.46) in females. In addition, a positive correlation between the temporal trends in ASIR for both cancers was observed at the global level across sexes (male r=0.98; female r=0.98).</p><p><strong>Conclusions: </strong>Our study shows that there was a significant spatial clustering of the incidence for esophageal and gastric cancer and a positive correlation between the risk of both cancers across countries was observed in males. In addition, a codescending incidence trend between both cancers was observed at the global level.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e66655"},"PeriodicalIF":3.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study.
IF 3.3 Q2 ONCOLOGY Pub Date : 2025-01-28 DOI: 10.2196/52886
Daphna Y Spiegel, Isabel D Friesner, William Zhang, Travis Zack, Gianna Yan, Julia Willcox, Nicolas Prionas, Lisa Singer, Catherine Park, Julian C Hong

Background: Early-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information and as a decision tool for patients, and awareness of these conversations is important for patient counseling.

Objective: The goal of this study was to compare sentiments and associated emotions in social media discussions surrounding BCS and mastectomy using natural language processing (NLP).

Methods: Reddit posts and comments from the Reddit subreddit r/breastcancer and associated metadata were collected using pushshift.io. Overall, 105,231 paragraphs across 59,416 posts and comments from 2011 to 2021 were collected and analyzed. Paragraphs were processed through the Apache Clinical Text Analysis Knowledge Extraction System and identified as discussing BCS or mastectomy based on physician-defined Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concepts. Paragraphs were analyzed with a VADER (Valence Aware Dictionary for Sentiment Reasoning) compound sentiment score (ranging from -1 to 1, corresponding to negativity or positivity) and GoEmotions scores (0-1) corresponding to the intensity of 27 different emotions and neutrality.

Results: Of the 105,231 paragraphs, there were 7306 (6.94% of those analyzed) paragraphs mentioning BCS and mastectomy (2729 and 5476, respectively). Discussion of both increased over time, with BCS outpacing mastectomy. The median sentiment score for all discussions analyzed in aggregate became more positive over time. In specific analyses by topic, positive sentiments for discussions with mastectomy mentions increased over time; however, discussions with BCS-specific mentions did not show a similar trend and remained overall neutral. Compared to BCS, conversations about mastectomy tended to have more positive sentiments. The most commonly identified emotions included neutrality, gratitude, caring, approval, and optimism. Anger, annoyance, disappointment, disgust, and joy increased for BCS over time.

Conclusions: Patients are increasingly participating in breast cancer therapy discussions with a web-based community. While discussions surrounding mastectomy became increasingly positive, BCS discussions did not show the same trend. This mirrors national clinical trends in the United States, with the increasing use of mastectomy over BCS in early-stage breast cancer. Recognizing sentiments and emotions surrounding the decision-making process can facilitate patient-centric and emotionally sensitive treatment recommendations.

{"title":"Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study.","authors":"Daphna Y Spiegel, Isabel D Friesner, William Zhang, Travis Zack, Gianna Yan, Julia Willcox, Nicolas Prionas, Lisa Singer, Catherine Park, Julian C Hong","doi":"10.2196/52886","DOIUrl":"https://doi.org/10.2196/52886","url":null,"abstract":"<p><strong>Background: </strong>Early-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information and as a decision tool for patients, and awareness of these conversations is important for patient counseling.</p><p><strong>Objective: </strong>The goal of this study was to compare sentiments and associated emotions in social media discussions surrounding BCS and mastectomy using natural language processing (NLP).</p><p><strong>Methods: </strong>Reddit posts and comments from the Reddit subreddit r/breastcancer and associated metadata were collected using pushshift.io. Overall, 105,231 paragraphs across 59,416 posts and comments from 2011 to 2021 were collected and analyzed. Paragraphs were processed through the Apache Clinical Text Analysis Knowledge Extraction System and identified as discussing BCS or mastectomy based on physician-defined Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concepts. Paragraphs were analyzed with a VADER (Valence Aware Dictionary for Sentiment Reasoning) compound sentiment score (ranging from -1 to 1, corresponding to negativity or positivity) and GoEmotions scores (0-1) corresponding to the intensity of 27 different emotions and neutrality.</p><p><strong>Results: </strong>Of the 105,231 paragraphs, there were 7306 (6.94% of those analyzed) paragraphs mentioning BCS and mastectomy (2729 and 5476, respectively). Discussion of both increased over time, with BCS outpacing mastectomy. The median sentiment score for all discussions analyzed in aggregate became more positive over time. In specific analyses by topic, positive sentiments for discussions with mastectomy mentions increased over time; however, discussions with BCS-specific mentions did not show a similar trend and remained overall neutral. Compared to BCS, conversations about mastectomy tended to have more positive sentiments. The most commonly identified emotions included neutrality, gratitude, caring, approval, and optimism. Anger, annoyance, disappointment, disgust, and joy increased for BCS over time.</p><p><strong>Conclusions: </strong>Patients are increasingly participating in breast cancer therapy discussions with a web-based community. While discussions surrounding mastectomy became increasingly positive, BCS discussions did not show the same trend. This mirrors national clinical trends in the United States, with the increasing use of mastectomy over BCS in early-stage breast cancer. Recognizing sentiments and emotions surrounding the decision-making process can facilitate patient-centric and emotionally sensitive treatment recommendations.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e52886"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
User and Developer Views on Using AI Technologies to Facilitate the Early Detection of Skin Cancers in Primary Care Settings: Qualitative Semistructured Interview Study.
IF 3.3 Q2 ONCOLOGY Pub Date : 2025-01-28 DOI: 10.2196/60653
Owain Tudor Jones, Natalia Calanzani, Suzanne E Scott, Rubeta N Matin, Jon Emery, Fiona M Walter
<p><strong>Background: </strong>Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals. There are few qualitative studies examining the views of relevant stakeholders or evidence about the implementation and positioning of AI technologies in the skin cancer diagnostic pathway.</p><p><strong>Objective: </strong>This study aimed to understand the views of several stakeholder groups on the use of AI technologies to facilitate the early diagnosis of skin cancer, including patients, members of the public, general practitioners, primary care nurse practitioners, dermatologists, and AI researchers.</p><p><strong>Methods: </strong>This was a qualitative, semistructured interview study with 29 stakeholders. Participants were purposively sampled based on age, sex, and geographical location. We conducted the interviews via Zoom between September 2022 and May 2023. Transcribed recordings were analyzed using thematic framework analysis. The framework for the Nonadoption, Abandonment, and Challenges to Scale-Up, Spread, and Sustainability was used to guide the analysis to help understand the complexity of implementing diagnostic technologies in clinical settings.</p><p><strong>Results: </strong>Major themes were "the position of AI in the skin cancer diagnostic pathway" and "the aim of the AI technology"; cross-cutting themes included trust, usability and acceptability, generalizability, evaluation and regulation, implementation, and long-term use. There was no clear consensus on where AI should be placed along the skin cancer diagnostic pathway, but most participants saw the technology in the hands of either patients or primary care practitioners. Participants were concerned about the quality of the data used to develop and test AI technologies and the impact this could have on their accuracy in clinical use with patients from a range of demographics and the risk of missing skin cancers. Ease of use and not increasing the workload of already strained health care services were important considerations for participants. Health care professionals and AI researchers reported a lack of established methods of evaluating and regulating AI technologies.</p><p><strong>Conclusions: </strong>This study is one of the first to examine the views of a wide range of stakeholders on the use of AI technologies to facilitate early diagnosis of skin cancer. The optimal approach and position in the diagnostic pathway for these technologies have not yet been determined. AI technologies need to be developed and implemented carefully and thoughtfully, with attention paid to the quality and representativeness of the data used for development,
{"title":"User and Developer Views on Using AI Technologies to Facilitate the Early Detection of Skin Cancers in Primary Care Settings: Qualitative Semistructured Interview Study.","authors":"Owain Tudor Jones, Natalia Calanzani, Suzanne E Scott, Rubeta N Matin, Jon Emery, Fiona M Walter","doi":"10.2196/60653","DOIUrl":"https://doi.org/10.2196/60653","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals. There are few qualitative studies examining the views of relevant stakeholders or evidence about the implementation and positioning of AI technologies in the skin cancer diagnostic pathway.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to understand the views of several stakeholder groups on the use of AI technologies to facilitate the early diagnosis of skin cancer, including patients, members of the public, general practitioners, primary care nurse practitioners, dermatologists, and AI researchers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This was a qualitative, semistructured interview study with 29 stakeholders. Participants were purposively sampled based on age, sex, and geographical location. We conducted the interviews via Zoom between September 2022 and May 2023. Transcribed recordings were analyzed using thematic framework analysis. The framework for the Nonadoption, Abandonment, and Challenges to Scale-Up, Spread, and Sustainability was used to guide the analysis to help understand the complexity of implementing diagnostic technologies in clinical settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Major themes were \"the position of AI in the skin cancer diagnostic pathway\" and \"the aim of the AI technology\"; cross-cutting themes included trust, usability and acceptability, generalizability, evaluation and regulation, implementation, and long-term use. There was no clear consensus on where AI should be placed along the skin cancer diagnostic pathway, but most participants saw the technology in the hands of either patients or primary care practitioners. Participants were concerned about the quality of the data used to develop and test AI technologies and the impact this could have on their accuracy in clinical use with patients from a range of demographics and the risk of missing skin cancers. Ease of use and not increasing the workload of already strained health care services were important considerations for participants. Health care professionals and AI researchers reported a lack of established methods of evaluating and regulating AI technologies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study is one of the first to examine the views of a wide range of stakeholders on the use of AI technologies to facilitate early diagnosis of skin cancer. The optimal approach and position in the diagnostic pathway for these technologies have not yet been determined. AI technologies need to be developed and implemented carefully and thoughtfully, with attention paid to the quality and representativeness of the data used for development, ","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e60653"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143060978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study.
IF 3.3 Q2 ONCOLOGY Pub Date : 2025-01-27 DOI: 10.2196/58834
Kelly Voigt, Yingtao Sun, Ayush Patandin, Johanna Hendriks, Richard Hendrik Goossens, Cornelis Verhoef, Olga Husson, Dirk Grünhagen, Jiwon Jung

Background: The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals' daily lives during their patient journey, qualitative studies are crucial. However, not all patients wish to share their stories with researchers.

Objective: This study aims to identify and assess patient experiences on a large scale using a novel machine learning-supported approach, leveraging data from patient forums.

Methods: Forum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source. Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. Researchers read the most relevant 50 posts on each topic, dividing them into "home" or "hospital" contexts. A patient community journey map, derived from patients stories, was developed to visually illustrate our findings. CRC medical doctors and a quality-of-life expert evaluated the identified topics of patient experience and the map.

Results: Based on 212,107 posts, 37 topics and 10 upper clusters were produced. Dominant clusters included "Daily activities while living with CRC" (38,782, 18.3%) and "Understanding treatment including alternatives and adjuvant therapy" (31,577, 14.9%). Topics related to the home context had more emotional content compared with the hospital context. The patient community journey map was constructed based on these findings.

Conclusions: Our study highlighted the diverse concerns and experiences of patients with CRC. The more emotional content in home context discussions underscores the personal impact of CRC beyond clinical settings. Based on our study, we found that a machine learning-supported approach is a promising solution to analyze patients' experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges in patients' daily lives, which is essential for delivering appropriate support at the right moment.

{"title":"A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study.","authors":"Kelly Voigt, Yingtao Sun, Ayush Patandin, Johanna Hendriks, Richard Hendrik Goossens, Cornelis Verhoef, Olga Husson, Dirk Grünhagen, Jiwon Jung","doi":"10.2196/58834","DOIUrl":"https://doi.org/10.2196/58834","url":null,"abstract":"<p><strong>Background: </strong>The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals' daily lives during their patient journey, qualitative studies are crucial. However, not all patients wish to share their stories with researchers.</p><p><strong>Objective: </strong>This study aims to identify and assess patient experiences on a large scale using a novel machine learning-supported approach, leveraging data from patient forums.</p><p><strong>Methods: </strong>Forum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source. Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. Researchers read the most relevant 50 posts on each topic, dividing them into \"home\" or \"hospital\" contexts. A patient community journey map, derived from patients stories, was developed to visually illustrate our findings. CRC medical doctors and a quality-of-life expert evaluated the identified topics of patient experience and the map.</p><p><strong>Results: </strong>Based on 212,107 posts, 37 topics and 10 upper clusters were produced. Dominant clusters included \"Daily activities while living with CRC\" (38,782, 18.3%) and \"Understanding treatment including alternatives and adjuvant therapy\" (31,577, 14.9%). Topics related to the home context had more emotional content compared with the hospital context. The patient community journey map was constructed based on these findings.</p><p><strong>Conclusions: </strong>Our study highlighted the diverse concerns and experiences of patients with CRC. The more emotional content in home context discussions underscores the personal impact of CRC beyond clinical settings. Based on our study, we found that a machine learning-supported approach is a promising solution to analyze patients' experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges in patients' daily lives, which is essential for delivering appropriate support at the right moment.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e58834"},"PeriodicalIF":3.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143060940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation.
IF 3.3 Q2 ONCOLOGY Pub Date : 2025-01-23 DOI: 10.2196/57275
Yosuke Yamagishi, Yuta Nakamura, Shouhei Hanaoka, Osamu Abe
<p><strong>Background: </strong>The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology reports, facilitating the interpretation of textual content, and enhancing data utility through clustering techniques. Clustering allows for the identification of similar lesions and disease patterns across a broad dataset, making it useful for aggregating information and discovering new insights in medical imaging. However, most publicly available medical datasets are in English, with limited resources in other languages. This scarcity poses a challenge for development of models geared toward non-English downstream tasks.</p><p><strong>Objective: </strong>This study aimed to develop and evaluate an algorithm that uses large language models (LLMs) to extract information from Japanese lung cancer radiology reports and perform clustering analysis. The effectiveness of this approach was assessed and compared with previous supervised methods.</p><p><strong>Methods: </strong>This study employed the MedTxt-RR dataset, comprising 135 Japanese radiology reports from 9 radiologists who interpreted the computed tomography images of 15 lung cancer patients obtained from Radiopaedia. Previously used in the NTCIR-16 (NII Testbeds and Community for Information Access Research) shared task for clustering performance competition, this dataset was ideal for comparing the clustering ability of our algorithm with those of previous methods. The dataset was split into 8 cases for development and 7 for testing, respectively. The study's approach involved using the LLM to extract information pertinent to lung cancer findings and transforming it into numeric features for clustering, using the K-means method. Performance was evaluated using 135 reports for information extraction accuracy and 63 test reports for clustering performance. This study focused on the accuracy of automated systems for extracting tumor size, location, and laterality from clinical reports. The clustering performance was evaluated using normalized mutual information, adjusted mutual information , and the Fowlkes-Mallows index for both the development and test data.</p><p><strong>Results: </strong>The tumor size was accurately identified in 99 out of 135 reports (73.3%), with errors in 36 reports (26.7%), primarily due to missing or incorrect size information. Tumor location and laterality were identified with greater accuracy in 112 out of 135 reports (83%); however, 23 reports (17%) contained errors mainly due to empty values or incorrect data. Clustering performance of the test data yielded an normalized mutual information of 0.6414, adjusted mutual information of 0.5598, and Fowlkes-Mallows index of 0.5354. The proposed method demonstrated superior performance across all evaluation metrics compared to previous methods.</p><p><str
{"title":"Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation.","authors":"Yosuke Yamagishi, Yuta Nakamura, Shouhei Hanaoka, Osamu Abe","doi":"10.2196/57275","DOIUrl":"https://doi.org/10.2196/57275","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology reports, facilitating the interpretation of textual content, and enhancing data utility through clustering techniques. Clustering allows for the identification of similar lesions and disease patterns across a broad dataset, making it useful for aggregating information and discovering new insights in medical imaging. However, most publicly available medical datasets are in English, with limited resources in other languages. This scarcity poses a challenge for development of models geared toward non-English downstream tasks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop and evaluate an algorithm that uses large language models (LLMs) to extract information from Japanese lung cancer radiology reports and perform clustering analysis. The effectiveness of this approach was assessed and compared with previous supervised methods.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study employed the MedTxt-RR dataset, comprising 135 Japanese radiology reports from 9 radiologists who interpreted the computed tomography images of 15 lung cancer patients obtained from Radiopaedia. Previously used in the NTCIR-16 (NII Testbeds and Community for Information Access Research) shared task for clustering performance competition, this dataset was ideal for comparing the clustering ability of our algorithm with those of previous methods. The dataset was split into 8 cases for development and 7 for testing, respectively. The study's approach involved using the LLM to extract information pertinent to lung cancer findings and transforming it into numeric features for clustering, using the K-means method. Performance was evaluated using 135 reports for information extraction accuracy and 63 test reports for clustering performance. This study focused on the accuracy of automated systems for extracting tumor size, location, and laterality from clinical reports. The clustering performance was evaluated using normalized mutual information, adjusted mutual information , and the Fowlkes-Mallows index for both the development and test data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The tumor size was accurately identified in 99 out of 135 reports (73.3%), with errors in 36 reports (26.7%), primarily due to missing or incorrect size information. Tumor location and laterality were identified with greater accuracy in 112 out of 135 reports (83%); however, 23 reports (17%) contained errors mainly due to empty values or incorrect data. Clustering performance of the test data yielded an normalized mutual information of 0.6414, adjusted mutual information of 0.5598, and Fowlkes-Mallows index of 0.5354. The proposed method demonstrated superior performance across all evaluation metrics compared to previous methods.&lt;/p&gt;&lt;p&gt;&lt;str","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e57275"},"PeriodicalIF":3.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Patient Voices: Multimethod Study on the Feasibility of Implementing Electronic Patient-Reported Outcome Measures in a Comprehensive Cancer Center.
IF 3.3 Q2 ONCOLOGY Pub Date : 2025-01-22 DOI: 10.2196/56625
Cinzia Brunelli, Sara Alfieri, Emanuela Zito, Marco Spelta, Laura Arba, Linda Lombi, Luana Caselli, Augusto Caraceni, Claudia Borreani, Anna Roli, Rosalba Miceli, Gabriele Tine', Ernesto Zecca, Marco Platania, Giuseppe Procopio, Nicola Nicolai, Luigi Battaglia, Laura Lozza, Morena Shkodra, Giacomo Massa, Daniele Loiacono, Giovanni Apolone

Background: "Patient Voices" is a software developed to promote the systematic collection of electronic patient-reported outcome measures (ePROMs) in routine oncology clinical practice.

Objective: This study aimed to assess compliance with and feasibility of the Patient Voices ePROM system and analyze patient-related barriers in an Italian comprehensive cancer center.

Methods: Consecutive patients with cancer attending 3 outpatient clinics and 3 inpatient wards were screened for eligibility (adults, native speakers, and being able to fill in the ePROMs) and enrolled in a quantitative and qualitative multimethod study. Compliance, reasons for not administering the ePROMs, patients' interaction needs, and patient-perceived System Usability Scale (range 0-100) were collected; semistructured interviews were carried out in a subsample of patients.

Results: From June 2020 to September 2021, a total of 435 patients were screened, 421 (96.7%) were eligible, and 309 completed the ePROMs (309/421, 73.4%; 95% CI 69.8%-77.5%; mean age 63.3, SD 13.7 years). Organization problems and patient refusal were the main reasons for not administering the ePROMs (outpatients: 40/234, 17.1% and inpatients: 44/201, 21.9%). Help for tablet use was needed by 27.8% (47/169) of outpatients and 10.7% (15/140) of inpatients, while the support received for item interpretation was similar in the 2 groups (outpatients: 36/169, 21.3% and inpatients: 26/140, 18.6%). Average System Usability Scale scores indicated high usability in both groups (outpatients: mean 86.8, SD 15.8 and inpatients: mean 83.9, SD 18.8). Overall, repeated measurement compliance was 76.9% (173/225; outpatients only). Interviewed patients showed positive attitudes toward ePROMs. However, there are barriers to implementation related to the time and cognitive effort required to complete the questionnaires. There is also skepticism about the usefulness of ePROMs in interactions with health care professionals.

Conclusions: This study provides useful information for future ePROM implementation strategies, aimed at effectively supporting the routine clinical management and care of patients with cancer. In addition, these findings may be relevant to other organizations willing to systematically collect PROMs or ePROMs in their clinical routines.

Trial registration: ClinicalTrials.gov NCT03968718; https://clinicaltrials.gov/study/NCT03968718.

{"title":"Patient Voices: Multimethod Study on the Feasibility of Implementing Electronic Patient-Reported Outcome Measures in a Comprehensive Cancer Center.","authors":"Cinzia Brunelli, Sara Alfieri, Emanuela Zito, Marco Spelta, Laura Arba, Linda Lombi, Luana Caselli, Augusto Caraceni, Claudia Borreani, Anna Roli, Rosalba Miceli, Gabriele Tine', Ernesto Zecca, Marco Platania, Giuseppe Procopio, Nicola Nicolai, Luigi Battaglia, Laura Lozza, Morena Shkodra, Giacomo Massa, Daniele Loiacono, Giovanni Apolone","doi":"10.2196/56625","DOIUrl":"https://doi.org/10.2196/56625","url":null,"abstract":"<p><strong>Background: </strong>\"Patient Voices\" is a software developed to promote the systematic collection of electronic patient-reported outcome measures (ePROMs) in routine oncology clinical practice.</p><p><strong>Objective: </strong>This study aimed to assess compliance with and feasibility of the Patient Voices ePROM system and analyze patient-related barriers in an Italian comprehensive cancer center.</p><p><strong>Methods: </strong>Consecutive patients with cancer attending 3 outpatient clinics and 3 inpatient wards were screened for eligibility (adults, native speakers, and being able to fill in the ePROMs) and enrolled in a quantitative and qualitative multimethod study. Compliance, reasons for not administering the ePROMs, patients' interaction needs, and patient-perceived System Usability Scale (range 0-100) were collected; semistructured interviews were carried out in a subsample of patients.</p><p><strong>Results: </strong>From June 2020 to September 2021, a total of 435 patients were screened, 421 (96.7%) were eligible, and 309 completed the ePROMs (309/421, 73.4%; 95% CI 69.8%-77.5%; mean age 63.3, SD 13.7 years). Organization problems and patient refusal were the main reasons for not administering the ePROMs (outpatients: 40/234, 17.1% and inpatients: 44/201, 21.9%). Help for tablet use was needed by 27.8% (47/169) of outpatients and 10.7% (15/140) of inpatients, while the support received for item interpretation was similar in the 2 groups (outpatients: 36/169, 21.3% and inpatients: 26/140, 18.6%). Average System Usability Scale scores indicated high usability in both groups (outpatients: mean 86.8, SD 15.8 and inpatients: mean 83.9, SD 18.8). Overall, repeated measurement compliance was 76.9% (173/225; outpatients only). Interviewed patients showed positive attitudes toward ePROMs. However, there are barriers to implementation related to the time and cognitive effort required to complete the questionnaires. There is also skepticism about the usefulness of ePROMs in interactions with health care professionals.</p><p><strong>Conclusions: </strong>This study provides useful information for future ePROM implementation strategies, aimed at effectively supporting the routine clinical management and care of patients with cancer. In addition, these findings may be relevant to other organizations willing to systematically collect PROMs or ePROMs in their clinical routines.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT03968718; https://clinicaltrials.gov/study/NCT03968718.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e56625"},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Changes in Physical Activity Across Cancer Diagnosis and Treatment Based on Smartphone Step Count Data Linked to a Japanese Claims Database: Retrospective Cohort Study. 生活日志数据表明,癌症诊断和治疗期间身体活动减少:使用与日本索赔数据库相关的智能手机步数数据进行分析。
IF 3.3 Q2 ONCOLOGY Pub Date : 2025-01-20 DOI: 10.2196/58093
Yoshihide Inayama, Ken Yamaguchi, Kayoko Mizuno, Sachiko Tanaka-Mizuno, Ayami Koike, Nozomi Higashiyama, Mana Taki, Koji Yamanoi, Ryusuke Murakami, Junzo Hamanishi, Satomi Yoshida, Masaki Mandai, Koji Kawakami

Background: Although physical activity (PA) is recommended for patients with cancer, changes in PA across cancer diagnosis and treatment have not been objectively evaluated.

Objective: This study aimed to assess the impact of cancer diagnosis and treatment on PA levels.

Methods: This was a retrospective cohort study using a Japanese claims database provided by DeSC Healthcare Inc, in which daily step count data, derived from smartphone pedometers, are linked to the claims data. In this study, we included patients newly diagnosed with cancer, along with those newly diagnosed with diabetes mellitus for reference. We collected data between April 2014 and September 2021 and analyzed them. The observation period spanned from 6 months before diagnosis to 12 months after diagnosis. We applied a generalized additive mixed model with a cubic spline to describe changes in step counts before and after diagnosis.

Results: We analyzed the step count data of 326 patients with malignant solid tumors and 1388 patients with diabetes. Patients with cancer exhibited a 9.6% (95% CI 7.1%-12.1%; P<.001) reduction in step counts from baseline at the start of the diagnosis month, which further deepened to 12.4% (95% CI 9.5%-15.2%; P<.001) at 3 months and persisted at 7.1% (95% CI 4.2%-10.0%; P<.001) at 12 months, all relative to baseline. Conversely, in patients with diabetes, step counts remained relatively stable after diagnosis, with a slight upward trend, resulting in a change of +0.6% (95% CI -0.6% to 1.9%; P=.31) from baseline at 3 months after diagnosis. At 12 months after diagnosis, step counts remained decreased in the nonendoscopic subdiaphragmatic surgery group, with an 18.0% (95% CI 9.1%-26.2%; P<.001) reduction, whereas step counts returned to baseline in the laparoscopic surgery group (+0.3%, 95% CI -6.3% to 7.5%; P=.93).

Conclusions: The analysis of objective pre- and postdiagnostic step count data provided fundamental information crucial for understanding changes in PA among patients with cancer. While cancer diagnosis and treatment reduced PA, the decline may have already started before diagnosis. The study findings may help tailor exercise recommendations based on lifelog data for patients with cancer in the future.

背景:虽然癌症患者被推荐进行体育锻炼,但在癌症诊断和治疗过程中体育锻炼的变化尚未得到客观评价。目的:探讨癌症诊断和治疗对身体活动水平的影响。方法:这是一项回顾性队列研究,使用由DeSC Healthcare Inc.提供的日本索赔数据库,其中每日步数数据来自智能手机计步器,与索赔数据相关联。在本研究中,我们纳入了新诊断为癌症的患者,以及新诊断为糖尿病的患者作为参考。我们收集了2014年4月至2021年9月的数据并进行了分析。观察期为诊断前6个月至诊断后12个月。我们应用了一个广义的三次样条加性混合模型来描述诊断前后步数的变化。结果:我们分析了326例恶性实体瘤患者和1388例糖尿病患者的步数数据。在诊断月开始时,癌症患者的步数比基线减少了9.6%[95%置信区间(CI) 7.1%, 12.1%;P < .001], 3个月时进一步加深至12.4% [95% CI 9.5%, 15.2%;P < .001],并在12个月时持续为7.1% [95% CI 4.2%, 10.0%;P < 0.001],均相对于基线。相反,在糖尿病患者中,诊断后步数保持相对稳定,有轻微上升趋势,在诊断后3个月较基线变化+0.6% [95% CI -0.6%, +1.9%;P = .31]。在诊断后12个月,非内窥镜下膈下手术组的步数仍然减少,减少18.0% [95% CI 9.1%, 26.2%;P < .001],而腹腔镜手术组的步数恢复到基线(+0.3% [95% CI -6.3%, 7.5%;P = .93])。结论:对诊断前和诊断后的客观步数数据的分析为了解癌症患者身体活动的变化提供了至关重要的基础信息。虽然癌症诊断和治疗减少了身体活动,但这种减少可能在诊断之前就已经开始了。这项研究结果可能有助于未来癌症患者根据生活日志数据量身定制锻炼建议。临床试验:
{"title":"Changes in Physical Activity Across Cancer Diagnosis and Treatment Based on Smartphone Step Count Data Linked to a Japanese Claims Database: Retrospective Cohort Study.","authors":"Yoshihide Inayama, Ken Yamaguchi, Kayoko Mizuno, Sachiko Tanaka-Mizuno, Ayami Koike, Nozomi Higashiyama, Mana Taki, Koji Yamanoi, Ryusuke Murakami, Junzo Hamanishi, Satomi Yoshida, Masaki Mandai, Koji Kawakami","doi":"10.2196/58093","DOIUrl":"10.2196/58093","url":null,"abstract":"<p><strong>Background: </strong>Although physical activity (PA) is recommended for patients with cancer, changes in PA across cancer diagnosis and treatment have not been objectively evaluated.</p><p><strong>Objective: </strong>This study aimed to assess the impact of cancer diagnosis and treatment on PA levels.</p><p><strong>Methods: </strong>This was a retrospective cohort study using a Japanese claims database provided by DeSC Healthcare Inc, in which daily step count data, derived from smartphone pedometers, are linked to the claims data. In this study, we included patients newly diagnosed with cancer, along with those newly diagnosed with diabetes mellitus for reference. We collected data between April 2014 and September 2021 and analyzed them. The observation period spanned from 6 months before diagnosis to 12 months after diagnosis. We applied a generalized additive mixed model with a cubic spline to describe changes in step counts before and after diagnosis.</p><p><strong>Results: </strong>We analyzed the step count data of 326 patients with malignant solid tumors and 1388 patients with diabetes. Patients with cancer exhibited a 9.6% (95% CI 7.1%-12.1%; P<.001) reduction in step counts from baseline at the start of the diagnosis month, which further deepened to 12.4% (95% CI 9.5%-15.2%; P<.001) at 3 months and persisted at 7.1% (95% CI 4.2%-10.0%; P<.001) at 12 months, all relative to baseline. Conversely, in patients with diabetes, step counts remained relatively stable after diagnosis, with a slight upward trend, resulting in a change of +0.6% (95% CI -0.6% to 1.9%; P=.31) from baseline at 3 months after diagnosis. At 12 months after diagnosis, step counts remained decreased in the nonendoscopic subdiaphragmatic surgery group, with an 18.0% (95% CI 9.1%-26.2%; P<.001) reduction, whereas step counts returned to baseline in the laparoscopic surgery group (+0.3%, 95% CI -6.3% to 7.5%; P=.93).</p><p><strong>Conclusions: </strong>The analysis of objective pre- and postdiagnostic step count data provided fundamental information crucial for understanding changes in PA among patients with cancer. While cancer diagnosis and treatment reduced PA, the decline may have already started before diagnosis. The study findings may help tailor exercise recommendations based on lifelog data for patients with cancer in the future.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":" ","pages":"e58093"},"PeriodicalIF":3.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Identifying Complex Scheduling Patterns Among Patients With Cancer With Transportation and Housing Needs: Feasibility Pilot Study. 在有交通和住房需求的癌症患者中识别复杂的调度模式:可行性试点研究。
IF 3.3 Q2 ONCOLOGY Pub Date : 2025-01-17 DOI: 10.2196/57715
Allan Fong, Christian Boxley, Laura Schubel, Christopher Gallagher, Katarina AuBuchon, Hannah Arem
<p><strong>Background: </strong>Patients with cancer frequently encounter complex treatment pathways, often characterized by challenges with coordinating and scheduling appointments at various specialty services and locations. Identifying patients who might benefit from scheduling and social support from community health workers or patient navigators is largely determined on a case-by-case basis and is resource intensive.</p><p><strong>Objective: </strong>This study aims to propose a novel algorithm to use scheduling data to identify complex scheduling patterns among patients with transportation and housing needs.</p><p><strong>Methods: </strong>We present a novel algorithm to calculate scheduling complexity from patient scheduling data. We define patient scheduling complexity as an aggregation of sequence, resolution, and facility components. Schedule sequence complexity is the degree to which appointments are scheduled and arrived to in a nonchronological order. Resolution complexity is the degree of no shows or canceled appointments. Location complexity reflects the proportion of appointment dates at 2 or more different locations. Schedule complexity captures deviations from chronological order, unresolved appointments, and coordination across multiple locations. We apply the scheduling complexity algorithm to scheduling data from 38 patients with breast cancer enrolled in a 6-month comorbidity management intervention at an urban hospital in the Washington, DC area that serves low-income patients. We compare the scheduling complexity metric with count-based metrics: arrived ratio, rescheduled ratio, canceled ratio, and no-show ratio. We defined an aggregate count-based adjustment metric as the harmonic mean of rescheduled ratio, canceled ratio, and no-show ratio. A low count-based adjustment metric would indicate that a patient has fewer disruptions or changes in their appointment scheduling.</p><p><strong>Results: </strong>The patients had a median of 88 unique appointments (IQR 60.3), 62 arrived appointments (IQR 47.8), 13 rescheduled appointments (IQR 13.5), 9 canceled appointments (IQR 10), and 1.5 missed appointments (IQR 5). There was no statistically significant difference in count-based adjustments and scheduling complexity bins (χ24=6.296, P=.18). In total, 5 patients exhibited high scheduling complexity with low count-based adjustments. A total of 2 patients exhibited high count-based adjustments with low scheduling complexity. Out of the 15 patients that indicated transportation or housing insecurity issues in conversations with community health workers, 86.7% (13/15) patients were identified as medium or high scheduling complexity while 60% (9/15) were identified as medium or high count-based adjustments.</p><p><strong>Conclusions: </strong>Scheduling complexity identifies patients with complex but nonchronological scheduling behaviors who would be missed by traditional count-based metrics. This study shows a potential link between
背景:癌症患者经常遇到复杂的治疗途径,通常以在各种专业服务和地点协调和安排预约的挑战为特征。确定可能受益于社区卫生工作者或患者导航员的日程安排和社会支持的患者,在很大程度上是根据具体情况确定的,需要耗费大量资源。目的:提出一种利用调度数据识别有交通和住房需求患者复杂调度模式的新算法。方法:提出了一种利用患者调度数据计算调度复杂度的新算法。我们将患者日程安排复杂性定义为序列、决议和设施组件的集合。日程顺序复杂性是指约会按非时间顺序安排和到达的程度。解决复杂性是指没有出现或取消约会的程度。地点复杂性反映预约日期在两个或两个以上不同地点的比例。进度复杂性捕获了时间顺序的偏差,未解决的约会,以及跨多个位置的协调。我们将调度复杂度算法应用于38名乳腺癌患者的调度数据,这些患者在华盛顿特区的一家城市医院参加了为期6个月的合并症管理干预,该医院为低收入患者提供服务。我们将调度复杂度度量与基于计数的度量进行比较:到达比率、重新调度比率、取消比率和未出现比率。我们定义了一个基于总数的调整度量,作为重排率、取消率和缺席率的谐波平均值。一个低计数为基础的调整指标将表明患者在他们的预约安排中有更少的中断或变化。结果:患者首次预约88次(IQR为60.3),到达预约62次(IQR为47.8),重新预约13次(IQR为13.5),取消预约9次(IQR为10),错过预约1.5次(IQR为5)。基于计数的调整和预约复杂度指标差异无统计学意义(χ24=6.296, P= 0.18)。总共有5例患者表现出高调度复杂性和低基于计数的调整。共有2例患者表现出高计数调整和低调度复杂性。在与社区卫生工作者交谈时指出交通或住房不安全问题的15名患者中,86.7%(13/15)的患者被确定为中等或高度调度复杂性,而60%(9/15)的患者被确定为中等或高度基于计数的调整。结论:日程安排的复杂性可以识别出具有复杂但非时间安排行为的患者,而传统的基于计数的指标可能会忽略这些患者。这项研究显示了交通和住房需求与时间表复杂性之间的潜在联系。在确定可能需要额外护理协调支持的患者时,特别是在涉及交通和住房需求时,调度复杂性可以补充基于计数的指标。
{"title":"Identifying Complex Scheduling Patterns Among Patients With Cancer With Transportation and Housing Needs: Feasibility Pilot Study.","authors":"Allan Fong, Christian Boxley, Laura Schubel, Christopher Gallagher, Katarina AuBuchon, Hannah Arem","doi":"10.2196/57715","DOIUrl":"10.2196/57715","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Patients with cancer frequently encounter complex treatment pathways, often characterized by challenges with coordinating and scheduling appointments at various specialty services and locations. Identifying patients who might benefit from scheduling and social support from community health workers or patient navigators is largely determined on a case-by-case basis and is resource intensive.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to propose a novel algorithm to use scheduling data to identify complex scheduling patterns among patients with transportation and housing needs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We present a novel algorithm to calculate scheduling complexity from patient scheduling data. We define patient scheduling complexity as an aggregation of sequence, resolution, and facility components. Schedule sequence complexity is the degree to which appointments are scheduled and arrived to in a nonchronological order. Resolution complexity is the degree of no shows or canceled appointments. Location complexity reflects the proportion of appointment dates at 2 or more different locations. Schedule complexity captures deviations from chronological order, unresolved appointments, and coordination across multiple locations. We apply the scheduling complexity algorithm to scheduling data from 38 patients with breast cancer enrolled in a 6-month comorbidity management intervention at an urban hospital in the Washington, DC area that serves low-income patients. We compare the scheduling complexity metric with count-based metrics: arrived ratio, rescheduled ratio, canceled ratio, and no-show ratio. We defined an aggregate count-based adjustment metric as the harmonic mean of rescheduled ratio, canceled ratio, and no-show ratio. A low count-based adjustment metric would indicate that a patient has fewer disruptions or changes in their appointment scheduling.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The patients had a median of 88 unique appointments (IQR 60.3), 62 arrived appointments (IQR 47.8), 13 rescheduled appointments (IQR 13.5), 9 canceled appointments (IQR 10), and 1.5 missed appointments (IQR 5). There was no statistically significant difference in count-based adjustments and scheduling complexity bins (χ24=6.296, P=.18). In total, 5 patients exhibited high scheduling complexity with low count-based adjustments. A total of 2 patients exhibited high count-based adjustments with low scheduling complexity. Out of the 15 patients that indicated transportation or housing insecurity issues in conversations with community health workers, 86.7% (13/15) patients were identified as medium or high scheduling complexity while 60% (9/15) were identified as medium or high count-based adjustments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Scheduling complexity identifies patients with complex but nonchronological scheduling behaviors who would be missed by traditional count-based metrics. This study shows a potential link between","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e57715"},"PeriodicalIF":3.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11758709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013843","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}
引用次数: 0
Developing and Assessing a Scalable Digital Health Tool for Pretest Genetic Education in Patients With Early-Onset Colorectal Cancer: Mixed Methods Design. 开发和评估可扩展的数字健康工具,用于早发性结直肠癌患者的检测前遗传教育:混合方法设计。
IF 3.3 Q2 ONCOLOGY Pub Date : 2025-01-17 DOI: 10.2196/59464
Jessica N Rivera Rivera, Moran Snir, Emilie Simmons, Tara Schmidlen, Misha Sholeh, Melinda Leigh Maconi, Carley Geiss, Hayden Fulton, Laura Barton, Brian D Gonzalez, Jennifer Permuth, Susan Vadaparampil

Background: National guidelines recommend germline genetic testing (GT) for all patients with early-onset colorectal cancer. With recent advances in targeted therapies and GT, these guidelines are expected to expand to include broader groups of patients with colorectal cancer. However, there is a shortage of genetic professionals to provide the necessary education and support for informed consent. As such, there is a pressing need to identify alternative approaches to facilitate and expedite access to GT.

Objective: This study describes the development of a pretest education intervention, Nest-CRC, to facilitate the uptake of germline GT among patients with early-onset colorectal cancer. Patients with early-onset colorectal cancer and health care providers reviewed Nest-CRC, and their reactions and recommendations were captured using a nested mixed methods approach.

Methods: Using the learner verification approach, we conducted 2 sequential phases of surveys and interviews with English- and Spanish-speaking patients with early-onset colorectal cancer and health care providers. The surveys assessed participants' experiences with genetic services and provided immediate feedback on the Nest-CRC genetic education modules. Semistructured interviews evaluated participants' perceptions of self-efficacy, attraction, comprehension, cultural acceptability, and usability of Nest-CRC. Survey data were analyzed using descriptive statistics (mean, median, and proportions), while interview data were analyzed through line-by-line coding of the transcribed interviews. After each phase, Nest-CRC was refined based on participants' recommendations.

Results: A total of 52 participants, including 39 patients with early-onset colorectal cancer and 13 providers, participated in the study. Of these, 19 patients and 6 providers participated in phase 1 (N=25), and 20 patients and 7 providers participated in phase 2 (N=27). Most participants (phase 1: 23/25, 92%, to 25/25, 100%; phase 2: 24/27, 89%, to 27/27, 100%) agreed that each of the 5 education modules was easy to understand and helpful; 13 patients reported no history of GT, with 11 (85%) expressing interest in GT and 2 (15%) remaining unsure after completing Nest-CRC. Participants reported that Nest-CRC provided sufficient information to help them decide about GT. The tool was deemed acceptable by individuals from diverse backgrounds, and participants found it visually attractive, easy to comprehend, and user-friendly.

Conclusions: The findings revealed that Nest-CRC is a promising strategy for facilitating pretest education and promoting GT. Nest-CRC has been refined based on participant recommendations and will be re-evaluated.

背景:国家指南推荐所有早发性结直肠癌患者进行生殖系基因检测(GT)。随着靶向治疗和GT的最新进展,这些指南有望扩大到包括更广泛的结直肠癌患者群体。然而,缺乏遗传专业人员为知情同意提供必要的教育和支持。因此,迫切需要确定替代方法来促进和加快获得GT。目的:本研究描述了一种测试前教育干预的发展,Nest-CRC,以促进早发性结直肠癌患者对种系GT的吸收。早发性结直肠癌患者和卫生保健提供者回顾了Nest-CRC,并使用嵌套混合方法捕获了他们的反应和建议。方法:采用学习者验证法,对英语和西班牙语的早发性结直肠癌患者和卫生保健提供者进行了2个连续阶段的调查和访谈。这些调查评估了参与者在遗传服务方面的经验,并就Nest-CRC遗传教育模块提供了即时反馈。半结构化访谈评估了参与者对Nest-CRC的自我效能感、吸引力、理解、文化可接受性和可用性的看法。调查数据采用描述性统计(平均值、中位数和比例)进行分析,而访谈数据则通过对转录的访谈逐行编码进行分析。每个阶段结束后,根据参与者的建议对Nest-CRC进行改进。结果:共有52名参与者参与了研究,其中包括39名早发性结直肠癌患者和13名提供者。其中,19名患者和6名提供者参加了第一阶段(N=25), 20名患者和7名提供者参加了第二阶段(N=27)。大多数参与者(阶段1:23/25,92%,到25/25,100%;阶段2:24/27(89%,到27/27(100%))同意5个教育模块中的每一个都很容易理解和有用;13例患者报告无GT病史,11例(85%)表示对GT感兴趣,2例(15%)在完成Nest-CRC后仍不确定。参与者报告说,Nest-CRC提供了足够的信息来帮助他们决定是否使用GT。该工具被来自不同背景的个人认为是可接受的,参与者发现它在视觉上很有吸引力,易于理解,并且用户友好。结论:研究结果表明,Nest-CRC是促进测试前教育和促进GT的一种有前景的策略。Nest-CRC已根据参与者的建议进行了改进,并将进行重新评估。
{"title":"Developing and Assessing a Scalable Digital Health Tool for Pretest Genetic Education in Patients With Early-Onset Colorectal Cancer: Mixed Methods Design.","authors":"Jessica N Rivera Rivera, Moran Snir, Emilie Simmons, Tara Schmidlen, Misha Sholeh, Melinda Leigh Maconi, Carley Geiss, Hayden Fulton, Laura Barton, Brian D Gonzalez, Jennifer Permuth, Susan Vadaparampil","doi":"10.2196/59464","DOIUrl":"https://doi.org/10.2196/59464","url":null,"abstract":"<p><strong>Background: </strong>National guidelines recommend germline genetic testing (GT) for all patients with early-onset colorectal cancer. With recent advances in targeted therapies and GT, these guidelines are expected to expand to include broader groups of patients with colorectal cancer. However, there is a shortage of genetic professionals to provide the necessary education and support for informed consent. As such, there is a pressing need to identify alternative approaches to facilitate and expedite access to GT.</p><p><strong>Objective: </strong>This study describes the development of a pretest education intervention, Nest-CRC, to facilitate the uptake of germline GT among patients with early-onset colorectal cancer. Patients with early-onset colorectal cancer and health care providers reviewed Nest-CRC, and their reactions and recommendations were captured using a nested mixed methods approach.</p><p><strong>Methods: </strong>Using the learner verification approach, we conducted 2 sequential phases of surveys and interviews with English- and Spanish-speaking patients with early-onset colorectal cancer and health care providers. The surveys assessed participants' experiences with genetic services and provided immediate feedback on the Nest-CRC genetic education modules. Semistructured interviews evaluated participants' perceptions of self-efficacy, attraction, comprehension, cultural acceptability, and usability of Nest-CRC. Survey data were analyzed using descriptive statistics (mean, median, and proportions), while interview data were analyzed through line-by-line coding of the transcribed interviews. After each phase, Nest-CRC was refined based on participants' recommendations.</p><p><strong>Results: </strong>A total of 52 participants, including 39 patients with early-onset colorectal cancer and 13 providers, participated in the study. Of these, 19 patients and 6 providers participated in phase 1 (N=25), and 20 patients and 7 providers participated in phase 2 (N=27). Most participants (phase 1: 23/25, 92%, to 25/25, 100%; phase 2: 24/27, 89%, to 27/27, 100%) agreed that each of the 5 education modules was easy to understand and helpful; 13 patients reported no history of GT, with 11 (85%) expressing interest in GT and 2 (15%) remaining unsure after completing Nest-CRC. Participants reported that Nest-CRC provided sufficient information to help them decide about GT. The tool was deemed acceptable by individuals from diverse backgrounds, and participants found it visually attractive, easy to comprehend, and user-friendly.</p><p><strong>Conclusions: </strong>The findings revealed that Nest-CRC is a promising strategy for facilitating pretest education and promoting GT. Nest-CRC has been refined based on participant recommendations and will be re-evaluated.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e59464"},"PeriodicalIF":3.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
JMIR Cancer
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1