Huitong Ding, Katherine Gifford, Ludy C Shih, Kristi Ho, Salman Rahman, Akwaugo Igwe, Spencer Low, Zachary Popp, Edward Searls, Zexu Li, Sanskruti Madan, Alexa Burk, Phillip H Hwang, Ileana De Anda-Duran, Vijaya B Kolachalama, Rhoda Au, Honghuang Lin
{"title":"利用自然语言处理技术探索老年人对数字脑健康平台的看法:队列研究。","authors":"Huitong Ding, Katherine Gifford, Ludy C Shih, Kristi Ho, Salman Rahman, Akwaugo Igwe, Spencer Low, Zachary Popp, Edward Searls, Zexu Li, Sanskruti Madan, Alexa Burk, Phillip H Hwang, Ileana De Anda-Duran, Vijaya B Kolachalama, Rhoda Au, Honghuang Lin","doi":"10.2196/60453","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the perspectives of older adults on an integrated digital brain health platform have not been investigated.</p><p><strong>Objective: </strong>This study aims to understand the perspectives of older adults on a digital brain health platform by conducting semistructured interviews and analyzing their transcriptions by natural language processing.</p><p><strong>Methods: </strong>The study included 28 participants from the Boston University Alzheimer's Disease Research Center, all of whom engaged with a digital brain health platform over an initial assessment period of 14 days. Semistructured interviews were conducted to collect data on participants' experiences with the digital brain health platform. The transcripts generated from these interviews were analyzed using natural language processing techniques. The frequency of positive and negative terms was evaluated through word count analysis. A sentiment analysis was used to measure the emotional tone and subjective perceptions of the participants toward the digital platform.</p><p><strong>Results: </strong>Word count analysis revealed a generally positive sentiment toward the digital platform, with \"like,\" \"well,\" and \"good\" being the most frequently mentioned positive terms. However, terms such as \"problem\" and \"hard\" indicated certain challenges faced by participants. Sentiment analysis showed a slightly positive attitude with a median polarity score of 0.13 (IQR 0.08-0.15), ranging from -1 (completely negative) to 1 (completely positive), and a median subjectivity score of 0.51 (IQR 0.47-0.53), ranging from 0 (completely objective) to 1 (completely subjective). These results suggested an overall positive attitude among the study cohort.</p><p><strong>Conclusions: </strong>The study highlights the importance of understanding older adults' attitudes toward digital health platforms amid the comprehensive evolution of the digitalization era. Future research should focus on refining digital solutions to meet the specific needs of older adults, fostering a more personalized approach to brain health.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"8 ","pages":"e60453"},"PeriodicalIF":2.0000,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring the Perspectives of Older Adults on a Digital Brain Health Platform Using Natural Language Processing: Cohort Study.\",\"authors\":\"Huitong Ding, Katherine Gifford, Ludy C Shih, Kristi Ho, Salman Rahman, Akwaugo Igwe, Spencer Low, Zachary Popp, Edward Searls, Zexu Li, Sanskruti Madan, Alexa Burk, Phillip H Hwang, Ileana De Anda-Duran, Vijaya B Kolachalama, Rhoda Au, Honghuang Lin\",\"doi\":\"10.2196/60453\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the perspectives of older adults on an integrated digital brain health platform have not been investigated.</p><p><strong>Objective: </strong>This study aims to understand the perspectives of older adults on a digital brain health platform by conducting semistructured interviews and analyzing their transcriptions by natural language processing.</p><p><strong>Methods: </strong>The study included 28 participants from the Boston University Alzheimer's Disease Research Center, all of whom engaged with a digital brain health platform over an initial assessment period of 14 days. Semistructured interviews were conducted to collect data on participants' experiences with the digital brain health platform. The transcripts generated from these interviews were analyzed using natural language processing techniques. The frequency of positive and negative terms was evaluated through word count analysis. A sentiment analysis was used to measure the emotional tone and subjective perceptions of the participants toward the digital platform.</p><p><strong>Results: </strong>Word count analysis revealed a generally positive sentiment toward the digital platform, with \\\"like,\\\" \\\"well,\\\" and \\\"good\\\" being the most frequently mentioned positive terms. However, terms such as \\\"problem\\\" and \\\"hard\\\" indicated certain challenges faced by participants. Sentiment analysis showed a slightly positive attitude with a median polarity score of 0.13 (IQR 0.08-0.15), ranging from -1 (completely negative) to 1 (completely positive), and a median subjectivity score of 0.51 (IQR 0.47-0.53), ranging from 0 (completely objective) to 1 (completely subjective). These results suggested an overall positive attitude among the study cohort.</p><p><strong>Conclusions: </strong>The study highlights the importance of understanding older adults' attitudes toward digital health platforms amid the comprehensive evolution of the digitalization era. Future research should focus on refining digital solutions to meet the specific needs of older adults, fostering a more personalized approach to brain health.</p>\",\"PeriodicalId\":14841,\"journal\":{\"name\":\"JMIR Formative Research\",\"volume\":\"8 \",\"pages\":\"e60453\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR Formative Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2196/60453\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Formative Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/60453","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Exploring the Perspectives of Older Adults on a Digital Brain Health Platform Using Natural Language Processing: Cohort Study.
Background: Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the perspectives of older adults on an integrated digital brain health platform have not been investigated.
Objective: This study aims to understand the perspectives of older adults on a digital brain health platform by conducting semistructured interviews and analyzing their transcriptions by natural language processing.
Methods: The study included 28 participants from the Boston University Alzheimer's Disease Research Center, all of whom engaged with a digital brain health platform over an initial assessment period of 14 days. Semistructured interviews were conducted to collect data on participants' experiences with the digital brain health platform. The transcripts generated from these interviews were analyzed using natural language processing techniques. The frequency of positive and negative terms was evaluated through word count analysis. A sentiment analysis was used to measure the emotional tone and subjective perceptions of the participants toward the digital platform.
Results: Word count analysis revealed a generally positive sentiment toward the digital platform, with "like," "well," and "good" being the most frequently mentioned positive terms. However, terms such as "problem" and "hard" indicated certain challenges faced by participants. Sentiment analysis showed a slightly positive attitude with a median polarity score of 0.13 (IQR 0.08-0.15), ranging from -1 (completely negative) to 1 (completely positive), and a median subjectivity score of 0.51 (IQR 0.47-0.53), ranging from 0 (completely objective) to 1 (completely subjective). These results suggested an overall positive attitude among the study cohort.
Conclusions: The study highlights the importance of understanding older adults' attitudes toward digital health platforms amid the comprehensive evolution of the digitalization era. Future research should focus on refining digital solutions to meet the specific needs of older adults, fostering a more personalized approach to brain health.