{"title":"Real-Time Medical Data Analytics in Internet of Things-based Smart Healthcare Systems","authors":"","doi":"10.22381/ajmr7120209","DOIUrl":"https://doi.org/10.22381/ajmr7120209","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68351697","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}
{"title":"Medical Wearables and Biosensor Technologies as Tools of Internet of Things-based Health Monitoring Systems","authors":"","doi":"10.22381/ajmr7120201","DOIUrl":"https://doi.org/10.22381/ajmr7120201","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68351141","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}
{"title":"Patient-oriented Decision Making, Real‐Time Healthcare Monitoring Systems, and Mobile Health Smartphone Applications","authors":"","doi":"10.22381/ajmr6120194","DOIUrl":"https://doi.org/10.22381/ajmr6120194","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68350894","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}
{"title":"Medical Big Data Processing Mechanisms and Real-Time Patient Health Monitoring","authors":"","doi":"10.22381/ajmr6220198","DOIUrl":"https://doi.org/10.22381/ajmr6220198","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68351482","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}
{"title":"Mobile Health Applications, Smart Medical Devices, and Big Data Analytics Technologies","authors":"","doi":"10.22381/ajmr6120193","DOIUrl":"https://doi.org/10.22381/ajmr6120193","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68350883","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}
{"title":"Real-Time and Remote Health Monitoring Internet of Things-based Systems: Digital Therapeutics, Wearable and Implantable Medical Devices, and Body Sensor Networks","authors":"","doi":"10.22381/ajmr6220196","DOIUrl":"https://doi.org/10.22381/ajmr6220196","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68351380","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}
{"title":"Real-Time Health-related Data, Wearable Medical Sensor Devices, and Smart Cyber-Physical Systems","authors":"","doi":"10.22381/ajmr6220193","DOIUrl":"https://doi.org/10.22381/ajmr6220193","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68350767","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}
Background: Tiredness is used in some characterizations of major depressive disorder (MDD). Transcranial magnetic stimulation (TMS) lowers all symptoms of MDD. Objective: To explore whether, 1) a visual analogue scale (VAS) for tiredness is a valid and reliable measure of a feature of MDD, and 2) TMS treatment reduces subjective tiredness occurring in MDD. Method: A naturalistic study of treatment with 10 Hz TMS. Completed pre- and post-treatment: HAMD-6, a visual analogue scale (VAS-6), the Clinical Global Impression – Severity (CGI-S) and a ‘VAS-tiredness’. Two groups received TMS. Acute course: N=52 participants suffering acute MDD, received 20 treatment courses (total courses 86). Relapse prevention (RP) course: N=26 participants suffering chronic relapsing MDD received scheduled episodic courses over 3 days; (total courses 266). VAS-tiredness scores were compared with the standardized tool results. Results: There were significant medium to large correlations between pre- and post-treatment VAS-tiredness and the standard depression measures (HAMD-6 .406 to .447, VAS-6 .446 to .525, CGI-S .348 to .407; all p<.001). TMS treatment produced a significant reduction in VAS tiredness in both (Acute course and RP) groups (main effect: F(1,350)=147.3, p<.001, η2=.30). The two groups displayed difference in the pre-treatment VAStiredness with the Acute group having higher scores pre-treatment. Post-treatment tiredness scores were similar. Conclusion: -tiredness is a valid measure of a feature of MDD. VAS-tiredness provides potentially useful information and complements standard mood tools. TMS treatment can reduce tiredness in MDD.
{"title":"Tiredness in Acute and Chronic Depression Treated with Transcranial Magnetic Stimulation","authors":"S. Pridmore, S. Erger, M. Rybak, T. May","doi":"10.22381/ajmr6220191","DOIUrl":"https://doi.org/10.22381/ajmr6220191","url":null,"abstract":"Background: Tiredness is used in some characterizations of major depressive disorder (MDD). Transcranial magnetic stimulation (TMS) lowers all symptoms of MDD. Objective: To explore whether, 1) a visual analogue scale (VAS) for tiredness is a valid and reliable measure of a feature of MDD, and 2) TMS treatment reduces subjective tiredness occurring in MDD. Method: A naturalistic study of treatment with 10 Hz TMS. Completed pre- and post-treatment: HAMD-6, a visual analogue scale (VAS-6), the Clinical Global Impression – Severity (CGI-S) and a ‘VAS-tiredness’. Two groups received TMS. Acute course: N=52 participants suffering acute MDD, received 20 treatment courses (total courses 86). Relapse prevention (RP) course: N=26 participants suffering chronic relapsing MDD received scheduled episodic courses over 3 days; (total courses 266). VAS-tiredness scores were compared with the standardized tool results. Results: There were significant medium to large correlations between pre- and post-treatment VAS-tiredness and the standard depression measures (HAMD-6 .406 to .447, VAS-6 .446 to .525, CGI-S .348 to .407; all p<.001). TMS treatment produced a significant reduction in VAS tiredness in both (Acute course and RP) groups (main effect: F(1,350)=147.3, p<.001, η2=.30). The two groups displayed difference in the pre-treatment VAStiredness with the Acute group having higher scores pre-treatment. Post-treatment tiredness scores were similar. Conclusion: -tiredness is a valid measure of a feature of MDD. VAS-tiredness provides potentially useful information and complements standard mood tools. TMS treatment can reduce tiredness in MDD.","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68351123","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}
{"title":"Intelligent Sensing Technology, Smart Healthcare Services, and Internet of Medical Things-based Diagnosis","authors":"","doi":"10.22381/ajmr6120192","DOIUrl":"https://doi.org/10.22381/ajmr6120192","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68350874","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}
{"title":"Big Data and Machine Learning in Medicine: Enhancing the Quality of Patient Care in Clinical Practice","authors":"","doi":"10.22381/ajmr6120199","DOIUrl":"https://doi.org/10.22381/ajmr6120199","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68350912","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}
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