J. Med. Internet Res. - What are health-related users tweeting? A qualitative content analysis of health-related users and their messages on twitter.

Tópicos

{ health(1844) social(1437) communiti(874) }
{ research(1085) discuss(1038) issu(1018) }
{ data(1737) use(1416) pattern(1282) }
{ chang(1828) time(1643) increas(1301) }
{ studi(2440) review(1878) systemat(933) }
{ general(901) number(790) one(736) }
{ studi(1410) differ(1259) use(1210) }
{ import(1318) role(1303) understand(862) }
{ patient(2837) hospit(1953) medic(668) }
{ medic(1828) order(1363) alert(1069) }
{ use(2086) technolog(871) perceiv(783) }
{ high(1669) rate(1365) level(1280) }
{ motion(1329) object(1292) video(1091) }
{ framework(1458) process(801) describ(734) }
{ model(2341) predict(2261) use(1141) }
{ record(1888) medic(1808) patient(1693) }
{ patient(1821) servic(1111) care(1106) }
{ analysi(2126) use(1163) compon(1037) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ activ(1452) weight(1219) physic(1104) }
{ sequenc(1873) structur(1644) protein(1328) }
{ assess(1506) score(1403) qualiti(1306) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ design(1359) user(1324) use(1319) }
{ care(1570) inform(1187) nurs(1089) }
{ howev(809) still(633) remain(590) }
{ perform(999) metric(946) measur(919) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ can(981) present(881) function(850) }
{ use(976) code(926) identifi(902) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ learn(2355) train(1041) set(1003) }
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{ clinic(1479) use(1117) guidelin(835) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

CKGROUND: Twitter is home to many health professionals who send messages about a variety of health-related topics. Amid concerns about physicians posting inappropriate content online, more in-depth knowledge about these messages is needed to understand health professionals' behavior on Twitter.OBJECTIVE: Our goal was to characterize the content of Twitter messages, specifically focusing on health professionals and their tweets relating to health.METHODS: We performed an in-depth content analysis of 700 tweets. Qualitative content analysis was conducted on tweets by health users on Twitter. The primary objective was to describe the general type of content (ie, health-related versus non-health related) on Twitter authored by health professionals and further to describe health-related tweets on the basis of the type of statement made. Specific attention was given to whether a tweet was personal (as opposed to professional) or made a claim that users would expect to be supported by some level of medical evidence (ie, a "testable" claim). A secondary objective was to compare content types among different users, including patients, physicians, nurses, health care organizations, and others.RESULTS: Health-related users are posting a wide range of content on Twitter. Among health-related tweets, 53.2% (184/346) contained a testable claim. Of health-related tweets by providers, 17.6% (61/346) were personal in nature; 61% (59/96) made testable statements. While organizations and businesses use Twitter to promote their services and products, patient advocates are using this tool to share their personal experiences with health.CONCLUSIONS: Twitter users in health-related fields tweet about both testable claims and personal experiences. Future work should assess the relationship between testable tweets and the actual level of evidence supporting them, including how Twitter users-especially patients-interpret the content of tweets posted by health providers.

Resumo Limpo

ckground twitter home mani health profession send messag varieti healthrel topic amid concern physician post inappropri content onlin indepth knowledg messag need understand health profession behavior twitterobject goal character content twitter messag specif focus health profession tweet relat healthmethod perform indepth content analysi tweet qualit content analysi conduct tweet health user twitter primari object describ general type content ie healthrel versus nonhealth relat twitter author health profession describ healthrel tweet basi type statement made specif attent given whether tweet person oppos profession made claim user expect support level medic evid ie testabl claim secondari object compar content type among differ user includ patient physician nurs health care organ othersresult healthrel user post wide rang content twitter among healthrel tweet contain testabl claim healthrel tweet provid person natur made testabl statement organ busi use twitter promot servic product patient advoc use tool share person experi healthconclus twitter user healthrel field tweet testabl claim person experi futur work assess relationship testabl tweet actual level evid support includ twitter usersespeci patientsinterpret content tweet post health provid

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