Background:　Users　searching　for　health　information　on　the　Internet　may　be　searching　for　their　own　health　issue,　searching　for　someone　else＇s　health　issue,　or　browsing　with　no　particular　health　issue　in　mind.　Previous　research　has　found　that　these　three　categories　of　users　focus　on　different　types　of　health　information.　However,　most　health　information　websites　provide　static　content　for　all　users.　If　the　three　types　of　user　health　information　need　contexts　can　be　identified　by　the　Web　application,　the　search　results　or　information　offered　to　the　user　can　be　customized　to　increase　its　relevance　or　usefulness　to　the　user.　Objective:　The　aim　of　this　study　was　to　investigate　the　possibility　of　identifying　the　three　user　health　information　contexts　(searching　for　self,　searching　for　others,　or　browsing　with　no　particular　health　issue　in　mind)　using　just　hyperlink　clicking　behavior;　using　eye-tracking　information;　and　using　a　combination　of　eye-tracking,　demographic,　and　urgency　information.　Predictive　models　are　developed　using　multinomial　logistic　regression.　Methods:　A　total　of　74　participants　(39　females　and　35　males)　who　were　mainly　staff　and　students　of　a　university　were　asked　to　browse　a　health　discussion　forum,　Healthboards.　com.　An　eye　tracker　recorded　their　examining　(eye　fixation)　and　skimming　(quick　eye　movement)　behaviors　on　2　types　of　screens:　summary　result　screen　displaying　a　list　of　post　headers,　and　detailed　post　screen.　The　following　three　types　of　predictive　models　were　developed　using　logistic　regression　analysis:　model　1　used　only　the　time　spent　in　scanning　the　summary　result　screen　and　reading　the　detailed　post　screen,　which　can　be　determined　from　the　user＇s　mouse　clicks;　model　2　used　the　examining　and　skimming　durations　on　each　screen,　recorded　by　an　eye　tracker;　and　model　3　added　user　demographic　and　urgency　information　to　model　2.　Results:　An　analysis　of　variance　(ANOVA)　analysis　found　that　users＇　browsing　durations　were　significantly　different　for　the　three　health　information　contexts　(P＜.001).　The　logistic　regression　model　3　was　able　to　predict　the　user＇s　type　of　health　information　context　with　a　10-fold　cross　validation　mean　accuracy　of　84%　(62/74),　followed　by　model　2　at　73%　(54/74)　and　model　1　at　71%　(52/78).　In　addition,　correlation　analysis　found　that　particular　browsing　durations　were　highly　correlated　with　users＇　age,　education　level,　and　the　urgency　of　their　information　need.　Conclusions:　A　user＇s　type　of　health　information　need　context　(ie,　searching　for　self,　for　others,　or　with　no　health　issue　in　mind)　can　be　identified　with　reasonable　accuracy　using　just　user　mouse　clicks　that　can　easily　be　detected　by　Web　applications.　Higher　accuracy　can　be　obtained　using　Google　glass　or　future　computing　devices　with　eye　tracking　function.