***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Background The Internet has potential as a medium for health behavior change programs , but no controlled studies have yet evaluated the impact of a fully automated physical activity intervention over several months with real-time objective feedback from a monitor . Objective The aim was to evaluate the impact of a physical activity program based on the Internet and mobile phone technology provided to individuals for 9 weeks . Methods A single-center , r and omized , stratified controlled trial was conducted from September to December 2005 in Bedfordshire , United Kingdom , with 77 healthy adults whose mean age was 40.4 years ( SD = 7.6 ) and mean body mass index was 26.3 ( SD = 3.4 ) . Participants were r and omized to a test group that had access to an Internet and mobile phone – based physical activity program  ( n = 47 ) or to a control group ( n = 30 ) that  received no support . The test group received tailored solutions for perceived barriers , a schedule to plan weekly exercise sessions with mobile phone and email reminders , a message board to share their experiences with others , and feedback on their level of physical activity . Both groups were issued a wrist-worn accelerometer to monitor their level of physical activity ;  only the test group received real-time feedback via the Internet . The main outcome measures were accelerometer data and self-report of physical activity . Results At the end of the study period , the test group reported a significantly greater increase over baseline than did the control group for perceived control ( P < .001 ) and intention/expectation to exercise ( P < .001 ) . Intent-to-treat analyses of both the accelerometer data ( P = .02 ) and leisure time self-report data ( P = .03 ) found a higher level of moderate physical activity in the test group . The average increase ( over the control group ) in accelerometer-measured moderate physical activity was 2 h 18 min per week . The test group also lost more percent body fat than the control group ( test group : −2.18 , SD = 0.59 ; control group : −0.17 , SD = 0.81 ; P = .04 ) . Conclusions A fully automated Internet and mobile phone – based motivation and action support system can significantly increase and maintain the level of physical activity in healthy adults PURPOSE The present study investigates the effect of the Activ-O-Meter , an internet-based computer-tailored physical activity intervention in adolescents in six European centers involved in the HELENA study . METHODS Adolescents ( 12 - 17 years old ) from Vienna , Ghent , Heraklion , Dortmund , Athens , and Stockholm were r and omized into intervention and control schools . Participants in the intervention condition received the computer-tailored advice at baseline and after 1 month . Participants in the control condition received a generic st and ard advice . Effects were evaluated after 1 ( n = 675 ) and 3 months ( n = 494 ) using multi-level modeling . Physical activity levels were measured using the International Physical Activity Question naire for adolescents ( IPAQ-A ) . RESULTS After 1 month , the intervention group reported higher levels of moderate ( beta = -32.8 , 95 % CI ( confidence interval ) : -64.2 to -1.4 ) and vigorous ( beta = -28.0 , 95 % CI : -50.7 to -5.3 ) physical activity in leisure time , as well as higher levels of cycling for transport ( beta = -19.1 , 95 % CI : -34.4 to -7.6 ) compared to the control group . After 3 months , when the intervention group had received the tailored feedback twice , intervention effects were even stronger . Favorable changes in physical activity levels of all intensities and in different context s were found in the tailored group compared to the control group . Among adolescents not reaching the physical activity recommendations at baseline similar effects as in the total sample were found . CONCLUSIONS The data indicated that the computer-tailored physical activity intervention had positive effects on physical activity levels among the adolescents . However , the implementation of the computer-tailored intervention in the schools was not feasible in all countries Background Postnatal women ( < 12 months postpartum ) are at increased risk of physical inactivity . Purpose To evaluate the efficacy and feasibility of a theory-based physical activity ( PA ) intervention delivered to postnatal women primarily via mobile telephone short message service ( SMS ) . Methods Eighty-eight women were r and omized to the intervention ( n = 45 ) or minimal contact control ( n = 43 ) condition . The 12-week intervention consisted of a face-to-face PA goal - setting consultation , a goal - setting magnet , three to five personally tailored SMS/week and a nominated support person who received two SMS per week . SMS content targeted constructs of social cognitive theory . Frequency ( days/week ) and duration ( min/week ) of PA participation and walking for exercise  were assessed via self-report at baseline , 6 and 13 weeks . Results Intervention participants increased PA frequency by 1.82 days/week ( SE ± 0.18 ) by 13 weeks ( F(2,85 ) = 4.46 , p = 0.038 ) and walking for exercise frequency by 1.08 days/week ( SE ± 0.24 ) by 13 weeks ( F(2,85 ) = 5.38 , p = 0.02 ) . Positive trends were observed for duration ( min/week ) of PA and walking for exercise . Conclusions Intervention exposure result ed in increased frequency of PA and walking for exercise in postnatal women Background Effective interventions are needed to reduce the chronic disease epidemic . The Internet has the potential to provide large population s with individual advice at relatively low cost . Objective The focus of the study was the Web-based tailored physical activity intervention Active-online . The main research questions were ( 1 ) How effective is Active-online , compared to a nontailored website , in increasing self-reported and objective ly measured physical activity levels in the general population when delivered in a real-life setting ? ( 2 ) Do respondents recruited for the r and omized study differ from spontaneous users of Active-online , and how does effectiveness differ between these groups ? ( 3 ) What is the impact of frequency and duration of use of Active-online on changes in physical activity behavior ? Methods Volunteers recruited via different media channels completed a Web-based baseline survey and were r and omized to Active-online ( intervention group ) or a nontailored website ( control group ) . In addition , spontaneous users were recruited directly from the Active-online website . In a subgroup of participants , physical activity was measured objective ly using accelerometers . Follow-up assessment s took place 6 weeks ( FU1 ) , 6 months ( FU2 ) , and 13 months ( FU3 ) after baseline . Results A total of 1531 respondents completed the baseline question naire ( intervention group n = 681 ,  control group  n = 688 , spontaneous users n = 162 ) ; 133 individuals had valid accelerometer data at baseline . Mean age of the total sample was 43.7 years , and 1146 ( 74.9 % ) were women . Mixed linear models ( adjusted for sex , age , BMI category , and stage of change ) showed a significant increase in self-reported mean minutes spent in moderate- and vigorous-intensity activity from baseline to FU1 ( coefficient = 0.14 , P = .001 ) and to FU3 ( coefficient = 0.19 , P < .001 ) in all participants with no significant differences between groups . A significant increase in the proportion of individuals meeting the HEPA recommendations ( self-reported ) was observed in all participants between baseline and FU3 ( OR = 1.47 , P = .03 ) , with a higher increase in spontaneous users compared to the r and omized groups ( interaction between FU3 and spontaneous users , OR = 2.95 , P = .02 ) . There were no increases in physical activity over time in any group for objective ly measured physical activity . A significant relation was found between time spent on the tailored intervention and changes in self-reported physical activity between baseline and FU3 ( coefficient = 1.13 , P = .03 , intervention group and spontaneous users combined ) . However , this association was no longer significant when adjusting for stage of change . Conclusions In a real-life setting , Active-online was not more effective than a nontailored website in increasing physical activity levels in volunteers from the general population . Further research may investigate ways of integrating Web-based physical activity interventions in a wider context , for example , primary care or workplace health promotion The aim of this study was to evaluate the effectiveness of a computer-tailored physical activity intervention delivered through the Internet in a real-life setting . Healthy adults ( n=526 ) , recruited in six worksites , between 25 and 55 years of age were r and omized to one of three conditions receiving , respectively , ( i ) online-tailored physical activity advice + stage-based reinforcement e-mails , ( ii ) online-tailored physical activity advice only , ( iii ) online non-tailored st and ard physical activity advice . At 6-month follow-up , no differences in physical activity between study conditions were found ; total physical activity , physical activity at moderate intensity and physical activity in leisure time significantly increased in all study conditions between baseline and follow-up . Further evaluation of the intervention material s showed that the tailored advice was more read , printed and discussed with others than the st and ard advice . Most of the respondents in the e-mail group indicated to be satisfied about the number , frequency and usefulness of the stage-based e-mails . In conclusion , although tailored advice was appreciated more than st and ard advice , no evidence was found that an online-tailored physical activity intervention program outperformed online st and ard information Background Inactive people are often not aware of the fact that they are insufficiently active . Providing insight into their actual physical activity ( PA ) levels may raise awareness and could , in combination with tailored PA advice , stimulate a physically active lifestyle . Objective This study evaluated the feasibility and effectiveness of a 3-month intervention in which Dutch office workers were provided with a personal activity monitor ( PAM ) coupled to simple and concise Web-based tailored PA advice ( PAM COACH ) . Method Participants were r and omly assigned to the 3-month PAM intervention ( n = 51 ) or received a single written information brochure with brief general PA recommendations ( n = 51 ) . Study outcome measures were changes in PA ( recall of minutes per week spent on PA , as measured by the Activity Question naire for Adolescents and Adults ) , determinants of PA , aerobic fitness , and body composition . Follow-up measurements were performed immediately after the 3-month intervention and at 8-months , 5 months after the end of the 3-month intervention period . Results A total of 102 workers , 23 to 39 years old , completed the baseline measurement at the worksite . 48 completed the 3-month follow up and 38 the 8-month follow-up in the intervention group , 50 completed the 3-month follow up and 42 the 8-month follow up in the control group . 35 out of 48 ( 73 % ) participants in the PAM intervention group reported wearing the PAM regularly , and the PAM COACH was used almost once a week ; 24 out of 46 ( 52 % ) PAM users set a personal goal , and 33 ( 72 % ) entered their favorite activities on the website . Main reasons for not using these items were lack of interest or not being able to find the item on the website . The majority of PAM users ( 34 out of 46 , 74 % ) read the advice , of whom 14 ( 39 % ) found it unappealing . After the 3-month intervention , no significant intervention effect was observed ( adjusted difference in min/week ) for sedentary behavior ( β = 10 , 95 % CI = −435 to 455 ) , light-intensity PA ( β = −129 , 95 % CI = −337 to 79 ) , moderate-intensity PA ( β = −13 , 95 % CI = −89 to 63 ) , vigorous-intensity PA ( β= −6 , 95 % CI = −75 to 62 ) , and moderate- to vigorous-intensity PA ( β = −23 , 95 % CI = −121 to 76 ) . No significant intervention effect was observed in the PA outcomes at the 8-month follow-up . For the determinants of PA , aerobic fitness , and body composition , no statistically significant intervention effect was observed in the total study population immediately after the 3-month intervention or the 8-month follow-up . Conclusions The intervention appeared to be easily applicable to real-life setting s. The intervention was ineffective in improving PA behavior or its determinants in healthy office workers . More attention should have been given to the quality and appropriateness of the tailored advice . Trial Registration International St and ard R and omized Controlled Trial Number ( IS RCT N ) : 93896459 ; http://www.controlled-trials.com/IS RCT N93896459/ ( Archived by WebCite at http://www.webcitation.org/5iR3mf7ex The primary objective of this pilot study was to evaluate the effect of active video games on  children 's physical activity levels . Twenty children ( mean ± SD age = 12 ± 1.5 years ; 40 % female ) were r and omised to receive either an active video game up grade package or to a control group ( no intervention ) . Effects on physical activity over the 12-week intervention period were measured using objective  ( Actigraph accelerometer ) and subjective ( Physical Activity Question naire for Children [ PAQ-C ] ) measures . An activity log was used to estimate time spent playing active and non-active video games . Children in the intervention group spent less mean time over the total 12-week intervention period playing all video games compared to those in the control group ( 54 versus 98 minutes/day [ difference = -44 minutes/day , 95 % CI [ -92 , 2 ] ] , p = 0.06 ) . Average time spent in all physical activities 
    Output:
    
No notable patterns emerged among the studies that did show support for e-health .