Several studies of good quality demonstrated changes in lifestyle habits arising from the provision of genetic interventions .
The most promising lifestyle changes were changes in nutrition .
Conclusions : It is possible to facilitate behaviour change using genetic testing as the catalyst .
Background : Study ing the impact of  genetic testing interventions on lifestyle behaviour change  has been a priority area of research in recent years .
Substantial heterogeneity exists in the results and conclusions of this literature , which has yet to be explained using vali date d behaviour change theory and an assessment of the quality of genetic interventions .
The theory of planned behaviour ( TPB ) helps to explain key contributors to behaviour change .
It has been hypothesized that personalization could be added to this theory to help predict changes in health behaviours .
Purpose : This systematic review provides a detailed , comprehensive identification , assessment , and summary of primary research articles pertaining to  lifestyle behaviour change  ( nutrition , physical activity , sleep , and smoking ) result ing from  genetic testing interventions .
The present review further aims to provide in-depth analyses of studies conducted to date within the context of the TPB and the quality of genetic interventions provided to participants while aim ing to determine whether or not genetic testing facilitates changes in lifestyle habits .
This review is timely in light of a recently published “ call-to-action ” paper , highlighting the need to incorporate the TPB into personalized healthcare behaviour change research .
Effects of a Web-Based Personalized Intervention on Physical Activity in European Adults: A Randomized Controlled Trial
Background The high prevalence of physical inactivity worldwide calls for innovative and more effective ways to promote physical activity ( PA ) . There are limited objective data on the effectiveness of Web-based personalized feedback on increasing PA in adults . Objective It is hypothesized that providing personalized advice based on PA measured objective ly alongside diet , phenotype , or genotype information would lead to larger and more sustained changes in PA , compared with nonpersonalized advice . Methods A total of 1607 adults in seven European countries were r and omized to either a control group ( nonpersonalized advice , Level 0 , L0 ) or to one of three personalized groups receiving personalized advice via the Internet based on current PA plus diet ( Level 1 , L1 ) , PA plus diet and phenotype ( Level 2 , L2 ) , or PA plus diet , phenotype , and genotype ( Level 3 , L3 ) .  PA  was measured for 6 months using triaxial accelerometers , and self-reported using the  Baecke question naire . Outcomes were objective and self-reported PA after 3 and 6 months . Results While 1270 participants ( 85.81 % of 1480 actual starters ) completed the 6-month trial , 1233 ( 83.31 % ) self-reported PA at both baseline and month 6 , but only 730 ( 49.32 % ) had sufficient objective PA data at both time points . For the total cohort after 6 months , a greater improvement in self-reported total PA ( P=.02 ) and PA during leisure ( nonsport ) ( P=.03 ) was observed in personalized groups compared with the control group . For individuals advised to increase PA , we also observed greater improvements in those two self-reported indices ( P=.006 and P=.008 , respectively ) with increased personalization of the advice ( L2 and L3 vs L1 ) . However , there were no significant differences in accelerometer results between personalized and control groups , and no significant effect of adding phenotypic or genotypic information to the tailored feedback at month 3 or 6 . After 6 months , there were small but significant improvements in the objective ly measured physical activity level ( P<.05 ) , moderate PA ( P<.01 ) , and sedentary time ( P<.001 ) for individuals advised to increase PA , but these changes were similar across all groups . Conclusions Different levels of personalization produced similar small changes in objective PA . We found no evidence that personalized advice is more effective than conventional “ one size fits all ” guidelines to promote changes in PA in our Web-based intervention when PA was measured objective ly . Based on self-reports , PA increased to a greater extent with more personalized advice . Thus , it is crucial to measure PA objective ly in any PA intervention study . Trial Registration Clinical Trials.gov NCT01530139 ; http:// clinical trials.gov/show/NCT01530139 ( Archived by WebCite at : http://www.webcitation.org/6XII1QwHz
Several studies of good quality demonstrated changes in lifestyle habits arising from the provision of genetic interventions .
The most promising lifestyle changes were changes in nutrition .
Conclusions : It is possible to facilitate behaviour change using genetic testing as the catalyst .
Background : Study ing the impact of  genetic testing interventions on lifestyle behaviour change  has been a priority area of research in recent years .
Substantial heterogeneity exists in the results and conclusions of this literature , which has yet to be explained using vali date d behaviour change theory and an assessment of the quality of genetic interventions .
The theory of planned behaviour ( TPB ) helps to explain key contributors to behaviour change .
It has been hypothesized that personalization could be added to this theory to help predict changes in health behaviours .
Purpose : This systematic review provides a detailed , comprehensive identification , assessment , and summary of primary research articles pertaining to  lifestyle behaviour change  ( nutrition , physical activity , sleep , and smoking ) result ing from  genetic testing interventions .
The present review further aims to provide in-depth analyses of studies conducted to date within the context of the TPB and the quality of genetic interventions provided to participants while aim ing to determine whether or not genetic testing facilitates changes in lifestyle habits .
This review is timely in light of a recently published “ call-to-action ” paper , highlighting the need to incorporate the TPB into personalized healthcare behaviour change research .