J. Med. Internet Res. - Attrition revisited: adherence and retention in a web-based alcohol trial.

Tópicos

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Resumo

CKGROUND: Attrition is a noted feature of eHealth interventions and trials. In 2005, Eysenbach published a landmark paper calling for a "science of attrition," suggesting that the 2 forms of attrition--nonusage attrition (low adherence to the intervention) and dropout attrition (poor retention to follow-up)--may be related and that this potential relationship deserved further study.OBJECTIVE: The aim of this paper was to use data from an online alcohol trial to explore Eysenbach's hypothesis, and to answer 3 research questions: (1) Are adherence and retention related? If so, how, and under which circumstances? (2) Do adherence and retention have similar predictors? Can these predictors adequately explain any relationship between adherence and retention or are there additional, unmeasured predictors impacting on the relationship? (3) If there are additional unmeasured predictors impacting on the relationship, are there data to support Eysenbach's hypothesis that these are related to overall levels of interest?METHODS: Secondary analysis of data from an online trial of an online intervention to reduce alcohol consumption among heavy drinkers. The 2 outcomes were adherence to the intervention measured by number of log-ins, and retention to the trial measured by provision of follow-up data at 3 months (the primary outcome point). Dependent variables were demographic and alcohol-related data collected at baseline. Predictors of adherence and retention were modeled using logistic regression models.RESULTS: Data were available on 7932 participants. Adherence and retention were related in a complex fashion. Participants in the intervention group were more likely than those in the control group to log in more than once (42% vs 28%, P<.001) and less likely than those in the control group to respond at 3 months (40% vs 49%, P<.001). Within each randomized group, participants who logged in more frequently were more likely to respond than those who logged in less frequently. Response rates in the intervention group for those who logged in once, twice, or =3 times were 34%, 46%, and 51%, respectively (P<.001); response rates in the control group for those who logged in once, twice, or =3 times were 44%, 60%, and 67%, respectively (P<.001). Relationships between baseline characteristics and adherence and retention were also complex. Where demographic characteristics predicted adherence, they tended also to predict retention. However, characteristics related to alcohol consumption and intention or confidence in reducing alcohol consumption tended to have opposite effects on adherence and retention, with factors that predicted improved adherence tending to predict reduced retention. The complexity of these relationships suggested the existence of an unmeasured confounder.CONCLUSIONS: In this dataset, adherence and retention were related in a complex fashion. We propose a possible explanatory model for these data.TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN): 31070347; http://www.controlled-trials.com/ISRCTN31070347 (Archived by WebCite at http://www.webcitation.org/6IEmNnlCn).

Resumo Limpo

ckground attrit note featur ehealth intervent trial eysenbach publish landmark paper call scienc attrit suggest form attritionnonusag attrit low adher intervent dropout attrit poor retent followupmay relat potenti relationship deserv studyobject aim paper use data onlin alcohol trial explor eysenbach hypothesi answer research question adher retent relat circumst adher retent similar predictor can predictor adequ explain relationship adher retent addit unmeasur predictor impact relationship addit unmeasur predictor impact relationship data support eysenbach hypothesi relat overal level interestmethod secondari analysi data onlin trial onlin intervent reduc alcohol consumpt among heavi drinker outcom adher intervent measur number login retent trial measur provis followup data month primari outcom point depend variabl demograph alcoholrel data collect baselin predictor adher retent model use logist regress modelsresult data avail particip adher retent relat complex fashion particip intervent group like control group log vs p less like control group respond month vs p within random group particip log frequent like respond log less frequent respons rate intervent group log twice time respect p respons rate control group log twice time respect p relationship baselin characterist adher retent also complex demograph characterist predict adher tend also predict retent howev characterist relat alcohol consumpt intent confid reduc alcohol consumpt tend opposit effect adher retent factor predict improv adher tend predict reduc retent complex relationship suggest exist unmeasur confounderconclus dataset adher retent relat complex fashion propos possibl explanatori model datatri registr intern standard random control trial number isrctn httpwwwcontrolledtrialscomisrctn archiv webcit httpwwwwebcitationorgiemnnlcn

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