Feedback in web-based questionnaires as incentive to increase compliance in studies on lifestyle factors

Bälter, Olle; Fondell, Elinor; Bälter, Katarina

doi:10.1017/s1368980011003041

Cited by 9 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Marcus, Bosnjak, Lindner, Pilischenko, and Schütz (2007) find that for individuals who are not especially interested in the survey topic, personalized feedback provided after an initial web survey increases response rates in follow-up online surveys. This is in line with Bälter, Fondell, and Bälter (2011), who observed higher response rates in follow-up surveys for respondents provided with personalized feedback on responses regarding energy expenditure during the first interview.…”

Section: Background and State Of Researchsupporting

confidence: 89%

Personalized Feedback in Web Surveys

Kühne

Kroh

2016

Social Science Computer Review

View full text Add to dashboard Cite

Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.

show abstract

Section: Background and State Of Researchsupporting

confidence: 89%

Personalized Feedback in Web Surveys

Kühne

Kroh

2016

Social Science Computer Review

View full text Add to dashboard Cite

show abstract

“…However, the response rate for rating the page content was very low and may be due to the fact that the page rating did not provide the users with feedback as was done in the questionnaire. A recent study explored using personalized feedback as incentive to increase compliance in Web-based questionnaires [38]. In addition, the variation in the rating responses may be due to the design of the rating process, as we have included the rating of each information page at the end of it.…”

Section: Discussionmentioning

confidence: 99%

Who Uses Smoking Cessation Apps? A Feasibility Study Across Three Countries via Smartphones

BinDhim¹,

McGeechan²,

Trevena³

2014

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

BackgroundSmartphone use is growing worldwide. While hundreds of smoking cessation apps are currently available in the app stores, there is no information about who uses them. Smartphones also offer potential as a research tool, but this has not previously been explored.ObjectiveThis study aims to measure and compare the uptake of a smoking cessation app over one year in Australia, the United Kingdom, and the United States. It also assesses the feasibility of conducting research via an app, describing respondents’ characteristics (demographics, smoking status, and other health related app use), and examining differences across countries.MethodsThis is a cross-sectional exploratory study of adults 18 years and older, passively recruited over one year in 2012, who downloaded this study app (Quit Advisor) via the two largest app stores (Apple and Android).ResultsThe total number of app downloads after one year was 1751, 72.98% (1278/1751) of them were Apple operation system users. Of these 1751 participants, 47.68% (835/1751) were from the United States, 29.18% (511/1751) were from the United Kingdom, and 16.68% (292/1751) were from Australia. There were 602 participants, 36.75% (602/1638) that completed a questionnaire within the app. Of these 602 participants, 58.8% (354/602) were female and the mean age was 32 years. There were no significant differences between countries in terms of age, operation system used, number of quitting attempts, and language spoken at home. However, there were significant differences between countries in terms of gender and stage of change. There were 77.2% (465/602) of the respondents that were ready to quit in the next 30 days and the majority of these had never sought professional help (eg, “Quitline”). More than half had downloaded smoking cessation apps in the past and of these, three-quarters had made quitting attempts (lasted at least 24 hours) using an app before. Respondents who had attempted to quit three times or more in the previous year were more likely to have tried smoking cessation apps (OR 3.3, 95% CI 2.1-5.2). There were 50.2% (302/602) of the respondents that had used other health related apps before. Of these, 89.4% (270/302) were using health related apps at least once a week, but 77.5% (234/302) never checked the credibility of the health app publishers before downloading.ConclusionsA smartphone app was able to reach smokers across three countries that were not seeking professional help, but were ready to quit within the next 30 days. Respondents were relatively young and almost demographically similar across all three countries. They also frequently used other health related apps, mostly without checking the credibility of their publishers.

show abstract

“…Incentives may improve response rates, but may have to have considerable value before having an impact [25]. Providing feedback on questionnaires may improve response rates [26]. There has also been some work characterizing participants who are more likely to drop out from studies, with better response rates reported for people who are white, older, better educated, with good Internet skills [27-31].…”

Section: Introductionmentioning

confidence: 99%

Attrition Revisited: Adherence and Retention in a Web-Based Alcohol Trial

Murray¹,

White²,

Varagunam³

et al. 2013

J Med Internet Res

117

View full text Add to dashboard Cite

BackgroundAttrition 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.ObjectiveThe 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?MethodsSecondary 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.ResultsData 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.ConclusionsIn this dataset, adherence and retention were related in a complex fashion. We propose a possible explanatory model for these data.Trial RegistrationInternational Standard Randomized Controlled Trial Number (ISRCTN): 31070347; http://www.controlled-trials.com/...

show abstract

Feedback in web-based questionnaires as incentive to increase compliance in studies on lifestyle factors

Cited by 9 publications

References 18 publications

Personalized Feedback in Web Surveys

Personalized Feedback in Web Surveys

Who Uses Smoking Cessation Apps? A Feasibility Study Across Three Countries via Smartphones

Attrition Revisited: Adherence and Retention in a Web-Based Alcohol Trial

Contact Info

Product

Resources

About