Barriers encountered during enrollment in an internet-mediated randomized controlled trial

Buis, Lorraine R; Janney, Adrienne W; Heß, Michael; Culver, Silas A; Richardson, Caroline R.

doi:10.1186/1745-6215-10-76

Cited by 13 publications

(14 citation statements)

References 11 publications

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“…This study had a higher rate of enrollment (58%) compared to many health targeted internet clinical trials previously reported [35–39]. However, to reach these high enrollment completion rates, extensive effort was required for up to a year to enroll all participants who expressed interest but did not get through the consenting and baseline assessment process despite indicating interest and intent to do so.…”

Section: Discussionmentioning

confidence: 97%

“…Security for personal health information during assessments can be balanced with the provision of ready, unrestricted access to the internet site content. The INSPIRE project used similar techniques as other behavior change websites to engage participants and keep the website interesting and fresh, including tailoring, push emails, updated ‘News You Can Use’ and ‘Our Space’ comments’ [35–41]. Our procedures for the development of the website were similar to those used by Mayer and colleagues [62].…”

Section: Discussionmentioning

confidence: 99%

“…However, there is a wide range of enrollment rates among internet based interventions (12%–54%) and high attrition [35–39]. The extent to which internet interventions are effective may in part depend on their ‘ingredients:’ components, delivery methodology, design, and tailoring, among other implementation specifics [40].…”

Section: Introductionmentioning

confidence: 99%

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Development and implementation of an Internet-based survivorship care program for cancer survivors treated with hematopoietic stem cell transplantation

Syrjala

Stover

et al. 2011

J Cancer Surviv

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Introduction The internet provides a widely accessible modality for meeting survivorship care needs of cancer survivors. In this paper we describe the development and implementation of an internet site designed as a base from which to conduct a randomized controlled trial to meet psycho-educational needs of hematopoietic stem cell transplantation (HSCT) survivors. Methods A cross-disciplinary team designed, wrote content and programmed an internet site for online study registration, consent, assessment, and study implementation. All 3–18 year survivors of HSCT for hematologic malignancy treated at one transplant center were approached by mail for participation. All study activities could be conducted without study staff contact. However, participants had options for phone or email contact with study staff as desired. Results Of 1775 participants approached for the study, 775 (58% of those eligible) consented and completed baseline assessment. Mean age was 51.7 (SD=12.5, age range 18–79), with 56% male. 57% required staff contact one or more times; a majority were for minor technical issues or delays in completion of enrollment or baseline assessment. Discussions/Conclusions This study demonstrated the potential for providing internet-based survivorship care to long-term survivors of HSCT. Although building a survivorship internet site requires a team with diverse expertise, once built, these resources can be implemented rapidly with large numbers of survivors. Implications for Cancer Survivors While internet-based services will not meet all the needs of cancer survivors, this methodology represents an important modality for augmenting onsite clinical services as a method for meeting psycho-educational, information and resource needs of cancer survivors.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

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Development and implementation of an Internet-based survivorship care program for cancer survivors treated with hematopoietic stem cell transplantation

Syrjala

Stover

et al. 2011

J Cancer Surviv

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“…However, the mean age at enrolment of 61 years appears to be reasonably representative of the hypertensive population. Running a clinical trial online, where participants are entrusted to submit accurate data does raise data accuracy issues [20][21][22]. However, TIME does corroborate participant-reported data by record linkage of endpoints.…”

Section: Discussionmentioning

confidence: 99%

Treatment in the Morning versus Evening (TIME) Study: Feasibility of an Online Study

Rorie¹,

Rogers²

2016

J Clin Trials

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Aims: The Treatment in Morning versus Evening (TIME) pilot study sought to establish the feasibility of an onlineonly study detecting whether evening dosing of antihypertensives is more cardio protective than morning dosing. Methods:The TIME study uses a prospective, randomised, open-label, blinded end-point (PROBE) design. In response to various forms of advertising, patients from primary and secondary care, and databases of patients who had previously consented to being contacted about research in the UK, enrolled on the study website (www.timestudy.co.uk). Furthermore, 1,794 hypertensive subjects were written to in three primary care practices as a form of targeted advertising. Participants had to be over 18, prescribed at least one hypertensive drug and have a valid email address. Subjects self-registered, consented, and entered demographics and drug treatments online, before being randomised to taking their antihypertensive therapy in the morning or evening. Automated email followup was used to track patient reported cardiovascular outcomes for the year-long pilot study.Result: 355 participants were randomised and followed up for ≥ 12 months. During this period, 14 participants withdrew from the randomised time of treatment. 59 patients were randomised from 3 practices which wrote to patients publicising the study, giving a rate of 33 randomised per 1,000 patients written to. The 10-year ASSIGN cardiovascular risk of the randomised participants varied by age; 21% for all ages (n=355), 25% for >55 yrs (n=269), 27% for >60 yrs (n=227) and 30% for >65 yrs (n=150). Based on participant cardiovascular risk during the pilot, a full trial with 80% power to detect a 20% improved outcome of nocturnal dosing would require 631 events to occur.

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“… 4 , 11–34 At the patient level, demographic characteristics such as age, race, and gender have been commonly recognized as influential factors on patients’ participation. 4 , 19 , 21 , 23 , 33 , 35 , 36 Patients’ financial and socioeconomic status (SES), measured through factors such as insurance payer and education level, have also been shown to correlate with decision-making. 11 , 12 , 19 , 29 , 32 Besides these objective factors, the impact of patients’ subjective attitudes towards research is thought to be considerable – attitudes are also potentially influenced by family members and care providers, particularly in a pediatric setting.…”

Section: Introductionmentioning

confidence: 99%

Will they participate? Predicting patients’ response to clinical trial invitations in a pediatric emergency department

Beck

Taylor

et al. 2016

Journal of the American Medical Informatics Association

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Objective (1) To develop an automated algorithm to predict a patient’s response (ie, if the patient agrees or declines) before he/she is approached for a clinical trial invitation; (2) to assess the algorithm performance and the predictors on real-world patient recruitment data for a diverse set of clinical trials in a pediatric emergency department; and (3) to identify directions for future studies in predicting patients’ participation response.Materials and Methods We collected 3345 patients’ response to trial invitations on 18 clinical trials at one center that were actively enrolling patients between January 1, 2010 and December 31, 2012. In parallel, we retrospectively extracted demographic, socioeconomic, and clinical predictors from multiple sources to represent the patients’ profiles. Leveraging machine learning methodology, the automated algorithms predicted participation response for individual patients and identified influential features associated with their decision-making. The performance was validated on the collection of actual patient response, where precision, recall, F-measure, and area under the ROC curve were assessed.Results Compared to the random response predictor that simulated the current practice, the machine learning algorithms achieved significantly better performance (Precision/Recall/F-measure/area under the ROC curve: 70.82%/92.02%/80.04%/72.78% on 10-fold cross validation and 71.52%/92.68%/80.74%/75.74% on the test set). By analyzing the significant features output by the algorithms, the study confirmed several literature findings and identified challenges that could be mitigated to optimize recruitment.Conclusion By exploiting predictive variables from multiple sources, we demonstrated that machine learning algorithms have great potential in improving the effectiveness of the recruitment process by automatically predicting patients’ participation response to trial invitations.

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Barriers encountered during enrollment in an internet-mediated randomized controlled trial

Cited by 13 publications

References 11 publications

Development and implementation of an Internet-based survivorship care program for cancer survivors treated with hematopoietic stem cell transplantation

Development and implementation of an Internet-based survivorship care program for cancer survivors treated with hematopoietic stem cell transplantation

Treatment in the Morning versus Evening (TIME) Study: Feasibility of an Online Study

Will they participate? Predicting patients’ response to clinical trial invitations in a pediatric emergency department

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