Dataset Information

Do people have differing motivations for participating in a stated-preference study? Results from a latent-class analysis.

ABSTRACT:

Background

Researchers and policy makers have long suspected that people have differing, and potentially nefarious, motivations for participating in stated-preference studies such as discrete-choice experiments (DCE). While anecdotes and theories exist on why people participate in surveys, there is a paucity of evidence exploring variation in preferences for participating in stated-preference studies.

Methods

We used a DCE to estimate preferences for participating in preference research among an online survey panel sample. Preferences for the characteristics of a study to be conducted at a local hospital were assessed across five attributes (validity, relevance, bias, burden, time and payment) and described across three levels using a starring system. A D-efficient experimental design was used to construct three blocks of 12 choice tasks with two profiles each. Respondents were also asked about factors that motivated their choices. Mixed logistic regression was used to analyze the aggregate sample and latent class analysis identified segments of respondents.

Results

629 respondents completed the experiment. In aggregate "study validity" was most important. Latent class results identified two segments based on underlying motivations: a quality-focused segment (76%) who focused most on validity, relevance, and bias and a convenience-focused segment (24%) who focused most on reimbursement and time. Quality-focused respondents spent more time completing the survey (p?ConclusionsThis information can be used to better understand variability in motivations to participate in stated-preference surveys and the impact of motivations on response quality.

SUBMITTER: Hollin IL

PROVIDER: S-EPMC7868025 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Do people have differing motivations for participating in a stated-preference study? Results from a latent-class analysis.

Hollin Ilene L IL Janssen Ellen E Kelley Marcella A MA Bridges John F P JFP

BMC medical informatics and decision making 20210206 1

<h4>Background</h4>Researchers and policy makers have long suspected that people have differing, and potentially nefarious, motivations for participating in stated-preference studies such as discrete-choice experiments (DCE). While anecdotes and theories exist on why people participate in surveys, there is a paucity of evidence exploring variation in preferences for participating in stated-preference studies.<h4>Methods</h4>We used a DCE to estimate preferences for participating in preference re ...[more]

PMID: 33549068

Similar Datasets

Project description:BackgroundSeveral pharmaceutical treatments for chronic pain caused by osteoarthritis (OA) and chronic low back pain (CLBP) are available or currently under development, each associated with different adverse events (AEs) and efficacy profiles. It is therefore important to understand what trade-offs patients are willing to make when choosing between treatments.MethodsA discrete-choice experiment (DCE) was conducted with 437 adults with chronic pain caused by OA and/or CLBP. Respondents were presented with a series of scenarios and asked to choose between pairs of hypothetical treatments, each defined by six attributes: level of symptom control; risks of heart attack, rapidly progressive osteoarthritis and dependency; frequency and mode of administration and cost. Attributes were based on known profiles of oral nonsteroidal anti-inflammatory drugs, opioids and injected nerve growth factor inhibitors, the last of which were under clinical development at the time of the study. Data were analysed using a latent class (LC) model to explore preference heterogeneity.ResultsOverall, respondents considered improving symptom control and reducing risk of physical dependency to be the most important attributes. The LC analysis identified four participant classes: an 'efficacy-focused' class (33.7%), a 'cost-averse' class (29.4%), a 'physical-dependence-averse' class (19.6%) and a 'needle-averse' class (17.3%). Subgroup membership was incompletely predicted by participant age and their responses to comprehension questions.ConclusionsPreference heterogeneity across respondents indicates a need for a personalized approach to offering treatment options. Symptom improvement, cost, physical dependence and route of administration might be important to different patients.SignificanceMultiple treatment options that differ substantially in terms of efficacy and adverse events are available for the management of chronic pain. With a growing emphasis on a patient-centred care model that incorporates patients' priorities and values into treatment decisions, there is a need to understand how individuals with chronic musculoskeletal pain balance the benefits and risks of treatment and how treatment priorities vary among individuals. This study was designed to identify patient preferences for different characteristics of treatments for the management of chronic pain and to investigate how preferences differ among respondents.

Project description:BACKGROUND:One prominent barrier faced by healthcare consumers when accessing health services is a common requirement to complete repetitive, inefficient paper-based documentation at multiple registration sites. Digital innovation has a potential role to reduce the burden in this area, through the collection and sharing of data between healthcare providers. While there is growing evidence for digital innovations to potentially improve the effectiveness and efficiency of health systems, there is less information on the willingness of healthcare consumers to embrace and utilise technology to provide data. AIM:The study aims to improve understanding of consumers' preference for utilising a digital health administration mobile app. METHODS:The online study used a stated preference experiment design to explore aspects of consumers' preference for a mobile health administration app and its impact on the likelihood of using the app. The survey was answered by a representative sample (by age and gender) of Australian adults, and sociodemographic factors were also recorded for analysis. Each participant answered eight choice sets in which a hypothetical app (defined by a set of dimensions and levels) was presented and the respondent was asked if they would be willing to provide data using that app. Analysis was conducted using bivariate logistic regression. RESULTS:For the average respondent, the two most important dimensions were the time it took to register on the app and the electronic governance arrangements around their personal information. Willingness to use any app was found to differ based on respondent characteristics: people with higher education, and women, were relatively more willing to utilise the mobile health app. CONCLUSION:This study investigated consumers' willingness to utilise a digital health administration mobile app. The identification of key characteristics of more acceptable apps provide valuable insight and recommendations for developers of similar digital health administration technologies. This would increase the likelihood of achieving successful acceptance and utilisation by consumers. The results from this study provide evidence-based recommendations for future research and policy development, planning and implementation of digital health administration mobile applications in Australia.

Project description:Background: Current evidence suggests that the information needs of people with diabetes mellitus differ across patient groups. With a view to being able to provide individualized information, this study aims to identify (i) the diabetes-related information needs of people with diabetes mellitus; (ii) different subgroups of people with specific information needs; and (iii) associated characteristics of the identified subgroups, such as sociodemographic characteristics, diabetes-related comorbidities, and well-being.Methods: This cross-sectional study was based on data from 837 respondents with diabetes mellitus who participated in the population-based KORA (Cooperative Health Research in the Augsburg Region) Health Survey 2016 in Southern Germany (KORA GEFU 4 study) (45.6% female, mean age 71.1?years, 92.8% Type 2 diabetes). Diabetes-related information needs were assessed with a questionnaire asking about patients' information needs concerning 11 diabetes-related topics, e.g. 'long-term complications' and 'treatment/therapy'. Subgroups of people with different information needs and associated characteristics were identified using latent class analysis.Results: We identified the following four classes of people with different information needs: 'high needs on all topics', 'low needs on all topics', 'moderate needs with a focus on complications and diabetes in everyday life', and 'advanced needs with a focus on social and legal aspects and diabetes research'. The classes differed significantly in age, years of education, type of diabetes, diabetes duration, diabetes-related comorbidities, smoking behaviour, diabetes education, current level of information, and time preference.Conclusions: Knowledge about different patient subgroups can be useful for tailored information campaigns or physician-patient interactions. Further research is needed to analyse health care needs in these groups, changes in information needs over the course of the disease, and prospective health outcomes.

Dataset Information

Do people have differing motivations for participating in a stated-preference study? Results from a latent-class analysis.

Background

Methods

Results

Publications

Do people have differing motivations for participating in a stated-preference study? Results from a latent-class analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets