ENOrphanet Journal of Rare Diseases | Evaluating the Efficiency of Online Sampling Strategies in Rare Disease Research
Date: January 31, 2026
Classification: Frontiers
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This study systematically compared three sampling strategies for rare disease patients and found that online sampling is the most effective method for recruiting participants within a limited timeframe, providing practical guidance for future participant recruitment in rare disease research.
Literature Overview
This article, 'Optimizing recruitment in rare disease research: a cross-sectional online study evaluating sampling strategies for hard-to-reach populations,' published in the journal Orphanet Journal of Rare Diseases, reviews and summarizes differences in recruitment efficiency and sample characteristics across three distinct sampling strategies used for rare disease patients. Using a cross-sectional online survey, the study compares the real-world performance of web-based online sampling, geographically based sampling, and respondent-driven sampling. The results show that although all three methods successfully recruited patient cohorts exhibiting typical psychological and clinical features of rare diseases, online sampling significantly outperformed the other two in terms of sample size acquisition. This study provides empirical support for research design involving hard-to-reach populations such as those with rare diseases, emphasizing the central role of digital recruitment pathways in contemporary research environments.Background Knowledge
Rare diseases are defined as conditions with a prevalence of less than 1 in 2,000, with over 6,000 identified globally, affecting more than 30 million people in Europe alone. Although individual diseases have few patients, the overall burden is substantial. Most are genetic, often involving multi-system symptoms, delayed diagnosis, and lack of effective treatments. Due to the wide geographic distribution of patients and difficulties in diagnosis, conducting statistically powerful research faces significant challenges, and traditional sampling methods often fail to achieve sufficient sample sizes. Therefore, sampling strategies for 'hard-to-reach populations'—such as respondent-driven sampling (RDS), online sampling (OS), and location-based sampling (LBS)—have become essential tools for studying rare disease populations. RDS relies on seed individuals making chain referrals through social networks, suitable for closed or hidden groups; OS leverages social media and online communities for rapid, low-cost, and broad-coverage recruitment; LBS involves direct contact with patients at specific physical locations (e.g., specialty clinics), ensuring diagnostic validity. However, the relative effectiveness of these methods in rare disease research has not been systematically compared. This study fills that gap, offering critical evidence to optimize research design and improve recruitment efficiency, with significant methodological and practical implications.
Methods and Experiment
This study employed a cross-sectional online design, conducted between July 1 and November 30, 2023, to compare the effectiveness of three sampling strategies in recruiting rare disease patients. Three highly heterogeneous rare diseases—Marfan syndrome, Huntington’s disease, and lysosomal storage disorders—were included to reflect the diversity of the rare disease population. An anonymous online questionnaire was distributed via the REDCap platform, with different links and QR codes used to distinguish recruitment sources. The three strategies were implemented as follows: online sampling involved posting study information in Facebook patient groups; respondent-driven sampling contacted self-organized support groups to recruit 'seed' members who forwarded the link to their social networks (each recommending up to five people); location-based sampling distributed paper flyers at specialty clinics across Germany. The primary outcome was the number of participants successfully recruited by each strategy, while secondary outcomes included demographic, clinical, and psychological characteristics (assessed using scales such as PHQ-9, GAD-7, SF-12, and SSD-12). Data were analyzed using descriptive statistics and intergroup comparisons (ANOVA, Kruskal-Wallis, chi-square tests), with significance set at p < 0.05.Key Findings and Insights
Research Implications and Outlook
This study provides empirical evidence for participant recruitment strategies in rare disease research, clearly identifying the advantages of online sampling in terms of efficiency and feasibility, especially for time-constrained projects. The high participation rate may align with the common behavior of patients using online health communities to seek information and support, making digital platforms a natural entry point for reaching this population.
However, the study also reveals that online sampling may introduce gender bias, which future research should correct using weighted analysis or mixed-methods designs. Additionally, although respondent-driven and location-based sampling are less efficient, they still offer value in capturing patient characteristics within specific clinical settings; optimizing their implementation (e.g., providing incentives, extending timelines, strengthening collaboration with medical centers) could enhance their utility. As rare disease research advances toward precision medicine, integrating patient registries, electronic health records, and multi-modal recruitment strategies will be key to improving sample representativeness and research quality.
Conclusion
This study systematically evaluated the performance of three sampling strategies in rare disease research and found that web-based online sampling is the most efficient method, capable of recruiting the largest number of participants in a relatively short time, with simple operation and low cost. In contrast, respondent-driven and location-based sampling faced numerous structural and operational barriers in practice, resulting in significantly lower recruitment efficiency. Although online sampling may introduce a potential bias toward higher female participation, its advantage in reaching geographically dispersed patients cannot be overlooked. All recruited samples exhibited typical psychosocial characteristics of rare disease populations, including high rates of depression, anxiety, somatic symptom burden, and reduced quality of life, confirming the clinical representativeness of the samples. The findings emphasize that online recruitment strategies should be prioritized when designing rare disease-related studies, while being mindful of potential selection biases. Future work should explore integrating multiple sampling approaches and leveraging patient registries and digital health tools to enhance the external validity and scientific value of research. This study provides important reference for optimizing research methodology in the field of rare diseases.
