Daniore Paola, Yan Chuqiao, Stanikic Mina, Iaquinto Stefania, Ammann Sabin, Kamm Christian P, Zecca Chiara, Calabrese Pasquale, Steinemann Nina, von Wyl Viktor
Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.
Digital Society Initiative, University of Zurich, Zurich, Switzerland.
PLOS Digit Health. 2024 Nov 6;3(11):e0000645. doi: 10.1371/journal.pdig.0000645. eCollection 2024 Nov.
Remote longitudinal studies are on the rise and promise to increase reach and reduce participation barriers in chronic disease research. However, maintaining long-term retention in these studies remains challenging. Early identification of participants with different patterns of long-term retention offers the opportunity for tailored survey adaptations. Using data from the online arm of the Swiss Multiple Sclerosis Registry (SMSR), we assessed sociodemographic, health-related, and daily-life related baseline variables against measures of long-term retention in the follow-up surveys through multivariable logistic regressions and unsupervised clustering analyses. We further explored follow-up survey completion measures against survey requirements to inform future survey designs. Our analysis included data from 1,757 participants who completed a median of 4 (IQR 2-8) follow-up surveys after baseline with a maximum of 13 possible surveys. Survey start year, age, citizenship, MS type, symptom burden and independent driving were significant predictors of long-term retention at baseline. Three clusters of participants emerged, with no differences in long-term retention outcomes revealed across the clusters. Exploratory assessments of follow-up surveys suggest possible trends in increased survey complexity with lower rates of survey completion. Our findings offer insights into characteristics associated with long-term retention in remote longitudinal studies, yet they also highlight the possible influence of various unexplored factors on retention outcomes. Future studies should incorporate additional objective measures that reflect participants' individual contexts to understand their ability to remain engaged long-term and inform survey adaptations accordingly.
远程纵向研究正在兴起，并有望扩大慢性病研究的覆盖范围，降低参与障碍。然而，在这些研究中保持长期参与度仍然具有挑战性。早期识别具有不同长期参与模式的参与者，为针对性的调查调整提供了机会。利用瑞士多发性硬化症登记处（SMSR）在线部分的数据，我们通过多变量逻辑回归和无监督聚类分析，将社会人口统计学、健康相关和日常生活相关的基线变量与后续调查中的长期参与度指标进行了评估。我们还根据调查要求进一步探讨了后续调查的完成情况，以为未来的调查设计提供参考。我们的分析纳入了1757名参与者的数据，这些参与者在基线后平均完成了4次（四分位间距为2 - 8次）后续调查，最多可能进行13次调查。调查开始年份、年龄、公民身份、MS类型、症状负担和独立驾驶是基线时长期参与度的重要预测因素。出现了三类参与者，各聚类在长期参与度结果上没有差异。对后续调查的探索性评估表明，调查复杂性增加可能伴随着调查完成率降低的趋势。我们的研究结果为远程纵向研究中与长期参与度相关的特征提供了见解，但也突出了各种未探索因素对参与度结果的可能影响。未来的研究应纳入更多反映参与者个体情况的客观指标，以了解他们长期参与的能力，并据此为调查调整提供依据。
