Axén Iben, Bodin Lennart
Institute of Environmental Medicine, Unit of Intervention and Implementation Research in Worker Health, Karolinska Institutet, Nobels väg 13, S-171 77, Stockholm, Sweden.
BMC Med Res Methodol. 2016 Sep 13;16(1):119. doi: 10.1186/s12874-016-0221-4.
Mobile technology has opened opportunities within health care and research to allow for frequent monitoring of patients. This has given rise to detailed longitudinal information and new insights concerning behaviour and development of conditions over time. Responding to frequent questionnaires delivered through mobile technology has also shown good compliance, far exceeding that of traditional paper questionnaires. However, to optimize compliance, the burden on the subjects should be kept at a minimum. In this study, the effect of using fewer data points compared to the full data set was examined, assuming that fewer measurements would lead to better compliance.
Weekly text-message responses for 6 months from subjects recovering from an episode of low back pain (LBP) were available for this secondary analysis. Most subjects showed a trajectory with an initial improvement and a steady state thereafter. The data were originally used to subgroup (cluster) patients according to their pain trajectory. The resulting 4-cluster solution was compared with clusters obtained from five datasets with fewer data-points using Kappa agreement as well as inspection of estimated pain trajectories. Further, the relative risk of experiencing a day with bothersome pain was compared week by week to show the effects of discarding some weekly data.
One hundred twenty-nine subjects were included in this analysis. Using data from every other weekly measure had the highest agreement with the clusters from the full dataset, weighted Kappa = 0.823. However, the visual description of pain trajectories favoured using the first 18 weekly measurements to fully capture the phases of improvement and steady-state. The weekly relative risks were influenced by the pain trajectories and 18 weeks or every other weekly measure were the optimal designs, next to the full data set.
A population recovering from an episode of LBP could be described using every other weekly measurement, an option which requires fewer weekly measures than measuring weekly for 18 weeks. However a higher measuring frequency might be needed in the beginning of a clinical course to fully map the pain trajectories.
移动技术为医疗保健和研究带来了机遇,可实现对患者的频繁监测。这产生了详细的纵向信息以及关于病情随时间的行为和发展的新见解。通过移动技术回复频繁的问卷也显示出良好的依从性,远远超过传统纸质问卷。然而,为了优化依从性,应将受试者的负担降至最低。在本研究中,假设较少的测量次数会带来更好的依从性,研究了与完整数据集相比使用较少数据点的效果。
对从下背痛(LBP)发作中恢复的受试者进行为期6个月的每周短信回复,用于本次二次分析。大多数受试者呈现出最初改善然后稳定的轨迹。这些数据最初用于根据疼痛轨迹对患者进行亚组(聚类)。使用卡帕一致性以及估计疼痛轨迹的检查,将所得的4聚类解决方案与从五个数据点较少的数据集中获得的聚类进行比较。此外,逐周比较经历烦扰性疼痛一天的相对风险,以显示丢弃一些每周数据的影响。
本分析纳入了129名受试者。每隔一周测量的数据与完整数据集的聚类具有最高的一致性,加权卡帕值 = 0.823。然而,疼痛轨迹的直观描述倾向于使用前18周的测量数据来充分捕捉改善和稳定阶段。每周相对风险受疼痛轨迹影响,除完整数据集外,18周或每隔一周测量是最佳设计。
从LBP发作中恢复的人群可以每隔一周测量来描述,这一选择比连续测量18周所需的每周测量次数更少。然而,在临床过程开始时可能需要更高的测量频率来全面描绘疼痛轨迹。