School of Computing and Information Systems, University of Melbourne, Melbourne, Australia.
Department of Computer Science, University of Bath, Bath, United Kingdom.
JMIR Mhealth Uhealth. 2021 Jan 26;9(1):e22846. doi: 10.2196/22846.
Physical activity trackers such as the Fitbit can allow clinicians to monitor the recovery of their patients following surgery. An important issue when analyzing activity tracker data is to determine patients' daily compliance with wearing their assigned device, using an appropriate criterion to determine a valid day of wear. However, it is currently unclear as to how different criteria can affect the reported compliance of patients recovering from ambulatory surgery. Investigating this issue can help to inform the use of activity data by revealing factors that may impact compliance calculations.
This study aimed to understand how using different criteria can affect the reported compliance with activity tracking in ambulatory surgery patients. It also aimed to investigate factors that explain variation between the outcomes of different compliance criteria.
A total of 62 patients who were scheduled to undergo total knee arthroplasty (TKA, ie, knee replacement) volunteered to wear a commercial Fitbit Zip activity tracker over an 8-week perioperative period. Patients were asked to wear the Fitbit Zip daily, beginning 2 weeks prior to their surgery and ending 6 weeks after surgery. Of the 62 patients who enrolled in the study, 20 provided Fitbit data and underwent successful surgery. The Fitbit data were analyzed using 5 different daily compliance criteria, which consider patients as compliant with daily tracking if they either register >0 steps in a day, register >500 steps in a day, register at least one step in 10 different hours of the day, register >0 steps in 3 distinct time windows, or register >0 steps in 3 out of 4 six-hour time windows. The criteria were compared in terms of compliance outcomes produced for each patient. Data were explored using heatmaps and line graphs. Linear mixed models were used to identify factors that lead to variation between compliance outcomes across the sample.
The 5 compliance criteria produce different outcomes when applied to the patients' data, with an average 24% difference in reported compliance between the most lenient and strictest criteria. However, the extent to which each patient's reported compliance was impacted by different criteria was not uniform. Some individuals were relatively unaffected, whereas others varied by up to 72%. Wearing the activity tracker as a clip-on device, rather than on the wrist, was associated with greater differences between compliance outcomes at the individual level (P=.004, r=.616). This effect was statistically significant (P<.001) in the first 2 weeks after surgery. There was also a small but significant main effect of age on compliance in the first 2 weeks after surgery (P=.040). Gender and BMI were not associated with differences in individual compliance outcomes. Finally, the analysis revealed that surgery has an impact on patients' compliance, with noticeable reductions in activity following surgery. These reductions affect compliance calculations by discarding greater amounts of data under strict criteria.
This study suggests that different compliance criteria cannot be used interchangeably to analyze activity data provided by TKA patients. Surgery leads to a temporary reduction in patients' mobility, which affects their reported compliance when strict thresholds are used. Reductions in mobility suggest that the use of lenient compliance criteria, such as >0 steps or windowed approaches, can avoid unnecessary data exclusion over the perioperative period. Encouraging patients to wear the device at their wrist may improve data quality by increasing the likelihood of patients wearing their tracker and ensuring that activity is registered in the 2 weeks after surgery.
ClinicalTrials.gov NCT03518866; https://clinicaltrials.gov/ct2/show/NCT03518866.
活动追踪器（如 Fitbit）可以让临床医生监测患者手术后的恢复情况。在分析活动追踪器数据时，一个重要的问题是确定患者日常佩戴指定设备的依从性，使用适当的标准来确定有效的佩戴日。然而，目前尚不清楚不同的标准如何影响接受门诊手术的患者的报告依从性。研究这个问题可以帮助了解活动数据的使用情况，揭示可能影响依从性计算的因素。
本研究旨在了解使用不同的标准如何影响门诊手术患者的活动跟踪报告依从性。还旨在调查导致不同依从性标准结果差异的因素。
共有 62 名计划接受全膝关节置换术（TKA，即膝关节置换术）的患者自愿在围手术期佩戴商业 Fitbit Zip 活动追踪器 8 周。要求患者从手术前 2 周开始每天佩戴 Fitbit Zip，一直持续到手术后 6 周。在 62 名参加研究的患者中，有 20 名提供了 Fitbit 数据并成功接受了手术。使用 5 种不同的日常依从性标准对 Fitbit 数据进行分析，这些标准将每天记录 >0 步、记录 >500 步、记录 10 小时内至少有 1 次记录、记录 3 个不同时间窗口内 >0 步或记录 4 个六小时时间窗口中 >0 步的患者视为符合日常跟踪。比较了每个患者的依从性结果产生的标准。使用热图和线图进行数据探索。使用线性混合模型来确定导致样本间依从性结果差异的因素。
当应用于患者数据时，这 5 个依从性标准产生了不同的结果，最宽松和最严格的标准之间报告的依从性差异平均为 24%。然而，每个患者的报告依从性受到不同标准的影响程度并不均匀。一些人相对不受影响，而另一些人则差异高达 72%。将活动追踪器作为夹式设备佩戴，而不是佩戴在手腕上，与个体水平上的依从性结果差异更大（P=.004，r=.616）。这种影响在手术后的头 2 周内具有统计学意义（P<.001）。手术后的前 2 周，年龄对依从性也有较小但显著的主要影响（P=.040）。性别和 BMI 与个体依从性结果的差异无关。最后，分析显示手术对患者的依从性有影响，手术后活动明显减少。这些减少通过在严格标准下丢弃更多数据来影响依从性计算。
本研究表明，不能互换使用不同的依从性标准来分析 TKA 患者提供的活动数据。手术会导致患者活动能力暂时下降，这会影响严格阈值下的报告依从性。活动能力下降表明，使用宽松的依从性标准（如 >0 步或窗口方法）可以避免在围手术期不必要的数据排除。鼓励患者将设备佩戴在手腕上可以通过增加患者佩戴追踪器的可能性并确保在手术后的 2 周内记录活动来提高数据质量。
ClinicalTrials.gov NCT03518866; https://clinicaltrials.gov/ct2/show/NCT03518866.
