[Purpose] This study aimed to examine the characteristics of preoperative physical activity and its impact on the postoperative period in patients who underwent surgery for esophageal cancer. [Participants and Methods] The participants were 30 patients who were diagnosed with esophageal cancer, underwent surgery, and fulfilled their conditions. Preoperative physical activity was measured using the step count, and metabolic equivalents as the amount of physical activity. We examined the relationships between preoperative step count and METs, patient demographics, treatment-related factors, preoperative physical function, and activities of daily living. Moreover, we examined the relationships of preoperative step count and METs with postoperative mobilization, physical activity, physical function, and activities of daily living. [Results] Preoperative step count was related to age, Glasgow prognostic score, and preoperative functional independence and associated with step count on postoperative days 7-13, METs on postoperative days 7-9, 6-min walking distance, and functional independence measures at discharge. [Conclusion] Improving the nutritional status and increasing preoperative physical activity by walking for esophageal cancer may help improve physical activity after postoperative day 7, exercise tolerance, and activities of daily after discharge.

OBJECTIVE: The aim of this study is to determine the validity of consumer grade step counter devices during the early recovery period after knee replacement surgery.
METHODS: Twenty-three participants wore a Fitbit Charge or Apple Watch Series 4 smart watch and performed a walking test along a 50-metre hallway. There were 9 males and 14 females included in the study with an average age of 68.5 years and BMI of 32. Each patient wore both the Fitbit Charge and Apple Watch while completing the walking test and an observer counted the ground truth value using a thumb-push tally counter. This test was repeated pre-operatively with no gait aid, immediately post operatively with a walker, at 6 weeks follow up with a cane and at 6 months with no gait aid. Bland-Altman plots were performed for all walking tests to compare the agreement between measurement techniques.
RESULTS: Mean overall agreement of step count for pre-operative and at 6 months for subjects walking without gait aids was excellent for both the Apple Watch vs. actual and Fitbit vs. actual with bias values ranging from - 0.87 to 1.36 with limits of agreement (LOA) ranging between - 10.82 and 15.91. While using a walker both devices showed extremely little agreement with the actual step count with bias values between 22.5 and 24.37 with LOA between 11.7 and 33.3. At 6 weeks post-op while using a cane, both the Apple Watch and Fitbit devices had a range of bias values between - 2.8 and 5.73 with LOA between - 13.51 and 24.97.
CONCLUSIONS: These devices show poor validity in the early post operative setting, especially with the use of gait aids, and therefore results should be interpreted with caution.

With the increased push for personalized medicine, researchers and clinicians have begun exploring the use of wearable sensors to track patient activity. These sensors typically prioritize device life over robust onboard analysis, which results in lower accuracies in step count, particularly at lower cadences. To optimize the accuracy of activity-monitoring devices, particularly at slower walking speeds, proven methods must be established to identify suitable settings in a controlled and repeatable manner prior to human validation trials. Currently, there are no methods for optimizing these low-power wearable sensor settings prior to human validation, which requires manual counting for in-laboratory participants and is limited by time and the cadences that can be tested. This article proposes a novel method for determining sensor step counting accuracy prior to human validation trials by using a mechanical camshaft actuator that produces continuous steps. Sensor error was identified across a representative subspace of possible sensor setting combinations at cadences ranging from 30 steps/min to 110 steps/min. These true errors were then used to train a multivariate polynomial regression to model errors across all possible setting combinations and cadences. The resulting model predicted errors with an R2 of 0.8 and root-mean-square error (RMSE) of 0.044 across all setting combinations. An optimization algorithm was then used to determine the combinations of settings that produced the lowest RMSE and median error for three ranges of cadence that represent disabled low-mobility ambulators, disabled high-mobility ambulators, and healthy ambulators (30-60, 20-90, and 30-110 steps/min, respectively). The model identified six setting combinations for each range of interest that achieved a ±10% error in cadence prior to human validation. The anticipated range of errors from the optimized settings at lower walking speeds are lower than the reported errors of wearable sensors (±30%), suggesting that pre-human-validation optimization of sensors may decrease errors at lower cadences. This method provides a novel and efficient approach to optimizing the accuracy of wearable activity monitors prior to human validation trials.

OBJECTIVE: Individuals with chronic pain due to knee osteoarthritis (OA) are insufficiently physically active, and alterations of facilitatory and inhibitory nociceptive signaling are common in this population. Our objective was to examine the association of these alterations in nociceptive signaling with objective accelerometer-based measures of physical activity in a large observational cohort.
METHODS: We used data from the Multicenter Osteoarthritis Study. Measures of peripheral and central pain sensitivity included pressure pain threshold at the knee and mechanical temporal summation at the wrist, respectively. The presence of descending pain inhibition was assessed by conditioned pain modulation (CPM). Physical activity was quantitatively assessed over 7 days using a lower back-worn activity monitor. Summary metrics included steps/day, activity intensity, and sedentary time. Linear regression analyses were used to evaluate the association of pain sensitivity and the presence of descending pain inhibition with physical activity measures.
RESULTS: Data from 1873 participants was analyzed (55.9% female, age = 62.8 ± 10.0 years). People having greater peripheral and central sensitivity showed lower step counts. CPM was not significantly related to any of the physical activity measures, and none of the exposures were significantly related to sedentary time.
CONCLUSIONS: In this cohort, greater peripheral and central sensitivity were associated with reduced levels of objectively-assessed daily step counts. Further research may investigate ways to modify or treat heightened pain sensitivity as a means to increase physical activity in older adults with knee OA.

[This corrects the article DOI: 10.3389/fpubh.2023.1190464.].

Background and Objectives: Mild cognitive impairment (MCI) is an early stage of dementia in which everyday tasks can be maintained; however, notable challenges may occur in memory, focus, and problem-solving skills. Therefore, motor-cognitive dual-task training is warranted to prevent cognitive decline and improve cognition in aging populations. This study aimed to determine the influence of such dual-task activities during straight and curved walking on the activities of the prefrontal cortex and associated gait variables in older adults with MCI. Materials and Methods: Twenty-seven older adults aged ≥65 years and identified as having MCI based on their scores (18-23) on the Korean Mini-Mental State Examination were enrolled. The participants performed four task scenarios in random order: walking straight, walking straight with a cognitive task, walking curved, and walking curved with a cognitive task. The activation of the prefrontal cortex, which is manifested by a change in the level of oxyhemoglobin, was measured using functional near-infrared spectroscopy. The gait speed and step count were recorded during the task performance. Results: Significant differences were observed in prefrontal cortex activation and gait variables (p < 0.05). Specifically, a substantial increase was observed in prefrontal cortex activation during a dual task compared with that during a resting-state (p < 0.013). Additionally, significant variations were noted in the gait speed and step count (p < 0.05). Conclusions: This study directly demonstrates the impact of motor-cognitive dual-task training on prefrontal cortex activation in older adults with MCI, suggesting the importance of including such interventions in enhancing cognitive function.

Chronic pain is associated with substantial impairment in physical function, which has been identified as a top concern among persons with pain. GetActive-Fitbit, a mind-body activity program, is feasible, acceptable, and associated with improvement in physical function among primarily White, sedentary individuals with pain. In preparation for a multisite efficacy trial, we must examine feasibility across multiple sites with diverse patient populations. Here we describe the protocol of a multisite, feasibility RCT comparing GetActive-Fitbit with a time- and attention-matched educational comparison (Healthy Living for Pain). We aim to 1) test multisite fidelity of clinician training; 2) evaluate multisite feasibility benchmarks, including recruitment of chronic pain patients taking <5000 steps/day and racial and ethnic minorities; and 3) optimize fidelity and study protocol in preparation for a future multisite efficacy trial.
Clinician training fidelity was assessed via roleplays and mock group sessions. Feasibility (i.e., recruitment, acceptability, credibility, adherence, satisfaction), multimodal physical function (e.g., self-report, 6-Minute Walk Test, step-count), and other psychosocial outcomes are assessed at baseline, posttest, and 6 months. Protocol optimization will be assessed using exit interviews and cross-site meetings.
The trial is ongoing. Clinician training is complete. 87 participants have been recruited. 54 completed baseline assessments and randomization, 44 are mid-intervention, and 9 have completed the intervention and posttest.
This study addresses the critical need for feasible, acceptable mind-body-activity interventions for chronic pain that follow evidence-based guidelines and improve all aspects of physical function across diverse populations. Results will inform a future fully-powered multisite efficacy trial.
NCT05700383.

BACKGROUND: Established health-related quality of life scores do not consider both subjective and objective indices of health. We propose the subjective and objective quality of life score (SOQOL) for the comprehensive assessment of health-related quality of life and aim to provide normative population data. The SOQOL is compatible with smartphone applications, allowing widespread use on a global scale.
METHODS: Normative SOQOL population data was sourced from pre-existing datasets on the EQ-5D-5L, daily step count, and walking speed. Normative values were calculated using weighted grand means. We trialled the SOQOL in a group of five patients presenting to a spinal neurosurgery clinic.
RESULTS: SOQOL scores decreased with age, and women had lower scores in every age group. In our case series, the spine patients with the biggest SOQOL deficit compared to age- and sex-matched population averages were found to be surgical while the rest were non-surgical.
CONCLUSIONS: The SOQOL shows promise as a simple and effective scoring tool that is compatible with smartphones, potentially useful for screening in primary and specialized care settings, and for assessment following healthcare interventions. This study, however, is preliminary, and the findings are primarily suggestive. They underline the necessity for future, more comprehensive studies to validate and expand upon these initial observations. The conclusion of both this abstract and the full paper will clearly state these limitations and the preliminary nature of the study.

Physical activity (PA) is suggested to reduce osteoarthritis pain; however, it may be avoided by patients requiring arthroplasty. Our goal was to investigate objective and patient-reported outcomes as a function of pre-operative PA levels in patients undergoing total knee arthroplasty (TKA). A total of 1941 patients enrolled in a multicenter prospective cohort study investigating a smartphone-based care management platform for self-directed rehabilitation underwent TKA and were included in the analysis. Activity was categorized based on the cohort\'s step count quartiles into low, moderate, and high pre-operative PA. Pre-operative and post-operative pain, EQ5D5L, KOOS JR, and step counts were compared by ANOVA according to activity group. Pre-operative pain scores increased with the decreasing activity level (all, p < 0.05) and were most improved post-operatively in the low PA group. High PA patients demonstrated the smallest improvements in EQ-5D-5L and KOOS JR. Low and moderate PA patients increased physical activity by three months, reaching 176% and 104% of pre-operative steps; high PA patients did not return to full step counts by one year post-operatively. Patients undergoing TKA who present with higher levels of physical activity report lower levels of pain and higher function pre-operatively but appreciate less improvement up to one year post-operatively. These results may be helpful in appropriate counseling of patient expectations before TKA.

BACKGROUND: Step counts are increasingly used in public health and clinical research to assess well-being, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states.
OBJECTIVE: Our goal was to evaluate an open-source, step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations (\"cross-body\" validation), manually ascertained ground truth (\"visually assessed\" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer (\"commercial wearable\" validation).
METHODS: We used 8 independent data sets collected in controlled, semicontrolled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. A total of 5 data sets (n=103) were used for cross-body validation, 2 data sets (n=107) for visually assessed validation, and 1 data set (n=45) was used for commercial wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw subsecond-level accelerometer data. We calculated the mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis.
RESULTS: In the cross-body validation data sets, participants performed 751.7 (SD 581.2) steps, and the mean bias was -7.2 (LoA -47.6, 33.3) steps, or -0.5%. In the visually assessed validation data sets, the ground truth step count was 367.4 (SD 359.4) steps, while the mean bias was -0.4 (LoA -75.2, 74.3) steps, or 0.1%. In the commercial wearable validation data set, Fitbit devices indicated mean step counts of 1931.2 (SD 2338.4), while the calculated bias was equal to -67.1 (LoA -603.8, 469.7) steps, or a difference of 3.4%.
CONCLUSIONS: This study demonstrates that our open-source, step-counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.