Association of Electronic Self-Scheduling and Screening Mammogram Completion.

INTRODUCTION: The purpose of this study was to evaluate if an electronic health record (EHR) self-scheduling function was associated with changes in mammogram completion for primary care patients who were eligible for a screening mammogram using U.S. Preventive Service Task Force recommendations. METHODS: This was a retrospective cohort study (September 1, 2014-August 31, 2019, analyses completed in 2022) using a difference-in-differences design to examine mammogram completion before versus after the implementation of self-scheduling. The difference-in-differences estimate was the interaction between time (pre-versus post-implementation) and group (active EHR patient portal versus inactive EHR patient portal). The primary outcome was mammogram completion among all eligible patients, with completion defined as receiving a mammogram within 6 months post-visit. The secondary outcome was mammogram completion among patients who received a clinician order during their visit. RESULTS: The primary analysis included 35,257 patient visits. The overall mammogram completion rate in the pre-period was 22.2% and 49.7% in the post-period. EHR self-scheduling was significantly associated with increased mammogram completion among those with an active EHR portal, relative to patients with an inactive portal (adjusted difference 13.2 percentage points [95% CI 10.6-15.8]). For patients who received a clinician mammogram order at their eligible visit, self-scheduling was significantly associated with increased mammogram completion among patients with an active EHR portal account (adjusted difference 14.7 percentage points, [95% CI 10.9-18.5]). CONCLUSIONS: EHR-based self-scheduling was associated with a significant increase in mammogram completion among primary care patients. Self-scheduling can be a low-cost, scalable function for increasing preventive cancer screenings.

Patient-reported sleep and physical function during and after hospitalization.

PURPOSE: Poor sleep is associated with morbidity and mortality in the community; however, the health impact of poor sleep during and after hospitalization is poorly characterized. Our purpose was to describe trends in patient-reported sleep and physical function during and after hospitalization and evaluate sleep as a predictor of function after discharge. METHODS: This is a secondary analysis of trial data with 232 adults followed for 3months after hospital discharge. Main measures were patient-reported surveys on sleep (Pittsburgh Sleep Quality Index) and physical function (Katz Activities of Daily Living, Lawton Instrumental Activities of Daily Living, and Nagi Mobility Scale) were collected during hospitalization and at 1, 5, 9, and 13weeks postdischarge. RESULTS: Patient-reported sleep declined significantly during hospitalization and remained worse for 3months postdischarge (median Pittsburgh Sleep Quality Index=8 vs. 6, p < .001). In parallel, mobility declined significantly from baseline and remained worse at each follow-up time (median Nagi score=2 vs. 0, p < .001). Instrumental activities of daily living similarly decreased during and after hospitalization, but basic activities of daily living were unaffected. In adjusted time-series logistic regression models, the odds of mobility impairment were 1.48 times higher for each 1-point increase in Pittsburgh Sleep Quality Index score over time (95% CI 1.27-1.71, p < .001). CONCLUSIONS: Patient-reported sleep worsened during hospitalization, did not improve significantly for 3months after hospitalization, and poor sleep was a significant predictor of functional impairment over this time. Sleep dysfunction that begins with hospitalization may persist and prevent functional recovery after discharge. TRIAL REGISTRATION: The primary study was registered at ClinicalTrials.gov NCT03321279.

Association between Timing of Clinical Evaluation by a Physician or Advanced Practitioner and Risk of Rehospitalization in Older Adults Admitted to a Skilled Nursing Facility Following Hospitalization: A Cohort Study.

OBJECTIVES: How transitional care services are provided to patients receiving post-acute care in skilled nursing facilities (SNFs) is not well understood. We aimed to determine the association of timing of physician or advanced practice provider (APP) visit after SNF admission with rehospitalization risk in a national cohort of older adults. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: 2,482,616 Medicare fee-for-service beneficiaries aged ≥66 years who entered an SNF for post-acute care following hospitalization. METHODS: We measured the relative risk of being rehospitalized within 14 days of SNF admission as a function of time to the first PAP visit, using time to follow-up as a time-dependent covariate, adjusted for patient demographics and clinical characteristics. We also evaluated whether findings extended across groups with different SNF prognosis on admission. RESULTS: Patients seen sooner after admission to an SNF (0-1 days) were less likely to be rehospitalized compared to patients seen later (≥2 days). The relative difference was similar across different risk groups. CONCLUSIONS AND IMPLICATIONS: Timely evaluation by a physician or APP after SNF admission may protect against rehospitalization. Investment in the workforce such as training programs, practice innovations, and equitable reimbursement for SNF visits after hospital discharge may mitigate labor shortages that were exacerbated by the COVID pandemic.

Development and validation of a model predicting mild stroke severity on admission using electronic health record data.

OBJECTIVE: Initial stroke severity is a potent modifier of stroke outcomes but this information is difficult to obtain from electronic health record (EHR) data. This limits the ability to risk-adjust for evaluations of stroke care and outcomes at a population level. The purpose of this analysis was to develop and validate a predictive model of initial stroke severity using EHR data elements. METHODS: This observational cohort included individuals admitted to a US Department of Veterans Affairs hospital with an ischemic stroke. We extracted 65 independent predictors from the EHR. The primary analysis modeled mild (NIHSS score 0-3) versus moderate/severe stroke (NIHSS score ≥4) using multiple logistic regression. Model validation included: (1) splitting the cohort into derivation (65%) and validation (35%) samples and (2) evaluating how the predicted stroke severity performed in regard to 30-day mortality risk stratification. RESULTS: The sample comprised 15,346 individuals with ischemic stroke (n = 10,000 derivation; n = 5,346 validation). The final model included 15 variables and correctly classified 70.4% derivation sample patients and 69.4% validation sample patients. The areas under the curve (AUC) were 0.76 (derivation) and 0.76 (validation). In the validation sample, the model performed similarly to the observed NIHSS in terms of the association with 30-day mortality (AUC: 0.72 observed NIHSS, 0.70 predicted NIHSS). CONCLUSIONS: EHR data can be used to construct a surrogate measure of initial stroke severity. Further research is needed to better differentiate moderate and severe strokes, enhance stroke severity classification, and how to incorporate these measures in evaluations of stroke care and outcomes.

Deploying Digital Health Technologies for Remote Physical Activity Monitoring of Rural Populations With Chronic Neurologic Disease.

Objective: The objective of this pilot study was to examine the feasibility of a remote physical activity monitoring program, quantify baseline activity levels, and examine predictors of activity among rurally residing adults with Parkinson disease (PD) or stroke. Design: Thirty-day observational study. Participants completed standardized assessments, connected a wearable device, and synced daily step counts via a remote monitoring platform. Setting: Community-based remote monitoring. Participants: Rurally residing adults with PD or stroke enrolled in the Veterans Health Administration. Intervention: N/A. Main Outcome Measures: Feasibility was evaluated using recruitment data (response rates), study completion (completed assessments and connected the wearable device), and device adherence (days recording ≥100 steps). Daily step counts were examined descriptively. Predictors of daily steps were explored across the full sample, then by diagnosis, using linear mixed-effects regression analyses. Results: Forty participants (n=20 PD; n=20 stroke) were included in the analysis with a mean (SD) age of 72.9 (7.6) years. Participants resided 252.6 (105.6) miles from the coordinating site. Recruitment response rates were 11% (PD) and 6% (stroke). Study completion rates were 71% (PD) and 80% (stroke). Device adherence rates were 97.0% (PD) and 95.2% (stroke). Participants with PD achieved a median [interquartile range] of 2618 [3896] steps per day and participants with stroke achieved 4832 [7383] steps. Age was the only significant predictor of daily steps for the full sample (-265 steps, 95% confidence interval [-407, -123]) and by diagnosis (PD, -175 steps, [-335, -15]; stroke, -357 steps [-603, -112]). Conclusions: A remote physical activity monitoring program for rurally residing individuals with PD or stroke was feasible. This study establishes a model for a scalable physical activity program for rural, older populations with neurologic conditions from a central coordinating site.

Exploring Wearables to Focus on the "Sweet Spot" of Physical Activity and Sleep After Hospitalization: Secondary Analysis.

BACKGROUND: Inadequate sleep and physical activity are common during and after hospitalization, but their impact on patient-reported functional outcomes after discharge is poorly understood. Wearable devices that measure sleep and activity can provide patient-generated data to explore ideal levels of sleep and activity to promote recovery after hospital discharge. OBJECTIVE: This study aimed to examine the relationship between daily sleep and physical activity with 6 patient-reported functional outcomes (symptom burden, sleep quality, physical health, life space mobility, activities of daily living, and instrumental activities of daily living) at 13 weeks after hospital discharge. METHODS: This secondary analysis sought to examine the relationship between daily sleep, physical activity, and patient-reported outcomes at 13 weeks after hospital discharge. We utilized wearable sleep and activity trackers (Withings Activité wristwatch) to collect data on sleep and activity. We performed descriptive analysis of device-recorded sleep (minutes/night) with patient-reported sleep and device-recorded activity (steps/day) for the entire sample with full data to explore trends. Based on these trends, we performed additional analyses for a subgroup of patients who slept 7-9 hours/night on average. Differences in patient-reported functional outcomes at 13 weeks following hospital discharge were examined using a multivariate linear regression model for this subgroup. RESULTS: For the full sample of 120 participants, we observed a "T-shaped" distribution between device-reported physical activity (steps/day) and sleep (patient-reported quality or device-recorded minutes/night) with lowest physical activity among those who slept <7 or >9 hours/night. We also performed a subgroup analysis (n=60) of participants that averaged the recommended 7-9 hours of sleep/night over the 13-week study period. Our key finding was that participants who had both adequate sleep (7-9 hours/night) and activity (>5000 steps/day) had better functional outcomes at 13 weeks after hospital discharge. Participants with adequate sleep but less activity (<5000 steps/day) had significantly worse symptom burden (z-score 0.93, 95% CI 0.3 to 1.5; P=.02), community mobility (z-score -0.77, 95% CI -1.3 to -0.15; P=.02), and perceived physical health (z-score -0.73, 95% CI -1.3 to -0.13; P=.003), compared with those who were more physically active (≥5000 steps/day). CONCLUSIONS: Participants within the "sweet spot" that balances recommended sleep (7-9 hours/night) and physical activity (>5000 steps/day) reported better functional outcomes after 13 weeks compared with participants outside the "sweet spot." Wearable sleep and activity trackers may provide opportunities to hone postdischarge monitoring and target a "sweet spot" of recommended levels for both sleep and activity needed for optimal recovery. TRIAL REGISTRATION: ClinicalTrials.gov NCT03321279; https://clinicaltrials.gov/ct2/show/NCT03321279.

Accuracy of an Algorithm in Predicting Upper Limb Functional Capacity in a United States Population.

OBJECTIVE: To determine the accuracy of an algorithm, using clinical measures only, on a sample of persons with first-ever stroke in the United States (US). It was hypothesized that algorithm accuracy would fall in a range of 70%-80%. DESIGN: Secondary analysis of prospective, observational, longitudinal cohort; 2 assessments were done: (1) within 48 hours to 1 week poststroke and (2) at 12 weeks poststroke. SETTING: Recruited from a large acute care hospital and followed over the first 6 months after stroke. PARTICIPANTS: Adults with first-ever stroke (N=49) with paresis of the upper limb (UL) at ≤48 hours who could follow 2-step commands and were expected to return to independent living at 6 months. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: The overall accuracy of the algorithm with clinical measures was quantified by comparing predicted (expected) and actual (observed) categories using a correct classification rate. RESULTS: The overall accuracy (61%) and weighted κ (62%) were significant. Sensitivity was high for the Excellent (95%) and Poor (81%) algorithm categories. Specificity was high for the Good (82%), Limited (98%), and Poor (95%) categories. Positive predictive value (PPV) was high for Poor (82%) and negative predictive value (NPV) was high for all categories. No differences in participant characteristics were found between those with accurate or inaccurate predictions. CONCLUSIONS: The results of the present study found that use of an algorithm with clinical measures only is better than chance alone (chance=25% for each of the 4 categories) at predicting a category of UL capacity at 3 months post troke. The moderate to high values of sensitivity, specificity, PPV, and NPV demonstrates some clinical utility of the algorithm within health care settings in the US.

Upper Limb Performance in Daily Life Approaches Plateau Around Three to Six Weeks Post-stroke.

Background. Wearable sensors allow for direct measurement of upper limb (UL) performance in daily life. Objective. To map the trajectory of UL performance and its relationships to other factors post-stroke. Methods. Participants (n = 67) with first stroke and UL paresis were assessed at 2, 4, 6, 8, 12, 16, 20, and 24 weeks after stroke. Assessments captured UL impairment (Fugl-Meyer), capacity for activity (Action Research Arm Test), and performance of activity in daily life (accelerometer variables of use ratio and hours of paretic limb activity), along with other potential modifying factors. We modeled individual trajectories of change for each measurement level and the moderating effects on UL performance trajectories. Results. Individual trajectories were best fit with a 3-parameter logistic model, capturing the rapid growth early after stroke within the longer data collection period. Plateaus (90% of asymptote) in impairment (bootstrap mean ± SE: 32 ± 4 days post-stroke) preceded those in capacity (41 ± 4 days). Plateau in performance, as measured by the use ratio (24 ± 5 days), tended to precede plateaus in impairment and capacity. Plateau in performance, as measured by hours of paretic activity (41 ± 6 days), occurred at a similar time to that of capacity and slightly lagged impairment. Modifiers of performance trajectories were capacity, concordance, UL rehabilitation, depressive symptomatology, and cognition. Conclusions. Upper limb performance in daily life approached plateau 3 to 6 weeks post-stroke. Individuals with stroke started to achieve a stable pattern of UL use in daily life early, often before neurological impairments and functional capacity started to stabilize.

Effect of Gamification With and Without Financial Incentives to Increase Physical Activity Among Veterans Classified as Having Obesity or Overweight: A Randomized Clinical Trial.

Importance: Gamification is increasingly being used for health promotion but has not been well tested with financial incentives or among veterans. Objective: To test the effectiveness of gamification with social support, with and without a loss-framed financial incentive, to increase physical activity among veterans classified as having overweight and obesity. Design, Setting, and Participants: This 3-group randomized clinical trial had a 12-week intervention period and an 8-week follow-up period. Participants included veterans with a body mass index greater than or equal to 25 who were receiving care from a single site in Philadelphia, Pennsylvania. Participants underwent a remotely monitored intervention from March 19, 2019, to August 9, 2020. Data analyses were conducted between October 1, 2020, and November 14, 2020. Interventions: All participants received a wearable device to track step counts and selected a step goal. The control group received feedback from their devices only. Participants in the 2 gamification groups were entered into a 12-week game with points and levels designed using behavioral economic principles and selected a support partner to receive weekly updates. Participants in the loss-framed financial incentive group had $120 allocated to a virtual account and lost $10 if weekly goals were not achieved. Main Outcomes and Measures: The primary outcome was the change in mean daily steps from baseline during the intervention. Secondary outcomes include proportion of days goals were achieved and changes during follow-up. Results: A total of 180 participants were randomized, 60 to the gamification with social support group, 60 to the gamification with social support and loss-framed financial incentives group, and 60 to the control group. The participants had a mean (SD) age of 56.5 (12.9) years and a mean (SD) body mass index of 33.0 (5.6); 71 participants (39.4%) were women, 90 (50.0%) were White, and 67 (37.2%) were Black. During the intervention period, compared with control group participants, participants in the gamification with financial incentives group had a significant increase in mean daily steps from baseline (adjusted difference, 1224 steps; 95% CI, 451 to 1996 steps; P = .005), but participants in the gamification without financial incentives group did not (adjusted difference, 433 steps; 95% CI, -337 to 1203 steps; P = .81). The increase for the gamification with financial incentives group was not sustained during the follow-up period, and the step count was not significantly different than that of the control group (adjusted difference, 564 steps; 95% CI, -261 to 1389 steps; P = .37). Compared with the control group, participants in the intervention groups had a significantly higher adjusted proportion of days meeting their step goal during the main intervention and follow-up period (gamification with social support group, adjusted difference from control, 0.21 participant-day; 95% CI, 0.18-0.24 participant-day; P < .001; gamification with social support and loss-framed financial incentive group, adjusted difference from control, 0.34 participant-day; 95% CI, 0.31-0.37 participant-day; P < .001). Conclusions and Relevance: Among veterans classified as having overweight and obesity, gamification with social support combined with loss-framed financial incentives was associated with a modest increase in physical activity during the intervention period, but the increase was not sustained during follow-up. Gamification without incentives did not significantly change physical activity. Trial Registration: ClinicalTrials.gov Identifier: NCT03563027.

Leveraging insights from behavioral economics to improve mobility for adults with stroke: Design and rationale of the BE Mobile clinical trial.

Physical inactivity post-stroke can negatively impact long-term health outcomes and contribute to cardiovascular deconditioning, muscle loss, and increased risk for recurrent stroke. The limited number of interventions designed to improve daily physical activity post-stroke have lacked precision in step goals, are resource intensive, and difficult to scale. The purpose of the Leveraging Insights from Behavioral Economics to Improve Mobility for Adults with Stroke (BE Mobile) trial is to examine the preliminary effectiveness of a novel gamification with social incentives intervention for improving physical activity post-stroke. This trial includes adults who have experienced an ischemic or hemorrhagic stroke ≥3 months prior to the time of recruitment who are randomized to a control or gamification arm. All participants receive a Fitbit Inspire 2 wearable device to quantify daily steps and complete a 2-week baseline run-in period followed by an 8-week intervention period. All participants select a daily step goal and the gamification arm is enrolled in a game with loss-framed points and levels to help participants achieve their daily step goal. Participants in the gamification arm also select a support partner who receives weekly updates on their progress in the game. The primary outcome is change in daily steps from baseline during the intervention period. The secondary outcome is difference in the proportion of days participants achieved their daily step goal. Results from this trial will inform future, larger studies that leverage insights from behavioral economics to help improve daily physical activity post-stroke. Trial registration: NCT #04607811.

Association of Behavioral Phenotypes With Changes in Sleep During a Workplace Wellness Program.

PURPOSE: Examine changes in sleep duration by 3 behavioral phenotypes during a workplace wellness program with overweight and obese adults. DESIGN: Secondary analysis of a randomized clinical trial. SETTING: Remotely monitored intervention conducted across the United States. SUBJECTS: 553 participants with a body mass index ≥25. INTERVENTION: Participants were randomized to 1 of 4 study arms: control, gamification with support, gamification with collaboration, and gamification with competition to increase their physical activity. All participants were issued a wrist-worn wearable device to record their daily physical activity and sleep duration. MEASURES: The primary outcome was change in daily sleep duration from baseline during the 24 week intervention and follow-up period by study arm within behavioral phenotype class. ANALYSIS: Linear mixed effects regression. RESULTS: Participants who had a phenotype of less physically active and less social at baseline, in the gamification with collaboration arm, significantly increased their sleep duration during the intervention period (30.2 minutes [95% CI 6.9, 53.5], P = 0.01), compared to the control arm. There were no changes in sleep duration among participants who were more extroverted and motivated or participants who were less motivated and at-risk. CONCLUSIONS: Changes in sleep during a physical activity intervention varied by behavioral phenotype. Behavioral phenotypes may help to precisely identify who is likely to improve sleep duration during a physical activity intervention.

Using Behavioral Economics and Technology to Improve Outcomes in Cardio-Oncology.

Patients with cancer are often at elevated risk for cardiovascular disease due to overlapping risk factors and cardiotoxic anticancer treatments. Their cancer diagnoses may be the predominant focus of clinical care, with less of an emphasis on concurrent cardiovascular risk management. Widely adopted technology platforms, including electronic health records and mobile devices, can be leveraged to improve the cardiovascular outcomes of these patients. These technologies alone may be insufficient to change behavior and may have greater impact if combined with behavior change strategies. Behavioral economics is a scientific field that uses insights from economics and psychology to help explain why individuals are often predictably irrational. Combining insights from behavioral economics with these scalable technology platforms can positively impact medical decision-making and sustained healthy behaviors. This review focuses on the principles of behavioral economics and how "nudges" and scalable technology can be used to positively impact clinician and patient behaviors.

Upper Limb Performance in Daily Life Improves Over the First 12 Weeks Poststroke.

Background. Upper limb (UL) performance, or use, in daily life is complex and likely influenced by many factors. While the recovery trajectory of UL impairment poststroke is well documented, little is known about the recovery trajectory of sensor-measured UL performance in daily life early after stroke and the potential moderating role of psychosocial factors. Objective. To examine the recovery trajectory of UL performance within the first 12 weeks poststroke and characterize the potential moderating role of belief, confidence, and motivation on UL performance. Methods. This was a longitudinal, prospective cohort study quantifying UL performance and related psychosocial factors early after stroke. UL performance was quantified via bilateral, wrist-worn accelerometers over 5 assessment sessions for 24 hours. Belief, confidence, and motivation to use the paretic UL, and self-perceived barriers to UL recovery were quantified via survey. Change in 4 accelerometer variables and the moderating role of psychosocial factors was tested using hierarchical linear modeling. The relationship between self-perceived barriers and UL performance was tested via Spearman rank-order correlation analysis. Results. UL performance improved over the first 12 weeks after stroke. Belief, confidence, and motivation did not moderate UL performance over time. There was a negative relationship between UL performance and self-perceived barriers to UL recovery at week 2, which declined over time. Conclusions. Sensor-measured UL performance can improve early after stroke. Early after stroke, rehabilitation interventions may not need to directly target belief, confidence, and motivation but may instead focus on reducing self-perceived barriers to UL recovery.

Belief, Confidence, and Motivation to Use the Paretic Upper Limb in Daily Life Over the First 24 Weeks After Stroke.

BACKGROUND AND PURPOSE: The recovery patterns of upper limb (UL) impairment after stroke are established. Psychosocial factors such as belief that paretic UL recovery is possible, confidence, and motivation to use the paretic UL in everyday tasks are unexplored early after stroke. The purpose of this exploratory study was to characterize belief, confidence, and motivation to use the paretic UL in daily life, and self-perceived barriers to UL recovery over the first 24 weeks after stroke. METHODS: This was a longitudinal cohort study (N = 30) with 8 assessment sessions over the first 24 weeks after stroke. Belief, confidence, and motivation to use the paretic UL and self-perceived barriers were quantified via survey and analyzed using descriptive statistics. Change in the number of self-perceived barriers between weeks 2 and 24 was tested using a paired-samples t test. The relationship between UL capacity, depressive symptomatology, cognition, and each psychosocial factor was examined using Spearman rank-order correlation analyses. RESULTS: Twenty-two participants completed all study assessments. Belief, confidence, and motivation were high across the 24 weeks, with little variation. There was no difference between the average number of barriers from weeks 2 to 24. There was no relationship between the clinical measures and psychosocial factors at week 2, 12, or 24. DISCUSSION AND CONCLUSIONS: High levels of belief, confidence, and motivation appear consistent over the first 6 months after stroke. The lack of correlations between psychosocial factors and clinical measures suggests belief, confidence, and motivation may not be vulnerable to functional status early after stroke.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1 available at: http://links.lww.com/JNPT/A283).

Comparison of Self-Report Versus Sensor-Based Methods for Measuring the Amount of Upper Limb Activity Outside the Clinic.

OBJECTIVE: To compare self-reported with sensor-measured upper limb (UL) performance in daily life for individuals with chronic (≥6mo) UL paresis poststroke. DESIGN: Secondary analysis of participants enrolled in a phase II randomized, parallel, dose-response UL movement trial. This analysis compared the accuracy and consistency between self-reported UL performance and sensor-measured UL performance at baseline and immediately post an 8-week intensive UL task-specific intervention. SETTING: Outpatient rehabilitation. PARTICIPANTS: Community-dwelling individuals with chronic (≥6mo) UL paresis poststroke (N=64). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Motor Activity Log amount of use scale and the sensor-derived use ratio from wrist-worn accelerometers. RESULTS: There was a high degree of variability between self-reported UL performance and the sensor-derived use ratio. Using sensor-based values as a reference, 3 distinct categories were identified: accurate reporters (reporting difference ±0.1), overreporters (difference >0.1), and underreporters (difference <-0.1). Five of 64 participants accurately self-reported UL performance at baseline and postintervention. Over half of participants (52%) switched categories from pre-to postintervention (eg, moved from underreporting preintervention to overreporting postintervention). For the consistent reporters, no participant characteristics were found to influence whether someone over- or underreported performance compared with sensor-based assessment. CONCLUSIONS: Participants did not consistently or accurately self-report UL performance when compared with the sensor-derived use ratio. Although self-report and sensor-based assessments are moderately associated and appear similar conceptually, these results suggest self-reported UL performance is often not consistent with sensor-measured performance and the measures cannot be used interchangeably.