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Purpose: We examined the one-year test re-test reliability and validity criterion of survey-assessed sleep duration collected from two separate questions. Methods: The Activity Validation Sub Study included 751 participants of the Cancer Prevention Study-3 study to further investigate rest/activity cycles. Sleep duration was collected using three methods: survey, Daysimeter device, and sleep diary. Survey-assessed sleep duration was collected using 2 different questions, each with different response options (categorical and continuous). Selected participants (n = 170) were asked to wear a Daysimeter device for seven consecutive days for two non-consecutive quarters. Participants were excluded from the current study due to incomplete/implausible survey or device data or reported working night shift. We calculated reliability of pre- and post-survey sleep duration for both survey question using Spearman correlation. We used the method of triads to estimate the validity coefficient (VC) between the three sleep duration measurements in the present study and the "true" latent sleep duration measure, and bootstrapping methods to calculate the 95% confidence intervals (95%CI). Results: Of 119 participants included in the study (52.10% male), test-retest correlation showed strong and moderate correlations for sleep duration collected continuously and categorically, respectively. The VC for survey-assessed continuous sleep duration was 0.82 (95%CI 0.71, 0.90) for weekday and 0.68 (95%CI 0.46, 0.83) for weekend. Performance of the VC was slightly weaker for survey-assessed categorical sleep duration (weekday VC = 0.57 95%CI 0.42, 0.71; weekend VC = 0.47 95%CI 0.29, 0.62). Conclusion: The two survey-assessed sleep duration questions used in the AVSS and CPS-3 cohorts are valid approximations of sleep duration. [ABSTRACT FROM AUTHOR]

Background: Performing physical activity may provide analgesic benefit, although this effect is more established for noncancer pain rather than cancer pain. The relationship between physical activity and pain outcomes in adults with and without a history of cancer was examined. Methods: Totals of 51,439 adults without a cancer history and 10,651 adults with a cancer history from the Cancer Prevention Study II Nutrition Cohort were included. Exposures included self‐reported moderate to vigorous physical activity (MVPA) as well as 2‐year change in MVPA. Pain outcomes included pain intensity (primary outcome) and analgesic use (secondary outcome). Results: MVPA was inversely associated with pain intensity for adults with (odds ratio [OR], 0.84 [≥15 metabolic equivalent of task (MET) h/week vs. <7.5 MET h/week]; 95% confidence interval [CI], 0.76–0.93) and without (OR, 0.79; 95% CI, 0.75–0.82) a history of cancer. Compared to remaining inactive, participants who became sufficiently active (cancer: OR, 0.76; 95% CI, 0.68–0.86; no cancer: OR, 0.73; 95% CI, 0.69–0.77), became inactive (cancer: OR, 0.79; 95% CI, 0.71–0.88; no cancer: OR, 0.84; 95% CI, 0.80–0.89), or remained sufficiently active (cancer: OR, 0.66; 95% CI, 0.60–0.72; no cancer: OR, 0.62; 95% CI, 0.60–0.65) also reported less pain. Physical activity was not related to analgesic use. Conclusions: The relationship between physical activity and pain intensity was not substantially different between people with and without a history of cancer. Cancer survivors who perform more activity, or who increase their activity, may experience less pain than cancer survivors who consistently perform less. Plain language summary: People who have had cancer often experience ongoing pain.Being physically active may help reduce the intensity of the pain they experience. This study examined physical activity and pain in people with and without a history of cancer. More physical activity was related to less pain in both groups. [ABSTRACT FROM AUTHOR]

Introduction Foodborne illness is a significant public health problem in the U.S. (Angelo, Nisler, Hall, Brown, & Gould, 2017; Centers for Disease Control and Prevention [CDC], 2013; Gould et al., [...], Husiraci Environmental health specialists often perform environmental assessments (EAs) when a suspected or confirmed foodborne illness outbreak is linked to a food establishment. Information from EAs helps officials determine the cause of the outbreak and develop strategies to prevent future outbreaks; however, EAs are not always conducted. To determine facilitators and barriers to conducting EAs, we analyzed open-ended responses reported to the National Environmental Assessment Reporting System about these assessments. We found that EAs were conducted most often when illness was identified, a jurisdiction had a policy to investigate illnesses, and there were resources for such a response. EAs were not conducted in instances such as limited resources, insufficient training, uncooperative facility personnel, or if the establishment fell outside of health department jurisdiction. Identifying the facilitators and barriers to conducting EAs can enable health departments to develop strategies that improve their ability to conduct EAs.

Background: Multimorbidity is associated with premature mortality and excess health care costs. The burden of multimorbidity is highest among patients with cancer, yet trends and determinants of multimorbidity over time are poorly understood. Methods: Via Medicare claims linked to Cancer Prevention Study II data, group‐based trajectory modeling was used to compare National Cancer Institute comorbidity index score trends for cancer survivors and older adults without a cancer history. Among cancer survivors, multinomial logistic regression analyses evaluated associations between demographics, health behaviors, and comorbidity trajectories. Results: In 82,754 participants (mean age, 71.6 years [SD, 5.1 years]; 56.9% female), cancer survivors (n = 11,265) were more likely than older adults without a cancer history to experience the riskiest comorbidity trajectories: (1) steady, high comorbidity scores (remain high; odds ratio [OR], 1.36; 95% CI, 1.29–1.45), and (2) high scores that increased over time (start high and increase; OR, 1.51; 95% CI, 1.38–1.65). Cancer survivors who were physically active postdiagnosis were less likely to fall into these two trajectories (OR, 0.73; 95% CI, 0.64–0.84, remain high; OR, 0.42; 95% CI, 0.33–0.53, start high and increase) compared to inactive survivors. Cancer survivors with obesity were more likely to have a trajectory that started high and increased (OR, 2.83; 95% CI, 2.32–3.45 vs. normal weight), although being physically active offset some obesity‐related risk. Cancer survivors who smoked postdiagnosis were also six times more likely to have trajectories that started high and increased (OR, 6.86; 95% CI, 4.41–10.66 vs. never smokers). Conclusions: Older cancer survivors are more likely to have multiple comorbidities accumulated at a faster pace than older adults without a history of cancer. Weight management, physical activity, and smoking avoidance postdiagnosis may attenuate that trend. Older cancer survivors are more likely to have multiple comorbidities accumulated at a faster pace than older adults without a history of cancer. Weight management, physical activity, and smoking avoidance postdiagnosis may attenuate the trend. [ABSTRACT FROM AUTHOR]

Introduction: Limited research has examined reasons for polytobacco use, an increasing public health problem, particularly among young adults. We examined reasons for polytobacco use among users of more than one tobacco product in the past 4 months enrolled in an ongoing six-wave longitudinal study of 3,418 students aged 18-25 from seven US colleges and universities., Methods: An expert panel generated items related to reasons for polytobacco use, included in Wave 3 (administered in Summer 2015). Participants reporting use of more than one tobacco product in the past four months (n=540) were asked to complete the Reasons for Polytobacco Use scale and measures related to tobacco/nicotine use/dependence, use motives, perceptions of tobacco, parental/friend use, other substance use, and mental health. We conducted a factor analysis and then examined convergent and discriminant validity for the derived factors., Results: Our sample was an average age of 20.40 (SD=1.84), 48.0% male, and 21.9% Black. Four factors were identified: Instrumentality, Social Context, Displacement, and Experimentation. Instrumentality was the only factor associated with little cigar/cigarillo and marijuana use. Displacement and Social Context showed similar associations; however, Social Context was associated with having friends who used tobacco while Displacement was not. Experimentation was associated with greater perceived addictiveness and harm of using tobacco products as well as greater perceived social acceptability of tobacco use., Conclusions: Each of the four factors identified demonstrated unique convergent and discriminant validity. The use of this scale to characterize polytobacco using young adults may help inform and target cessation or prevention interventions.

Limb tracking is an important aspect of human-machine interfaces (HMI). These systems, however, can often be limited by complex algorithms requiring significant processing power, obtrusive and immobile sensing techniques, and high costs. In this work, we utilize a sensor fusion algorithm implemented in commercial inertial measurement units (IMU) to combine accelerometer and gyroscope measurements in an effort to minimize computational requirements of the limb tracking system. In addition, previously developed methods were implemented to eliminate sensor drift by including information from a magnetometer. We tested the accuracy of our system by computing the root mean squared error (RMSE) of the true angle between the headings of two sensors and the estimate of that angle through quaternion-vector manipulations. An average RMSE of approximately 2.9° was achieved. Our limb tracking system is wearable, minimally complex, low-cost, and simple to use which has proven useful in multiple HMI applications discussed herein.

Myoelectric control of prosthetic devices tend to rely on classification schemes of extracted features of EMG data. Those features however, may be sensitive to arm position resulting in decreased performance in real-world applications. The effect of varying limb position in a pattern recognition system have been illustrated by documenting the change in classification accuracy as the user achieves particular limb configurations. We continue to investigate this limb position effect by observing its impact on classification accuracy as well as through an analysis of how each extracted feature of the raw EMG varies in each position. Finally, LDA classification schemes are applied both to demonstrate the effect varying limb position has on classification accuracy and to increase classification accuracy without the use of additional hardware or sensors such as accelerometers as has been done in the past. It is shown that high classification accuracy can be achieved by (1) training an LDA classifier with data from many positions, as well as (2) by utilizing an extra position LDA classifier which can weigh the grasp classifiers appropriately. The classification accuracies achieved by these methods approached that of a model relying on a perfect knowledge of arm position.