Authors Background Acknowledgments User Guide UpdatesChanges from the Previous Database Version AbstractThis document is based on previous documentation of the nationally standardized Forest Inventory and Analysis database (Hansen and others 1992;Woudenberg and Farrenkopf 1995; Miles and others 2001; Woudenberg and others 2010). Documentation of the structure of the Forest Inventory and Analysis database (FIADB) for Phase 2 data, as well as codes and definitions, is provided. Examples for producing population-level estimates are also presented. This database provides a consistent framework for storing forest inventory data across all ownerships for the entire United States. These data are available to the public. Keywords:Forest Inventory and Analysis, inventory database, user manual, user guide, monitoringThe use of trade or firm names in this publication is for reader information and does not imply endorsement by the U.S. Department of Agriculture of any product or service. BackgroundThe Forest Inventory and Analysis (FIA) research program has been in existence since mandated by Congress in 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the Nation's forest land. Before 1999, all inventories were conducted on a periodic basis. The passage of the 1998 Farm Bill requires FIA to collect data annually on plots within each State. This kind of up-to-date information is essential to frame realistic forest policies and programs. USDA Forest Service regional research stations are responsible for conducting these inventories and publishing summary reports for individual States.In addition to published reports, the Forest Service provides data collected in each inventory to those interested in further analysis. This report describes a standard format in which data can be obtained. This standard format, referred to as the Forest Inventory and Analysis Database (FIADB) structure, was developed to provide users with as much data as possible in a consistent manner among States. A number of inventories conducted prior to the implementation of the annual inventory are available in the FIADB. However, various data attributes may be empty or the items may have been collected or computed differently. Annual inventories use a common plot design and common data collection procedures nationwide, resulting in greater consistency among FIA work units than earlier inventories. Data field definitions note inconsistencies caused by different sampling designs and processing methods. AcknowledgmentsIn addition to those listed as authors, the following people provided additional contributions to this document: This particular document, version 6.0.1, has undergone some major updates and reorganization since the last version. Many of the updates were made to make this document more accessible to all users. Other changes to this document, such as the addition of hypertext links, are reflective of the ePUB environment and will allow users to quickly access particular sec...

Authors Background Acknowledgments User Guide UpdatesChanges from the Previous Database Version AbstractThis document is based on previous documentation of the nationally standardized Forest Inventory and Analysis database (Hansen and others 1992;Woudenberg and Farrenkopf 1995; Miles and others 2001; Woudenberg and others 2010). Documentation of the structure of the Forest Inventory and Analysis database (FIADB) for Phase 2 data, as well as codes and definitions, is provided. Examples for producing population-level estimates are also presented. This database provides a consistent framework for storing forest inventory data across all ownerships for the entire United States. These data are available to the public. Keywords:Forest Inventory and Analysis, inventory database, user manual, user guide, monitoringThe use of trade or firm names in this publication is for reader information and does not imply endorsement by the U.S. Department of Agriculture of any product or service. BackgroundThe Forest Inventory and Analysis (FIA) research program has been in existence since mandated by Congress in 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the Nation's forest land. Before 1999, all inventories were conducted on a periodic basis. The passage of the 1998 Farm Bill requires FIA to collect data annually on plots within each State. This kind of up-to-date information is essential to frame realistic forest policies and programs. USDA Forest Service regional research stations are responsible for conducting these inventories and publishing summary reports for individual States.In addition to published reports, the Forest Service provides data collected in each inventory to those interested in further analysis. This report describes a standard format in which data can be obtained. This standard format, referred to as the Forest Inventory and Analysis Database (FIADB) structure, was developed to provide users with as much data as possible in a consistent manner among States. A number of inventories conducted prior to the implementation of the annual inventory are available in the FIADB. However, various data attributes may be empty or the items may have been collected or computed differently. Annual inventories use a common plot design and common data collection procedures nationwide, resulting in greater consistency among FIA work units than earlier inventories. Data field definitions note inconsistencies caused by different sampling designs and processing methods. AcknowledgmentsIn addition to those listed as authors, the following people provided additional contributions to this document: This particular document, version 6.0.2, has undergone some major updates and reorganization since version 6.0. Many of the updates were made to make this document more accessible to all users. Other changes to this document, such as the addition of hypertext links, are reflective of the ePUB environment and will allow users to quickly access particular section...

Background Achilles tendinopathy (AT) is a common and often persistent musculoskeletal disorder affecting both athletic and non-athletic populations. Despite the relatively high incidence there is little insight into the impact and perceptions of tendinopathy from the individual's perspective. Increased awareness of the impact and perceptions around individuals' experiences with Achilles tendinopathy may provide crucial insights for the management of what is often a complex, persistent, and disabling MSK disorder. Purpose To qualitatively explore the lived experiences of individuals with AT. Design A qualitative, interpretive description design was performed using semi-structured telephone interviews. Methods Semi-structured interviews were conducted on 15 participants (8 male and 7 female) with AT. Thematic analysis was performed using the guidelines laid out by Braun and Clarke. The study has been reported in accordance with the consolidated criteria for reporting qualitative research (COREQ) checklist.

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The optimal set of return to sport (RTS) tests after anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) remains elusive. Many athletes fail to pass current RTS test batteries, fail to RTS, or sustain secondary ACL injuries if they do RTS. The purpose of this review is to summarize current literature regarding functional RTS testing after ACLR and to encourage clinicians to have patients “think” (add a secondary cognitive task) outside the “box” (in reference to the box used during the drop vertical jump task) when performing functional RTS tests. We review important criteria for functional tests in RTS testing, including task-specificity and measurability. Firstly, tests should replicate the sport-specific demands the athlete will encounter when they RTS. Many ACL injuries occur when the athlete is performing a dual cognitive-motor task (e.g., attending to an opponent while performing a cutting maneuver). However, most functional RTS tests do not incorporate a secondary cognitive load. Secondly, tests should be measurable, both through the athlete’s ability to complete the task safely (through biomechanical analyses) and efficiently (through measures of performance). We highlight and critically examine three examples of functional tests that are commonly used for RTS testing: the drop vertical jump, single-leg hop tests, and cutting tasks. We discuss how biomechanics and performance can be measured during these tasks, including the relationship these variables may have with injury. We then discuss how cognitive demands can be added to these tasks, and how these demands influence both biomechanics and performance. Lastly, we provide clinicians with practical recommendations on how to implement secondary cognitive tasks into functional testing and how to assess athletes’ biomechanics and performance.

Physical fitness is a pillar of U.S. Air Force (USAF) readiness and ensures that Airmen can fulfill their assigned mission and be fit to deploy in any environment. The USAF assesses the fitness of service members on a periodic basis, and discharge can result from failed assessments. In this study, a 21-feature dataset was analyzed related to 223 active-duty Airmen who participated in a comprehensive mental and social health survey, body composition assessment, and physical performance battery. Graphical analysis revealed pass/fail trends related to body composition and obesity. Logistic regression and limited-capacity neural network algorithms were then applied to predict fitness test performance using these biomechanical and psychological variables. The logistic regression model achieved a high level of significance (p < 0.01) with an accuracy of 0.84 and AUC of 0.89 on the holdout dataset. This model yielded important inferences that Airmen with poor sleep quality, recent history of an injury, higher BMI, and low fitness satisfaction tend to be at greater risk for fitness test failure. The neural network model demonstrated the best performance with 0.93 accuracy and 0.97 AUC on the holdout dataset. This study is the first application of psychological features and neural networks to predict fitness test performance and obtained higher predictive accuracy than prior work. Accurate prediction of Airmen at risk of failing the USAF fitness test can enable early intervention and prevent workplace injury, absenteeism, inability to deploy, and attrition.

Study Design Repeated-measures clinical measurement reliability study. Background While there are some shoulder functional tests for athletes, no widely used performance test of arm and shoulder function currently exists to assess lower-level upper extremity functional demands in, for example, a nonathlete population or elderly individuals. In these individuals, functional measures rely on patient self-report. Objectives Describe the development of the Timed Functional Arm and Shoulder Test (TFAST), age-related scores, and between-session reliability in a group of asymptomatic high school athletes, young adults, middle-aged adults, older adults, and a preliminary group of symptomatic patients. Methods One hundred forty asymptomatic individuals participated in the study: 36 high school athletes (14-18 years of age), 34 young adults (19-35 years of age), 37 middle-aged adults (36-65 years of age), 33 older adults (over 65 years of age), and 16 symptomatic patients (22-66 years of age). The TFAST is a functional test that includes 3 tasks: hand to head and back, wall wash, and gallon lift. Total repetitions were noted for each task, and the total TFAST score was calculated. Results Mean total TFAST scores were higher for young adults (107.9; 95% confidence interval [CI]: 102.5, 113.4) and middle-aged adults (105.2; 95% CI: 99.1, 111.3) as compared to the high school athletes (89.9; 95% CI: 81.2, 98.5) and older adults (74.5; 95% CI: 65.6, 83.5). All groups were significantly different (P<.05) from each other, except the young and middle-aged adults. For patients, the mean score for the symptomatic side was 100.1 (95% CI: 89.6, 110.5). The between-session reliability values for the total TFAST scores in the asymptomatic individuals were as follows: intraclass correlation coefficient (ICC) = 0.93; 95% CI: 0.60, 0.98; standard error of measurement, 6.7; and minimal detectable change based on a 95% CI, 18.5 repetitions. The ICC values for individual tasks ranged from 0.80 to 0.94 (95% CI range, 0.44-0.98). The reliability for the patient group was 0.83 (95% CI: 0.51, 0.94). Conclusion The TFAST was sensitive to detect differences in functional performance between age groups, demonstrated adequate between-session reliability, and demonstrated feasibility in a symptomatic patient group. Further assessment is needed to refine the TFAST. Development of a feasible and valid test of arm function would enhance clinical evaluation and outcome measurement. J Orthop Sports Phys Ther 2017;47(6):420-431. Epub 3 Mar 2017. doi:10.2519/jospt.2017.7136.

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