Patient-based outcome assessment instruments in acupuncture research.

BACKGROUND: Outcome assessment can support the therapeutic process by providing a way to track symptoms and functionality over time, providing insights to clinicians and patients, as well as offering a common language to discuss patient behavior/functioning. OBJECTIVES: In this article, we examine the patient-based outcome assessment (PBOA) instruments that have been used to determine outcomes in acupuncture clinical research and highlight measures that are feasible, practical, economical, reliable, valid, and responsive to clinical change. The aims of this review were to assess and identify the commonly available PBOA measures, describe a framework for identifying appropriate sets of measures, and address the challenges associated with these measures and acupuncture. Instruments were evaluated in terms of feasibility, practicality, economy, reliability, validity, and responsiveness to clinical change. METHODS: This study was a systematic review. A total of 582 abstracts were reviewed using PubMed (from inception through April 2009). RESULTS: A total of 582 citations were identified. After screening of title/abstract, 212 articles were excluded. From the remaining 370 citations, 258 manuscripts identified explicit PBOA; 112 abstracts did not include any PBOA. The five most common PBOA instruments identified were the Visual Analog Scale, Symptom Diary, Numerical Pain Rating Scales, SF-36, and depression scales such as the Beck Depression Inventory. CONCLUSIONS: The way a questionnaire or scale is administered can have an effect on the outcome. Also, developing and validating outcome measures can be costly and difficult. Therefore, reviewing the literature on existing measures before creating or modifying PBOA instruments can significantly reduce the burden of developing a new measure.

Computer vision tools for the non-invasive assessment of autism-related behavioral markers

The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated that promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests behavioral markers can be observed late in the first year of life. Many of these studies involved extensive frame-by-frame video observation and analysis of a child's natural behavior. Although non-intrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are impractical for clinical and large population research purposes. Diagnostic measures for ASD are available for infants but are only accurate when used by specialists experienced in early diagnosis. This work is a first milestone in a long-term multidisciplinary project that aims at helping clinicians and general practitioners accomplish this early detection/measurement task automatically. We focus on providing computer vision tools to measure and identify ASD behavioral markers based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure three critical AOSI activities that assess visual attention. We augment these AOSI activities with an additional test that analyzes asymmetrical patterns in unsupported gait. The first set of algorithms involves assessing head motion by tracking facial features, while the gait analysis relies on joint foreground segmentation and 2D body pose estimation in video. We show results that provide insightful knowledge to augment the clinician's behavioral observations obtained from real in-clinic assessments.

Mapping the structure of perceptual and visual-motor abilities in healthy young adults.

The ability to quickly detect and respond to visual stimuli in the environment is critical to many human activities. While such perceptual and visual-motor skills are important in a myriad of contexts, considerable variability exists between individuals in these abilities. To better understand the sources of this variability, we assessed perceptual and visual-motor skills in a large sample of 230 healthy individuals via the Nike SPARQ Sensory Station, and compared variability in their behavioral performance to demographic, state, sleep and consumption characteristics. Dimension reduction and regression analyses indicated three underlying factors: Visual-Motor Control, Visual Sensitivity, and Eye Quickness, which accounted for roughly half of the overall population variance in performance on this battery. Inter-individual variability in Visual-Motor Control was correlated with gender and circadian patters such that performance on this factor was better for males and for those who had been awake for a longer period of time before assessment. The current findings indicate that abilities involving coordinated hand movements in response to stimuli are subject to greater individual variability, while visual sensitivity and occulomotor control are largely stable across individuals.