From alpha to omega: a practical solution to the pervasive problem of internal consistency estimation

Coefficient alpha is the most popular measure of reliability (and certainly of internal consistency reliability) reported in psychological research. This is noteworthy given the numerous deficiencies of coefficient alpha documented in the psychometric literature. This mismatch between theory and practice appears to arise partly because users of psychological scales are unfamiliar with the psychometric literature on coefficient alpha and partly because alternatives to alpha are not widely known. We present a brief review of the psychometric literature on coefficient alpha, followed by a practical alternative in the form of coefficient omega. To facilitate the shift from alpha to omega we also present a brief guide to the calculation of point and interval estimates of omega using a free, open source software environment

Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab

The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors. These behaviors often violate the assumptions of clinimetric testing protocols, where these protocols are designed to probe for specific symptoms. Such violations are frequent outside the lab and affect the accuracy of the subsequent data analysis and scientific conclusions. To address these problems, we report on a unified algorithmic framework for automated sensor data quality control, which can identify those parts of the sensor data that are sufficiently reliable for further analysis. Combining both parametric and nonparametric signal processing and machine learning techniques, we demonstrate that across 100 subjects and 300 clinimetric tests from three different types of behavioral clinimetric protocols, the system shows an average segmentation accuracy of around 90%. By extracting reliable sensor data, it is possible to strip the data of confounding factors in the environment that may threaten reproducibility and replicability

Probabilistic modelling of gait for robust passive monitoring in daily life

Passive monitoring in daily life may provide valuable insights into a person's health throughout the day. Wearable sensor devices play a key role in enabling such monitoring in a non-obtrusive fashion. However, sensor data collected in daily life reflect multiple health and behavior-related factors together. This creates the need for a structured principled analysis to produce reliable and interpretable predictions that can be used to support clinical diagnosis and treatment. In this work we develop a principled modelling approach for free-living gait (walking) analysis. Gait is a promising target for non-obtrusive monitoring because it is common and indicative of many different movement disorders such as Parkinson's disease (PD), yet its analysis has largely been limited to experimentally controlled lab settings. To locate and characterize stationary gait segments in free-living using accelerometers, we present an unsupervised probabilistic framework designed to segment signals into differing gait and non-gait patterns. We evaluate the approach using a new video-referenced dataset including 25 PD patients with motor fluctuations and 25 age-matched controls, performing unscripted daily living activities in and around their own houses. Using this dataset, we demonstrate the framework's ability to detect gait and predict medication induced fluctuations in PD patients based on free-living gait. We show that our approach is robust to varying sensor locations, including the wrist, ankle, trouser pocket and lower back

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study

Background: Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments. Objective: This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD. Methods: The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation. Results: From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor. Conclusions: We present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders

Determinants of different aspects of everyday outcome in schizophrenia: The roles of negative symptoms, cognition, and functional capacity

Cognition, negative symptoms, and depression are potential predictors of disability in schizophrenia. We present analyses of pooled data from four separate studies (all n>169; total n=821) that assessed differential aspects of disability and their potential determinants. We hypothesized that negative symptoms would predict social outcomes, but not vocational functioning or everyday activities and that cognition and functional capacity would predict vocational functioning and everyday activities but not social outcomes. The samples were rated by clinician informants for their everyday functioning in domains of social and vocational outcomes, and everyday activities, examined with assessments of cognition and functional capacity, rated clinically with the Positive and Negative Syndrome Scale (PANSS) and self-reporting depression. We computed a model that tested the hypotheses described above and compared it to a model that predicted that negative symptoms, depression, cognition, and functional capacity had equivalent influences on all aspects of everyday functioning. The former, specific relationship model fit the data adequately and we subsequently confirmed a similar fit within all four samples. Analyses of the relative goodness of fit suggested that this specific model fit the data better than the more general, equivalent influence predictor model. We suggest that treatments aimed at cognition may not affect social functioning as much as other aspects of disability, a finding consistent with earlier research on the treatment of cognitive deficits in schizophrenia, while negative symptoms predicted social functioning. These relationships are central features of schizophrenia and treatment efforts should be aimed accordingly

Measuring the positive psychological well-being of people with rheumatoid arthritis: a cross-sectional validation of the subjective vitality scale

Introduction: People with rheumatoid arthritis (RA) frequently suffer from compromised physical and psychological health, however, little is known about positive indicators of health, due to a lack of validated outcome measures. This study aims to validate a clinically relevant outcome measure of positive psychological well-being for people with RA. The first study examined the reliability and factorial validity of the Subjective Vitality Scale (SVS), whilst study 2 tested the instruments convergent validity. Methods: In study 1, National Rheumatoid Arthritis Society members (N = 333; M age = 59.82 years SD = 11.00) completed a postal questionnaire. For study 2, participants (N = 106; M age = 56 years, SD = 12 years) were those recruited to a randomized control trial comparing two physical activity interventions who completed a range of health-related questionnaires. Results: The SVS had a high level of internal consistency (α = .93, Rho = .92). Confirmatory factor analysis supported the uni-dimensional factor structure of the questionnaire among RA patients [χ = 1327 (10), CFI = 1.0, SRMSR = .01 and RMSEA = .00 (.00 - .08)]. Support for the scales convergent validity was revealed by significant (p < .05) relationships, in expected directions, with health related quality of life (r = .59), physical function (r = .58), feelings of fatigue (r = −.70), anxiety (r = −.57) and depression (r = −.73). Conclusions: Results from two studies have provided support for the internal consistency, factorial structure and convergent validity of the Subjective Vitality Scale. Researchers and healthcare providers may employ this clinically relevant, freely available and brief assessment with the confidence that it is a valid and reliable measure of positive psychological well-being for RA patients

Measurement invariance of the Internet Gaming Disorder Scale–Short-Form (IGDS9-SF) between Australia, the USA, and the UK

The Internet Gaming Disorder Scale-Short-Form (IGDS9-SF) is widely used to assess Internet Gaming Disorder behaviors. Investigating cultural limitations and implications in its applicability is imperative. One way to evaluate the cross-cultural feasibility of the measure is through measurement invariance analysis. The present study used Multigroup Confirmatory Factor Analysis (MGCFA) to examine the IGDS9-SF measurement invariance across gamers from Australia, the United States of America (USA), and the United Kingdom (UK). To accomplish this, 171 Australian, 463 USA, and 281 UK gamers completed the IGDS9-SF. Although results supported the one-factor structure of the IGD construct, they indicated cross-country variations in the strength of the relationships between the indicators and their respective factor (i.e., non-invariant loadings of items 1, 2, 5), and that the same scores may not always indicate the same level of IGD severity across the three groups (i.e., non-invariant intercepts for items 1, 5, 7, 9)

Factor structure and factorial invariance of the strengths and difficulties questionnaire among children of prisoners and their parents

Parental imprisonment has been linked to a variety of adverse psychological outcomes for children and adolescents. The Strengths and Difficulties Questionnaire (SDQ) has been widely used to assess behavioural and emotional difficulties among 7-17 year olds in the general population and more recently has been utilised among samples of children of prisoners. Previous research has variously tested traditional one-, three- and five- factor solutions to the SDQ, and more recently one bifactor solution has been examined. Based on a sample of children of prisoners (N = 724) and their non-imprisoned parent or caregiver (N = 658), the aim of the present study was to simultaneously compare nine alternative factor structures, including previously tested models and alternative bifactor solutions. Tests of factorial invariance and composite reliability were also performed. The five-factor model was found to provide the best fit for the data. Tests of factorial invariance revealed that the five-factor model provided an equally acceptable, but not identical fit, among boys and girls. Composite reliability scores were low for the Conduct Problems and Peer Problems subscales. The utility of the SDQ in measuring psychological functioning in response to parental imprisonment is discussed