Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer

Smartphone-Based Hand Function Assessment: Systematic Review

BACKGROUND

Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity and reliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems further aggravate the issue. However, smartphones provide a promising opportunity to address these challenges. Thus, the built-in, high-efficiency sensors in smartphones can be used as effective tools for hand function assessment.

OBJECTIVE

This review aims to evaluate existing studies on hand function evaluation using smartphones.

METHODS

An information specialist searched 8 databases on June 8, 2023. The search criteria included two major concepts: (1) smartphone or mobile phone or mHealth and (2) hand function or function assessment. Searches were limited to human studies in the English language and excluded conference proceedings and trial register records. Two reviewers independently screened all studies, with a third reviewer involved in resolving discrepancies. The included studies were rated according to the Mixed Methods Appraisal Tool. One reviewer extracted data on publication, demographics, hand function types, sensors used for hand function assessment, and statistical or machine learning (ML) methods. Accuracy was checked by another reviewer. The data were synthesized and tabulated based on each of the research questions.

RESULTS

In total, 46 studies were included. Overall, 11 types of hand dysfunction-related problems were identified, such as Parkinson disease, wrist injury, stroke, and hand injury, and 6 types of hand dysfunctions were found, namely an abnormal range of motion, tremors, bradykinesia, the decline of fine motor skills, hypokinesia, and nonspecific dysfunction related to hand arthritis. Among all built-in smartphone sensors, the accelerometer was the most used, followed by the smartphone camera. Most studies used statistical methods for data processing, whereas ML algorithms were applied for disease detection, disease severity evaluation, disease prediction, and feature aggregation.

CONCLUSIONS

This systematic review highlights the potential of smartphone-based hand function assessment. The review suggests that a smartphone is a promising tool for hand function evaluation. ML is a conducive method to classify levels of hand dysfunction. Future research could (1) explore a gold standard for smartphone-based hand function assessment and (2) take advantage of smartphones' multiple built-in sensors to assess hand function comprehensively, focus on developing ML methods for processing collected smartphone data, and focus on real-time assessment during rehabilitation training. The limitations of the research are 2-fold. First, the nascent nature of smartphone-based hand function assessment led to limited relevant literature, affecting the evidence's completeness and comprehensiveness. This can hinder supporting viewpoints and drawing conclusions. Second, literature quality varies due to the exploratory nature of the topic, with potential inconsistencies and a lack of high-quality reference studies and meta-analyses.

Virtual goniometric measurement of the forearm, wrist, and hand: A double-blind psychometric study of a digital goniometer

BACKGROUND

Various virtual goniometers have been used for photographic measurements. However, there is no single method that is both reliable and valid for measuring the forearm, wrist, and finger joints.

PURPOSE

This study aimed to investigate the criterion validity and intra- and inter-rater reliability of a virtual goniometer for assessing forearm, wrist, and finger joints using screenshots from video recordings and to calculate the standard error of measurement (SEM) and minimum detectable change (MDC).

STUDY DESIGN

This is a clinical measurement study.

METHODS

Goniometric measurements were performed independently by two observers in 26 healthy participants (49 hands) using a virtual goniometer. Criterion validity was assessed by examining the agreement between virtual and manual goniometer measurements. Reliability was calculated using the intraclass correlation coefficient (ICC) to assess agreement between virtual and manual goniometers and interobserver agreement for virtual measurements. The difference between measurements was analyzed using the Student test and Bland-Altman plots. SEM and MDC were both used to determine the error associated with the measurements.

RESULTS

Strong agreement between measurements (ICC = 0.69-0.98) and positive moderate to high correlation (r = 0.52-0.96; p < 0.001) were observed. Bland-Altman plots showed the agreement between the two measurement methods. Intra-rater (ICC = 0.80-0.99) and inter-rater reliability (ICC = 0.76-0.99) were high. SEM was low (2°-4°) and MDC ranged from 4°-12°.

CONCLUSIONS

The virtual goniometer proved to be a valid and reliable method for measuring joint angles from screenshots. The inter-rater and intra-rater reliability of the virtual goniometer was high. The average bias between the virtual and manual goniometer was small. Measurement errors were low for forearm, wrist, and hand movements, with the largest measurement errors observed for the second and third fingers.

Are patients satisfied with online video consultations for assessing their hand disorder?

OBJECTIVES

The aim was to evaluate patient satisfaction with online video consultations in assessing hand disorder.

MATERIAL AND METHODS

This prospective study included patients who attended a video consultation, either as an initial meeting to assess the need for further evaluation or treatment or as an early postoperative follow-up consultation. After the consultation, they completed a satisfaction questionnaire. Regression models were used to reveal determinants of patient satisfaction.

RESULTS

We included 100 patients, with a mean age of 55 years (range 17-81 years). 95% were satisfied or very satisfied. The main reasons for choosing this form of consultation were shorter travel and wait times. Age, gender and educational level did not determine satisfaction. Significant factors for dissatisfaction were insufficient video and sound quality.

CONCLUSION

Online video consultation in hand surgery is a valuable alternative to in-clinic appointments for all age groups. However, it is crucial to ensure adequate video and audio quality.

Novel Tools to Approach and Measure Outcomes in Patients with Fractures

Upper extremity fractures are prevalent and pose a great burden to patients and society. In the US alone, the annual incidence of upper extremity fractures is 67.6 fractures per 10,000 persons. While the majority of patients with upper extremity fractures demonstrate satisfactory outcomes when treated appropriately (the details of which are discussed in prior articles), the importance of follow-up and outcome measurement cannot be understated. Outcome measurement allows for accountability and improvement in clinical outcomes and research. The purpose of this article is to describe recent advances in methods and tools for assessing clinical and research outcomes in hand and upper extremity care. Three specific advances that are broadly changing the landscape of follow-up care of our patients include: 1) telemedicine, 2) patient-reported outcome measurement, and 3) wearables/remote patient monitoring.

Wearable rehabilitation wristband for distal radius fractures

Background

Distal radius fractures are a common type of fracture. For patients treated with closed reduction with splinting, a period of rehabilitation is still required after the removal of the splint. However, there is a general lack of attention and low compliance to rehabilitation training during this period, so it is necessary to build a rehabilitation training monitoring system to improve the efficiency of patients' rehabilitation.

Methods

A wearable rehabilitation training wristband was proposed, which could be used in the patient's daily rehabilitation training scenario and could recognize four common wrist rehabilitation actions in real-time by using three thin film pressure sensors to detect the pressure change curve at three points on the wrist. An algorithmic framework for classifying rehabilitation training actions was proposed. In our framework, an action pre-detection strategy was designed to exclude false detections caused by switching initial gestures during rehabilitation training and wait for the arrival of the complete signal. To classify the action signals into four categories, firstly an autoencoder was used to downscale the original signal. Six SVMs were then used for evaluation and voting, and the final action with the highest number of votes would be used as the prediction result.

Results

Experimental results showed that the proposed algorithmic framework achieved an average recognition accuracy of 89.62%, an average recognition recall of 88.93%, and an f1 score of 89.27% on the four rehabilitation training actions.

Conclusion

The developed device has the advantages of being small size and easy to wear, which can quickly and accurately identify and classify four common rehabilitation training actions. It can easily be combined with peripheral devices and technologies (e.g., cell phones, computers, Internet) to build different rehabilitation training scenarios, making it worthwhile to use and promote in clinical settings.

Telerehabilitation Technology Used for Remote Wrist/Finger Range of Motion Evaluation: A Scoping Review

Background

Monitoring finger/wrist range of motion (ROM) is an important component of routine hand therapy after surgery. Telerehabilitation is a field that may potentially address various barriers of in-person hand therapy appointments. Therefore, the purpose of this scoping review is to identify telerehabilitation technologies that can be feasibly used in a patient's home to objectively measure finger/wrist ROM.

Methods

Following PRISMA-ScR guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases using alternative word spellings for the following core concepts: "wrist/hand," "rehabilitation," and "telemedicine." Studies were imported into Covidence, and systematic two-level screening was done by two independent reviewers. Patient demographics and telerehabilitation information were extracted from the selected articles, and a narrative synthesis of the findings was done.

Results

There were 28 studies included in this review, of which the telerehabilitation strategies included smartphone angle measurement applications, smartphone photography, videoconference, and wearable or external sensors. Most studies measured wrist ROM with the most accurate technologies being wearable and external sensors. For finger ROM, the smartphone angle application and photography had higher accuracy than sensor systems. The telerehabilitation strategies that had the highest level of usability in a remote setting were smartphone photographs and estimation during virtual appointments.

Conclusions

Telerehabilitation can be used as a reliable substitute to in-person goniometer measurements, particularly the smartphone photography and motion sensor ROM measurement technologies. Future research should investigate how to improve the accuracy of motion sensor applications that are available on easy-to-access devices.

Dorsal bridging plates for the treatment of high and low energy distal radius fractures

Distal radius fractures are common and treatment of complex fracture pattens can be challenging. We assessed functional outcomes, radiographic analysis, and complications of 26 distal radius fractures treated with dorsal bridging plate (DBP) at a mean of 14 months post plate removal (6-34 months). Radiographic parameters were measured pre- and post-operatively and patient reported wrist evaluation scores, patient reported wrist range of movement and satisfaction scores. Mean post-operative total PRWE was 26 (range 0-76) and mean wrist mobility 52° flexion (range 10°-85°) and 50° extension (range 10°-85°). Mean post-operative patient satisfaction score was 89% (range 50-100%). Four patients developed complications (one EPL rupture and three developed CRPS). DBP can reliably restore distal radius anatomy and is associated with good functional outcome scores, return of functional range of wrist movement and high levels of patient satisfaction.

Level of Evidence

III.

Smartphone goniometer application for reliable and convenient measurement of finger range of motion: a comparative study

ObjectiveGoniometer measurements are commonly used in hand clinics to follow progress or as an outcome measure for acute and chronic conditions. The use of an Apple iPhone goniometer application (smartphone app) may provide a practical alternative for clinicians. This study sought to assess accuracy in range-of-motion measurements obtained by a smartphone app compared to a traditional universal goniometer (UG) in healthy and diseased hands. MethodsPatients with various hand disease processes were recruited through a tertiary hospital hand clinic, as well as healthy volunteers. Middle and little fingers were assessed at the metacarpophalangeal (MCP), proximal interphalangeal (PIP) and distal interphalangeal (DIP) joints for flexion and extension. Measurements were taken using a UG and a smartphone app by two independent evaluators using a standardised proforma. ResultsTwenty patients were recruited (50% male, 50% female; 13 healthy volunteers and seven with hand pathology). There were no significant differences in measurements using the smartphone app. The only significant difference found using the UG was for little finger PIP joint flexion; however, the difference was only 2.5°. Comparison of the average measurements using a UG and smartphone app found significant differences for six joints, with none of these > 5° (largest 4.6°). ConclusionThe smartphone app is as reliable as traditional UG and would be a practical tool for everyday use in hand clinics. Its uptake may provide broader utility for patient self-measurement and telemedicine.

Comparison of the Wrist Range of Motion Measurement between Inertial Measurement Unit Glove, Smartphone Device and Standard Goniometer

A goniometer is currently the gold standard for range of motion (ROM) measurements. However, trained staff are required for accurate measurements. The objective of this study is to assess an agreement between the proposed standalone inertial measurement unit glove, smartphone device, and a standard goniometer for the measurement of wrist range of motion. Twenty participants performed wrist flexion, wrist extension, pronation, supination, ulnar deviation, and radial deviation movements with three operators measuring the movements with three devices. Average measurements from the three approaches had within 1.5 degrees of difference from each other for all of the movements. Both the proposed IMU glove and smartphone showed a strong correlation to the goniometer in most of the movements, with an intraclass correlation coefficient (ICC) between 0.914 and 0.961, and between 0.929 and 0.951, respectively. Only wrist supination using the smartphone has an ICC of 0.828. In comparison with a standard goniometer, a smartphone device is a more convenient method and readily available. The proposed IMU glove requires additional hardware but is easier to use and is more suitable for measuring and monitoring dynamic motion than a smartphone or a goniometer. These patient-friendly approaches could be used by the patients at home and provide remote quantitative monitoring during the wrist rehabilitation process.

Remote self-measurement of wrist range of motion performed on normal wrists by a minimally trained individual using the iPhone level application only demonstrated good reliability in measuring wrist flexion and extension

STUDY DESIGN

This is a reliability study using the intraclass correlation coefficient.

PURPOSE

The purpose of this study was to determine whether an individual with minimal training could use the iPhone Level application to self-measure the range of motion of the forearm and wrist from a remote location.

METHODS

Forty healthy participants (80 wrists) were measured twice by two examiners using a universal goniometer and the iPhone Level application. After measurement, each participant received a training session in the self-measurement method. They were then asked to perform remote self-measurements two to three days later and report their findings to the examiners using Skype or FaceTime.

RESULTS

SPSS, version 26, was used to run intraclass correlation coefficients using a two-way random analysis at a 95% confidence interval with absolute agreement. Comparisons of single measurements were used to determine reliability. Good inter-rater reliability was found between wrist flexion and extension in all testing conditions. Measurement of active motion in supination, pronation, radial, and ulnar deviation demonstrated moderate reliability compared with the universal goniometer where the measurements were performed by the investigators. Self-measurement of the participant resulted in moderate reliability for supination and poor reliability in pronation, radial, and ulnar deviation.

DISCUSSION

Some participants found the procedures technologically and perceptually challenging. Anatomical variances, positional requirements, and substitution patterns complicated the process.

CONCLUSIONS

The iPhone Level application may be used to perform reliable self-measurements of wrist flexion and extension from a remote location. Further research exploring methods for remote selfmeasurement is indicated.

The Telemedicine-Based Pediatric Examination of the Neck and Upper Limbs: A Narrative Review

With the COVID-19 pandemic hastening the adoption of telemedicine into clinical practice, it has also prompted an abundance of new literature highlighting its capabilities and limitations. The purpose of this review is to summarize the current state of the literature on telemedicine applied in the context of a musculoskeletal examination of the neck and upper limbs for children 3 to 18 years old. The PubMed and ScienceDirect databases were searched for relevant articles from January 2015 to August 2021 using a combination of keywords and nested searches. General examination components including inspection, guided self-palpation, range of motion, sensory and motor examination, as well as special testing are described. Although the literature is focused mainly on adult populations, we describe how each component of the exam can be reliably incorporated into a virtual appointment specific to pediatric patients. Caregivers are generally needed for most consultations, but certain maneuvers can be self-performed by older children and adolescents alone. There is general feasibility, validity, and substantial reliability in performing most examination components of the upper limbs remotely, except for the shoulder exam. Compared to those made in person, clinical diagnoses established virtually were found to be either the same or similar in most cases, and management decisions also had high agreement. Despite this, there is evidence that some pediatric providers may not be able to collect all the information needed from a telemedicine visit to make a complete clinical assessment. Lastly, currently available smartphone applications measuring joint range of motion were found to have high reliability and validity. This narrative review not only establishes a foundation for a structured pediatric musculoskeletal examination, but also aims to increase physicians' confidence in incorporating telemedicine into their standard of care.

Accuracy and reliability of a smartphone application for measuring the knee joint angle

[Purpose] Recently, a photo-based smartphone application for angle measurement—“Grid line imaging application Professional”—was developed to evaluate joint disease treatments. The aim of this study was to determine the accuracy and reliability of the application. [Participants and Methods] We measured the knee joint of a mannequin using an application and a universal goniometer. Twelve examiners measured eight knee joints of mannequins at different arbitrary angles using the application and a universal goniometer. Correlations between the application and universal goniometer measurements were examined using scatter plots and correlation coefficients. Systematic errors of the application were visually confirmed using the Bland-Altman method. Intra-class correlation coefficients were used to evaluate the inter-examiner reliability of the application. [Results] The application and universal goniometer measurements showed a good correlation (r=0.99) and no systematic error. The intra-class correlation coefficient for inter-examiner reliability was 0.999. Furthermore, to evaluate intra-examiner reliability, six examiners measured six different knee joints twice using the application on a 2-day interval. The intra-class correlation coefficient for intra-examiner reliability was 0.982. [Conclusion] The accuracy of the application was equivalent to that of a universal goniometer, and both the inter- and intra-examiner reliabilities of the application were almost perfect.

The validity of an objective measurement method using the Leap Motion Controller for fingers wrist, and forearm ranges of motion

The present study aimed to validate the Leap Motion Controller (LMC)-based Fizyosoft® HandROM System developed by our team to evaluate range of motion (ROM) for fingers, wrist, and forearm in a new clinical setting. Thirty-five healthy individuals participated in the study (all right-handed, 20-30 years old). The LMC-based Fizyosoft® HandROM System is a licensed software ROM-measurement developed by our team. Pronation/supination, wrist flexion/extension, ulnar/radial deviation and metacarpophalangeal (MCP) flexion/extension of all fingers were measured with both the Fizyosoft® HandROM System and a universal goniometer. No significant differences were found between the two measurement methods for almost all mean ROMs except for ulnar and radial deviation (p > 0.05). Highly significant correlations were found between all ROMs of the forearm, wrist, and thumb (p < 0.01). MCP flexion showed significant correlation only in the index finger (r = 0.516, p = 0.003) and little finger (r = 0.517, p = 0.004). Besides, for both measures, the intraclass correlations were good to excellent on all ROMs of the forearm, wrist, and fingers except for MCP of the middle and ring fingers (0.68-0.88). The present study results indicated that the LMC-based Fizyosoft® HandROM System could sensitively track changes in the active motion of the thumb, wrist, and forearm. It is a viable alternative for assessing ROMs of the forearm, wrist, and thumb in patient follow-up.

Intelligent Rehabilitation Assistance Tools for Distal Radius Fracture: A Systematic Review Based on Literatures and Mobile Application Stores

Objective

To systematically analyze the existing intelligent rehabilitation mobile applications (APPs) related to distal radius fracture (DRF) and evaluate their features and characteristics, so as to help doctors and patients to make evidence-based choice for appropriate intelligent-assisted rehabilitation.

Methods

Literatures which in regard to the intelligent rehabilitation tools of DRF were systematic retrieved from the PubMed, the Cochrane library, Wan Fang, and VIP Data. The effective APPs were systematically screened out through the APP markets of iOS and Android mobile platform, and the functional characteristics of different APPs were evaluated and analyzed.

Results

A total of 8 literatures and 31 APPs were included, which were divided into four categories: intelligent intervention, angle measurement, intelligent monitoring, and auxiliary rehabilitation games. These APPs provide support for the patients' home rehabilitation guidance and training and make up for the high cost and space limitations of traditional rehabilitation methods. The intelligent intervention category has the largest download ratio in the APP market. Angle measurement tools help DRF patients to measure the joint angle autonomously to judge the degree of rehabilitation, which is the most concentrated type of literature research. Some of the APPs and tools have obtained good clinical verification. However, due to the restrictions of cost, geographic authority, and applicable population, a large number of APPs still lack effective evidence to support popularization.

Conclusion

Patients with DRF could draw support from different kinds of APPs in order to fulfill personal need and promote self-management. Intelligent rehabilitation APPs play a positive role in the rehabilitation of patients, but the acceptance of the utilization for intelligent rehabilitation APPs is relatively low, which might need follow-up research to address the conundrum.

A coordinate-system-independent method for comparing joint rotational mobilities

Three-dimensional studies of range of motion currently plot joint poses in a 'Euler space' whose axes are angles measured in the joint's three rotational degrees of freedom. Researchers then compute the volume of a pose cloud to measure rotational mobility. However, pairs of poses that are equally different from one another in orientation are not always plotted equally far apart in Euler space. This distortion causes a single joint's mobility to change when measured based on different joint coordinate systems and precludes fair comparison among joints. Here, we present two alternative spaces inspired by a 16th century map projection - cosine-corrected and sine-corrected Euler spaces - that allow coordinate-system-independent comparison of joint rotational mobility. When tested with data from a bird hip joint, cosine-corrected Euler space demonstrated a 10-fold reduction in variation among mobilities measured from three joint coordinate systems. This new quantitative framework enables previously intractable, comparative studies of articular function.

Developing Smartphone-Based Objective Assessments of Physical Function in Rheumatoid Arthritis Patients: The PARADE Study

Background

Digital biomarkers that measure physical activity and mobility are of great interest in the assessment of chronic diseases such as rheumatoid arthritis, as it provides insights on patients' quality of life that can be reliably compared across a whole population.

Objective

To investigate the feasibility of analyzing iPhone sensor data collected remotely by means of a mobile software application in order to derive meaningful information on functional ability in rheumatoid arthritis patients.

Methods

Two objective, active tasks were made available to the study participants: a wrist joint motion test and a walk test, both performed remotely and without any medical supervision. During these tasks, gyroscope and accelerometer time-series data were captured. Processing schemes were developed using machine learning techniques such as logistic regression as well as explicitly programmed algorithms to assess data quality in both tasks. Motion-specific features including wrist joint range of motion (ROM) in flexion-extension (for the wrist motion test) and gait parameters (for the walk test) were extracted from high quality data and compared with subjective pain and mobility parameters, separately captured via the application.

Results

Out of 646 wrist joint motion samples collected, 289 (45%) were high quality. Data collected for the walk test included 2,583 samples (through 867 executions of the test) from which 651 (25%) were high quality. Further analysis of high-quality data highlighted links between reduced mobility and increased symptom severity. ANOVA testing showed statistically significant differences in wrist joint ROM between groups with light-moderate (220 participants) versus severe (36 participants) wrist pain (p < 0.001) as well as in average step times between groups with slight versus moderate problems walking about (p < 0.03).

Conclusion

These findings demonstrate the potential to capture and quantify meaningful objective clinical information remotely using iPhone sensors and represent an early step towards the development of patient-centric digital endpoints for clinical trials in rheumatoid arthritis.

An observational study of the maintenance of the 30° side-lying lateral tilt position among aged care residents at risk of developing pressure injuries when using the standard care pillow and a purpose-designed positioning device

The aim of the study was to evaluate the maintenance of the 30° side-lying lateral tilt position among aged care residents at the risk of developing pressure injuries when using the standard care pillow and a purpose-designed positioning device. An observational study was conducted. Participants were monitored during positioning under two conditions, with pillows and with a fluidised positioner. Body angle measurements were taken at three time points (baseline, 1 hour, and 2 hours) on 10 occasions. Repeated-measures analysis assessed the difference in the degree of the angle of the body. The sample (n = 12) had an average age of 83 years, and the participants were immobile when in bed. The average angle with the pillow condition was 26.7° at baseline, 21.5° at 1 hour, and 16.6° at 2 hours. The average angle with the fluidised positioner condition was 30.7° at baseline, 29.3° at 1 hour, and 26.8° at 2 hours. The main effects of Condition and Time were significant: Condition: F(1,11) = 14.378, P < .001, Time: F(2,22) = 45.858, P < .001. There was a statistically significant interaction between the effects of Condition and Time on the average lateral tilt position, F(2,22) = 15.574, P < .001. The lateral tilt body position was better maintained with the positioning device than the pillow. Further research is required to determine the effectiveness of the fluidised positioner for pressure injury prevention.

Reliability and validity of clinically accessible smartphone applications to measure joint range of motion: A systematic review

Measuring joint range of motion is an important skill for many allied health professionals. While the Universal Goniometer is the most commonly utilised clinical tool for measuring joint range of motion, the evolution of smartphone technology and applications (apps) provides the clinician with more measurement options. However, the reliability and validity of these smartphones and apps is still somewhat uncertain. The aim of this study was to systematically review the literature regarding the intra- and inter-rater reliability and validity of smartphones and apps to measure joint range of motion. Eligible studies were published in English peer-reviewed journals with full text available, involving the assessment of reliability and/or validity of a non-videographic smartphone app to measure joint range of motion in participants >18 years old. An electronic search using PubMed, Medline via Ovid, EMBASE, CINAHL, and SPORTSDiscus was performed. The risk of bias was assessed using a standardised appraisal tool. Twenty-three of the eligible 25 studies exceeded the minimum 60% score to be classified as a low risk of bias, although 3 of the 13 criteria were not achieved in >50% of the studies. Most of the studies demonstrated adequate intra-rater or inter-rater reliability and/or validity for >50% of the range of motion tests across all joints assessed. However, this level of evidence appeared weaker for absolute (e.g. mean difference ± limit of agreement, minimal detectable change) than relative (e.g. intraclass correlation, correlation) measures; and for spinal rotation than spinal extension, flexion and lateral flexion. Our results provide clinicians with sufficient evidence to support the use of smartphones and apps in place of goniometers to measure joint motion. Future research should address some methodological limitations of the literature, especially including the inclusion of absolute and not just relative reliability and validity statistics.