The Gross Motor Function Measure (GMFM) has become the best evaluative measure of motor function designed for quantifying change in the gross motor abilities of children with cerebral palsy. The new version of the scoring programme has now been released, and includes two abbreviated methods of estimating GMFM-66 scores using the GMFM-66-Item sets and the GMFM-66-Basal & Ceiling.
The measure is very widely used internationally, and is now the standard outcome assessment tool. This new edition builds on the wide success of the first edition and:
- Is now based on an updated version of the GMFM scoring program (GMAE-2)
- Includes an update on the psychometric properties of the GMFM-88 and GMFM-66
- Covers development and validation of two abbreviated methods of estimating GMFM-66 scores using the GMFM-66 Item sets (GMFM-66-IS) and the GMFM-66-B&C (Basal and Ceiling)
- Includes a new longitudinal case illustration of the development, application and interpretation of the meaure
- Presents a forward look to an upward extension of the GMFM to assess skill development in highly functional young people with cerebral palsy
- Presents a forward look to ‘Quality Function Measure’ to quantify changes in quality of gross motor function
- Physical Therapists and other health professionals working with children, youth and adults with cerebral palsy
- Clinical and health services researchers as an outcome measure to assess the impact of numerous interventions that are proposed as methods of enhancing gross motor function in this population
- Any clinician who is assessing and describing current gross motor function and evaluating its change over time
- It serves the needs of clinical and health services researchers as an outcome measure to assess the impact of any of a host of interventions that are proposed as methods of enhancing gross motor function in this population.
Table of Contents
1. Overview of the Gross Motor Function Measure (GMFM)
2. Conceptual Background
3. Development and Validation of the GMFM-88
4. Development and Validation of the GMFM-66
5. GMFM-66: Two Short Forms of Administration
6. Administration and Scoring Guidelines for the GMFM-88 and GMFM-66
7. Interpretation and Uses of the GMFM-88 and GMFM-66
8. Applications of the GMFM: What Have We Learned and Where Can We Go Next?
Dianne J. Russell is Research and Knowledge Exchange Specialist
with CanChild Centre for Childhood Disability Research. She has been a health
services researcher for over 20 years and a key individual in the development,
evaluation and dissemination of clinical outcome measures such as the Gross
Motor Function Measure (GMFM). Her current focus is on facilitating the use
of research evidence in practice by engaging with research users throughout
the research process and by making research results easily accessible in multiple
formats to families, service providers, and policy decision-makers.
Peter L. Rosenbaum is a Developmental Paediatrician, health services research, teacher, writer and editor. His career has been devoted to childhood disability, and all his roles have involved work with, or about, children with impairments and their families. He has held over 75 research grants and has contributed to almost 300 peer-reviewed papers, book chapters, editorials and invited commentaries in his field.
Marilyn Wright is a pediatric physiotherapist. She has worked with children with physical disabilities or cancer in acute, rehabilitation, and community settings. In addition to clinical work, she has been involved in research, teaching, administrative, and policy roles. Work in these areas has contributed to inter-professional and multi-site best practice initiatives. Current interests include physical activity promotion, lifespan services, and motion analysis.
Lisa M. Avery is an independent statistician and founder of Avery Information Services. She provides statistical consulting and analysis services to various academic institutions and is primarily involved in pediatric disability research. Her primary interests are outcome measurement and causation modeling.