Evidence-Based Clinical Algorithm for Hypotonia Assessment: To Pardon the Errs

Abstract

Despite the many advances in diagnostics, the clinical assessment of children with hypotonia presents a diagnostic challenge for clinicians due to the current subjectivity of the initial clinical assessment. The aim of this paper is to report on an evidence-based clinical algorithm (EBCA) that was developed for the clinical assessment of hypotonia in children as part of the output of a multiphased study towards assisting clinicians in more accurate assessments. This study formed part of a larger advanced mixed methods design. The preceding phases of the study included a systematic review, a survey amongst clinicians, a consensus process (Delphi technique), and a qualitative critique with multiple focus groups. Samples were drawn from three professional groups (occupational therapists, physiotherapists, and paediatricians). Data were analysed at each stage and merged in the development of the EBCA. The EBCA followed a rigorous process of development and critique. The methods for formulating changes in the revision and development of the EBCA are presented together with a description and presentation of the final algorithm for practice. The overarching concepts that guided the development and refinement of the EBCA are described, taking into consideration knowledge translation, evidence-based practice, and the value of EBCAs in addition to recommendations for stakeholder uptake. The EBCA is envisaged to be useful in practice for clinicians who are faced with the assessment of a child that is suspected as having hypotonia via a systematic process in identifying specific characteristics that are associated with low muscle tone.