Developing a competency model for telerehabilitation therapists and patients: Results of a cross-sectional online survey

Abstract

Telerehabilitation is a new form of care that provides digital access to rehabilitative services. However, it places many demands on the users–both patients and therapists. The aim of this study was to determine the requirements and competencies needed for successful usage, identify person- and context-specific differences and develop a competency model. We conducted two cross-sectional online surveys with telerehabilitation patients and therapists from Germany during June-August 2023. The adjusted dataset of 262 patients and 73 therapists was quantitatively analyzed including descriptive and bivariate statistics. Group differences were assessed using t-tests or U-tests. The development of two telerehabilitation competency models was guided by a competency modeling process. The surveys show that patients need to gather program information before program start, follow therapist’s instructions, adapt therapy, deal with health problems, as well as motivate and remind oneself during the program. Therapists need to inform and instruct patients, adapt therapy, carry out technical set-up and support, give medical support, guide and monitor patients, give feedback, motivation and reminder, as well as documentation. The competency model for patients includes 23 and the model for therapists 24 core competencies, including various required areas of knowledge, skills, attitudes and experiences. The three most relevant competencies for patients are self-interest in the program, self-awareness and self-management. Also, disease severity, age, and language abilities can enable successful execution. Program type, technology affinity, and age significantly influence the rated relevance of competencies. The three most relevant competencies for therapists are therapeutic-professional skills, medical and telerehabilitation knowledge. The type of therapy practiced and language abilities can enable successful execution. Therapist’s age, technology affinity, and job type significantly impact the rated relevance. The models should be applied to develop tailored training formats and support decisions on the selection of suitable therapists and patients for telerehabilitation.