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Aleid de Rooij
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The physical hospital environment and physical activity
Patients’ perspectives on barriers, facilitators and preferences after cardiothoracic surgery
Journal article
(2026)
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Lisenka te Lindert, Machiel Van Dorst, Arend De Weger, Eric Vermeulen, Thea Vliet Vlieland, Rienk Dekker, Åsa Mennema, Aleid De Rooij, Jorit Meesters
Hospital environments often promote sedentary behaviour, despite physical activity being essential for recovery. This study explored patients’ perspectives on how the physical hospital environment supports physical activity after cardiothoracic surgery. A cross-sectional survey study was conducted among patients, who underwent elective cardiothoracic surgery, using a self-developed questionnaire. Descriptive statistics ranked barriers, facilitators and potential supportive features, while Spearman correlations explored associations of barriers/facilitators with participant characteristics and hospital factors. Of 221 eligible patients, 106 (48%) responded, with 70% being male, a median age of 65 years and a median hospital stay of seven days. Main barriers included medical devices, while main facilitators included convenient layout, comfortable furniture and information about physical activity. Greater support from healthcare professionals was associated with fewer barriers and more facilitators. The results showed that the physical hospital environment should promote physical activity, with a key role for healthcare professionals to help patients leverage these opportunities.
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Hospital environments often promote sedentary behaviour, despite physical activity being essential for recovery. This study explored patients’ perspectives on how the physical hospital environment supports physical activity after cardiothoracic surgery. A cross-sectional survey study was conducted among patients, who underwent elective cardiothoracic surgery, using a self-developed questionnaire. Descriptive statistics ranked barriers, facilitators and potential supportive features, while Spearman correlations explored associations of barriers/facilitators with participant characteristics and hospital factors. Of 221 eligible patients, 106 (48%) responded, with 70% being male, a median age of 65 years and a median hospital stay of seven days. Main barriers included medical devices, while main facilitators included convenient layout, comfortable furniture and information about physical activity. Greater support from healthcare professionals was associated with fewer barriers and more facilitators. The results showed that the physical hospital environment should promote physical activity, with a key role for healthcare professionals to help patients leverage these opportunities.
The Push Forward in Rehabilitation
Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type
Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim of this study was to validate this new detection method by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single-wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation.
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Within rehabilitation, there is a great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self- or attendant-pushed wheelchair propulsion. The aim of this study was to validate this new detection method by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single-wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation.