Background: Low levels of physical activity are common during the hospital stay and have been associated with negative health outcomes. Understanding barriers and enablers to physical activity during a hospital stay can improve the development and implementation of tailored interventions aimed at improving physical activity. Previous studies have identified many barriers and enablers, but a comprehensive overview is lacking. This study aimed to identify and categorize all published patient- and healthcare professional-reported barriers and enablers to physical activity during a hospital stay for acute care, using the Theoretical Domains Framework (TDF). Methods: We conducted a scoping review of Dutch and English articles using MEDLINE, CINAHL Plus, EMBASE, PsycINFO, and Cochrane Library (inception to September 2020), which included quantitative, qualitative, and mixed-methods studies reporting barriers and enablers to physical activity during a hospital stay for acute care, as perceived by patients or healthcare professionals. Two reviewers systematically extracted, coded, and categorized all barriers and enablers into TDF domains. Results: Fifty-six articles were included in this review (32 qualitative, 7 quantitative, and 17 mixed-methods). In total, 264 barriers and 228 enablers were reported by patients, and 415 barriers and 409 enablers by healthcare professionals. Patient-reported barriers were most frequently assigned to the TDF domains Environmental Context & Resources (ECR, n = 148), Social Influences (n = 32), and Beliefs about Consequences (n = 25), while most enablers were assigned to ECR (n = 67), Social Influences (n = 54), and Goals (n = 32). Barriers reported by healthcare professionals were most frequently assigned to ECR (n = 210), Memory, Attention and Decision Process (n = 45), and Social/Professional Role & Identity (n = 31), while most healthcare professional-reported enablers were assigned to the TDF domains ECR (n = 143), Social Influences (n = 76), and Behavioural Regulation (n = 54). Conclusions: Our scoping review presents a comprehensive overview of all barriers and enablers to physical activity during a hospital stay and highlights the prominent role of the TDF domains ECR and Social Influences in hospitalized patients’ physical activity behavior. This TDF-based overview provides a theoretical foundation to guide clinicians and researchers in future intervention development and implementation. Scoping review registration: No protocol was registered for this review.

Low physical activity (PA) levels are common in hospitalized patients. Digital health tools could be valuable in preventing the negative effects of inactivity. We therefore developed Hospital Fit; which is a smartphone application with an accelerometer, designed for hospitalized patients. It enables objective activity monitoring and provides patients with insights into their recovery progress and offers a tailored exercise program. The aim of this study was to investigate the potential of Hospital Fit to enhance PA levels and functional recovery following orthopedic surgery. PA was measured with an accelerometer postoperatively until discharge. The control group received standard physiotherapy, while the intervention group used Hospital Fit in addition to physiotherapy. The time spent active and functional recovery (modified Iowa Level of Assistance Scale) on postoperative day one (POD1) were measured. Ninety-seven patients undergoing total knee or hip arthroplasty were recruited. Hospital Fit use, corrected for age, resulted in patients standing and walking on POD1 for an average increase of 28.43 min (95% confidence interval (CI): 5.55–51.32). The odds of achieving functional recovery on POD1, corrected for the American Society of Anesthesiologists classification, were 3.08 times higher (95% CI: 1.14–8.31) with Hospital Fit use. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients’ PA levels and functional recovery during hospitalization.

Background: Older adults admitted with an acute medical illness spent little time active during hospitalisation and this has been associated with negative health outcomes. Understanding which barriers and enablers influence the physical activity behaviour of hospitalised older adults is a first step towards identifying potentially modifiable factors and developing, evaluating and implementing targeted interventions aimed at increasing their physical activity behaviour. Using a theoretical framework has been found to be more successful in changing behaviour than using a non-theory driven approach. This study aimed to explore barriers and enablers to physical activity behaviour in older adults admitted to a hospital with an acute medical illness, as perceived by patients and healthcare professionals, and to categorise them using the Theoretical Domains Framework (TDF). Methods: A qualitative study was conducted at a combined university and regional hospital in the Netherlands between January 2019 and February 2020. Older adults (≥70 years) admitted with an acute medical illness, and healthcare professionals (nurses, physicians, physiotherapists) were recruited using purposive sampling. Semi-structured interviews were audiotaped, transcribed and analysed using directed qualitative content analysis. Barriers and enablers to physical activity behaviour during hospitalisation were identified and coded using the TDF. Results: Meaning saturation was determined after interviews with 12 patients and 16 healthcare professionals. A large number of barriers and enablers were identified and each categorised to 11 of the 14 domains of the TDF. The ‘Environmental Context and Resources’ domain in particular yielded many examples, and revealed that the hospital environment exerts an inactivating influence on patients. Conclusions: The large number of identified barriers and enablers highlights the complexity of influencing older adults’ physical activity behaviour during hospitalisation. This overview of barriers and enablers to physical activity behaviour in older adults admitted to a hospital with an acute medical illness represents an initial step towards developing, evaluating and implementing theory-informed behaviour change interventions to improve hospitalised older adults’ physical activity levels. It can assist clinicians and researchers in selecting modifiable factors that can be targeted in future interventions.

Low amounts of physical activity (PA) and prolonged periods of sedentary activity are common in hospitalized patients. Objective PA monitoring is needed to prevent the negative effects of inactivity, but a suitable algorithm is lacking. The aim of this study is to optimize and validate a classification algorithm that discriminates between sedentary, standing, and dynamic activities, and records postural transitions in hospitalized patients under free-living conditions. Optimization and validation in comparison to video analysis were performed in orthopedic and acutely hospitalized elderly patients with an accelerometer worn on the upper leg. Data segmentation window size (WS), amount of PA threshold (PA Th) and sensor orientation threshold (SO Th) were optimized in 25 patients, validation was performed in another 25. Sensitivity, specificity, accuracy, and (absolute) percentage error were used to assess the algorithm’s performance. Optimization resulted in the best performance with parameter settings: WS 4 s, PA Th 4.3 counts per second, SO Th 0.8 g. Validation showed that all activities were classified within acceptable limits (>80% sensitivity, specificity and accuracy, ±10% error), except for the classification of standing activity. As patients need to increase their PA and interrupt sedentary behavior, the algorithm is suitable for classifying PA in hospitalized patients.

Inactive behavior is common in hospitalized patients. This study investigated the effectiveness of using a smartphone app with an accelerometer (Hospital Fit) in addition to usual care physiotherapy on increasing patients’ physical activity (PA) behavior. A randomized controlled trial was performed at Maastricht University Medical Centre. Patients receiving physiotherapy while hospitalized at the department of Pulmonology or Internal Medicine were randomized to usual care physiotherapy or using Hospital Fit additionally. Daily time spent walking, standing, and upright (standing/walking) (min) and daily number of postural transitions were measured with an accelerometer between the first and last treatment. Multiple linear regression analysis was performed to determine the association between PA behavior and Hospital Fit use, corrected for functional independence (mILAS). Seventy-eight patients were included with a median (IQR) age of 63 (56–68) years. Although no significant effects were found, a trend was seen in favor of Hospital Fit. Effects increased with length of use. Corrected for functional independence, Hospital Fit use resulted in an average increase of 27.4 min (95% CI: −2.4–57.3) standing/walking on day five and 29.2 min (95% CI: −6.4–64.7) on day six compared to usual care. Hospital Fit appears valuable in increasing PA in functionally independent patients.

Inactive behaviour is common in older adults during hospitalisation and associated with poor health outcomes. If patients at high risk of spending little time standing/walking could be identified early after admission, they could be given interventions aimed at increasing their time spent standing/walking. This study aims to identify older adults at high risk of low physical activity (PA) levels during hospitalisation. Prospective cohort study of 165 older adults (≥ 70 years) admitted to the department of Internal Medicine of Maastricht University Medical Centre for acute medical illness. Two prediction models were developed to predict the probability of low PA levels during hospitalisation. Time spent standing/walking per day was measured with an accelerometer until discharge (≤ 12 days). The average time standing/walking per day between inclusion and discharge was dichotomized into low/high PA levels by dividing the cohort at the median (50.0%) in model 1, and lowest tertile (33.3%) in model 2. Potential predictors—Short Physical Performance Battery (SPPB), Activity Measure for Post-Acute Care (AM-PAC), age, sex, walking aid use, and disabilities in activities of daily living—were selected based on literature and analysed using logistic regression analysis. Models were internally validated using bootstrapping. Model performance was quantified using measures of discrimination (area under the receiver operating characteristic curve (AUC)) and calibration (Hosmer and Lemeshow (H–L) goodness-of-fit test and calibration plots). Model 1 predicts a probability of spending ≤ 64.4 min standing/walking and holds the predictors SPPB, AM-PAC and sex. Model 2 predicts a probability of spending ≤ 47.2 min standing/walking and holds the predictors SPPB, AM-PAC, age and walking aid use. AUCs of models 1 and 2 were .80 (95% confidence interval (CI) = .73—.87) and .86 (95%CI = .79—.92), respectively, indicating good discriminative ability. Both models demonstrate near perfect calibration of the predicted probabilities and good overall performance, with model 2 performing slightly better. The developed and internally validated prediction models may enable clinicians to identify older adults at high risk of low PA levels during hospitalisation. External validation and determining the clinical impact are needed before applying the models in clinical practise.