Age-related decreases in physical activity (PA) and a decline in physical functioning lead to increased fall risk. As falls are a major cause of accidental deaths and hospitalization in older adults, PA promotion and fall prevention are important measures, especially in nursing homes (NH). With advances in information and communication technology, e- and m-health solutions have been developed to positively influence various health-related factors. To date, only little research exists on the implementation of these technologies to promote health in NH. Therefore, the objective of this systematic review was to provide an overview of the effectiveness, acceptability, and feasibility of e- and m-health interventions aimed at promoting PA and preventing falls in NH. Additionally, the effectiveness of such interventions regarding the secondary outcomes physical function, cognitive function, neuropsychiatric symptoms, and psychosocial status was examined. A systematic literature search was performed in five databases and studies published until 15 November 2021, were considered for inclusion. All studies that examined the effectiveness and/or the acceptability and feasibility of e- or m-health interventions in promoting PA and preventing falls in NH, without restriction on language or date of publication, were included in the final synthesis. Of the 1,358 records retrieved, 28 studies were included in this systematic review. Twenty-four studies contained digital exergaming as an intervention or as a part of the intervention, the four additional studies on e-health interventions only examined a small number of outcomes. No m-health intervention study was identified. Data synthesis indicates that exergaming may be effective in reducing the number of falls and fall risk in NH residents. Several significant improvements were also reported regarding secondary outcomes albeit not consistent across studies. No conclusion can be drawn about the effects of exergaming and other e-health interventions on PA, as data is scarce. E-health interventions were mostly reported as feasible and well accepted by NH residents. However, these findings may not be applicable to NH residents with advanced physical and/or cognitive impairments, since they were excluded in many studies. Therefore, more research examining other digital solutions besides exergaming to promote PA in this specific population is critical.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/, identifier CRD42021289488

Measuring resting metabolic rate (RMR) is time-consuming and expensive, and thus various equations for estimating RMR have been developed. This study’s objective was to compare five equations in elderly people with type 2 diabetes (T2DM). RMR was measured in 90 older adults (≥65 years) with T2DM (mean body mass index (BMI) of 31.5 kg/m2), using indirect calorimetry. Results were compared to four frequently used equations (those of Cunningham, Harris and Benedict, and Gougeon developed for young adults with T2DM, and that of Lührmann, which was developed for the elderly), in addition to a new equation developed recently at the Academic College at Wingate (Nachmani) for overweight individuals. Estimation accuracy was defined as the percentage of subjects with calculated RMR within ±10% of measured RMR. Measured RMR was significantly underestimated by all equations. The equations of Nachmani and Lührmann had the best estimation accuracy: 71.4% in males and 50.9% in females. Skeletal muscle mass, fat mass, hemoglobin A1c (HbA1c), and the use of insulin explained 70.6% of the variability in measured RMR. RMR in elderly participants with T2DM was higher than that calculated using existing equations. The most accurate equations for this specific population were those developed for obesity or the elderly. Unbalanced T2DM may increase caloric demands in the elderly. It is recommended to adjust the RMR equations used for the target population.