Introduction
The ability to navigate safely and efficiently through the environment is a critical aspect of an individual’s level of independence. Maintaining physical mobility such as gait (locomotor task) is essential for social participation and maintaining quality of life (Metz,
2000; Shafrin, Sullivan, Goldman, & Gill,
2017). However, a decline in an individual’s level of independence can negatively affect personal safety and quality of life (Stubbs, Schofield, & Patchay,
2016; Johnen
2017). Due to diminished independent mobility, it is becoming increasingly difficult for older adults to visit grocery stores, the doctor’s office, the hairdresser, or participate in the choir, craft group, or other sociocultural activities (Giannouli, Bock, Mellone, & Zijlstra,
2016). Gait disturbances represent a functional limitation in elderly people with a predictive potential for the development of comorbidities. Thus, a reduced level of independent mobility is a predictor of institutionalization (Von Bonsdorff, Rantanen, Laukkanen, Suutama, & Heikkinen,
2006), fall incidents (Sterke, Huisman, van Beeck, Looman, & van der Cammen,
2010; McGough, Logsdon, Kelly, & Teri,
2013), dependence, and mortality and is also inversely associated with quality of life (Davis et al.,
2015). Furthermore, mobility in daily life depends on an intact sensorimotor system and this is also associated with intact cognition and psychosocial factors in older adults (Bunn, Dickinson, Barnett-Page, Mcinnes, & Horton,
2008; Costello, Kafchinski, Vrazel, & Sullivan,
2011).
Independent mobility is routinely assessed with field tests such as the Timed Up and Go (TUG) test (Podsiadlo & Richardson,
1991). This test can be used diagnostically to guide appropriate interventions, especially considering the subphases within the TUG. Traditionally, only the total duration of the TUG test is assessed using a stopwatch. In contrast, using an instrumented TUG (iTUG) with sensors (e.g., Cimolin et al.,
2019; Zarzeczny et al.,
2017) provides the opportunity to examine the subphases more closely, including getting up from a chair, walking, turning around, and sitting down again. This helps to identify individual weaknesses and provides the opportunity of targeted training to improve functional mobility (Schoene et al.,
2013). While some studies used the iTUG in different populations and clinical conditions (e.g., Zampieri et al.,
2010 in Parkinson’s disease; Mirelman et al.,
2014 in mild cognitive impairment), to our knowledge, there is only one study with nursing home residents (Zarzeczny et al.,
2017). This study investigated the subphases of the iTUG in nursing home residents based on quantitative wearable sensors. The authors demonstrated that vertical sit-to-stand acceleration correlated best with subject age (
r2 = 0.430,
p < 0.05), suggesting that age-related decreases in TUG performance are primarily associated with decreases in “explosive” lower extremity muscle strength. However, Zarzeczny et al. (
2017) only considered cross-sectional findings; studies on intervention effects are not available.
Similarly, the TUG has been widely used to evaluate the effects of different intervention programs related to physical functioning and balance (Baum, Jarjoura, Polen, Faur, & Rutecki,
2003; Toulotte, Fabre, Dangremont, Lensel, & Thévenon,
2003; Netz, Axelrad, & Argov,
2007; Wollesen et al.,
2020). There is strong evidence that supports the claim that strength-, balance- and gait-training can improve mobility in older adults (de Labra, Guimaraes-Pinheiro, Maseda, Lorenzo, & Millán-Calenti,
2015). In their meta-analysis, Hortobágyi et al. (
2015) found that coordination training, resistance training, and multimodal training significantly improved gait speed in healthy older adults, obtaining the highest effect size for multimodal training interventions (effect size = 0.86). Multimodal interventions have also shown to effectively maintain (Trautwein et al.,
2020) or even improve gait abilities (Kocic et al.,
2018; Kovacs, Sztruhar Jonasne, Karoczi, Korpos, & Gondos,
2013; Sakamoto & Miura,
2016) in nursing home residents. Arrieta, Rezola-Pardo, Gil, Irazusta, & Rodriguez-Larrad (
2018) published a systematic review to identify randomized controlled physical exercise intervention studies that assessed gait ability in older long-term nursing home residents using concurrent walking speed and TUG tests. This overview shows that some studies that evaluated multicomponent exercise interventions reported improvements in the TUG test after the intervention (Cadore et al.,
2014; Lazowski et al.,
1999), whereas other studies showed similar (Au-Yeung et al.,
2002) or worse results (Dechamps et al.,
2010; Serra-Rexach et al.,
2011). However, no subphases were considered in all these studies.
To determine the generalizability of these findings to larger cohorts of institutionalized older adults, the purpose of the study was to (1) examine the iTUG as an instrument to measure the effects of a multicomponent exercise intervention on physical function and balance in nursing home residents and to identify subphases of the iTUG that are more responsive to intervention effects than others. We hypothesized that older adults in long-term care show positive effects in all subphases of the iTUG, and in particular, show improvements in the walking phase because the intervention focused heavily on that aspect. We also wanted to (2) evaluate the impact of the attendance rate on iTUG improvement, since little is known about the dose–response ratio concerning actual attendance. In some cases, the attendance rate is reported in intervention studies, but a defined adherence rate above which participation can be described as successful are hardly to be found. For a more differentiated consideration of the intervention effects, it is therefore necessary to take into account the attendance rate. In a comparable setting, Fairhall et al. (
2012) were able to show that in a multifactorial interdisciplinary intervention higher adherence was significantly associated with better performance for most mobility outcomes. Thus, we expect the intervention effects to be significant only at higher attendance rates.
Discussion
This study aimed to evaluate the iTUG as a tool to measure the effects of a multicomponent exercise intervention on the iTUG subphases in nursing home residents, particularly concerning the subphases, and to evaluate the impact of the attendance rate on iTUG changes.
One may assume that the nursing home residents participating in our study would be among the fitter individuals, since participation required specific physical abilities. This should be considered when assessing the representativity of the sample. Indeed, the range of TUG performance in nursing home residents was extensive (< 10 up to > 150 s). Some studies reported longer durations in a similar sample (Baum et al.,
2003; Johnen & Schott,
2018; Henskens, Nauta, Van Eekeren, & Scherder,
2018), although some examined nursing home residents with dementia. Other studies reported shorter TUG total durations at baseline (Arrieta et al.,
2018; Benavent-Caballer, Rosado-Calatayud, Segura-Ortí, Amer-Cuenca, & Lisón,
2014; Cadore et al.,
2014; Meng et al.,
2017; Kocic et al.,
2018); however, some of them studied cognitively unimpaired individuals or older adults in the assisted living environment. Other findings in this setting and age group are similar to our results at baseline (Cancela, Ayán, Varela, & Seijo,
2016; Mouton et al.,
2017; Zarzeczny et al.,
2017; Holmerová et al.,
2010). A significant interaction of pretest performance × time with a concurrent interaction effect time × group for iTUG total duration suggests that residents with high iTUG performance at baseline benefit more from the intervention than residents who started at lower iTUG performance levels. Our results contradict the findings by Fairhall et al. (
2012), in which they found a higher effect of the intervention on gait speed among frail older people. It is not surprising, as mobile residents were less dependent on caregivers and were able to come to interventions independently. This could have led to lower attendance rates for less mobile residents, as it was not always possible to ensure that they were ready on time or that the caregivers always reliably brought them to the intervention. The moderating effect of a person’s functional independence (which is above a Barthel Index of 70.07) on the relationship between attendance rate and intervention effect in the iTUG total duration also confirmed that. The moderation was able to explain an additional 8.34% of the variance, which can be interpreted as moderate according to Cohen (
1988).
The significant interaction effect time × group for the iTUG total duration indicated that a high attendance rate positively affected the iTUG performance and its subphases. With increasing attendance, we saw larger effects for the total duration and the stand-to-sit subphase, indicating a dose–response effect of the intervention. This is consistent with Fairhall et al. (
2012), who showed that higher adherence was significantly associated with better performance for most outcomes. Nevertheless, the absence or slowing down of the decline in physical performance can, in principle, be interpreted as a sign of the effectiveness of the intervention since the natural decline in physical function is considered normal in nursing home residents. Masciocchi et al. (
2019) reported in their narrative review that performance in the TUG test declined by an average of 2.8% (range 0.7–6.2%) per month when nursing home residents did not attend any additional physical exercise therapy. This natural decline can be explained by the high sedentary times among nursing home residents (Harvey, Chastin, & Skelton,
2015; Healy et al.,
2011; McArthur,
2019; Jansen, Diegelmann, Schnabel, Wahl, & Hauer,
2017). Applied to our intervention duration of 4 months, this would predict a decline of 11.2% if a linear decline is assumed. In our study, we observed even higher declines of 22.7% in the group with a low attendance rate; however, in the group with a high attendance rate, we saw a positive effect of the intervention in 78.1% (
n = 25) of the residents.
Regarding the subphases, we observed that residents in the group with a high attendance rate improved or maintained their TUG performance in all subphases compared to the other groups. However, these group differences were not significant. A possible explanation could be the relatively small number of participants and values that were not provided by the system because the Mobility Lab™ algorithm could not detect them. The sit-to-stand subphase, for example, was the least reliable component (with 22 missing values), probably due to the large degrees of freedom available to nursing home residents, who can use a variety of strategies to perform this activity (Janssen, Bussmann, & Stam,
2002). As seen, the acceleration patterns in these subphases of the iTUG can be very heterogeneous, which makes detection based on the acceleration peaks more difficult. In addition, the training program focused on improving walking performance, coordination, balance, dual-task performance, mobility and cognitive performance. Strength exercises, e.g., for the lower extremities, which appeared to be important for the sit-to-stand subphase, were addressed only secondarily. In previous studies, lower extremity training has been shown to affect standing up and mobilization in general. For example, Johnen & Schott (
2018) showed that nursing home residents significantly improved their physical performance in the TUG and 30-second Chair Stand test after resistance training for the upper and lower extremities with both free weights and machines. In this study however, the subphases were not considered. Regarding the sit-to-stand subphase, a meta-analysis on intervention effects in stroke patients indicated a significant overall effect estimate in favor of the intervention group (standardized mean difference [SMD] −0.34; 95% CI [−0.62,−0.06], seven studies; Pollock, Gray, Culham, Durward, & Langhorne,
2014), and a recently published study by Kasch (
2021) showed that 12 weeks of progressive strength training decreased the duration in the sit-to-stand subphase up to 22% in patients with multiple sclerosis. The improvements in the sit-to-stand subphase in our study could be explained by the strength training and range of motion exercises for the hip and trunk within the intervention program (Cordes et al.,
2019). This apparently led to increased strength in the lower extremities and a better lean angle in the sit-to-stand phase, and thus to a shorter duration in the iTUG.
There are nevertheless some limitations that need to be addressed. In addition to cognitive performance, which may influence performance in the iTUG and the intervention effect, there are other factors that we did not examine in this study. These include depression, fear of falling, and other emotional factors that play a crucial role and affect one another (Kose, Cuvalci, Ekici, Otman, & Karakaya,
2005). Unlike the PROCARE study (Cordes et al.,
2019), we did not conduct a retention test to examine the persistent effects on iTUG performance. A retention test is mandatory but quite difficult in the nursing home setting given the high mortality rate in this age range, making it hard to provide suggestions on the sustained effects of a specific intervention.
Moreover, it would have been useful to compare the intervention group with a control group that did not receive this intervention. Since we did not have a traditional control group, we divided the groups according to their attendance rate. This did allow for a better illustration of the intervention effects as a function of visit frequency. We decided to divide participants who visited two-thirds of the units (Hawley-Hague et al.,
2016) and compared this group with those with lower attendance rates. Studies reporting mean attendance rates should provide more details, such as the range of attended sessions, at least in studies with small samples, (e.g., Henskens et al.,
2018, p. 69: “Mean attendance to the intended 72 exercise sessions was 55% [mean = 39.5, SD = 20.8; range = 0–64].”). Besides, it is important to consider how lower attendance rates occurred. This may have different outcome effects for someone who had to stop attending the intervention sessions for several weeks (maybe, due to some personal reasons) to someone who regularly attended the intervention sessions. We examined irregularities related to the attendance rate and factored in unpredictable circumstances (such as people suffering from stroke or a disease), but this did not justify excluding this group of participants. Overall, we had a relatively small number of participants, so the subphases between these groups did not become significant. Furthermore, a priori power analysis was not performed. Studies with a higher number of participants and additional measures to assess TUG performance (such as number of steps in the turning phase, turning strategies, lean angle in the sit-to-stand and stand-to-sit subphase) could have led to a more differentiated interpretation of the intervention effects. These additional parameters allow to detect obvious impairments or changes and capture subtle differences and thus provide a better description of motor processes. Sensor-based analysis systems and the associated algorithms (Caldas, Mundt, Potthast, de Lima Neto, & Markert,
2017), which can sensitively capture different measures (biomarkers), play a crucial role in long-term observations and for documenting intervention successes. The downside is that these systems are cost-intensive and can only be used in the care setting with considerable effort. In this regard, modern smartphones have a growing number of inertial and location sensors, such as accelerometers, GPS, gyroscopes, and magnetometers, and are comparably user-friendly. To what extent sensor-based systems will be used in the nursing home setting to investigate alternative motion parameters remains to be seen. Ponciano, Pires, Ribeiro, and Spinsante (
2020) conducted a systematic review of how inertial sensors embedded in mobile devices were used to measure various parameters of the iTUG test in older people. The authors stated that together with mobile devices using open source technologies, iTUG is very accessible to all. Persons without experience with nursing home residents and the application of the TUG should be alert to potential accidents. For safety reasons, the resident should be accompanied during the iTUG. Also, an alternative and secure realization of the iTUG is to use two chairs; one chair with the seat facing the wall and another against the backrest. This prevents the chair from tipping over and avoids subjects injuring their heads on the wall if they lose their balance and fall backwards while sitting down. This alternative was not applied but was considered the safer alternative during the course of data collection. For comparability reasons we did not change the setup. Our findings have potential implications for assessing intervention effects in nursing home residents. We have approved the iTUG test as a potential tool for measuring the effects of a multicomponent exercise intervention on physical function and balance in nursing home residents. We observed changes in the iTUG performance especially in the group with high attendance rates. Therefore, the iTUG performance can be highly recommended as an evaluation tool for intervention effects. In addition to the total TUG duration, other parameters should be considered in the different subphases. The exercises in the intervention programs could be adjusted accordingly to induce significant differences in these subphases. For this to work, however, gait analysis systems must measure these subphases reliably and sensitively. Factors emanating from the individual, such as fear of discomfort or pain, anxiety or depression, and limitations due to neuromuscular or musculoskeletal impairment, may influence the iTUG performance and the subphases. For example, external factors include forced rest for therapeutic purposes (Herdman et al., 2021). These factors must also be considered if we want to examine the effects on physical function and balance in nursing home residents. A more detailed view of the intervention effects on mobility will be provided by the results of the multicenter PROCARE project using different evaluation criteria (Cordes et al.,
2019).