Assessing the well-being of residents in nursing facilities

Several hundred thousand people live in German care facilities. At the end of the first half of 2020, around 731,000 people were cared for in Germany’s full inpatient care facilities. According to this, the number of residents in the facilities has risen steadily over the past 10 years: from 620,249 in 2010 to 676,584 in 2015 and now around 731,000. Among 80- to 85-year-olds, one in four people already need care (Statistisches Bundesamt, 2020). If the number of older adults in the population increases, the number of people in need of care and assistance is also likely to rise. Almost two-thirds of people in Germany suffer from three or more chronic diseases associated with physical and cognitive limitations in old age (RKI, 2015). However, there is no linear relationship between such limitations and subjectively perceived quality of life (QoL) and well-being (WB); QoL and WB can be subjectively experienced as high despite physical or cognitive limitations (Megari, 2013). Therefore, the maintenance and improvement of QoL are increasingly seen as essential tasks for health care.

Numerous studies and surveys show that people’s desires and expectations in need of care and their relatives are focused on providing good care and including WB factors such as consideration of individuality and participation, and respect for human dignity (García & Ramírez, 2018). Over the years, this has been recognized, and WB and QoL have become central in caring for older people and an essential topic for research (van Leeuwen et al., 2019). While the concept of QoL finds its scientific origins in various disciplines, especially in philosophy, sociology, and medicine, the term WB originates from psychology (Schumacher, Klaiberg, & Brähler, 2003). Earlier research focused mainly on QoL’s social and economic indicators, such as financial security, social equity, health care. More recently, subjective indicators of QoL, such as subjective WB and life satisfaction, have been increasingly considered (Diener & Suh, 1997).

Although research on QoL and WB has a long history, there is considerable debate in the literature about what is meant by QoL and WB. Attempts to describe and define QoL and WB in a universally valid way usually failed because they are individual and influenced by age and related circumstances (Oppikofer & Mayorova, 2016). Moreover, a clear distinction between the two concepts is hardly possible; sometimes they are used synonymously, or aspects of WB are used to define QoL; although they are associated with different theoretical concepts (e.g., Ryff’s Psychological Well-being, Ryff & Keyes, 1995): eudaimonic well-being emphasizing the realization of a person’s potential (Ryan & Deci, 2001); Diener’s (2000) hedonic approach of Subjective Well-Being; PERMA model (Seligman, 2018); Wilson & Cleary Model of HRQoL (1995) (for an extensive discussion see Das, Jones-Harrell, & Fan, 2020; and Skevington & Böhnke, 2018). Subjective WB and subjective QoL are overarchingly described as key concepts that describe experiences, abilities, states, behaviors, appraisals, and emotional responses to circumstances. Perhaps most widely used is the concept of QoL as a multidimensional construct that encompasses physical, emotional, mental, social, and everyday functional factors (Bullinger, 2014; Koller et al., 2009). Health-related QoL is defined as how subjective QoL is influenced by physical and mental health (Karimi & Brazier, 2016). Subjective WB (SWB) is composed of general life satisfaction, satisfaction with individual life domains, the presence of positive feelings, and a low level of negative feelings (Diener, 2000). In a recent study using network analysis, Skevington and Böhnke (2018) proposed an integrated model of SWB and QoL through testing its overlapping and exclusive dimensions, which resulted in 14 facets: energy, sleep (physical domain); positive feelings, self-esteem (psychological); dependence on medication, & treatment (independence); personal relationships, social support, sex-life (social relationships); home environment, financial resources, health & social care, recreation & leisure (environment); wholeness, inner peace (spiritual). In addition, they showed that health-related variables are closely linked to physical QoL facets on medication, activity, and mobility. Testa and Simonson (1996) proposed an interesting conceptual framework of the subjective and objective domains to unravel the different components of QoL and WB. Each domain of QoL can be viewed from two dimensions: as an objective examination of a person’s health status, based on an assessment of their physical, mental, and social functioning, and as a subjective perception of that status, in terms of physical, mental, and social well-being. Figure 1 shows a modified conceptual model of their QoL framework supplemented by socioeconomic (objective) factors and the classification into physical, psychological, and social well-being. The x‑axis represents the subjective, the y‑axis the objective dimensions of the three central domains of the concept of QoL. The concept of QoL spans between the two axes. The objective dimension of QoL is to be understood as a gauge of the state of health in physical, mental, and social terms, which is, however, only transformed into the concept of QoL by the person’s subjective judgment. Using the example of the physical domain, this means that the frequency and severity of limitations and their relevance from the individual’s point of view are important (Fig. 1).

However, there remains ongoing debate about how QoL and WB should be conceptualized and measured, and therefore, a variety of approaches exist to measure QoL and WB in surveys. More recently, Lindert, Bain, Kubzansky, and Stein (2015) identified 60 unidimensional and multidimensional instruments to survey subjective WB alone. Some of these are generic, i.e., appropriate for the general population, and can be applied to various conditions (e.g., diabetes, cancer; Ryffs Scales of Psychological Well-being (Ryff & Keyes, 1995); WHO (Five) Well-being Index (Bech, 1996); WHOQOL-100 (WHOQOL, 1998)), while others are disease-specific and relate to a particular pathology (e.g., depression, Parkinson’s disease; Beck Depression Inventory II (Beck, Steer, Ball, & Ranieri, 1996); Parkinson’s disease questionnaire (Hagell & Nygren, 2007)). However, few of these instruments have been developed specifically for older “healthy” adults; instead, there are mainly questionnaires for individuals with dementia (e.g., Qualidem). Also, no instruments are available specifically for the nursing home setting.

Previous studies on older adults in a nursing home setting (Ballmer, Wirz, & Gantschnig, 2019; van der Wolf, van Hooren, Waterink, & Lechner, 2019) often use questionnaires designed for young and middle-aged adults to evaluate WB or QoL, such as the SF-36 and its short form the SF-12 by Bullinger and Kirchberger (1998), the World Health Organization Quality of Life 100 (WHOQOL-100), or the 5‑item World Health Organization Well-Being Index (WHO-5). These questionnaires cover medical and physical aspects, while social and psychological factors are only marginally addressed. However, they do not address individual priorities and needs arising from different life experiences (Meyer, Drewniak, Hovorka, & Schenk, 2019). Another problem is that the questionnaires, which are also used for community-dwelling individuals, often do not correspond to the nursing home residents’ living environment. For example, the SF-36 or SF-12 asks whether there are limitations to activities such as running fast, lifting heavy objects, or engaging in strenuous sports due to current health status, to name just a few examples. One of the few questionnaires developed for nursing home residents is the Qualidem (Ettema, Dröes, de Lange, Mellenbergh, & Ribbe, 2007). In this questionnaire, residents are not directly interviewed, but their close caregivers, such as relatives or caregivers, can assess their situation. However, considering the resident’s perspective is important as the third party to self-assessment can be very different (Oppikofer & Mayorova, 2016).

The Laurens Well-Being Inventory for Gerontopsychiatry (LWIG; van der Wolf, van Hooren, Waterink, & Lechner, 2018) is a self-rated 30-item well-being measure (that relate to the last 7 days) for individuals residing in a nursing home driven by both theoretical and data-driven considerations. In creating their scale, van der Wolf et al. (2018) followed the WHO view that at least the physical, social, and psychological dimensions should be included (WHOQOL Group, 1995; see Fig. 1). Two models that, when used together, cover and explain these dimensions are the Social Production Function (SPF) model (Lindenberg, 1986; for both the physical and social domains) and Ryff’s model of psychological well-being (Ryff & Keyes, 1995; for the psychological domain). Based on previous matching question instruments, interviews with geriatric professionals, and focus groups, the authors created an item pool from which selections were made based on their chosen theoretical models. Further decisions about whether to retain or reject items in this process were based on an empirical study of nursing home residents, reducing the initial pool of more than 300 possible items to 30 items. The authors of the original scale (van der Wolf et al., 2018) analyzed the proposed 3‑factor structure of wellbeing through confirmatory analysis (PCA), but only on the questionnaire with 53 variables (preliminary stage). Overall, the fit statistics did not show a perfect model (CFI = 0.688, TLI = 0.675, RMSEA = 0.063). In addition, they examined whether one or more factors were present within the three different dimensions. The final instrument contains six subscales in three dimensions: physical well-being’ (6 items: items 1, 7, 13, 19, 24, 27), social well-being’ (subscales positive social experience, 6 items: 4, 10, 16, 22, 26, 29; negative social experience, 4 items: 5, 11, 17, 23; communal living, 3 items: items 6, 12, 18), and psychological well-being’ (subscales affect, 7 items: 2, 8, 14, 20, 2, 28, 30; self-worth, 4 items: 3, 9, 15, 21). The LWIG has demonstrated adequate reliability and validity (van der Wolf et al., 2018). Reliability of the dimensions and their underlying factors was assessed using McDonald ω and Cronbach α: All but one subscale “negative social experience” had values > 0.70. Although self-rated and observer-rated WB is generally weakly correlated, the LWIG subscales were significantly related to most of the observer-rated Qualidem subscales and the Cantril Ladder (Ettema et al., 2007; van der Wolf et al., 2018). In addition, older adults with depressive symptoms scored significantly lower on all subscales of the LWIG compared to individuals with no depression.

Following the Standards for Educational and Psychological Testing (Frey, 2018), the present study addressed evidence related to dimensionality, reliability, and construct validity (Boateng, Neilands, Frongillo, Melgar-Quinonez, & Young, 2018). Dimensionality, in general, refers to the structure of a specific phenomenon and examines the extent to which the internal components of a scale match the defined constructs and is concerned with item homogeneity. We examined dimensionality using both exploratory and confirmatory factor analytical procedures as well as Rasch analysis. Reliability refers to the consistency and relative freedom from error of an instrument. This study assessed internal consistency using the reliability coefficients Cronbach’s alpha (α), composite reliability (CR), and McDonald’s omega (ω) coefficient. Finally, construct validity refers to how well a scale measures the construct it is intended to measure and is based on, among other things, the relationships of the constructs to other variables. There are two subsets of construct validity: convergent construct validity and discriminant construct validity. Convergent construct validity tests the relationship between the construct and a similar measure; it shows that constructs that are supposed to be related are related. Discriminant construct validity tests the relationships between the construct and an unrelated measure; this indicates that constructs are not associated with something unexpected. Consistent with previous studies, factors such as physical and cognitive activities as well as physical function are found to be positively related to well-being in nursing home residents (Brett, Traynor, & Stapley, 2016; Grönstedt et al., 2011; Saadeh, Welmer, Dekhtyar, Fratiglioni, & Calderón-Larrañaga, 2020) and negatively related to impaired cognition, fear of falling, and depression (Morsch, Shenk, & Bos, 2015; Smalbrugge et al., 2006). Therefore, these constructs were selected to assess convergent construct validity using correlational analyses.

This study translated the LWIG—a questionnaire specially designed for nursing home residents reflecting their environment—into German (i.e., LWIG-GER) and examined its psychometric properties based on both factor analysis (EFA, CFA) and Rasch modeling. The CFA results supported the validity of the three-factor model of the original LWIG (psychological well-being, social well-being, physical well-being) but not the 6‑factor model. Based on classic test theory, the analysis produced a shorter version of 19 items of the LWIG-GER with acceptable reliability, validity, and moderate to strong correlations with other depression and functional performance measures. Rasch modeling suggested that the LWIG-GER was unidimensional, with acceptable INFIT and OUTFIT values, good person separation, and high person reliability. It also generated a reduced version of the LWIG_GER, excluding similar items as the classic test theory procedure.

In this convenience sample from different nursing homes, the answers of almost all items of the LWIG are somewhat on the side of high well-being, leading to left-skewed distributions of the scores. This should not necessarily be regarded as unfavorable; the values of other instruments measuring QoL or WB show a similar pattern (Lindert et al., 2015). However, classical test theory as well as item response theory analysis (using Rasch) clearly show that items 5, 22, and 23 have unacceptable values for skewness and kurtosis (compared to factor analysis, a normal distribution is not a requirement for Rasch analysis) and a low item difficulty (item analysis: 0.92, 0.93, 0.94; Rasch measure: −1.03, −1.28, −1.97). Therefore, we omitted these three items, which addressed the questions “be bothered by other residents”, “relationship with nurses”, and “be bullied”.

Factor analysis demonstrated the appropriateness of a three-factor structure for the LWIG. The structure for all 30 items was identified for the first time because factor analysis on all items was not conducted on all items simultaneously in the original LWIG development study in the Netherlands (van der Wolf et al., 2018). Subscales were labeled to reflect the theory guiding the original LWIG item pool. In the EFA, the factor loadings of nine items on the scale are below 0.40 or loaded significantly on more than one dimension. For example, according to van der Wolf et al. (2018), item 3 (“I think I am worth the effort”) belongs to the psychological dimension, but our results show that this item loads with a factor of 0.337 on the social WB dimension. In contrast, for item 18 (“How often did you enjoy the communal mealtimes?”; on the social dimension), we see a factor loading of 0.373 on the psychological dimension. The different assignment of the items to a specific dimension can be attributed to a different interpretation of the questions. Undeniably, short questionnaires improve assessment by saving response time and effort, increasing the response rate, minimizing burden, and decreasing fatigue. Therefore, based on the previous results for subscaling, items 2, 3, 8, 16, 17, 18, 21, and 24 were excluded. Concerning the results based on CFA, we found the LWIG-GER with 19 items had good performance on all psychometric indicators.

Item fit analysis for each item was performed to ensure that all items matched expected responses based on the Rasch model. INFIT and OUTFIT MnSq statistics for each item were within the acceptable range, indicating a satisfactory fit to the underlying global trait, i.e., the residents’ well-being. Nevertheless, our results also indicate that the LWIG-GER had some redundant or poorly constructed items. In this context, six items can be considered redundant (e.g., 8, 16, 28, 24). The Wright map shows that most of the items that are too easy to agree with can be assigned to the social WB dimension (e.g., items 5, 22, and 23), which corresponds with the item analysis (skewness and kurtosis of the item’s distribution and the pattern of response frequency).

A widely used way to measure a questionnaire’s reliability is to assess internal consistency. We preferred the use of McDonald’s ω rather than the widely used Cronbach’s α since Cronbach’s α is based on the assumption of equal factor loadings (however, for comparison, we also reported Cronbach’s α). Therefore, Cronbach’s α, McDonald’s ω values > 0.7, can be interpreted as good internal reliability. For the LWIG-GER, we obtained McDonald’s ω of 0.685, 0.795, and 0.827 indicating a moderate to good internal consistency for the three dimensions of the LWIG-GER. Both reliability measures were comparable to the Dutch instrument (van der Wolf et al., 2018).

In terms of convergent, divergent, and known group validity, the LWIG-GER was significantly correlated with education, depression, functional performance (TUG, gait speed), and fear of falling (measures as a one-item question). Like van der Wolf et al. (2018, 2019), we observe a negative correlation between the LWIG-GER total score and the depression scale (DIA-S). Our results are also consistent with those of Meeks and Murrell (2001), who showed that higher education levels are related to lower levels of negative affect in older adults. Also, Subaşı and Hayran (2005) demonstrated that educational level is a statistically significant independent predictor of life satisfaction among nursing home residents. We also see an association between fear of falling and the LWIG-GER total score and physical WB and an association between mobility (TUG and 5 m gait speed) and the LWIG-GER total score and in almost all of its dimensions. This study conducted a known-group comparison to examine whether the LWIG-GER and its subdimensions scores could discriminate between three groups according to depression and gait speed. Our results confirm this relationship with respect to DIA‑S (p < 0.001 to p = 0.003) and 5 m gait speed (p < 0.001 to p = 0.121). In this context, the study by Painter et al. (2012) aimed to examine the relationship of fear of falling, depression, anxiety, activity level, and activity limitation. They were able to show strong correlations for most of these constructs. This illustrates that these are multifaceted constructs that have a reciprocal relationship along with the construct of WB. Through these analyses, convergent validity and known group validity were confirmed to be satisfactory.

The present study has several limitations that should be noted. One issue is achieving a sufficient sample size to conduct an EFA or CFA since getting a sample in a clinical setting is not easy. Although our sample size was smaller than that recommended for traditional factor analysis (minimum subject to item ratio of at least 5:1, Hatcher, 1994; or more recently 3:1, Bujang, Ghani, Soelar, & Zulkifli, 2012), the Rasch rating scale model functions with a minimum of 10 observations per category (Boone et al., 2014). As a Likert-type scale of four categories was used for the present study, the minimum sample size for Rasch model measurement analysis for the measurement instrument in this study was n = 40. These recommendations suggest a valid sample size of n = 104 in the present study to analyze the LWIG-GER. However, more significant numbers of cases are needed to replicate our findings and draw reliable conclusions.

We are confronted with the same attendance problem mentioned by van der Wolf et al., (2018). Accordingly, it can be assumed that residents who did not attend are either more physically (e.g., gait speed, TUG) or mentally (e.g., MoCa) impaired, which should negatively affect WB. In particular, a more comprehensive range of facilities should be considered in terms of socioeconomic status.

Regarding reliability, an internally consistent questionnaire can be assumed based on correlations, Cronbach’s α, McDonald’s ω, and calculated Rasch reliability. However, no conclusions can be made about test–retest reliability and interrater reliability. In future studies, testing these reliability measures is an exciting aspect. Moreover, even though we did not include items 5, 22, and 23 in the factor analysis, and most of the items that are too easy to agree with belong to the social dimension, we would not recommend removing all of these items. This is because the present data were collected in only five different facilities, and thus it is not certain that the full range of facilities was covered. Therefore, it is important to replicate our results not only with larger samples in future studies to revisit the questionnaire’s undoubtedly reasonable shortening; but in addition, other formulations regarding items that are too “easy” should be tested with the residents to avoid falling into the trap of social desirability in response behavior.

Taken together, the German version of the short LWIG-GER with 19 items provides a valid and reliable measurement tool to evaluate well-being in nursing home residents. Although there is no consistent definition of WB, it seems to be a multidimensional construct (Lindert et al., 2015; Su et al., 2014). Based on the CFA and comparable to the results of van der Wolf et al. (2018), we can verify a three-factor structure (psychological, social, and physical well-being) with an acceptable fit.

We see great potential in this questionnaire because, on the one hand, there have been few specific questionnaires on WB in healthy nursing home residents. On the other hand, the questionnaire is meaningful, cost-effective, time-efficient, and easy to use. However, significantly more data are needed to determine the sensitivity, specificity, and cut-off values of the LWIG-GER. Nevertheless, this provides an instrument for future studies that measure WB validly and reliably.