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Path model explaining the association between fear of falling and health-related quality of life in (pre-)frail older adults

BMC Geriatrics volume 25, Article number: 87 (2025) Cite this article

Abstract

Background

Fear of falling (FoF) is estimated to be prevalent in over 50% of older adults and several studies suggest that it negatively affects health-related quality of life (HrQoL). Unlike previous studies that examined only few mediating variables, this study aimed to develop a more comprehensive path model explaining the association between FoF and HrQoL.

Methods

A theoretical path model was developed based on existing evidence and expert feedback and fitted to cross-sectional baseline data on 385 community-dwelling (pre-)frail older adults from the PromeTheus randomized controlled trial using robust weighted least squares estimation. FoF and HrQoL were operationalized by the Short Falls Efficacy Scale International and EQ-5D Index, respectively. The model included potential explanatory pathways through physical activity (German Physical Activity Questionnaire for middle-aged and older adults), physical capacity (Short Physical Performance Battery), physical performance (Late-Life Function and Disability Instrument [LLFDI] function component), disability (LLFDI disability component – short form), and affect (visual analogue scales on ‘happiness’, ‘sadness’, ‘calmness’ and ‘tension’). Age, sex, education, and previous falls were considered as covariates.

Results

The model demonstrated good fit to the data and the remaining direct effect of FoF on HrQoL was small (β=-0.05). Physical capacity and physical performance were the most important mediators (combined indirect effect of β=-0.17, accounting for > 50% of the total effect). Pathways of minor individual relevance (e.g. through disability or affect) contributed considerably to the total indirect effect when combined. Controlling for sociodemographic data and previous falls only had minor effects on model fit and path coefficients.

Conclusion

Physical capacity and physical performance are particularly important levers for reducing the impact of FoF on HrQoL through interventions. However, the other pathways also had a considerable influence when taken together. Hence, research on the association of FoF and HrQol should acknowledge the complexity of causal pathways that may explain this association and not neglect minor pathways. The proposed model should be tested on an alternative sample, using longitudinal data, and extended to include additional explanatory factors (e.g. activity avoidance).

Trial registration

German Clinical Trials Register, ID: DRKS00024638, https://drks.de/search/en/trial/DRKS00024638, date of registration: March 11th 2021.

Peer Review reports

Background

Falls are a prevalent health issue in older adults, having a relevant impact on the burden of disease and consequently on quality of life (QoL) in this population [1, 2]. Therefore, research into effective fall prevention has become a growing field [3, 4]. Fear of falling (FoF) is a psychological aspect of falling that can be described as “low perceived self-efficacy at avoiding falls during essential, nonhazardous activities of daily living” [5]. Prevalence estimates vary, but a recent meta-analysis estimated FoF being prevalent in almost half of the population aged 60 years and older [6]. In (pre-)frail older populations, the prevalence may even be as high as 75% [7, 8]. Frailty describes a state of reduced physiological reserves caused by declines in various systems, leading to an increased vulnerability to stressors. Persons who show only some elements of frailty (e.g. shrinking, weakness, poor endurance and energy, slowness, and low physical activity level) are considered pre-frail [9].

The (risk) factors associated with FoF include demographic characteristics (e.g., female gender), physical function, chronic diseases, and mental problems, while previous fall experience tends to play a minor role [6, 10, 11]. Several studies suggest that FoF is associated with lower (health-related) QoL (HrQoL) and this association also appears to be largely independent of whether a person has actually experienced a fall [12, 13]. (Hr)QoL is a key indicator for active aging and an important outcome in studies examining interventions aiming to promote active aging [14]. Understanding the mechanisms underlying the association between FoF and (Hr)QoL may help in designing effective strategies to address FoF and increase (Hr)QoL.

Just as FoF is not necessarily a consequence of previous fall experiences, perceived and physiological fall risk do not always appear to be congruent [15]. However, FoF may lead to changes in behavior such as fear-related activity restriction that cause gait speed adaptions, which (in the long term) potentially results in lower physical capacity and performance [16,17,18,19] and, in turn, increases the physiological fall risk and further intensifies FoF. These associations of FoF with physical capacity or physical performance (via activity restrictions/avoidance) present a potential linking factor in the association between FoF and (Hr)QoL. This is supported by previous studies that found, e.g., subjective functional capacity, gait speed, lower leg strength, or physical activity to partly explain the association of FoF and HrQoL (partial mediation) [20,21,22]. These studies used relatively simple path models that examined only one or two potential mediators at a time, making it difficult to estimate the relative importance of different explanatory factors. However, path analysis is capable of describing complex relations between various variables, allowing the evaluation of hypothesized models [23]. Compared to the existing literature, the current study aimed to include more parameters, such as physical capacity and affect, and thus provide a more detailed insight into the association between FoF and (Hr)QoL.

Therefore, the present study aimed to explain the relationship between FoF and HrQoL by including several factors known to be associated with FoF and/or HrQoL in a path model, making it possible to compare their importance and examine their interplay and dependencies.

Methods

Study design and sample

This study is a secondary, cross-sectional analysis using data from the baseline examination of the PromeTheus multicenter randomized-controlled trial (registered in the German Clinical Trials Register on March 11, 2021; ID: DRKS00024638) [24]. The study population consisted of (pre-)frail older adults (Clinical Frailty Scale [25] score 4–6) of at least 70 years who were living at home or in assisted living facilities in the areas of Stuttgart, Heidelberg, and Ulm (Baden-Wuerttemberg, Germany), were insured with the ‘Allgemeine Ortskrankenkasse (AOK) Baden-Württemberg’ (a German statutory health insurance), and were able to walk at least 10 m with or without walking aids but less than 800 m without walking aids and breaks. Eligibility criteria are described in detail elsewhere [24].

Hypothesized model

In a first step, two authors conducted a literature review on the relationship between FoF and HrQoL. Based on the quantitative and qualitative evidence identified, they hypothesized a first path model linking FoF and HrQoL through constructs that could be measured using data from the PromeTheus study. This was modified following discussion with a group of experts (physiotherapists/sports scientists/geriatric researchers from the PromeTheus study group), so that further instruments capturing the abstract concepts were identified. Inclusion of the expert group’s feedback on this updated model led to the hypothetical path model used for this analysis (Fig. 1).

Given the proposed association between FoF and HrQoL [12] and to be able to differentiate between the direct and indirect association between FoF & HrQoL, a direct path was drawn from FoF to HrQoL. We hypothesized that a key explanatory pathway is through mobility (defined as the ability to move [26]), which is a determinant of older people’s HrQoL [27]. We follow the recommendation to differentiate two constructs of mobility: physical capacity (the capability or the ‘can do’ measured under standardized/ideal conditions) and physical performance (measured embedded within a (daily) task/activity and representing the ‘do’) [26]. Previous research suggests that FoF leads to avoidance or restriction of activities [16]. As this was not directly measured in PromeTheus, we assumed that these activity restrictions would present as changes in physical capacity measures. These could be a direct (and possibly conscious) manifestation of fear-related avoidance behavior (e.g., reduction of gait speed) or a physiological consequence of fear-related avoidance and thus non-use, which manifests itself in an actual reduction in physical capacity [18, 19, 22]. Activity avoidance may also be reflected in FoF-related reduction in physical activity level [20], which is a determinant of maintaining physical capacity and physical performance [28]. On the one hand, physical capacity logically affects physical performance; on the other hand, FoF could have a direct negative influence on physical performance regardless of capacity limitations (i.e. a person who essentially ‘can do’ certain activities might not actually ‘do’ them in daily tasks/activities). Not doing daily tasks/activities (i.e. limited in physical performance) could lead to a reduction of physical activity, which in turn could start the vicious cycle of (further) decreasing physical capacity and performance. FoF-related limitations in physical performance may, depending on a person’s surroundings and adaptability, carry over into disability, i.e. limited performance of socially defined life tasks [26], but FoF could also result in a person experiencing disability without being limited in physical performance per se (e.g. through avoidance). Finally, a pathway was drawn connecting FoF and HrQoL through affect, assuming that FoF as a psychological construct might impact the affective state (feelings, mood) more globally [29], which in turn might be reflected in HrQoL [30].

Fig. 1
figure 1

Hypothesized path model

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Measures

Fear of Falling was assessed using the Falls-Efficacy Scale International – Short Form (Short FES-I), a seven-item questionnaire on concern about falling [31]. A total score was calculated from seven items regarding the concern about falling in executing everyday tasks, each item having response options from 1 (not at all concerned) to 4 (very concerned). Thus, the total score ranges from 7 (no concern about falling) to 28 (severe concern about falling). Measurement properties of the Short FES-I were sufficient, showing good test-retest-reliability (r = .87), very good score reliability (Cronbach’s alpha 0.92), and a strong correlation with the original and cross-culturally validated 16-item FES-I (r = .97) [31, 32].

HrQoL was determined by the EQ-5D-5 L index score [33]. It summarizes five dimensions of HrQoL (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) into a score using health state preferences of the German general population (0 representing ‘death’, 1 representing ‘full health’, and negative scores indicating health states valued worse than death). The EQ-5D-5 L showed sufficient construct validity in older populations [34].

Physical capacity was measured using the Short Physical Performance Battery (SPPB), which assesses lower extremity function based on the three subtests: a hierarchical standing balance test (Romberg, semi-tandem, and tandem stance), a usual gait speed test over 4 m, and a 5-chair stand test [35]. A total score ranging from 0 (worst) to 12 (best) was calculated. The SPPB demonstrated good validity and reliability in frail older adults without severe cognitive impairment [36].

Physical performance was operationalized using the Late-Life Function and Disability Instrument’s (LLFDI) function component, consisting of 32 items assessing limitations in a person’s ability to perform discrete actions/activities encountered in daily routines [37]. A scaled score was calculated ranging from 0 to 100, with higher scores indicating better performance. The measurement properties of the LLFDI function component are supported by several studies [38].

Self-reported physical activity was measured using the German Physical Activity Questionnaire for middle-aged and older adults (German PAQ 50+) [39]. Participants were asked how much time they spent on a number of activities in a typical week of the last month. These times were multiplied with the metabolic equivalent (MET) for the respective activity [40] and summed to calculate the activity level as MET-hours per week. The instrument was constructed from other validated instruments, indicating good construct validity; test-retest-reliability was insufficient (r = .53) [39].

Disability – the ability to perform socially defined life tasks within a typical sociocultural and physical environment [41] – was assessed with the short form of the LLFDI disability component’s limitation dimension [42, 43]. The raw score ranging from 8 to 40 was calculated, with higher scores indicating a lower level of disability. It has been found to have sufficient reliability and validity [43, 44].

Affect was measured on visual analogue scales to four questions regarding ‘happiness’, ‘sadness’, ‘calmness’ and ‘tension’, which were summarized to a score between 0 (high level) and 100 (low level of affect) [45]. Even though the instrument was designed to detect individual changes over time, its known-groups validity provides some evidence for its use in inter-individual comparison [45].

Furthermore, self-reported information on age, gender (male/female), years of formal education, and whether participants had fallen in the last six months (yes/no) were used as control variables.

Statistical analysis

All analyses were performed using R version 4.2.3 software. Data was complete for the variables of interest for this analysis, except for occasional missing values in the variables ‘affect’ (n = 1) and ‘years of education’ (n = 2), which were replaced by the median of the respective variable.

Descriptive and bivariate statistics were used to describe the sample characteristics and test statistical requirements for subsequent analyses.

The lavaan package (version 0.6–16) for R was used for path analysis [46]. In path analysis, simultaneous regression analyses are conducted between certain variables according to a pre-specified model, allowing the estimation of both direct and indirect effects. The theoretical model was fitted using robust weighted least squares estimation which does not assume normality of the data used and allows analysis of both metric and dichotomous variables [47]. Some variables – namely physical activity, physical performance, disability, HrQoL, education, sex, and previous falls – were multiplied with a constant factor to ensure model convergence. Modification indices were inspected to identify potential additional connections between variables that would improve the model’s fit. Removing regressions with minimal effect from the model was considered in the interest of parsimony, but was ultimately rejected because of the theoretical or practical significance of the affected pathways. The model was analyzed both in a raw version and in one that controlled for age, sex, education, and previous falls.

Following the recommendations by Kline [47], global fit of the model was examined by Chi-squared statistics, the robust Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR) as absolute fit measures, as well as the robust Comparative Fit Index (CFI) as measure for incremental fit. A non-significant Chi-squared statistic on a 0.05 confidence level, a CFI ≥ 0.90, RMSEA < 0.05, and SRMR values < 0.08 were considered indicative for good model fit [48]. Correlation residuals < 0.10 were taken as indicators of good local fit [47]. Standardized and unstandardized path coefficients as well as indirect effects were calculated alongside 95%-confidence intervals (CIs).

Results

Descriptives

Sample characteristics of the 385 baseline participants of PromeTheus are presented in Table 1. The mean age was 81.2 years, the majority was female (73.5%) and either married (30.6%) or widowed (51.7%). 32.2% were living in an assisted living facility, 38.5% had a care degree (qualifying for benefits from the German long-term care insurance), and 36.9% reported at least one fall in the last 6 months. Their median score on the clinical frailty scale was 4 (interquartile range 4–5), indicating very mild to mild frailty. The mean and standard deviation of variables of interest in the path model (HrQoL, FoF, physical activity, physical capacity, physical performance, disability, and affect) as well as their bivariate correlations (Spearman’s rank correlation coefficient, ρ) are provided in Table 2. There was a moderate negative bivariate correlation between FoF and HrQoL (ρ=-0.35). Further, HrQoL and FoF correlated moderately to strongly (ρ ≥ 0.3) with physical capacity, physical performance, and disability, while the associations with physical activity and affect were weaker.

Table 1 Sample characteristics (n = 385)
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Table 2 Correlation coefficients (spearman) and means (standard deviations) of main variables included in the model
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Path model

During model fitting, indicated by a high modification index, the hypothetical model was extended by adding a path from disability to affect, which has some theoretical support [49]. Model fit statistics indicated a satisfactory global (Table 3) and local fit (data not shown) of the resulting model in both its raw and corrected form. Fitted covariance matrices can be found in the Supplemental material (Tables S1-S2) along with the coefficients of the raw model (Table S3); coefficients of the final corrected model are presented in Table 4; Fig. 2. Overall, correcting for age, sex, education, and previous falls only slightly changed the path coefficients compared to the raw model.

Table 3 Model fit indices
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Fig. 2
figure 2

Final path model. Standardized path coefficients are shown

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Table 4 Path coefficients of the corrected model
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The direct effect of FoF on HrQoL was small with a standardized path coefficient (β) of -0.05 (95% CI -0.19 to 0.09), suggesting that the association between FoF and HrQoL is mediated by the remaining variables in the model (total indirect effect: β=-0.28, 95% CI -0.37 to -0.19). The strongest separate mediator was physical performance with an indirect effect of β=-0.11 (95% CI -0.18 to -0.03). Additionally considering the pathway from FoF through capacity and physical performance to HrQoL (β=-0.07, 95% CI -0.12 to -0.02), the indirect effect increased to β=-0.17 (95% CI -0.30 to -0.05), indicating that > 50% of the total effect and > 60% of the total indirect effect is explained through mobility limitations (capacity and performance combined). Other indirect effects, e.g. through disability alone (β=-0.01, 95% CI -0.02 to 0.01) or through performance and disability (β=-0.03, 95% CI -0.08 to 0.01), were of lesser importance. Similarly, affect did not seem to be a relevant separate mediator (β=-0.01, 95% CI -0.03 to 0.01). However, the pathways through affect alone, through disability and affect, through physical performance, disability and affect, and through physical capacity, physical performance, disability and affect combined still accounted for 11% (β=-0.03, 95% CI -0.06 to -0.01) of the total indirect effect.

Discussion

This study aimed to develop a path model explaining the association between FoF and HrQoL in a sample of community-dwelling (pre-)frail older adults. In the final model, the direct effect between FoF and HrQoL was negligible, suggesting that the association can mostly be explained via the other pathways in the model, the most relevant indirect effect going through mobility, mainly physical performance. The model showed very good local and global fit, indicating appropriateness for the data.

Discussion of the results in the context of existing evidence

The results are in line with previous studies finding that indicators of physical capacity and physical performance mediate the association between FoF and HrQoL [21, 22], but unlike the current study, these found a significant remaining direct effect of FoF on HrQoL. One explanation for this divergence could be the less complex models, considering only a few selected mediating pathways, e.g. only physical performance, disability [21], or leg strength/balance and/or gait speed [22]. The largest proportion of the indirect effect in the current study was also explained through physical capacity and physical performance. However, the remaining pathways, although less relevant individually, together made a considerable contribution to explaining the overall effect.

The standardized coefficients of individual paths in the model were often small and below the level of being considered meaningful [50]. For example, the direct effects between FoF and physical activity and between physical activity and physical capacity were β < 0.2, resulting in the explanatory pathways involving physical activity being close to zero. Despite this, physical activity was left in the model, as model fit worsened considerably when excluding the variable. It is also worth noting that the weak (bivariate) association between FoF or physical capacity and physical activity may be due to the measurement of physical activity in the PromeTheus study. In the German PAQ-50+, several low-intensity activities could be mentioned by the participants. These activities, which are probably barely affected by FoF, made up a large proportion of the overall activity level in the sample [51]. Thus, testing the model with alternative measurements of physical activity (e.g. objectively, sensor-based) is desirable. Overall, the current model provides a more comprehensive picture of the potential pathways that explain the relationship between FoF and HrQoL.

Interpretation of selected pathways

It was hypothesized that individuals with FoF exhibit lower physical performance, because their physical capacity does not allow it or because their FoF hinders them, independently of their physical capacity. The latter could be explained by avoidance behavior or deliberate activity restriction, which has been found to fully mediate the association between FoF and QoL in nursing home residents [52]. Furthermore, physical performance could also be affected by reasons other than limitations in lower extremity function (which is essentially what the SPPB, used to operationalize physical capacity, measures). FoF-related avoidance behavior was not assessed in PromeTheus and was therefore not included in the model. However, a future extension of the model to include this aspect would enable a differentiation between avoidance-related and actual physical performance limitations.

Contrary to what was expected, there was no relevant independent path through disability despite the strong association between function and disability. However, there was a small effect of the path going from FoF through mobility (physical performance alone or via physical capacity and performance), disability and affect to HrQoL. This suggests that by promoting physical capacity and physical performance, disability can also be positively influenced, which in turn translates into a more desirable level of affect and ultimately better HrQoL.

This implies that measures to improve or maintain physical capacity are the most important interventions to reduce the impact of FoF on HrQoL, particularly because of the feedback loop to physical capacity. The overall direction of our model from FoF to HrQoL was based on our research aim to examine the effect of FoF on HrQoL. This was in line with qualitative evidence [53] and is also the basis for improving HrQoL through geriatric interventions by addressing FoF. Therefore, we prioritized including the pathway from FoF to physical capacity. In addition, there may be an effect of physical capacity on FoF, as has been found for postural instability [54] and often theorized [12, 55, 56]. However, it was methodologically not possible to include this reverse pathway, so only the more practically relevant direction from FoF to physical capacity was included in our model. We therefore emphasize that future research should include a bidirectional pathway whenever possible. Given the major direct and indirect impact of FoF on physical performance, people with FoF should be equipped with strategies on how to safely perform daily tasks despite their FoF, e.g. through training programs that explicitly target a transfer of exercises to everyday tasks or integrate the training/exercises into everyday tasks. For example, the Lifestyle-integrated Functional Exercise (LiFE) program fulfils these criteria and has been shown to improve HrQoL as well as physical capacity, physical performance, and physical activity [57]. Based on the relative weakness of pathways going through disability, interventions which solely focus on adapting to limitations in physical capacity and physical performance might be less effective to mitigate the impact on the HrQoL of persons with FoF.

Limitations and further research directions

This study has several limitations that suggest directions for future research. First, FoF was assessed by the Short FES-I, which in fact asks about concern of falling in different activities. It would be interesting to examine whether the path model still holds true when FoF is measured by alternative instruments (e.g. a single question about fear of falling or the original 16-item FES-I [58]). Second, due to the cross-sectional design of this study, temporality cannot be used to support an assumption of causality. In particular, a model with an inverted structure, i.e. with pathways leading from HrQoL to FoF, would have exactly the same fit. Moreover, alternative models assuming different causal directions are theoretically conceivable (e.g. FoF may not only impact on physical capacity and physical performance, but the association may be bidirectional [59]). Third, measures used in structural equation models (of which path analysis is a subset) should have good content validity, score reliability, and construct validity [47, 60], which was not comprehensively investigated for all instruments used in the model. Future studies could furthermore fully exploit the capabilities of structural equation modelling by using a measurement model with latent constructs described by multiple indicators. In the present study, a measurement model was not used due to the limited number of indicators available per construct. Fourth, the model was fitted based on a relatively small sample of (pre-)frail older adults from a randomized controlled trial with specific characteristics determined by the eligibility criteria of the trial, which limits the generalizability of the findings. A sample size of 5 to 20 times the number of parameters to be estimated is a commonly recommended [47] but also debated rule of thumb for structural equation models [61]. Depending on the threshold applied, the sample size might be too small for the corrected model. Consequently, the model should be verified on an alternative and larger sample to improve confidence in the model [48].

Conclusions

The study suggests that the association between FoF and HrQoL can be explained by a number of explanatory pathways, leaving only a negligible direct effect of FoF on HrQoL. Most of the indirect effect was explained by mobility, mainly physical performance, indicating that people with FoF should be equipped with strategies to safely perform everyday tasks despite their FoF. The remaining pathways (e.g. through disability or affect) were less relevant individually, but together contributed considerably to the total indirect effect. Future studies may verify the model and the assumed causal directions using alternative samples and/or longitudinal data.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to ethical and confidentiality concerns but are available from the corresponding author upon reasonable request.

References

  1. James SL, Lucchesi LR, Bisignano C, Castle CD, Dingels ZV, Fox JT, Hamilton EB, Henry NJ, Krohn KJ, Liu Z, et al. The global burden of falls: global, regional and national estimates of morbidity and mortality from the global burden of Disease Study 2017. Inj Prev. 2020;26(Supp 1):i3–11.

    Article  PubMed  Google Scholar 

  2. Hartholt KA, van Beeck EF, Polinder S, van der Velde N, van Lieshout EM, Panneman MJ, van der Cammen TJ, Patka P. Societal consequences of falls in the older population: injuries, healthcare costs, and long-term reduced quality of life. J Trauma. 2011;71(3):748–53.

    PubMed  Google Scholar 

  3. Montero-Odasso MM, Kamkar N, Pieruccini-Faria F, Osman A, Sarquis-Adamson Y, Close J, Hogan DB, Hunter SW, Kenny RA, Lipsitz LA, et al. Evaluation of clinical practice guidelines on fall Prevention and Management for older adults: a systematic review. JAMA Netw Open. 2021;4(12):e2138911.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Dautzenberg L, Beglinger S, Tsokani S, Zevgiti S, Raijmann R, Rodondi N, Scholten R, Rutjes AWS, Di Nisio M, Emmelot-Vonk M, et al. Interventions for preventing falls and fall-related fractures in community-dwelling older adults: a systematic review and network meta-analysis. J Am Geriatr Soc. 2021;69(10):2973–84.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Tinetti ME, Richman D, Powell L. Falls efficacy as a measure of fear of falling. J Gerontol. 1990;45(6):P239–243.

    Article  CAS  PubMed  Google Scholar 

  6. Xiong W, Wang D, Ren W, Liu X, Wen R, Luo Y. The global prevalence of and risk factors for fear of falling among older adults: a systematic review and meta-analysis. BMC Geriatr. 2024;24(1):321.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Merchant RA, Chen MZ, Wong BLL, Ng SE, Shirooka H, Lim JY, Sandrasageran S, Morley JE. Relationship between fear of falling, fear-related activity restriction, Frailty, and Sarcopenia. J Am Geriatr Soc. 2020;68(11):2602–8.

    Article  PubMed  Google Scholar 

  8. Bahat Öztürk G, Kılıç C, Bozkurt ME, Karan MA. Prevalence and associates of fear of falling among Community-Dwelling older adults. J Nutr Health Aging. 2021;25(4):433–9.

    Article  PubMed  Google Scholar 

  9. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, et al. Frailty in older adults: evidence for a phenotype. J Gerontol Biol Sci Med Sci. 2001;56(3):M146–156.

    Article  CAS  Google Scholar 

  10. Denkinger MD, Lukas A, Nikolaus T, Hauer K. Factors associated with fear of falling and associated activity restriction in community-dwelling older adults: a systematic review. Am J Geriatric Psychiatry. 2015;23(1):72–86.

    Article  Google Scholar 

  11. Dos Santos EPR, Ohara DG, Patrizzi LJ, de Walsh IAP, Silva CFR, da Silva Neto JR, Oliveira NGN, Matos AP, Iosimuta NCR, Pinto A et al. Investigating factors Associated with fear of falling in Community-Dwelling older adults through structural equation modeling analysis: a cross-sectional study. J Clin Med 2023, 12(2).

  12. Schoene D, Heller C, Aung YN, Sieber CC, Kemmler W, Freiberger E. A systematic review on the influence of fear of falling on quality of life in older people: is there a role for falls? Clin Interv Aging. 2019;14:701–19.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Korenhof SSA, van Grieken AA, Franse CCB, Tan S, Verma AA, Alhambra TT, Raat HH. The association of fear of falling and physical and mental health-related quality of life (HRQoL) among community-dwelling older persons; a cross-sectional study of Urban Health Centres Europe (UHCE). BMC Geriatr. 2023;23(1):291.

    Article  PubMed  Google Scholar 

  14. World Health Organization. Active ageing: a policy framework. Geneva: World Health Organization; 2002.

    Google Scholar 

  15. Delbaere K, Close JC, Brodaty H, Sachdev P, Lord SR. Determinants of disparities between perceived and physiological risk of falling among elderly people: cohort study. BMJ. 2010;341:c4165.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Deshpande N, Metter EJ, Lauretani F, Bandinelli S, Guralnik J, Ferrucci L. Activity restriction induced by fear of falling and objective and subjective measures of physical function: a prospective cohort study. J Am Geriatr Soc. 2008;56(4):615–20.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Delbaere K, Crombez G, Vanderstraeten G, Willems T, Cambier D. Fear-related avoidance of activities, falls and physical frailty. A prospective community-based cohort study. Age Ageing. 2004;33(4):368–73.

    Article  PubMed  Google Scholar 

  18. Rochat S, Büla CJ, Martin E, Seematter-Bagnoud L, Karmaniola A, Aminian K, Piot-Ziegler C, Santos-Eggimann B. What is the relationship between fear of falling and gait in well-functioning older persons aged 65 to 70 years? Arch Phys Med Rehabil. 2010;91(6):879–84.

    Article  PubMed  Google Scholar 

  19. Reelick MF, van Iersel MB, Kessels RP, Rikkert MG. The influence of fear of falling on gait and balance in older people. Age Ageing. 2009;38(4):435–40.

    Article  PubMed  Google Scholar 

  20. Hsu Y, Alfermann D, Lu FJ, Lin LL. Pathways from fear of falling to quality of life: the mediating effect of the self-concept of health and physical independence. Aging Ment Health. 2013;17(7):816–22.

    Article  PubMed  Google Scholar 

  21. Gottschalk S, König HH, Schwenk M, Jansen CP, Nerz C, Becker C, Klenk J, Dams J. Mediating factors on the association between fear of falling and health-related quality of life in community-dwelling German older people: a cross-sectional study. BMC Geriatr. 2020;20(1):401.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Steckhan GMA, Fleig L, Schwarzer R, Warner LM. Perceived physical functioning and Gait Speed as Mediators in the Association between fear of falling and quality of life in Old Age. J Appl Gerontol. 2022;41(2):421–9.

    Article  PubMed  Google Scholar 

  23. Streiner DL. Finding our way: an introduction to path analysis. Can J Psychiatry. 2005;50(2):115–22.

    Article  PubMed  Google Scholar 

  24. Werner C, Wolf-Belala N, Nerz C, Abel B, Braun T, Gruneberg C, Thiel C, Buchele G, Muche R, Hendlmeier I, et al. A multifactorial interdisciplinary intervention to prevent functional and mobility decline for more participation in (pre-)frail community-dwelling older adults (PromeTheus): study protocol for a multicenter randomized controlled trial. BMC Geriatr. 2022;22(1):124.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173(5):489–95.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Reijnierse EM, Geelen SJG, van der Schaaf M, Visser B, Wüst RCI, Pijnappels M, Meskers CGM. Towards a core-set of mobility measures in ageing research: the need to define mobility and its constructs. BMC Geriatr. 2023;23(1):220.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Fagerström C, Borglin G. Mobility, functional ability and health-related quality of life among people of 60 years or older. Aging Clin Exp Res. 2010;22(5–6):387–94.

    Article  PubMed  Google Scholar 

  28. Cunningham C, R OS, Caserotti P, Tully MA. Consequences of physical inactivity in older adults: a systematic review of reviews and meta-analyses. Scand J Med Sci Sports. 2020;30(5):816–27.

    Article  PubMed  Google Scholar 

  29. Hajek A, Bock JO, Konig HH. Psychological correlates of fear of falling: findings from the German aging survey. Geriatr Gerontol Int. 2018;18(3):396–406.

    Article  PubMed  Google Scholar 

  30. Benyamini Y, Idler EL, Leventhal H, Leventhal EA. Positive affect and function as influences on self-assessments of health: expanding our view beyond illness and disability. J Gerontol B Psychol Sci Soc Sci. 2000;55(2):P107–116.

    Article  CAS  PubMed  Google Scholar 

  31. Kempen GI, Yardley L, van Haastregt JC, Zijlstra GA, Beyer N, Hauer K, Todd C. The short FES-I: a shortened version of the falls efficacy scale-international to assess fear of falling. Age Ageing. 2008;37(1):45–50.

    Article  PubMed  Google Scholar 

  32. Kempen GIJM, Todd CJ, Van Haastregt JCM, Rixt Zijlstra GA, Beyer N, Freiberger E, Hauer KA, Piot-Ziegler C, Yardley L. Cross-cultural validation of the Falls Efficacy Scale International (FES-I) in older people: results from Germany, the Netherlands and the UK were satisfactory. Disabil Rehabil. 2007;29(2):155–62.

    Article  PubMed  Google Scholar 

  33. Ludwig K, von der Graf JM, Greiner W. German value set for the EQ-5D-5L. PharmacoEconomics. 2018;36(6):663–74.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Gottschalk S, König HH, Nejad M, Dams J. Measurement properties of the EQ-5D in populations with a mean age of ≥ 75 years: a systematic review. Qual Life Res. 2023;32(2):307–29.

    Article  PubMed  Google Scholar 

  35. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, Scherr PA, Wallace RB. A short physical performance battery assessing lower extremity function: Association with Self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49(2):M85–94.

    Article  CAS  PubMed  Google Scholar 

  36. Kameniar K, Mackintosh S, Van Kessel G, Kumar S. The Psychometric properties of the short physical performance battery to assess physical performance in older adults: a systematic review. J Geriatr Phys Ther. 2024;47(1):43–54.

    Article  PubMed  Google Scholar 

  37. Haley SM, Jette AM, Coster WJ, Kooyoomjian JT, Levenson S, Heeren T, Ashba J. Late life function and disability instrument: II. Development and evaluation of the function component. J Gerontol Biol Sci Med Sci. 2002;57(4):M217–222.

    Article  Google Scholar 

  38. Beauchamp MK, Schmidt CT, Pedersen MM, Bean JF, Jette AM. Psychometric properties of the late-life function and disability instrument: a systematic review. BMC Geriatr. 2014;14(1):12.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Huy C, Schneider S. [Instrument for the assessment of middle-aged and older adults’ physical activity: design, eliability and application of the German-PAQ-50+]. Z Gerontol Geriatr. 2008;41(3):208–16.

    Article  PubMed  Google Scholar 

  40. Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, O’Brien WL, Bassett DR Jr., Schmitz KH, Emplaincourt PO, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32(9 Suppl):S498–504.

    Article  CAS  PubMed  Google Scholar 

  41. Jette AM, Haley SM, Coster WJ, Kooyoomjian JT, Levenson S, Heeren T, Ashba J. Late life function and disability instrument: I. Development and evaluation of the disability component. J Gerontol Biol Sci Med Sci. 2002;57(4):M209–216.

    Article  Google Scholar 

  42. McAuley E, Konopack JF, Motl RW, Rosengren K, Morris KS. Measuring disability and function in older women: psychometric properties of the late-life function and disability instrument. J Gerontol Biol Sci Med Sci. 2005;60(7):901–9.

    Article  Google Scholar 

  43. Denkinger MD, Weyerhäuser K, Nikolaus T, Coll-Planas L. [Reliability of the abbreviated version of the late life function and disability Instrument–a meaningful and feasible tool to assess physical function and disability in the elderly]. Z Gerontol Geriatr. 2009;42(1):28–38.

    Article  CAS  PubMed  Google Scholar 

  44. Abizanda P, López-Jiménez M, López-Torres J, Atienzar-Núñez P, Naranjo JM, McAuley E. Validation of the Spanish Version of the short-form late-life function and disability instrument. J Am Geriatr Soc. 2011;59(5):893–9.

    Article  PubMed  Google Scholar 

  45. Monk TH. A visual analogue scale technique to measure global vigor and affect. Psychiatry Res. 1989;27(1):89–99.

    Article  CAS  PubMed  Google Scholar 

  46. Rosseel Y. Lavaan: AnRPackage for Structural equation modeling. J Stat Softw 2012, 48(2).

  47. Kline RB. Principles and practice of structural equation modeling. 4 ed. New York: The Guilford Press; 2016.

    Google Scholar 

  48. Beran TN, Violato C. Structural equation modeling in medical research: a primer. BMC Res Notes. 2010;3:267.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lee S. An exploration of antecedents of positive affect among the elderly: a cross-sectional study. Eur J Public Health. 2016;26(1):187–91.

    Article  CAS  PubMed  Google Scholar 

  50. Chin WW. Commentary: issues and opinion on structural equation modeling. MIS Q. 1998;22(1):vii–xvi.

    Google Scholar 

  51. Gottschalk S, König HH, Werner C, Fleiner T, Thiel C, Büchele G, Schäufele M, Rapp K, Dams J. Association between physical activity and costs in very mild to moderately frail community-dwelling older adults: a cross-sectional study. BMC Public Health. 2024;24(1):2737.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Xu D, Wang Y, Zhu S, Zhao M, Wang K. Relationship between fear of falling and quality of life in nursing home residents: the role of activity restriction. Geriatr Nurs. 2024;57:45–50.

    Article  PubMed  Google Scholar 

  53. Baltes M, Herber OR, Meyer G, Stephan A. Fear of falling from the perspective of affected persons-A systematic review and qualitative meta-summary using Sandelowski and Barroso’s method. Int J Older People Nurs. 2023;18(1):e12520.

    Article  PubMed  Google Scholar 

  54. Valentine JD, Simpson J, Worsfold C, Fisher K. A structural equation modelling approach to the complex path from postural stability to morale in elderly people with fear of falling. Disabil Rehabil. 2011;33(4):352–9.

    Article  PubMed  Google Scholar 

  55. Patil R, Uusi-Rasi K, Kannus P, Karinkanta S, Sievanen H. Concern about falling in older women with a history of falls: associations with health, functional ability, physical activity and quality of life. Gerontology. 2014;60(1):22–30.

    Article  PubMed  Google Scholar 

  56. Scheffer AC, Schuurmans MJ, van Dijk N, van der Hooft T, de Rooij SE. Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing. 2008;37(1):19–24.

    Article  PubMed  Google Scholar 

  57. Clemson L, Fiatarone Singh MA, Bundy A, Cumming RG, Manollaras K, O’Loughlin P, Black D. Integration of balance and strength training into daily life activity to reduce rate of falls in older people (the LiFE study): randomised parallel trial. BMJ. 2012;345:e4547.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Yardley L, Beyer N, Hauer K, Kempen G, Piot-Ziegler C, Todd C. Development and initial validation of the Falls Efficacy Scale-International (FES-I). Age Ageing. 2005;34(6):614–9.

    Article  PubMed  Google Scholar 

  59. Peeters G, Bennett M, Donoghue OA, Kennelly S, Kenny RA. Understanding the aetiology of fear of falling from the perspective of a fear-avoidance model - A narrative review. Clin Psychol Rev. 2020;79:101862.

    Article  PubMed  Google Scholar 

  60. Morrison TG, Morrison MA, McCutcheon JM. Best practice recommendations for using Structural equation modelling in Psychological Research. Psychology. 2017;08(09):1326–41.

    Article  Google Scholar 

  61. Wolf EJ, Harrington KM, Clark SL, Miller MW. Sample size requirements for structural equation models: an evaluation of Power, Bias, and Solution Propriety. Educ Psychol Meas. 2013;76(6):913–34.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank all participants for their participation. We also thank all colleagues and institutions involved in the conduct of this trial, including the ‘Allgemeine Ortskrankenkasse (AOK) Baden-Württemberg’ and the ‘Kassenärztliche Vereinigung Baden-Württemberg’, the trainers and assessors, database managers, members of the advisory board, and all members of the PromeTheus study group: Jürgen M. Bauer, Christian Werner, Bastian Abel, Natalie Hezel, Michael Denkinger, Nacera Wolf-Belala, Dhayana Dallmeier, Vanessa Haug, Tim Fleiner, Kilian Rapp, Corinna Nerz, Christoph Endress, Rebekka Leonhardt, Erkin Uysal, Monika Schönfelder, Lars Schneider, Clemens Becker, Christian Grüneberg, Christian Thiel, Tobias Braun, Rainer Muche, Gisela Büchele, Dietrich Rothenbacher, Birgit Och, Sarah Enderle, Martin Rehm, Martina Schäufele, Ingrid Hendlmeier, Hans-Helmut König, Judith Dams, Sophie Gottschalk, Simone Deininger, Rüdiger Kucher, Anna Lena Flagmeier, Maria Gonzales Medina.

Funding

Open Access funding enabled and organized by Projekt DEAL. The PromeTheus project is funded by the German Innovation Fund (‘New Forms of Care’) coordinated by the Innovation Committee of the Federal Joint Committee (in German: “Innovationsausschuss beim Gemeinsamen Bundesausschuss”, G-BA; grant #01NVF19020). The funder had no role in the study design, the collection, management, analysis and interpretation of data, writing the manuscript, and the decision to submit this manuscript for publication. We acknowledge financial support from the Open Access Publication Fund of UKE - Universitätsklinikum Hamburg-Eppendorf.

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Author notes

  1. Tjard Sattler and Sophie Gottschalk contributed equally to this work.

Authors and Affiliations

  1. Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251, Hamburg, Germany

    Tjard Sattler, Sophie Gottschalk, Hans-Helmut König & Judith Dams

  2. Hamburg Center for Health Economics, Hamburg, Germany

    Tjard Sattler, Sophie Gottschalk, Hans-Helmut König & Judith Dams

  3. Department of Applied Health Sciences, Hochschule für Gesundheit, Bochum, Germany

    Tobias Braun

  4. Department of Health, HSD Hochschule Döpfer, Cologne, Germany

    Tobias Braun

  5. Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany

    Gisela Büchele

  6. Institute for Geriatric Research, Ulm University Medical Center, Ulm, Germany

    Michael Denkinger & Tim Fleiner

  7. Institute of Medical Engineering and Mechatronics, Ulm University of Applied Sciences, Ulm, Germany

    Tim Fleiner

  8. Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany

    Corinna Nerz & Kilian Rapp

  9. Department of Social Work, Mannheim University of Applied Sciences, Mannheim, Germany

    Martina Schäufele

  10. Geriatric Centre, Heidelberg University Hospital, Agaplesion Bethanien Hospital Heidelberg, Heidelberg, Germany

    Christian Werner

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  1. Tjard Sattler
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Contributions

KR developed the grant proposal for the PromeTheus trial approved for funding. TS and SG developed the methodological approach of this study. TS performed the data analysis, supervised by SG and JD. TS and SG produced the first draft of the manuscript. All other authors contributed to the conception and design of the PromeTheus study, acquisition of data in the PromeTheus study, and critically revised the manuscript for important intellectual content and approved the final version of the manuscript.

Corresponding author

Correspondence to Tjard Sattler.

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Ethical approval and consent to participate

Ethical approval was obtained for all study sites: Heidelberg (document number #S-072/2021), Stuttgart (document number #732/2020B01), and Ulm (document number #26/21), and the Ethics Committee of the State Medical Association Baden-Wuerttemberg (B-F-2021-042). The study is conforming to the respective policy and mandates of the Declaration of Helsinki. All participants gave written informed consent prior to participation.

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The authors declare no competing interests.

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Sattler, T., Gottschalk, S., König, HH. et al. Path model explaining the association between fear of falling and health-related quality of life in (pre-)frail older adults. BMC Geriatr 25, 87 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-025-05718-x

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Keywords

BMC Geriatrics

ISSN: 1471-2318

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