Next Article in Journal
Real-Life Study of Patient Preference for Dupilumab or Revision Surgery for Recurrent Chronic Rhinosinusitis with Nasal Polyps
Previous Article in Journal
Reference Values for the German Version of the Quality of Life after Brain Injury in Children and Adolescents (QOLIBRI-KID/ADO) from a General Population Sample
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JPM
Volume 14
Issue 4
10.3390/jpm14040337
jpm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The “Can Do, Do Do” Framework Applied to Assess the Association between Physical Capacity, Physical Activity and Prospective Falls, Subsequent Fractures, and Mortality in Patients Visiting the Fracture Liaison Service

by
Merle R. Schene
1,2,3,
Caroline E. Wyers
1,2,4,*,
Johanna H. M. Driessen
5,6,
Lisanne Vranken
2,
Kenneth Meijer
1,7,
Joop P. van den Bergh
1,2,4 and
Hanna C. Willems
3,8
1
NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
2
Department of Internal Medicine, VieCuri Medical Center, P.O. Box 1926, 5900 BX Venlo, The Netherlands
3
Internal Medicine and Geriatrics, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
4
Department of Internal Medicine, Maastricht University Medical Center+, P.O. Box 616, 6200 MD Maastricht, The Netherlands
5
Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
6
Department of Clinical Pharmacy, CARIM School for Cardiovascular Research Institute Maastricht, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
7
Department of Nutrition and Movement Sciences, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
8
Amsterdam Bone Center, Amsterdam Movement Sciences, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2024, 14(4), 337; https://doi.org/10.3390/jpm14040337
Submission received: 29 February 2024 / Revised: 14 March 2024 / Accepted: 16 March 2024 / Published: 23 March 2024
(This article belongs to the Special Issue Geriatric Medicine: Towards Personalized Medicine)
Browse Figures
Versions Notes

Abstract

:
The “can do, do do” framework combines measures of poor and normal physical capacity (PC, measured by a 6 min walking test, can do/can’t do) and physical activity (PA, measured by accelerometer, do do/don’t do) into four domains and is able to categorize patient subgroups with distinct clinical characteristics, including fall and fracture risk factors. This study aims to explore the association between domain categorization and prospective fall, fracture, and mortality outcomes. This 6-year prospective study included patients visiting a Fracture Liaison Service with a recent fracture. Outcomes were first fall (at 3 years of follow-up, measured by fall diaries), first subsequent fracture, and mortality (at 6 years). Cumulative incidences of all three outcomes were calculated. The association between domain categorization and time to the three outcomes was assessed by uni- and multivariate Cox proportional hazard analysis with the “can do, do do” group as reference. The physical performance of 400 patients with a recent fracture was assessed (mean age: 64 years; 70.8% female), of whom 61.5%, 20.3%, and 4.9% sustained a first fall, sustained a subsequent fracture, or had died. Domain categorization using the “can do, do do” framework was not associated with time to first fall, subsequent fracture, or mortality in the multivariate Cox regression analysis for all groups. “Can’t do, don’t do” group: hazard ratio [HR] for first fall: 0.75 (95% confidence interval [CI]: 0.45–1.23), first fracture HR: 0.58 (95% CI: 0.24–1.41), and mortality HR: 1.19 (95% CI: 0.54–6.95). Categorizing patients into a two-dimensional framework seems inadequate to study complex, multifactorial outcomes. A personalized approach based on known fall and fracture risk factors might be preferable.

1. Introduction

The global burden of disease of fractures is high. About 50% of females and 20% of males are estimated to sustain a fracture after their 50th life year [1]. In the European Union, the number of fragility fractures alone (e.g., fractures due to osteoporosis) was estimated to be 4.3 million in 2019, resulting in a total cost of EUR 56.9 billion, and it is expected to rise in the future [2]. Fractures are associated with short- and long-term disability and a decrease in quality of life [2]. Fracture patients have a high imminent risk of falls, subsequent fractures, and mortality [3]. Therefore, adequate and early secondary fall and fracture prevention is essential to lower the risk of subsequent falls, fractures, and mortality post-fracture [4,5]. The Fracture Liaison Service (FLS) is the recommended organizational approach for fracture prevention in terms of (cost-)effectiveness [6,7,8]. At the FLS, personal risk factors are evaluated, including causes of osteoporosis, comorbidities, and medication use. FLS care ideally includes a fall assessment [4].
In FLS patients, approximately 80% of subsequent fractures are fall-related [9]. The majority of falls in older persons occur during mobility tasks, such as walking, and most falls are caused by slips and trips [10,11]. An evaluation of physical performance including mobility tasks is therefore an essential part of fall risk evaluation [12]. The “can do, do do” framework is a recent approach for an integrated assessment of physical performance. It combines two distinct, yet related, domains of physical performance: physical capacity (PC; can do, can’t do) and physical activity (PA; do, do, don’t do) into four quadrant groups [13,14]. PC is objectively measured physical performance; in the “can do, do do” framework, the 6 min walking test (6MWT) is used to test walking ability [13,15]. Impaired walking ability (e.g., abnormalities in gait or balance) has been associated with future falls [12,16,17,18,19] and fractures and post-fracture mortality (in males) [20,21]. PA is defined as “any bodily movement produced by skeletal muscles that results in energy expenditure”, and is measured by an accelerometer [13,22]. PA might protect or expose (due to higher exposure to risk) persons to falls [23,24,25,26]. In fact, some studies found a U-shaped association with both inactive and highly active adults at increased fall risk [26]. The “can do, do do” framework has proven to be a useful and practical tool to identify patient subgroups with distinct clinical characteristics [13,15,27,28]. In patients with COPD, a preserved PC was found to be associated with a lower 6-year mortality risk compared to decreased PC, regardless of PA level [29]. A recent study in patients with a fracture showed that the framework was able to categorize patient groups in terms of fall and fracture risk factors, such as fall history, fear of falling, type of fracture, and osteoporosis [15].
Thus, for fracture patients the “can do, do do” framework could possibly be of value to test physical performance in the context of future falls in order to guide personalized exercise interventions. However, the prospective application of the “can do, do do” framework to assess associated clinical outcomes is scarce. The aim of our study was to explore the association between domain categorization and future falls, subsequent fractures, and all-cause mortality in patients visiting an FLS.

2. Materials and Methods

2.1. Study Population

This study used data from the FX MoVie study, a 3-year prospective cohort study of patients visiting the FLS of VieCuri Medical Center in the south of the Netherlands. The cohort was established as follows: 1. All patients who visited the emergency room with a recent clinical fracture were invited to the FLS by a nurse specialized in osteoporosis, according to the Dutch guidelines for osteoporosis and fracture prevention [30]. Patients with a radiologically confirmed fracture, aged 50–90 years old, and living in the referral area of the hospital were invited. Patients with fractures due to high-energy trauma, osteomyelitis, or bone metastasis and patients concurrently treated for a malignancy were not invited. 2. All patients attending the FLS for fracture risk evaluation between July 2014 and June 2016 were asked to participate in the FXMoVie study. Patients with cognitive impairment, patients with peri-prosthetic fractures, and non-Caucasians were excluded from participation in the prospective study cohort (FXMoVie). Prior to participation, patients were given study information through oral and written communication, and all participants gave written informed consent. The study protocols were approved by a medical ethical committee (NL45707.072.13 and 2022-3319). For this study, patients with missing scores in the PC or PA data were excluded from the analyses.

2.2. Data Collection

During the FLS visit, sex and demographics were collected for all patients. Cause of the fracture and risk factors for fractures and falls were evaluated by a detailed questionnaire according to the Dutch guidelines for osteoporosis and fracture prevention [30]. This included self-reported measures on smoking and alcohol use, fall history, dizziness and balance problems, fear of falling, and use of walking aids. Information on comorbidities was derived from the electronic patient files and labeled according to ICD-10 (international classification of disease) standards [31]. Index fracture location was grouped into: I. hip fractures; II. major fractures: vertebra, multiple rib, humerus, pelvis, distal femur, and proximal tibia; and III. minor fractures: all other fractures (including finger and toe fractures), according to Center et al. [32]. Measurements included height, weight, body mass index (BMI), and bone mineral density (BMD) measurement at the lumbar spine, total hip, and femoral neck, and vertebral fracture assessment was performed using dual-energy X-ray absorptiometry (DEXA). Anti-osteoporotic treatment (AOM; e.g., bisphosphonates, denosumab, or teriparatide) was started according to the Dutch guidelines for osteoporosis and fracture prevention—in summary: in case of T score ≤ −2.5, or in case of osteopenia with at least one Grade 2 or 3 prevalent vertebral fracture according to Genant et al. [33].

2.3. Can Do, Do Do Framework

The “can do, do do” framework and our methods of testing PC and PA have been previously described elsewhere in detail [15]. In short, the framework combines a measure of PC with a measure of PA to form four quadrant groups: 1. preserved PC, preserved PA (can do, do do); 2. low PC, preserved PA (can’t do, do do); 3. preserved PC, low PA (can do, don’t do); 4. low PC, low PA (can’t do, don’t do). PC was measured by a trained nurse at the baseline study visit with the 6MWT. The 6MWT assesses walking ability and overall physical performance and has excellent inter-rater and test–retest reliability [34,35]. Patients were asked to walk as far as possible, up and down a 10 m walkway, rest when needed, and the use of walking aids was permitted. No encouragements were given during the test. Sex-stratified cutoff scores for poor or normal performance were based on Beekman et al. and amounted to a cutoff at 385 m for males and 366 m for females [36]. PA was measured by the MOX activity monitor (Maastricht Instruments B.V., The Netherlands). The MOX is a small, waterproof device that was attached to the right thigh. PA was measured during eight consecutive days in which patients were asked to follow normal daily activity routines. Details on the properties, processing of data, activity classification, calibration, and validity are described in detail elsewhere [15,37,38]. Static and dynamic activity (DA) were determined using the signal magnitude area: a measure of the intensity of physical activity. DA (average minutes per day) was further categorized as low physical activity (LPA, <3 metabolic equivalents [METS]), moderate-to-vigorous physical activity (MVPA, 3–6 METS), and vigorous physical activity (VPA, >6 METS) [38], and the threshold for poor or normal PA was defined according to WHO recommendations on minimum weekly exercise for general health benefits at <150 min of MVPA/VPA per week [39].

2.4. Outcome Measures

The outcomes were time to fall, time to first subsequent fracture, and time to death (by any cause). Registration of falls, subsequent fractures, and mortality started after inclusion in the study (baseline), a mean (SD) 3.5 (0.99) months after the index fracture. Initial prospective follow-up of participants was three years. A fall was defined according to the WHO definition as “an event which results in a person coming to rest inadvertently on the ground or floor or other lower level” [40]. Falls were recorded by participants in weekly fall diaries, which were returned by postal service. Research assistants verified and checked the diaries for completeness at three and six months by telephone and during the regular study visits at one, two, and three years of follow-up. Subsequent fracture incidence and mortality were evaluated at the yearly study visits at one, two, and three years of follow-up. All subsequent fractures were radiologically confirmed by radiology reports. All reported vertebral fractures were clinical vertebral fractures, as throughout follow-up no routine imaging of the spine was performed. The follow-up period for the outcomes of subsequent fractures and mortality was extended until 6 years after index fracture for all patients. Fall data were not available for the period between 3 and 6 years of follow-up.

2.5. Statistical Analysis

Descriptive analyses were performed for the whole population and predefined quadrant groups separately. Continuous data are presented as mean and SD for normally distributed data and median and IQR for non-normally distributed data. Categorical data are presented as frequencies and percentages. Cumulative incidences of first fractures, first falls, and mortality were assessed at 1, 2, 3 (fractures, mortality, falls), and 6 years (only fractures and mortality) of follow-up for the total population and the different “can do, do do” quadrants. Outcomes were also stratified by sex and compared using chi-square or Fisher exact tests in cases of low expected counts. Cox proportional hazard regression models were used to calculate crude and adjusted hazard ratios and 95% CIs for first subsequent fractures, first falls, and mortality for the different “can do, do do” quadrants. The “can do, do do” group was used as the main reference category, but all other between-group comparisons were also carried out. Patients were followed from baseline to censoring: outcome of interest (i.e., first fall, first fracture, or death), or end of data collection, whichever came first. For the outcome “fall” and “fracture”, patients were censored in case of death during follow-up. For the outcome “fall”, censoring was also applied if the patient was lost to follow-up (defined as the moment when >1 week of fall data were missing). Multivariate models were adjusted for a predefined set of variables based on the literature, including age, sex (all outcomes), BMI, lowest BMD, and vertebral fractures (only for fracture outcome). Proportional hazards assumption was checked by visual assessment of LML plots and by performing time program functions with all quadrants and all covariables independently. Additional analyses included Cox proportional hazards regression in which PC and PA were entered as continuous variables, as well as their interaction term. As a sensitivity analysis, a competing risk analysis was performed using the cumulative incidence competing risk method, according to Fine and Grey [41], in which death was considered a competing risk for the outcome of first fall or fracture. However, only one death occurred during the first 3 years of follow-up, which was censored due to incomplete fall data of the patient. Composite endpoints at 3 years (all outcomes) and 6 years (fractures and mortality) were explored visually and compared between the poor and normal PC and PA groups and for the patients in the 1st quartile of PC and PA compared to the highest three quartiles. A p-value of <0.05 was considered statistically significant. The competing risk analysis for the outcome of first fracture was carried out in R statistical software (version 4.2.1, R Core Team, 2022, R Foundation for Statistical Computing, Vienna, Austria), and R studio (RStudio Team, 2020, RStudio: Integrated Development for R. RStudio, PBC, Boston, MA) using the “cprsk” package. All other statistical analyses were performed in SPSS (version 28, 2021, IBM SPSS Statistics for Windows, Armonk, NY: IBM Corp.)

3. Results

3.1. Baseline Characteristics

Baseline characteristics are presented in Table 1. Participants were unequally divided into groups, with the largest proportion of 69.5% of the participants included in the “can do, do do” group, and smaller proportions in the other groups: 19.3% in the “can’t do, do do”, 3.0% in the “can do, don’t do”, and 8.3% in the “can’t do don’t do” group (p < 0.001). Participants in the “can do, do do” group were younger, had less severe fracture types, had less often osteoporosis or a prevalent vertebral fracture and had a treatment indication for AOM less often, had fewer comorbidities and fewer fall risk factors including fear of falling, and higher scores on the physical performance tests.
A total of five male patients and seven female patients had >1% missing fall data and were excluded from the cumulative incidence analyses. Table 2 shows the cumulative incidence of first fractures, first falls, and mortality during follow-up, stratified by year of follow-up and sex.

3.2. Time to First Fall

In total, 61.5% of the participants with sufficient fall data (N = 388) experienced at least one fall during follow-up of 3 years (Table 2). No significant association was found between quadrant groups and time to first fall during the 3 years of follow-up in the uni- and multivariate analysis (Table 3). Between-group comparison using alternative reference groups also did not show significant associations. A sensitivity analysis using dichotomous outcomes—“can do” vs. “can’t do” and “do do” vs. “don’t do”—did not show significant associations of low PC or low PA with time to first fall.

3.3. Time to First Subsequent Fracture

In total, 20.3% of the participants had sustained a first subsequent fracture at 6-year follow-up (Table 2). No significant association was found between each of the quadrant groups and time to first subsequent fracture during the 6 years of follow-up (Table 3), nor in the between-group analyses. The sensitivity analyses with death considered as a competing risk did not show a significant association. Sensitivity analysis using the dichotomous outcomes “can do” vs. “can’t do” and “do do” vs. “don’t do” and continuous outcomes of PC and PA measures did not show a significant association with time to first fracture.

3.4. Time to Death

During 6 years of follow-up, 4.8% of the participants had died. The proportion of participants that died was significantly different between quadrant groups at six years of follow-up: “can do, do do”: 2.9%, “can’t do, do do”: 9.1%, “can do, don’t do”: 0%, and “can’t do, don’t do”: 12.1% (p = 0.019, Table 2). Compared to the reference group, the “can’t do, do do” quadrant group was also significantly associated with time to death during 6 years of follow-up in the univariate analysis for the “can’t do, do do” group: HR = 3.29 (95% CI 1.19–9.07) and the “can’t do, don’t do” group: HR = 4.40 (95% CI 1.33–14.62). However, this association did not remain significant in the multivariate analysis (Table 3), nor did any of the between-group analyses yield significant results. A sensitivity analysis using the dichotomous outcomes “can do” vs. “can’t do” and “do do” vs. “don’t do” showed a significant association between the “can’t do” group: HR = 3.73 (95%CI 1.50–9.28) with time to first fracture compared to the “can do” group in univariate analysis, but this association did not remain significant in the multivariate analysis adjusted for age and sex: HR = 1.75, (95% CI 0.65–4.70).

3.5. Composite Outcomes

The visualization of composite endpoints are presented in Figure 1A–D and Figure 2A–D. Figure 1A–D displays the outcome of the composite endpoints falls, fractures, and mortality at 3 years of follow-up for the “can do” and the “can’t do” groups and the “do do “and “don’t do” groups, respectively. Figure 2A–D displays the outcome of the composite endpoints of fractures and mortality at 6 years of follow-up for the “can do” and the “can’t do” groups and the “do do “and “don’t do” groups, respectively. For both endpoints, the composite outcomes did not differ between groups. Quadrant group categorization based on quartile distribution of PC and PA measures instead of categorization based on predefined thresholds did not show differences in composite endpoint visualization.

4. Discussion

In this prospective study of 400 patients visiting a FLS with a recent fracture, we did not find a significant association between physical performance at 3, 5 months after fracture, assessed with the “can do, do do” framework, and time to first fall, first fracture, or death.
A previous cross-sectional assessment of the “can do, do do” framework showed differences in fall and fracture risk factors between the quadrant groups; patients in the can’t do, don’t do” group were older; presented with more major index fractures; more often had osteoporosis, prevalent vertebral fractures, and comorbidities; scored lower on all physical performance tests; and more often used a walking aid [15]. Contrarily, our study did not show an association with the clinically relevant outcomes of first fall (at three years), first fracture, and mortality (at six years). Our results are also in contrast with studies in the general older population associating poor physical performance (including both PC and PA measures) with future falls [12,25], subsequent fractures [42,43,44], and mortality [39,45,46,47,48]. Studies in fracture populations are fewer in number, but several involving older individuals with hip, radius, or any low-trauma fracture have reported associations between poor PC (using balance and gait measures) and falls [49,50,51,52] and post-fracture mortality in older males [52]. Poor PA has been associated with subsequent fractures in older males [53,54], but not females [55], after fracture. Interestingly, one study reported that males with a recent fracture who sustained a subsequent fracture had higher PA and gait speed measures compared to those who did not sustain a subsequent fracture [52]. This suggests that they may be more likely to engage in risky behavior. However, comparability between the studies in fracture patients is low, as they differ in terms of the age and sex of the included population, index fracture type, timing of the measurement before or after fracture, and measurement method of PC (different performance tests were evaluated) and PA (mostly self-reported with questionnaire) [52,54]. Only one study previously explored the “can do, do do” framework prospectively. Vaes et al. explored mortality risk in the different quadrant groups in patients with COPD [29] and found that patients with preserved PC had significantly lower 6-year mortality risk than those with poor PC (HR = 0.36, 95% CI; 0.14–0.93). In our study, PC (can do vs. can’t do) was associated with mortality in the univariate but not multivariate analysis. However, in the study by Vaes et al. the threshold values were study population driven and not based on pre-determined cutoff scores for poor or normal PC and PA. The lack of association with mortality outcomes in our study might be explained by the low mortality incidence during follow-up. The survival of 95.5% was the same as the 6-year survival of 65-year-olds in the general Dutch population (95.8%) [56], indicating a healthy subset of fracture patients, who generally have an increased mortality risk [3].
The absence of associations in our study might also be attributed to the unequal distribution of participants in the framework that resulted in a small number of participants included in the “can do, don’t do” and “can’t do, don’t do” groups. This was partly due to the low proportion of patients with low PA. The use of predefined cutoff scores for poor and normal PA (<150 min/week and 21/min per day MVPA/VPA) was based on the WHO recommendation to prevent adverse health outcomes, such as falls and fall-related injuries and all-cause and cause-specific mortality [39,57]. These are in line with other cutoff scores: <25.1 min MVPA/day was associated with a higher fall risk in older (70+) females [58]; and in community-dwelling older males, those with a history of >2 falls had an average of 24 min MVPA/day compared to 40 min/day in non-fallers [59]. In our study, the assessment of composite endpoints using the quartile distribution (instead of predefined cutoff scores) did not show superior discrimination between groups. Thus, the chosen cutoff scores do not seem to have had a great influence on the absence of an association.
More likely, our study population is too homogeneous, as it consists primarily of individuals who have sustained a fracture and are nearly all “previous fallers”. In 86.5% of our study population, the fracture that led to FLS evaluation was caused by a fall, and this did not vary between quadrant groups. Patients with a recent fracture are at a high risk for subsequent falls and fractures, with 38% of our participants experiencing a fall in the first year and 60% in the first three years, and 21% sustaining a subsequent fracture during six years of follow-up, despite evaluation and treatment according to best practice standards. These incidences did not differ between quadrant groups. Therefore, our study suggests that measures of PC and PA, measured during the FLS visit, do not differentiate enough between patients with a recent fracture to show any association with the clinical outcomes of falls, fractures, and mortality—a conclusion that was also suggested in a previous study assessing physical performance and subsequent fracture risk [52].
To effectively evaluate the likelihood of future fractures, it is crucial to assess fall risk factors, as falls are a significant predictor. A comprehensive review of the World Falls Guidelines emphasized the multifactorial nature of fall prediction and concluded that no single measurement can guarantee a high level of confidence in fall risk assessment for older adults. The review recommended both balance and gait assessments in fall prediction [12,17]. They further underlined that mobility assessment is only one part of fall prediction, and many other factors (e.g., fall-risk-increasing drugs, cardiovascular management, and cognitive function and sensory function decline) are also important [12]. Consequently, fall prediction and prevention is highly personalized. Interventions, including physical performance interventions, need to target multiple domains and need to be tailored to the patients’ needs [12,60,61]. Our study underlines this; even though the “can do, don’t do” framework offers a more complex approach compared to single physical function tests, this framework remains on one hand too “simplistic” to capture the multifactorial causes of falls and fractures. On the other hand, in testing and constructing combined measures, we lose information for a tailor-made, individual evaluation of different physical performance domains and corresponding treatment options. Thus, the categorization into a two-dimensional framework is inadequate to study complex clinical outcomes such as falls, fractures, and death, and a personalized approach based on existing fall and fracture risk factors from international guidelines [4,12] seems preferable.

Strengths and Limitations

One of the key strengths of our study is the inclusion of a sizable cohort of patients who had recently experienced a fracture, along with the examination of three clinically relevant outcomes. Secondly, this study is one of the first to assess physical performance in fracture patients in terms of clinical outcomes. Another strength is that we used a competing risk analysis to check the robustness of our results. Failure to adjust for the competing risk of death would result in an overestimation of the risk of outcome [41]. However, several limitations apply to this study. First, this was an explorative study and results will have to be confirmed in other prospective studies. Second, as stated, our cohort is relatively healthy compared to the wider range of fracture patients presenting at the Emergency Room, which might be reflected in the low number of patients with poor PA. The fracture patients that visit the FLS are only 60% of all consecutive patients with a clinical fracture [15], and previous studies showed that those who visit the FLS are more often female, younger, less frail, and less likely to have a hip fracture compared to those who do not attend [62,63]. Moreover, the participation in this prospective study was voluntary, and mostly younger patients with less severe types of fracture were willing and able to participate. The low incidence of mortality results in a lower reliability for this outcome, limited options of covariables in the multivariable models, and limited added benefit of the competing risk analysis. Lastly, we did not account for changes in physical performance parameters and patients’ exercise and rehabilitation activities during follow-up. Some studies suggest that the patients’ change in physical performance over time is equally, if not more, important to predict falls, fractures, and mortality as single test values [42,52]. Future studies should assess the association between physical performance measures and fall, subsequent fracture, and mortality risk in larger cohorts of fracture patients, and assess changes in performance over time.

5. Conclusions

The “can do, do do” framework was not associated with time to fracture, time to fall, or time to death in a 6-year prospective study. This study suggests that categorizing patients into a two-dimensional framework is inadequate to study complex, multifactorial outcomes and that a personalized approach based on individual assessment of existing fall and fracture risk factors might be preferable.

Author Contributions

Conceptualization, M.R.S., J.P.v.d.B., H.C.W. and C.E.W.; methodology, M.R.S., J.H.M.D., J.P.v.d.B., H.C.W. and C.E.W.; formal analysis, M.R.S. and J.H.M.D.; investigation, L.V., C.E.W. and J.P.v.d.B.; resources, J.P.v.d.B.; data curation, L.V., C.E.W., K.M. and J.P.v.d.B.; writing—original draft preparation, M.R.S. and H.C.W.; writing—review and editing, M.R.S., J.H.M.D., J.P.v.d.B., H.C.W., L.V., K.M. and C.E.W.; visualization, M.R.S.; supervision, J.P.v.d.B., H.C.W., J.H.M.D. and C.E.W.; project administration, J.P.v.d.B. and C.E.W.; funding acquisition, J.P.v.d.B. All authors have read and agreed to the published version of the manuscript.

Funding

The FX MoVie study was conducted with funding from the Weijerhorst Foundation. The Weijerhorst Foundation was not involved in conducting the study, analyzing the data, or writing the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Nijmegen (NL45707.072.13, 17 December 2013).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Research data are not shared due to privacy or ethical restrictions.

Conflicts of Interest

M.R.S., C.E.W., L.V., K.M. and J.H.M.D. declare that they have no conflicts of interest. J.P.v.d.B. reports grants for lectures and a position on the advisory board from UCB and Amgen outside the submitted work. H.C.W. reports a speaker’s fee for lectures from UCB, outside the submitted work. The Weijerhorst Foundation had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. van Staa, T.P.; Dennison, E.M.; Leufkens, H.G.; Cooper, C. Epidemiology of fractures in England and Wales. Bone 2001, 29, 517–522. [Google Scholar] [CrossRef]
  2. Kanis, J.A.; Norton, N.; Harvey, N.C.; Jacobson, T.; Johansson, H.; Lorentzon, M.; McCloskey, E.V.; Willers, C.; Borgström, F. SCOPE 2021: A new scorecard for osteoporosis in Europe. Arch. Osteoporos. 2021, 16, 82. [Google Scholar] [CrossRef] [PubMed]
  3. Schene, M.R.; Wyers, C.E.; Driessen, A.M.H.; Souverein, P.C.; Gemmeke, M.; van den Bergh, J.P.; Willems, H.C. Imminent fall risk after fracture. Age Ageing 2023, 52, afad201. [Google Scholar] [CrossRef] [PubMed]
  4. Javaid, M.K.; Sami, A.; Lems, W.; Mitchell, P.; Thomas, T.; Singer, A.; Speerin, R.; Fujita, M.; Pierroz, D.D.; Akesson, K.; et al. A patient-level key performance indicator set to measure the effectiveness of fracture liaison services and guide quality improvement: A position paper of the IOF Capture the Fracture Working Group, National Osteoporosis Foundation and Fragility Fracture Network. Osteoporos. Int. 2020, 31, 1193–1204. [Google Scholar] [CrossRef] [PubMed]
  5. Li, N.; Hiligsmann, M.; Boonen, A.; van Oostwaard, M.M.; de Bot, R.; Wyers, C.E.; Bours, S.P.G.; van den Bergh, J.P. The impact of fracture liaison services on subsequent fractures and mortality: A systematic literature review and meta-analysis. Osteoporos. Int. 2021, 32, 1517–1530. [Google Scholar] [CrossRef] [PubMed]
  6. Geusens, P.; Bours, S.P.G.; Wyers, C.E.; van den Bergh, J.P. Fracture liaison programs. Best. Pract. Res. Clin. Rheumatol. 2019, 33, 278–289. [Google Scholar] [CrossRef]
  7. Ganda, K. Fracture liaison services: Past, present and future: Editorial relating to: The impact of Fracture Liaison Services on subsequent fractures and mortality: A systematic literature review and meta-analysis. Osteoporos. Int. 2021, 32, 1461–1464. [Google Scholar] [CrossRef]
  8. Lems, W.F.; Dreinhöfer, K.E.; Bischoff-Ferrari, H.; Blauth, M.; Czerwinski, E.; da Silva, J.; Herrera, A.; Hoffmeyer, P.; Kvien, T.; Maalouf, G.; et al. EULAR/EFORT recommendations for management of patients older than 50 years with a fragility fracture and prevention of subsequent fractures. Ann. Rheum. Dis. 2017, 76, 802–810. [Google Scholar] [CrossRef]
  9. Vranken, L.; Wyers, C.E.; Van der Velde, R.Y.; Janzing, H.M.J.; Kaarsemakers, S.; Driessen, J.; Eisman, J.; Center, J.R.; Nguyen, T.V.; Tran, T.; et al. Association between incident falls and subsequent fractures in patients attending the fracture liaison service after an index fracture: A 3-year prospective observational cohort study. BMJ Open 2022, 12, e058983. [Google Scholar] [CrossRef]
  10. Boyé, N.D.; Mattace-Raso, F.U.; Van der Velde, N.; Van Lieshout, E.M.; De Vries, O.J.; Hartholt, K.A.; Kerver, A.J.; Bruijninckx, M.M.; Van der Cammen, T.J.; Patka, P.; et al. Circumstances leading to injurious falls in older men and women in the Netherlands. Injury 2014, 45, 1224–1230. [Google Scholar] [CrossRef] [PubMed]
  11. Berg, W.P.; Alessio, H.M.; Mills, E.M.; Tong, C. Circumstances and consequences of falls in independent community-dwelling older adults. Age Ageing 1997, 26, 261–268. [Google Scholar] [CrossRef]
  12. Montero-Odasso, M.; van der Velde, N.; Martin, F.C.; Petrovic, M.; Tan, M.P.; Ryg, J.; Aguilar-Navarro, S.; Alexander, N.B.; Becker, C.; Blain, H.; et al. World guidelines for falls prevention and management for older adults: A global initiative. Age Ageing 2022, 51, afac205. [Google Scholar] [CrossRef] [PubMed]
  13. Koolen, E.H.; van Hees, H.W.; van Lummel, R.C.; Dekhuijzen, R.; Djamin, R.S.; Spruit, M.A.; van ‘t Hul, A.J. “Can do” versus “do do”: A Novel Concept to Better Understand Physical Functioning in Patients with Chronic Obstructive Pulmonary Disease. J. Clin. Med. 2019, 8, 340. [Google Scholar] [CrossRef] [PubMed]
  14. van Lummel, R.C.; Walgaard, S.; Pijnappels, M.; Elders, P.J.; Garcia-Aymerich, J.; van Dieën, J.H.; Beek, P.J. Physical Performance and Physical Activity in Older Adults: Associated but Separate Domains of Physical Function in Old Age. PLoS ONE 2015, 10, e0144048. [Google Scholar] [CrossRef]
  15. Schene, M.R.; Meijer, K.; Cheung, D.; Willems, H.C.; Driessen, J.H.M.; Vranken, L.; van den Bergh, J.P.; Wyers, C.E. Physical Functioning in Patients with a Recent Fracture: The “Can Do, Do Do” Framework Applied to Explore Physical Capacity, Physical Activity and Fall Risk Factors. Calcif. Tissue Int. 2023, 113, 195–206. [Google Scholar] [CrossRef]
  16. Lusardi, M.M.; Fritz, S.; Middleton, A.; Allison, L.; Wingood, M.; Phillips, E.; Criss, M.; Verma, S.; Osborne, J.; Chui, K.K. Determining Risk of Falls in Community Dwelling Older Adults: A Systematic Review and Meta-analysis Using Posttest Probability. J. Geriatr. Phys. Ther. 2017, 40, 1–36. [Google Scholar] [CrossRef] [PubMed]
  17. Beck Jepsen, D.; Robinson, K.; Ogliari, G.; Montero-Odasso, M.; Kamkar, N.; Ryg, J.; Freiberger, E.; Masud, T. Predicting falls in older adults: An umbrella review of instruments assessing gait, balance, and functional mobility. BMC Geriatr. 2022, 22, 615. [Google Scholar] [CrossRef]
  18. Ganz, D.A.; Bao, Y.; Shekelle, P.G.; Rubenstein, L.Z. Will my patient fall? Jama 2007, 297, 77–86. [Google Scholar] [CrossRef]
  19. Cöster, M.E.; Karlsson, M.; Ohlsson, C.; Mellström, D.; Lorentzon, M.; Ribom, E.; Rosengren, B. Physical function tests predict incident falls: A prospective study of 2969 men in the Swedish Osteoporotic Fractures in Men study. Scand. J. Public Health 2020, 48, 436–441. [Google Scholar] [CrossRef]
  20. Alajlouni, D.A.; Bliuc, D.; Tran, T.S.; Blank, R.D.; Center, J.R. Muscle strength and physical performance contribute to and improve fracture risk prediction in older people: A narrative review. Bone 2023, 172, 116755. [Google Scholar] [CrossRef]
  21. Alajlouni, D.; Tran, T.; Bliuc, D.; Blank, R.D.; Cawthon, P.M.; Orwoll, E.S.; Center, J.R. Muscle Strength and Physical Performance Improve Fracture Risk Prediction Beyond Garvan and FRAX: The Osteoporotic Fractures in Men (MrOS) Study. J. Bone Miner. Res. 2021, 37, 411–419. [Google Scholar] [CrossRef] [PubMed]
  22. Caspersen, C.J.; Powell, K.E.; Christenson, G.M. Physical activity, exercise, and physical fitness: Definitions and distinctions for health-related research. Public. Health Rep. 1985, 100, 126–131. [Google Scholar] [PubMed]
  23. Heesch, K.C.; Byles, J.E.; Brown, W.J. Prospective association between physical activity and falls in community-dwelling older women. J. Epidemiol. Community Health 2008, 62, 421–426. [Google Scholar] [CrossRef] [PubMed]
  24. Klenk, J.; Kerse, N.; Rapp, K.; Nikolaus, T.; Becker, C.; Rothenbacher, D.; Peter, R.; Denkinger, M.D. Physical Activity and Different Concepts of Fall Risk Estimation in Older People--Results of the ActiFE-Ulm Study. PLoS ONE 2015, 10, e0129098. [Google Scholar] [CrossRef]
  25. Sherrington, C.; Fairhall, N.; Kwok, W.; Wallbank, G.; Tiedemann, A.; Michaleff, Z.A.; Ng, C.; Bauman, A. Evidence on physical activity and falls prevention for people aged 65+ years: Systematic review to inform the WHO guidelines on physical activity and sedentary behaviour. Int. J. Behav. Nutr. Phys. Act. 2020, 17, 144. [Google Scholar] [CrossRef]
  26. Lu, Z.; Lam, F.M.H.; Leung, J.C.S.; Kwok, T.C.Y. The U-Shaped Relationship Between Levels of Bouted Activity and Fall Incidence in Community-Dwelling Older Adults: A Prospective Cohort Study. J. Gerontol. A Biol. Sci. Med. Sci. 2020, 75, e145–e151. [Google Scholar] [CrossRef] [PubMed]
  27. Carl, J.; Schultz, K.; Janssens, T.; von Leupoldt, A.; Pfeifer, K.; Geidl, W. The “can do, do do” concept in individuals with chronic obstructive pulmonary disease: An exploration of psychological mechanisms. Respir. Res. 2021, 22, 260. [Google Scholar] [CrossRef]
  28. Janssen, S.M.J.; Spruit, M.A.; Antons, J.C.; Djamin, R.S.; Abbink, J.J.; van Helvoort, H.A.C.; van ‘t Hul, A.J. “Can Do” Versus “Do Do” in Patients with Asthma at First Referral to a Pulmonologist. J. Allergy Clin. Immunol. Pract. 2021, 9, 1278–1284. [Google Scholar] [CrossRef]
  29. Vaes, A.W.; Spruit, M.A.; Koolen, E.H.; Antons, J.C.; de Man, M.; Djamin, R.S.; van Hees, H.W.H.; van ‘t Hul, A.J. “Can Do, Do Do” Quadrants and 6-Year All-Cause Mortality in Patients with COPD. Chest 2022, 161, 1494–1504. [Google Scholar] [CrossRef]
  30. Dutch Institute for Healthcare Improvement CBO. Richtlijn Osteoporose en Fractuurpreventie, Derde Herziening [Dutch]; Dutch Institute for Healthcare Improvement CBO: Utrecht, The Netherlands, 2011. [Google Scholar]
  31. World Health Organisation. ICD-10: International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, 2nd ed.; World Health Organisation: Geneva, Switzerland, 2004. [Google Scholar]
  32. Center, J.R.; Nguyen, T.V.; Schneider, D.; Sambrook, P.N.; Eisman, J.A. Mortality after all major types of osteoporotic fracture in men and women: An observational study. Lancet 1999, 353, 878–882. [Google Scholar] [CrossRef]
  33. Genant, H.K.; Wu, C.Y.; van Kuijk, C.; Nevitt, M.C. Vertebral fracture assessment using a semiquantitative technique. J. Bone Miner. Res. 1993, 8, 1137–1148. [Google Scholar] [CrossRef] [PubMed]
  34. Rikli, R.E.; Jones, C.J. The reliability and validity of a 6-minute walk test as a measure of physical endurance in older adults. J. Aging Phys. Act. 1998, 6, 363–375. [Google Scholar] [CrossRef]
  35. Overgaard, J.A.; Larsen, C.M.; Holtze, S.; Ockholm, K.; Kristensen, M.T. Interrater Reliability of the 6-Minute Walk Test in Women With Hip Fracture. J. Geriatr. Phys. Ther. 2017, 40, 158–166. [Google Scholar] [CrossRef] [PubMed]
  36. Beekman, E.; Mesters, I.; Gosselink, R.; Klaassen, M.P.; Hendriks, E.J.; Van Schayck, O.C.; de Bie, R.A. The first reference equations for the 6-minute walk distance over a 10 m course. Thorax 2014, 69, 867–868. [Google Scholar] [CrossRef] [PubMed]
  37. Bijnens, W.; Aarts, J.; Stevens, A.; Ummels, D.; Meijer, K. Optimization and Validation of an Adjustable Activity Classification Algorithm for Assessment of Physical Behavior in Elderly. Sensors 2019, 19, 5344. [Google Scholar] [CrossRef]
  38. van der Weegen, S.; Essers, H.; Spreeuwenberg, M.; Verwey, R.; Tange, H.; de Witte, L.; Meijer, K. Concurrent validity of the MOX activity monitor compared to the ActiGraph GT3X. Telemed. J. E Health 2015, 21, 259–266. [Google Scholar] [CrossRef]
  39. World Health Organization. WHO Guidelines on Physical Activity and Sedentary Behaviour; World Health Organization: Geneva, Switzerland, 2020. [Google Scholar]
  40. World Health Organisation. Factsheet Falls. 2021. Available online: https://www.who.int/news-room/fact-sheets/detail/falls (accessed on 17 May 2021).
  41. Verduijn, M.; Grootendorst, D.C.; Dekker, F.W.; Jager, K.J.; le Cessie, S. The analysis of competing events like cause-specific mortality--beware of the Kaplan-Meier method. Nephrol. Dial. Transplant. 2011, 26, 56–61. [Google Scholar] [CrossRef]
  42. Barbour, K.E.; Lui, L.Y.; McCulloch, C.E.; Ensrud, K.E.; Cawthon, P.M.; Yaffe, K.; Barnes, D.E.; Fredman, L.; Newman, A.B.; Cummings, S.R.; et al. Trajectories of Lower Extremity Physical Performance: Effects on Fractures and Mortality in Older Women. J. Gerontol. A Biol. Sci. Med. Sci. 2016, 71, 1609–1615. [Google Scholar] [CrossRef]
  43. Cawthon, P.M.; Fullman, R.L.; Marshall, L.; Mackey, D.C.; Fink, H.A.; Cauley, J.A.; Cummings, S.R.; Orwoll, E.S.; Ensrud, K.E. Physical performance and risk of hip fractures in older men. J. Bone Miner. Res. 2008, 23, 1037–1044. [Google Scholar] [CrossRef]
  44. Harvey, N.C.; Odén, A.; Orwoll, E.; Lapidus, J.; Kwok, T.; Karlsson, M.K.; Rosengren, B.E.; Ribom, E.; Cooper, C.; Cawthon, P.M.; et al. Measures of Physical Performance and Muscle Strength as Predictors of Fracture Risk Independent of FRAX, Falls, and aBMD: A Meta-Analysis of the Osteoporotic Fractures in Men (MrOS) Study. J. Bone Miner. Res. 2018, 33, 2150–2157. [Google Scholar] [CrossRef]
  45. Studenski, S.; Perera, S.; Patel, K.; Rosano, C.; Faulkner, K.; Inzitari, M.; Brach, J.; Chandler, J.; Cawthon, P.; Connor, E.B.; et al. Gait speed and survival in older adults. Jama 2011, 305, 50–58. [Google Scholar] [CrossRef]
  46. Yazdanyar, A.; Aziz, M.M.; Enright, P.L.; Edmundowicz, D.; Boudreau, R.; Sutton-Tyrell, K.; Kuller, L.; Newman, A.B. Association Between 6-Minute Walk Test and All-Cause Mortality, Coronary Heart Disease-Specific Mortality, and Incident Coronary Heart Disease. J. Aging Health 2014, 26, 583–599. [Google Scholar] [CrossRef]
  47. Veronese, N.; Stubbs, B.; Fontana, L.; Trevisan, C.; Bolzetta, F.; Rui, M.; Sartori, L.; Musacchio, E.; Zambon, S.; Maggi, S.; et al. A Comparison of Objective Physical Performance Tests and Future Mortality in the Elderly People. J. Gerontol. A Biol. Sci. Med. Sci. 2017, 72, 362–368. [Google Scholar] [CrossRef] [PubMed]
  48. Hirsch, C.H.; Buzková, P.; Robbins, J.A.; Patel, K.V.; Newman, A.B. Predicting late-life disability and death by the rate of decline in physical performance measures. Age Ageing 2012, 41, 155–161. [Google Scholar] [CrossRef] [PubMed]
  49. Lloyd, B.D.; Williamson, D.A.; Singh, N.A.; Hansen, R.D.; Diamond, T.H.; Finnegan, T.P.; Allen, B.J.; Grady, J.N.; Stavrinos, T.M.; Smith, E.U.; et al. Recurrent and injurious falls in the year following hip fracture: A prospective study of incidence and risk factors from the Sarcopenia and Hip Fracture study. J. Gerontol. A Biol. Sci. Med. Sci. 2009, 64, 599–609. [Google Scholar] [CrossRef] [PubMed]
  50. Kristensen, M.T.; Foss, N.B.; Kehlet, H. Timed “up & go” test as a predictor of falls within 6 months after hip fracture surgery. Phys. Ther. 2007, 87, 24–30. [Google Scholar] [CrossRef] [PubMed]
  51. Dewan, N.; MacDermid, J.C.; Grewal, R.; Beattie, K. Risk factors predicting subsequent falls and osteoporotic fractures at 4 years after distal radius fracture-a prospective cohort study. Arch. Osteoporos. 2018, 13, 32. [Google Scholar] [CrossRef] [PubMed]
  52. Alajlouni, D.A.; Bliuc, D.; Tran, T.S.; Blank, R.D.; Cawthon, P.M.; Ensrud, K.E.; Lane, N.E.; Orwoll, E.S.; Cauley, J.A.; Center, J.R. Muscle Strength and Physical Performance Are Associated with Risk of Postfracture Mortality But Not Subsequent Fracture in Men. J. Bone Miner. Res. 2022, 37, 1571–1579. [Google Scholar] [CrossRef] [PubMed]
  53. Center, J.R.; Bliuc, D.; Nguyen, T.V.; Eisman, J.A. Risk of subsequent fracture after low-trauma fracture in men and women. Jama 2007, 297, 387–394. [Google Scholar] [CrossRef] [PubMed]
  54. Bliuc, D.; Nguyen, N.D.; Milch, V.E.; Nguyen, T.V.; Eisman, J.A.; Center, J.R. Mortality risk associated with low-trauma osteoporotic fracture and subsequent fracture in men and women. Jama 2009, 301, 513–521. [Google Scholar] [CrossRef]
  55. Iconaru, L.; Moreau, M.; Baleanu, F.; Kinnard, V.; Charles, A.; Mugisha, A.; Surquin, M.; Benoit, F.; Karmali, R.; Paesmans, M.; et al. Risk factors for imminent fractures: A substudy of the FRISBEE cohort. Osteoporos. Int. 2021, 32, 1093–1101. [Google Scholar] [CrossRef] [PubMed]
  56. CBS Statline. Overlevingskansen; Geslacht, Leeftijd (Dutch). Available online: https://opendata.cbs.nl/statline/#/CBS/nl/dataset/70701ned/table?fromstatwebdate (accessed on 12 February 2024).
  57. Ekelund, U.; Tarp, J.; Steene-Johannessen, J.; Hansen, B.H.; Jefferis, B.; Fagerland, M.W.; Whincup, P.; Diaz, K.M.; Hooker, S.P.; Chernofsky, A.; et al. Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: Systematic review and harmonised meta-analysis. Bmj 2019, 366, l4570. [Google Scholar] [CrossRef]
  58. Buchner, D.M.; Rillamas-Sun, E.; Di, C.; LaMonte, M.J.; Marshall, S.W.; Hunt, J.; Zhang, Y.; Rosenberg, D.E.; Lee, I.M.; Evenson, K.R.; et al. Accelerometer-Measured Moderate to Vigorous Physical Activity and Incidence Rates of Falls in Older Women. J. Am. Geriatr. Soc. 2017, 65, 2480–2487. [Google Scholar] [CrossRef] [PubMed]
  59. Jefferis, B.J.; Iliffe, S.; Kendrick, D.; Kerse, N.; Trost, S.; Lennon, L.T.; Ash, S.; Sartini, C.; Morris, R.W.; Wannamethee, S.G.; et al. How are falls and fear of falling associated with objectively measured physical activity in a cohort of community-dwelling older men? BMC Geriatr. 2014, 14, 114. [Google Scholar] [CrossRef]
  60. Tricco, A.C.; Thomas, S.M.; Veroniki, A.A.; Hamid, J.S.; Cogo, E.; Strifler, L.; Khan, P.A.; Robson, R.; Sibley, K.M.; MacDonald, H.; et al. Comparisons of Interventions for Preventing Falls in Older Adults: A Systematic Review and Meta-analysis. Jama 2017, 318, 1687–1699. [Google Scholar] [CrossRef]
  61. Sherrington, C.; Fairhall, N.J.; Wallbank, G.K.; Tiedemann, A.; Michaleff, Z.A.; Howard, K.; Clemson, L.; Hopewell, S.; Lamb, S.E. Exercise for preventing falls in older people living in the community. Cochrane Database Syst. Rev. 2019, 1, Cd012424. [Google Scholar] [CrossRef] [PubMed]
  62. van den Berg, P.; van Haard, P.M.M.; Geusens, P.P.; van den Bergh, J.P.; Schweitzer, D.H. Challenges and opportunities to improve fracture liaison service attendance: Fracture registration and patient characteristics and motivations. Osteoporos. Int. 2019, 30, 1597–1606. [Google Scholar] [CrossRef]
  63. Eekman, D.A.; van Helden, S.H.; Huisman, A.M.; Verhaar, H.J.; Bultink, I.E.; Geusens, P.P.; Lips, P.; Lems, W.F. Optimizing fracture prevention: The fracture liaison service, an observational study. Osteoporos. Int. 2014, 25, 701–709. [Google Scholar] [CrossRef] [PubMed]
Jpm 14 00337 g001
Figure 1. Composite endpoints during three years of follow-up. (A–D) Visualization of composite endpoints falls and fractures during 3 years of follow-up (in weeks) for normal and poor physical capacity and activity groups. The cutoff for low physical capacity is 366 m for females and 385 for males (can’t do); the cutoff for low physical activity is an average of 21 min/day (don’t do).
Figure 1. Composite endpoints during three years of follow-up. (A–D) Visualization of composite endpoints falls and fractures during 3 years of follow-up (in weeks) for normal and poor physical capacity and activity groups. The cutoff for low physical capacity is 366 m for females and 385 for males (can’t do); the cutoff for low physical activity is an average of 21 min/day (don’t do).
Jpm 14 00337 g001
Jpm 14 00337 g002
Figure 2. Composite endpoints during six years of follow-up. (A–D) Visualization of composite endpoints fractures and mortality during 6 years of follow-up (in weeks) for normal and poor physical capacity and activity groups. The cutoff for low physical capacity is 366 m for females and 385 for males (can’t do), the cutoff for low physical activity is an average of 21 min/day (don’t do).
Figure 2. Composite endpoints during six years of follow-up. (A–D) Visualization of composite endpoints fractures and mortality during 6 years of follow-up (in weeks) for normal and poor physical capacity and activity groups. The cutoff for low physical capacity is 366 m for females and 385 for males (can’t do), the cutoff for low physical activity is an average of 21 min/day (don’t do).
Jpm 14 00337 g002
Table 1. Baseline characteristics.
Table 1. Baseline characteristics.
Total PopulationCan Do, Do
Do		Can’t Do, Do DoCan Do, Don’t DoCan’t Do, Don’t Dop-Value
Number of patients b400 (100)278 (69.5)77 (19.3)12 (3.0)33 (8.3)<0.001 *
Females b283 (70.8) 191 (67.5)53 (18.7)12 (4.2)27 (9.5) <0.001 *
Males b117 (29.2)87 (74.4)24 (20.5)0 (0)6 (5.1)<0.001 *
Age a64.6 ± 8.662.7 ± 7.968.6 ± 9.067.4 ± 870.4 ± 8.2<0.001 *
BMI a27.2 ± 4.427.3 ± 4.229.0 ± 4.226.2 ± 5.027.2 ± 6.00.02 *
Time since fracture (days) a108.0 ± 30.2107.1 ± 29.2110.6 ± 31.4105.7 ± 30.3110.4 ± 35.80.77
Alcohol use b316 (79.0) 239 (86.0)50 (64.9)7 (58.3)20 (60.6)<0.001 *
Smoking b269 (67.3)190 (68.3)54 (70.1)4 (33.3)21 (63.6)0.08
Fracture type b <0.001 *
Hip20 (5)6 (2.2)5 (6.5)1 (8.3)8 (24.2)
Major80 (20)39 (14.0)22 (28.6)4 (33.3)15 (45.5)
Minor250 (62.5)191 (68.7)45 (58.4)6 (50.0)8 (24.2)
Finger and toe50 (12.5)42 (15.1)5 (6.5)1 (8.3)2 (6.1)
BMD b <0.001 *
Normal BMD112 (28.0)87 (31.3)21 (27.3)2 (16.7)2 (6.1)
Osteopenia 200 (50.0)142 (51.1)40 (51.9)5 (41.7)13 (39.4)
Osteoporosis 88 (22)49 (17.6)16 (20.8)5 (41.7) 18 (54.4)
≥1 Prevalent VF gr 2–3 b49 (12.3)23 (8.3)15 (19.5)1 (8.3)10 (30.3)<0.001 *
AOM treatment b 135 (33.7)79 (28.4)24 (31.4)7 (58.3)25 (75.8)<0.001 *
Cardiovascular comorbidity b163 (40.8)99 (35.6)41 (53.2)2 (16.7)21 (63.6)<0.001 *
Asthma/COPD b40 (10.0)21 (7.6)12 (15.6)07 (21.2)0.019 *
Diabetes b29 (72)13 (4.7)10 (13.0)06 (18.2)0.006 *
Fracture caused by a fall b346 (86.5)241 (86.7)66 (85.7)11 (91.7)28 (84.8)0.97
≥2 Falls in past year b51 (12.8)32 (11.6)14 (18.2)1 (8.3)4 (12.1)0.46
Dizziness–balance b94 (25.3)52 (20.2)25 (34.2)5 (45.5)12 (40.0) 0.006 *
Walking aid b15 (3.8)2 (0.7)7 (9.1)06 (18.2)<0.001 *
Fear of falling b39 (9.8)16 (5.8)15 (19.7)08 (24.2)<0.001 *
MVPA-VPA (Av. min/day) a56 (26.3–87.0)70 (48–100)43 (32–59)12 (11–18)14 (8–18)<0.001 *
6MWD (m) a428 (364.3–489.8)465 (420–510)330 (299–354)419 (396–476)280 (218–330)<0.001 *
a Continuous variable: mean ± SD, median (IQR); b categorical variable: number (%). AOM: anti-osteoporotic medication, BMI: body mass index, BMD: bone mineral density, 6MWD: six-minute walking distance, MVPA: moderate-to-vigorous physical activity, VF: vertebral fracture, VPA: vigorous physical activity, Av.: average. The cutoff for low physical capacity is 366 m for females and 385 for males (can’t do), the cutoff for low physical activity is an average 21 min/day (don’t do). * statistically significant difference between groups.
Table 2. Cumulative incidence of first fractures, first falls, and mortality.
Table 2. Cumulative incidence of first fractures, first falls, and mortality.
Can Do, Do DoCan’t Do, Do DoCan Do, Don’t DoCan’t Do, Don’t Dop-Value
Number of patients278 (69.5)77 (19.3)12 (3.0)33 (8.3)
First fall by follow-up †
1 year112 (40.3)28 (40.0)5 (41.5)8 (24.2)0.31
2 years146 (53.5)35 (50.0)6 (50.0)13 (39.4)0.49
3 years170 (62.3)45 (64.3)7 (58.3)18 (54.5)0.78
First fracture by follow-up
1 year14 (5.0)5 (6.5)0 (0)2 (6.1)0.84
2 years19 (6.8)5 (6.5)0 (0)4 (12.1)0.80
3 years32 (11.5)9 (11.7)0 (0)5 (15.20.65
6 years60 (21.6)14 (18.2)1 (8.3)6 (18.2)0.76
Mortality by follow-up
1 year0 (0)0 (0)0 (0)0 (0)-
2 years0 (0)1 (1.3)0 (0)0 (0)0.30
3 years0 (0)1 (1.3)0 (0)0 (0)0.30
6 years8 (2.9)7 (9.1)0 (0)4 (12.1)0.019 *
All cumulative incidences are presented as numbers (%). The cutoff for low physical capacity is 366 m for females and 385 for males (can’t do), the cutoff for low physical activity is an average of 21 min/day (don’t do). † Twelve participants were excluded due to >1% missing fall data. * Statistically significant at p < 0.05.
Table 3. Association of “can do, do do” groups with falls, subsequent fractures, and mortality.
Table 3. Association of “can do, do do” groups with falls, subsequent fractures, and mortality.
Time to First FallTime to First SubsequentTime to Death
Fracture
Univariate Multivariate Univariate Multivariate Univariate Multivariate
HR 95% CIHR 95% CIHR 95% CIHR 95% CIHR 95% CIHR 95% CI
Can do, do do (ref)------
Can’t do, do do0.99 (0.71, 1.38)0.99 (0.70, 1.39)0.86 (0.48, 1.53)0.80 (0.43, 1.48)3.29 (1.19, 9.07) *1.55 (0.53. 4.56)
Can do, don’t do0.74 (0.41, 1.88)0.83 (0.39, 1.78)0.34 (0.05, 2.48)0.25 (0.04, 1.84)--
Can’t do, don’t do 0.76 (0.47, 1.24)0.75 (0.45, 1.23)0.88 (0.37, 2.04)0.58 (0.24, 1.41)4.40 (1.33, 4.62) *1.19 (0.54, 6.95)
Time to first fall was assessed during 3 years of follow-up; time to first subsequent fracture and death was assessed during 6 years of follow-up. HR: hazard ratio. 95% CI: 95% confidence interval. Multivariate models included the following covariates: First falls: age and sex. First subsequent fractures: age, sex, BMI, prevalent VFs, and lowest BMD. Mortality: age and sex. * Statistically significant compared to reference group “can do, do do” at p < 0.05. Between-group comparisons with different reference groups did not yield significant results.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Schene, M.R.; Wyers, C.E.; Driessen, J.H.M.; Vranken, L.; Meijer, K.; van den Bergh, J.P.; Willems, H.C. The “Can Do, Do Do” Framework Applied to Assess the Association between Physical Capacity, Physical Activity and Prospective Falls, Subsequent Fractures, and Mortality in Patients Visiting the Fracture Liaison Service. J. Pers. Med. 2024, 14, 337. https://doi.org/10.3390/jpm14040337

AMA Style

Schene MR, Wyers CE, Driessen JHM, Vranken L, Meijer K, van den Bergh JP, Willems HC. The “Can Do, Do Do” Framework Applied to Assess the Association between Physical Capacity, Physical Activity and Prospective Falls, Subsequent Fractures, and Mortality in Patients Visiting the Fracture Liaison Service. Journal of Personalized Medicine. 2024; 14(4):337. https://doi.org/10.3390/jpm14040337

Chicago/Turabian Style

Schene, Merle R., Caroline E. Wyers, Johanna H. M. Driessen, Lisanne Vranken, Kenneth Meijer, Joop P. van den Bergh, and Hanna C. Willems. 2024. "The “Can Do, Do Do” Framework Applied to Assess the Association between Physical Capacity, Physical Activity and Prospective Falls, Subsequent Fractures, and Mortality in Patients Visiting the Fracture Liaison Service" Journal of Personalized Medicine 14, no. 4: 337. https://doi.org/10.3390/jpm14040337

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop