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Mediating Effects of Coping Strategies on Quality of Life Following Extremity Injury

Tonapa, Santo Imanuel; Lin, Wei-Ting; Kuo, Fang-Li; Lee, Bih-O

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doi: 10.1097/NNR.0000000000000581
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Over the years, the disease burden and productivity loss that extremity injuries impose on society have become a public health concern (Banerjee et al., 2013; Polinder et al., 2016). Extremity injury is more prevalent in low- and middle-income countries, including Indonesia (Cordero et al., 2020). Such injuries often result from traffic accidents (Tonapa et al., 2021), primarily caused by lack of road safety. Extremity injuries are a significant cause of disability in Indonesia; more than 70% of the reported injuries annually are extremity-related, and this number increases every year. Individuals who get injured often experience a decreased quality of life (QoL). Regaining preinjury QoL following extremity injury may take time and require participation in rehabilitation programs (Lin et al., 2016), which can be associated with high medical costs for both the patients and the healthcare system (Dalal & Svanström, 2015).

QoL has been recognized as an essential outcome in extremity injury research recently. Nurses at the forefront of injury care have given precise direction to their practice to maximize patients’ QoL after injury (Richmond & Aitken, 2011). Survivors of extremity injuries tend to have poor QoL during the first 2 months (de Putter et al., 2014), 6 months (Van Son et al., 2016), 12 months, and 2 years (Lin et al., 2016) following hospital discharge. Nevertheless, most of these studies have focused on long-term outcomes and did not explore how patients self-regulate during the early recovery phase following injuries that probably are vital for them to face rehabilitation programs.

In order to ensure that services such as hospital care and rehabilitation programs provide the most benefits possible, these services must be strongly evidence based. The self-regulation model (SRM) has been used to understand individuals’ self-regulation processes when coping with health problems and treatment (Benyamini & Karademas, 2019; Leventhal & Diefenbach, 1996). Based on the SRM, individuals’ self-regulation process started from representing their illness and, accordingly, coping with the situations that influence health outcomes (Leventhal & Diefenbach, 1996), such as QoL. This model positions individuals as problem-solvers who use common sense to represent an illness. The SRM structure comprises illness representations (IRs), coping, and appraisals from these regulation processes reflected in health outcomes such as QoL (Leventhal & Diefenbach, 1996). The SRM has been demonstrated to influence QoL in patients with chronic illnesses, such as alopecia (Willemse et al., 2019) and gastroparesis (Woodhouse et al., 2018). However, most injury-related studies have partially employed the model without evaluating coping strategies (Chaboyer et al., 2010; Lee et al., 2008). As suggested by Lee et al. (2008), studies should assess the effectiveness of coping strategies when utilizing the SRM in injured patients, as coping may define the underlying mechanisms through which patients practice self-regulation following injury.

IRs represent a core concept of the SRM that has been well-studied among injury populations. IRs are formed when individuals face health threats based on patients’ health-related beliefs and expectations. Although the patients’ health-related beliefs and expectations may not always appear rational from an outside perspective, they are guided by their internal logic. They seem logical from the patients’ perspective (Leventhal & Diefenbach, 1996). Longitudinal studies have demonstrated that IRs have a predictive effect on QoL at 3 and 6 months among patients with traumatic injuries (Chaboyer et al., 2010; Lee et al., 2008). Also, a recent experimental study examining patients with traumatic injuries revealed that using an IR-based intervention resulted in long-term benefits for patients’ QoL (Fann et al., 2021). Although IRs have been demonstrated empirically to influence QoL significantly, the mechanism of this influence remains unclear, and little evidence is available to explain this relationship among the injury population.

After experiencing an injury, an individual may employ various coping strategies (Gustafsson & Ahlström, 2006). Coping refers to conscious efforts to solve personal and interpersonal problems and control, reduce, or tolerate stress (Stephenson et al., 2016). Based on Leventhal and Diefenbach (1996), the SRM proposes that coping strategies play a mediating role in the relationship between IRs and health outcomes. Thus far, no evidence is available in the literature exploring the mediating role of coping on determining QoL among the injury population. However, among patients with inflammatory bowel disease, reductions in activity levels were found to mediate the relationship between IRs and QoL (van Erp et al., 2017). When patients harbor negative IRs, they decrease their activity levels, worsening QoL. This finding contrasted with the finding reported by Woodhouse et al. (2018) in gastroparesis patients; they concluded that coping did not mediate the relationship between IRs and QoL. Thus, because of these inconsistent findings from previous studies regarding the effects of coping on the relationship between IRs and QoL and because the mediating role played by coping on QoL among the injury populations remains understudied, further investigations remain necessary to understand the role played by coping on the relationship between IRs and QoL among the injury population.

Adopting the SRM seems appropriate to understand Indonesian patients’ self-regulation processes in the early recovery phases following an extremity injury. It is helpful because it provides an explicit and testable framework for understanding how cognitive and emotional constructs influence coping mechanisms and relating to people with injuries. However, there is a knowledge gap as limited studies utilize the entire SRM framework to examine coping among injured patients. Understanding patients’ self-regulation during the early recovery phase following an extremity injury may be the key to providing precise directions for delivering appropriate nursing care based on these self-regulatory processes. In turn, this could enable patients to manage their conditions better. The current study aimed to examine the relationships between IRs, coping, and QoL based on the SRM framework, assuming that adaptive and maladaptive coping strategies play mediating roles between IRs and QoL in patients with extremity injuries. Accordingly, this study hypothesizes:

  • H1: Positive IRs are related to higher QoL.
  • H2: Greater focus on the use of maladaptive coping is related to lower QoL.
  • H3: Lesser focus on the use of adaptive coping is related to lower QoL
  • H4: Adaptive and maladaptive coping has a mediating effect on the relationship between IRs and QoL.


Study Design

This study employed a correlational model testing design to examine the hypotheses that developed based on the proposed model or theory (LoBiondo-Wood & Haber, 2017). This study was reported according to STROBE (Strengthening the Reporting of Observational Studies in Epidemiology; von Elm et al., 2007).

Study Sample

Participants were recruited using a convenience sampling method in trauma wards. The inclusion criteria were that participants needed to be older than 18 years, can communicate in the Indonesian language, have unilateral trauma, have been primarily injured at an extremity, and have a score of ≥2 on the Abbreviated Injury Scale (AIS), a measure of the severity of extremity injury. In addition, participants were excluded if they had cognitive impairment, a severe brain injury or cerebrovascular disease, an injury not caused by force, or a history of psychiatric illness.

Power analysis was performed based on an a priori sample size calculation using G*Power (Faul et al., 2009). Assuming 11 explanatory factors—with an anticipated effect size of 0.14 (Vaske et al., 2017)—a desired coefficient power of 0.90, a significance level α of .05, and an additional 15% of missing data, a minimum of 186 participants were needed for this study. A total of 201 participants were initially screened. Nine participants were excluded because their extremity AIS score was less than 2, leaving a final sample of 192 participants with moderate to severe extremity injury.

Setting and Procedures

This study was conducted at the trauma wards of two equivalent academic medical centers, with each medical center having a capacity of over 200 beds located in Surabaya City, Indonesia. Both medical centers serve as referral centers for trauma cases in the healthcare system. The trauma wards in which this study was conducted are specialized units that admit patients with various traumatic injuries and are staffed by multidisciplinary professionals who care for these patients. This study was approved by the institutional review board of Universitas Airlangga (IRB 1764-KEPK). The protection of the participants’ rights was considered throughout this study. Written informed consent was obtained from all participants, and their responses were anonymized. After the investigator received ethics approval, data collection was conducted between August 2019 until January 2020. Once a discharge date was planned, the investigator approached potential participants, requested their consent to participate in the study, and booked them for an appointment to complete the survey.


Sociodemographic Characteristics

Participants’ characteristics were reported through a self-developed questionnaire, including age, gender, education level, and marital status. Meanwhile, injury characteristics were obtained from participants’ medical records and consisted of the patients’ length of stay (LOS), injury location, injury types, surgery status, AIS, and injury severity score (ISS).

IR Measures

IRs were measured using the eight Brief Illness Perception Questionnaire (BIPQ) items widely used in studies of injuries (Broadbent et al., 2006; Chaboyer et al., 2010; Chen et al., 2021). The BIPQ scale ranged from 0 (minimum) to 10 (maximum) to assess each dimension. A higher score represents a negative injury perception related to a more life-threatening condition. The questionnaire measures eight dimensions, including injury’s effect on life (consequences), duration of injury (timeline), control over injury (personal control), expectations about the effectiveness of treatment (treatment control), degree of understanding of symptoms as they relate to the injury (identity), patient concern about the injury (concern), patient’s degree of understanding of the injury (coherence), and emotional aspects that include negativity resulting from the injury (emotional response; Broadbent et al., 2006). The developer of the BIPQ has adapted this measure for use in the Indonesian population, and a recent study used this measure in patients with diabetic foot ulcers (Indrayana et al., 2019). The BIPQ was tested on patients with hand injuries with Cronbach’s alpha and showed test–retest reliability values of .70 and .73, respectively (Chen et al., 2021). In this study, the internal consistency of the BIPQ resulted in a Cronbach’s alpha score of .78 and test–retest reliability with an intraclass correlation (ICC) of .84.

Coping Measures

Coping strategies were measured using the Brief Coping Orientation to Problems Experienced (Brief-Cope) inventory. The Brief-Cope covers 14 different coping strategies, each assessed by two items, and asks participants how they cope with health threats (Carver, 1997). The Brief-Cope has 28 items, each item is scored by a 4-point Likert scale (1–4), and higher scores denote greater use of coping. Based on a previous study by Knowles et al. (2017), two main coping strategies were distinguished from the exploratory factor analysis: Adaptive coping (including active coping, planning, religion, emotional support, humor, acceptance, positive reframing, and instrumental support) and maladaptive coping (including venting, self-distraction, denial, behavioral disengagement, self-blame, and substance use). The Indonesian version of the Brief-Cope is publicly available, and a recent study used this measure in gynecological cancer patients (Nasution et al., 2020). Cronbach’s alphas from the previous study in patients with irritable bowel syndrome ranged from .70 to .85 (Knowles et al., 2017). The Cronbach’s alpha of the Brief-Cope ranged from .80 to .90, and its ICC ranged from .89 to .90.

QoL Measures

QoL was assessed by the abbreviated version of the World Health Organization Quality of Life (WHOQoL-BREF). Previously, the WHOQoL-BREF has been used on the extremity injury population (Van Son et al., 2016). The WHOQoL-BREF is a brief self-report QoL assessment developed by the WHO group in 24 international field centers, including Indonesia (The WHOQoL Group, 1998). This instrument consists of 26 items; each item considered an indicator of a person’s QoL and measured four broad domains, including physical, psychological, social relationships, and environment. Each item in this scale is scored by a 5-point Likert scale (1–5). After collecting the data, raw domain scores were transformed linearly to a 0- to 100-scale, and higher scores denote higher QoL (The WHOQoL Group, 1998). The WHOQoL-BREF has already been tested in a healthy (control) Indonesian population, with test–retest reliabilities varying between .70 to .79 (Purba et al., 2018). In this study, subscales’ Cronbach’s alphas were .73–.84 and .81 for the overall scale, and its reliability showed an ICC of .87.

Data Analysis

Data analyses were conducted with IBM SPSS Statistics for Windows (Version 24.0) and the PROCESS macro (Hayes, 2017). Significance was defined at .05 for all tests. Descriptive statistics were obtained for the sociodemographic data, injury data, IRs, coping, and QoL. Hierarchical regressions analyses were conducted to examine the explanatory factors contributing to QoL. Covariates consisting of sociodemographic and injury data were entered into the first block. To distinguish each SRM component’s effect, IRs were added in the second block, and in the third block, both coping strategies were entered as the last regressors. To save statistical power, only a significant explanatory variable entered mediation analysis.

The parallel multiple mediation analyses were performed following the recommendations made by Hayes (2017). A bootstrapping method with 5,000 bootstrapped samples was used to test the significance of both the specific and total indirect effects (paths a1a2 and b1b2). Based on Hayes (2017), bootstrapping has better control on Type I errors and has a powerful method that can assess indirect effect better than the Sobel test (Sobel, 1982) or the causal step approach (Baron & Kenny, 1986). The parallel mediating effect was significant when the 95% bootstrapped confidence interval (BCI) of the total indirect effect did not include zero.


Participant Characteristics

A total of 192 patients completed the survey. The participants were predominantly male (60.4%), with an average age of 38.80 (SD = 14.22) years. Most of them were married (72.90%) and had received education for ≤12 years (55.2%). Slightly more than half of the participants were injured in the lower extremities (55.7%), and dislocation, sprain, and strain of knee and ankle (18.2%) were the most common of injury types after lower leg and forearm fractures (10.9%). Most of the participants have undergone surgery procedures (70.3%), with the mean AIS score of 2.40 (SD = 0.50) and the average ISS score of 10.76 (SD = 5.63), and hospitalized for an average of 5.00 (SD = 2.18) days (Table 1).

TABLE 1 - Demographic Characteristics of Participants (N = 192)
Variable Frequency (%) Mean (SD) Min–max
Sociodemographic data
 Age 38.80 (14.22) 18–65
  Male 116 (60.4)
  Female 76 (39.6)
 Marital status
  Married 140 (72.9)
  Single 52 (27.1)
  ≤12 years 106 (55.2)
  >12 years 86 (44.8)
Injury characteristics
 Injury location
  Lower extremity 107 (55.7)
  Upper extremity 85 (44.3)
 Injury types
  Complex soft tissue injury (leg) 17 (8.9)
  Dislocation of hip 7 (3.6)
  Dislocation, sprain, or strain of knee/ankle 35 (18.2)
  Dislocation, sprain, or strain shoulder/elbow 14 (7.3)
  Dislocation, sprain, or strain wrist/hand/fingers 5 (2.6)
  Fracture clavicle/scapula 12 (6.3)
  Fracture hip 2 (1.0)
  Fracture knee/lower leg 21 (10.9)
  Fracture of ankle 3 (1.6)
  Fracture of elbow/forearm 21 (10.9)
  Fracture of foot 10 (5.2)
  Fracture of hand/fingers 12 (6.3)
  Fracture of upper arm 16 (8.3)
  Fracture of upper leg 17 (8.9)
  Yes 135 (70.3)
  No 57 (29.7)
 Extremity Abbreviated Injury Scale 2.4 (0.50) 2–3
 Injury severity score 10.76 (5.63) 4–22
 Length of stay 5.02 (2.18) 2–12

Descriptive Statistics for IRs, Coping, and QoL

The average score of IRs was 5.76 (SD = 1.48) out of 10, which was slightly high, indicating that participants tended to have negative beliefs and expectations toward their postinjury condition. Furthermore, among the IR components, emotional response had the highest mean, with 6.30 (SD = 1.48), suggesting that participants showed many negative emotions. Compared with maladaptive coping, participants tended to rely on adaptive coping with an average score of 2.66 (SD = 0.76) out of 4. The average QoL score was borderline at 61.94 (SD = 13.75) out of 100. Furthermore, the lowest score among the QoL domain was physical health, with a mean of 54.31 (SD = 12.99) out of 100, which indicated that participants with extremity injuries were physically impaired (Table 2).

TABLE 2 - Descriptive Statistics for Illness Representations, Coping, and Quality of Life (N = 192)
Variables Mean (SD) Min–max Possible range
Illness representations
 Illness representations (overall) 5.76 (1.48) 2.25–8.88 0–10
 Consequences 6.02 (1.97) 1–10
 Timeline 4.85 (1.62) 1–9
 Personal control 5.86 (1.77) 1–10
 Treatment control 5.56 (1.96) 1–9
 Identity 6.09 (1.66) 2–9
 Coherence 5.84 (1.63) 1–9
 Concern 5.46 (1.64) 3–10
 Emotional response 6.30 (1.49) 2–10
 Adaptive 2.66 (0.76) 1–4 1–4
 Maladaptive 2.36 (0.56) 1–4
Quality of life
 Overall quality of life 61.94 (13.75) 28–81 0–100
 Physical 54.31 (12.99) 13–81
 Psychological 63.05 (15.71) 25–88
 Social relationship 64.30 (15.52) 19–94
 Environment 66.11 (16.50) 13–94

Explanatory Factors Contributing to QoL

Explanatory variables that comprised ISS, LOS, IRs, adaptive coping, and maladaptive coping were significant in explaining 89% of QoL variance, F(2, 183) = 76.17, p < .001, adjusted R2 = .89 (Table 3). Initially, the covariates that were controlled in the first block significantly explained 69% of the variance in QoL, the addition of IRs into the model (Block 2) significantly explained an additional 11% variance of QoL, and IRs had a strong negative effect on QoL (β = −.53, p < .001). Moreover, an additional 9% variance of QoL was presented when adding maladaptive and adaptive coping strategies into the model (Block 3). Maladaptive coping had a negative effect on QoL (β = −.20, p < .001). On the other hand, adaptive coping had a protective effect on QoL (β = .29, p < .001). However, the explanatory power of IRs on QoL was decreased after both coping strategies entered the model (β = −.40, p < .001), which suggested a mediating role from both coping strategies.

TABLE 3 - Explanatory Factors of Quality of Life
Variables Quality of life
β SE 95% CI ΔR 2
Block 1: Covariates .69***
 Age −.01 .02 [−0.04, 0.06]
 Injury location (upper vs. lower) −.06 .86 [−3.34, 0.06]
 Extremity Abbreviated Injury Scale −.01 .97 [−1.61, 2.25]
 Injury severity score −.10* .10 [−0.44, −0.03]
 Length of stay −.13*** .22 [−1.24, −0.35]
 Surgery (no vs. yes) −.02 .74 [−2.01, 0.91]
Block 2: Illness representations .11***
 Illness representations −.40*** .38 [−4.45, −2.96]
Block 3: Coping .09***
 Maladaptive −.20*** .78 [−6.31, −3.24]
 Adaptive .29*** .73 [3.80, 6.68]
Adjusted R 2 .89***
Note. CI = confidence interval.
*p < .05.
**p < .01.
***p < .001.

Parallel Mediation of Coping on IRs and QoL

ISS and LOS were included as covariates in the parallel mediation model because these variables were significant in the regressions analyses. The effect of IRs on QoL was significant (path c, β = −.57, p < .001; Step 1). In addition, IRs had a negative effect on adaptive coping (path a1, β = −.38, p < .001) and a positive effect on maladaptive coping (path b1, β = .19, p = .024; Step 2). The effect of mediating variables on QoL showed that the positive effect of adaptive coping (path a2, β = .30, p < .001) and the negative effect of maladaptive coping (path b2, β = −.19, p < .001) were significant (Step 3). Furthermore, when the covariates, IRs, and both coping (mediators) were simultaneously added into the model, the IRs' effect on QoL reduced (path c′, β = −.42, p < .001; Step 4). Thus far, the current findings revealed the occurrence of mediations (Figure 1).

Parallel mediation of coping between illness representations and quality of life with standardized regression coefficient. CV = covariate. *p < .05, ***p < .001.

After controlling for all covariates in the model, both paths through single mediation of adaptive coping (path a1a2, β = −.11; 95% BCI [−0.17, −0.06]), and maladaptive coping (path b1b2, β = −.04; 95% BCI [−0.08, −0.01]) indicated that these single mediations effect were significant. In addition, the total indirect effect that included both paths (a1a2 + b1b2) indicated that the parallel mediation of adaptive and maladaptive coping was significant (β = −.15, 95% BCI [−0.23, −0.08]). Overall, these results revealed that adaptive and maladaptive coping has an independent and simultaneous mediating effect on the relationship between IRs and QoL (Table 4).

TABLE 4 - Total, Direct, and Indirect Effect of Mediating Role
Model β SE 95% BCI R 2
Model without mediators
 Total effect (c) −.57 .42 [−6.15, −4.49] .79***
   X(IR) → Y(QoL)
Model with mediators
 Direct effect (c′) −.42 .33 [−4.57, −3.25] .89***
   X(IR) → Y(QoL)
 Indirect effect (a 1 a 2) −.11 .03 [−0.17, −0.06]
   X(IR) → M 1(AC) → Y(QoL)
 Indirect effect (b 1 b 2) −.04 .02 [−0.08, −0.01]
   X(IR) → M 2(MC) → Y(QoL)
 Total indirect effect (a 1 a 2 + b 1 b 2) −.15 .04 [−0.23, −0.08]
Note. BCI = bootstrapped confidence interval; IR = illness representation; QoL = quality of life; AC = adaptive coping; MC = maladaptive coping.
***p < .001.


To the best of our knowledge, this was the first study that found a parallel mediating effect by adaptive and maladaptive coping in the relationship between IRs and QoL during the early recovery phase of extremity injury. The most noteworthy finding was that this study portrayed how patients viewed and coped with the extremity injury during the early recovery phase.

In addition to being influenced by ISS and LOS, QoL in the early recovery phase in patients with extremity injury was influenced by IRs and patients’ adjustment through adaptive and maladaptive coping. This finding is consistent with previous studies that found ISS and LOS were significant explanatory factors of QoL (Baecher et al., 2018; Baek et al., 2018; Lee et al., 2008). A higher ISS represented a more severe injury at hospital admission. More prolonged hospitalizations indicated that more time is needed to care for and cure the patients. Injury characteristics such as ISS and LOS are essential determinants of QoL in injured patients. This finding suggests that nurses should be attentive to these determinants when caring for patients with extremity injuries.

As the central part of the SRM, IRs were the most substantial QoL explanatory factor compared with other variables. This finding corresponds with previous studies on traumatic injury (Chaboyer et al., 2010; Lee et al., 2008) and traumatic brain injury (War & Rajeswaren, 2013). Patients develop perceptions toward their conditions and use them as guidance in coping with or managing their conditions, ultimately influencing QoL. This finding implies that nurses should spend more effort into fully understanding how patients view their conditions, and reframing patients’ perceptions to prevent adverse outcomes perhaps is necessary.

Coping strategies form the core processes of the SRM. In this study, both adaptive and maladaptive coping was found to be significant explanatory factors of QoL in patients with extremity injuries. Greater engagement in adaptive coping is well known to promote a better QoL. Using specific adaptive coping strategies such as emotional support and instrumental support may improve patients’ QoL (Turkington et al., 2018).

Conversely, greater engagement in maladaptive coping leads to poor QoL. This finding was also reported by Knowles et al. (2017), who found that the QoL for patients with a fecal ostomy was influenced by maladaptive coping. Moreover, specific maladaptive coping strategies such as venting and denial were significant explanatory factors of poorer QoL (Turkington et al., 2018). This finding indicates that patients who frequently attempt to cope negatively with their injury have a lower QoL. Shortly after the injury, patients may show various regulation responses such as venting their emotions, denying their current conditions, and blaming themselves (Gustafsson & Ahlström, 2006). However, because of this maladaptive response, patients are blinded by their feelings and have unsolved health problems. Accordingly, nurses should be aware of patients’ coping orientations because they could lead to advantageous or adverse health outcomes in patients with extremity injuries.

In addition, this study found that adaptive and maladaptive coping significantly mediated the relationship between IRs and QoL. Inappropriate IRs may impede adaptive coping and, in contrast, promote maladaptive coping, leading to poorer QoL. This corroborates the previous research by Vaske et al. (2017), which found that the relationship between IRs and QoL was mediated by depressive coping. Also, another study on patients with alopecia revealed that avoidant coping mediated the relationship between IRs and QoL (Willemse et al., 2019). Furthermore, it was somewhat surprising that both adaptive and maladaptive coping simultaneously mediated the effect of IRs on QoL. These findings may explain that coping appears as multiple approaches because individuals can take more than one approach when facing health problems such as an extremity injury. During the recovery process following extremity injuries, patients who have more positive representations of the injury may be prone to adaptive responses and less inclined to adopt maladaptive responses to cope with their postinjury conditions, an outcome leading to better QoL. In summary, the findings confirmed the premise of the SRM in the extremity injury population. These results may assist patients in selecting their coping strategies as they adjust to their postinjury conditions.

These findings imply that nurses in trauma care facilities should consider their patients’ psychological conditions and suggest conducting early screenings and developing care management. Because the data were collected before patients’ hospital discharge, an initial identification process should be performed before reintegrating them back into the community. Nurses can assess the patients using reliable instruments such as the BIPQ and Brief-COPE in clinical settings. Moreover, psychologically driven care management could help patients reshape their views and coping strategies to promote recovery outcomes. More importantly, this study provides new knowledge to nurses regarding the utilization of the SRM, especially in Indonesia, by offering a point of view regarding patients’ self-regulation processes and their effect on QoL after extremity injury.


This study has some limitations that should be considered. First, as this work was a cross-sectional study, the evidence cannot be overinterpreted as having a causal relationship. Further investigation through a longitudinal study may be needed to confirm the efficacy of our findings. Second, the data collection was conducted in the early recovery phase of extremity injury. This poses a possible limitation as IRs may change over time (Lee et al., 2010). Third, as this study used convenience sampling and participants were self-selected, the findings can only be generalized for patients with similar characteristics.


Enhancing the use of adaptive coping strategies, reducing reliance on maladaptive coping strategies, and reframing negative IRs during the early recovery phase are necessary to promote better postinjury QoL. These findings suggest that nurses should identify patients who utilize improper coping strategies and harbor negative IRs during the early phases of recovery following an extremity injury. Subsequently, nurses can apply psychologically driven interventions to reshape patients’ coping strategies and reframe their IRs before they are transitioned into the community.



Santo Imanuel Tonapa

Wei-Ting Lin

Fang-Li Kuo

Bih-O Lee


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coping; illness representations; injury; quality of life; traffic accident

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