Physical activity and posttraumatic growth in patients receiving maintenance hemodialysis: A prospective study

Abstract

This study used a prospective design to examine the association between self-reported physical activity and posttraumatic growth (PTG) over a 1-year period among 150 patients receiving maintenance hemodialysis. Transport-related, household, and leisure-time physical activity were positively associated with PTG at baseline and follow-up. Total physical activity could predict higher levels of PTG at follow-up, after controlling for baseline PTG and other covariates. The findings indicate that daily physical activity could be a modifiable behavioral factor associated with PTG among patients receiving maintenance hemodialysis. Further study is needed using a randomized controlled design and objective measures of physical activity.

Keywords

dialysis end-stage renal disease physical activity posttraumatic growth

Introduction

End-stage renal disease (ESRD), also known as kidney failure, is the last stage of chronic kidney disease. Renal replacement therapy, through either dialysis or renal transplantation, is the treatment available for patients with ESRD. Maintenance hemodialysis, typically performed in a health-care setting, is the most common treatment modality for ESRD in many countries, including the United States, mainland China, and many countries in southeastern Asia (Robinson et al., 2016). Although maintenance hemodialysis sustains life, it also raises concerns about the negative impact of treatment on the population (Ekelund and Andersson, 2007; Karamanidou et al., 2014). Patients on hemodialysis may experience a range of physical and emotional distress resulting from the serious disease and the time-consuming treatment (Chan et al., 2012; van der Borg et al., 2019; Yang, 2017). Nevertheless, patients gradually make an attempt to deal with the problems and adjust to their new life with hemodialysis (Nazly et al., 2013; Yang, 2017). A handful of studies have examined posttraumatic growth (PTG) in patients with ESRD, which refers to the positive psychological changes as a result of the struggle with the highly challenging life circumstances (Tedeschi and Calhoun, 2004). The adaptive significance of PTG has been found in patients with a serious illness, including reduced emotional distress, enhanced psychological well-being, greater adherence to treatment, and better perceived physical health (Barskova and Oesterreich, 2009; Pascoe and Edvardsson, 2013; Sawyer et al., 2010). Therefore, studying positive psychological changes and the factors associated with PTG will advance the understanding of how patients adjust to their new life and gain psychological well-being during maintenance hemodialysis.

Research on factors associated with PTG indicates that constructive cognitive processing, manageable emotional distress, and social support may facilitate the development of PTG following a life crisis (Joseph et al., 2012; Tedeschi and Calhoun, 2004). Moreover, a recent systematic review indicated that physical activity may be a modifiable behavioral facilitator for PTG (Chen et al., 2020). Engagement in physical activity may cause individuals with a chronic illness to perceive strength in their body and mind, relieve emotional distress, and develop meaningful social experiences with families and friends, which may in turn foster PTG (Crawford et al., 2014; Day, 2013; McDonough et al., 2011). Notably, most previous studies on the relationship between physical activity and PTG adopted a cross-sectional design and examined only the leisure-time domains of physical activity (e.g. exercise, sports; Chen et al., 2020), and the association between physical activity and PTG is yet to be developed among patients receiving maintenance dialysis. The cross-sectional design restricted the ability to understand the predictive effect of physical activity on PTG over time. Non-leisure-time physical activity, including household and transport-related physical activity, makes a considerable contribution to the total energy expenditure of a person, especially in individuals with a chronic illness and older adults (Manns et al., 2012; Zang and Ng, 2016). Studies carried out on the general population suggested that non-leisure-time physical activity was less optimal for mental health outcomes than physical activity during leisure time (White et al., 2017). Nevertheless, the effects of non-leisure-time physical activity on mental health outcomes may differ between the clinical population and the general population. For instance, traveling to and from hospitals in an active way and performing household chores may foster a sense of independence in patients receiving maintenance hemodialysis, which may contribute to better mental health outcomes. Therefore, using a longitudinal study design and assessing various contexts of physical activity will help to clarify the predictive effect of physical activity on PTG in patients receiving maintenance hemodialysis.

This study used a prospective design to examine the association between physical activity and PTG in different daily contexts over a 1-year period among patients receiving maintenance hemodialysis. Physical activity included occupational, transportation, household, leisure, and total physical activity. We hypothesized that transportation, household, leisure, and total physical activity would be positively associated with concurrent and subsequent levels of PTG, after controlling for other correlates of PTG.

Method

Participants and procedures

The study used a consecutive sampling method to recruit patients from two dialysis units in one secondary and one tertiary hospital in Shanghai, China. Patients were considered eligible for the study if they were between 18 and 85 years old and able to communicate verbally, had received maintenance hemodialysis for 3 months or more, and had no diagnosed cognitive impairment. Doctors or nurses in the participating units identified 248 potentially eligible patients, and research assistants approached them and introduced the study at one treatment appointment. Participation was voluntary, and a small gift (e.g. a small body cleanser) with a value of 1 US dollar (≈7 Chinese yuan) was provided as a token of gratitude. The study was approved by the Human Research Ethics Committee at The University of Hong Kong (EA1808035). From April 2017 to August 2017, a total of 215 patients consented to participate in the study, and 150 patients (60.5% of the eligible patients) completed the full package of questionnaires by themselves or with the help of a research assistant in the hospital. A follow-up survey was conducted 12 months after the first assessment. The research assistants had a bachelor’s or master’s degree in medical-related majors (e.g. public health, nursing). The first author and corresponding authors provided training for the research assistants and monitored the process of the study.

The analytic sample in this study included 150 participants who completed the main study measures (i.e. physical activity and PTG) at the first assessment (T1). Of the 150 participants, 117 (78.0%) responded to the 12-month follow-up survey (T2), 10 died, and 23 were not available for the follow-up (e.g. rejected, too ill to participate).

The sample characteristics are presented in Table 1. The participants were middle to old age (mean age = 59.80 ± 11.25 years), and more than half of them were male (56.7%) and received at least high school education. Most patients were married (80.7% at T1 and 78.6% at T2) and had no current employment (87.3% at T1). The mean length of dialysis was 7.32 years, with 72.0% of the participants receiving treatment for more than 3 years. The participants had an average of two comorbid conditions (2.14 at T1 and 2.15 at T2), with diabetes, cardiovascular disease, and hypertension being the most common comorbid conditions. The participants who completed the first assessment (n = 150) did not differ from the patients who consented to participate in the study but did not complete the full package of questionnaires (n = 65) on all the available sample characteristics, including sex, age, education level, marital status, length of dialysis, and number of comorbid conditions.

Table 1.

Sample characteristics.

Variables	T1 (n = 150)	T2 (n = 117)
	n (%)	n (%)
Sex
Female	65 (43.3)	50 (42.7)
Male	85 (56.7)	67 (57.3)
Age at T1 (in years) ^a	59.80 (11.25)	59.32 (10.10)
Education level
Middle school or under	73 (48.7)	56 (47.9)
High school	55 (36.7)	44 (37.6)
College or above	22 (14.7)	17 (14.5)
Marital status
Married	121 (80.7)	92 (78.6)
Not married	28 (18.7)	24 (20.5)
Missing	1 (0.7)	1 (0.9)
Employment status at T1
Unemployed	131 (87.3)	NA
Employed	19 (12.7)	NA
Self-perceived financial burden ^a	1.92 (1.10)	NA
Social support ^a	5.00 (1.12)	NA
Emotional distress
Depressive symptoms^a	6.50 (4.13)	6.18 (4.07)
Anxiety symptoms^a	4.37 (3.57)	3.49 (3.36)
Length of dialysis at T1 (in years) ^a	7.32 (5.99)	7.88 (6.35)
⩽1 year	23 (15.3)	16 (13.7)
>1 and ⩽3 years	17 (11.3)	13 (11.1)
>3 years	108 (72.0)	87 (74.4)
Missing	2 (1.3)	1 (0.8)
Comorbid conditions
Diabetes	94 (62.7)	84 (71.8)
Hypertension	50 (33.3)	25 (21.4)
Cardiovascular disease	68 (45.3)	56 (47.9)
Respiratory disease	20 (13.3)	19 (16.2)
Endocrine and metabolic disease	40 (26.7)	35 (29.9)
Others	49 (32.7)	33 (28.2)
Number of comorbid conditions ^a	2.14 ± 1.32	2.15 ± 1.31
Limitation in physical mobility ^a	1.65 (1.11)	NA

NA: not assessed at T2.

Presented as mean and standard deviation.

Measures

Posttraumatic growth was measured by the Posttraumatic Growth Inventory-Short Form (PTGI-SF) at the first assessment. The PTGI-SF has 10 items for measuring changes relating to others (two items), new possibilities (two items), personal strength (two items), appreciation for life (two items), and spiritual changes (two items) (Cann et al., 2010). The respondents rated the extent of change following the dialysis treatment on a 6-point scale ranging from 0 (no change at all) to 5 (very great change). All items were summed to yield an overall score for PTG, with a higher score indicating greater growth. A score >60% of the potential maximum score is regarded as a moderate to high level of domain-specific or total PTG (Wu et al., 2019). The PTGI-SF showed acceptable internal consistency in Chinese adult samples (Liu et al., 2018). Cronbach’s alpha for PTGI-SF in this study was 0.78 at T1 and T2.

Physical activity was measured by the long form of the International Physical Activity Questionnaire (IPAQ-LF) at T1. The Chinese version of the IPAQ-LF has demonstrated adequate test–retest reliability with intraclass correlation coefficients ranging from 0.74 to 0.97 and showed evidence of validity when compared with accelerometry (Macfarlane et al., 2011). It measured light-, moderate-, and vigorous-intensity physical activity in four daily contexts (i.e. occupational, transportation, household, and leisure time) in a usual week (Craig et al., 2003). The volume of physical activity was calculated by weighting each intensity of activity by metabolic equivalent task (MET) energy expenditure to yield a score in MET minutes (METs). Weekly METs were calculated for specific contexts (i.e. occupational, transport-related, household, and leisure-time physical activity) and across contexts (total).

Emotional distress was measured by the Hospital Anxiety and Depression Scale (HADS) at T1 and T2. The HADS has seven items for measuring depressive symptoms and seven items for measuring anxiety symptoms (Zigmond and Snaith, 1983). The HADS has been widely used in Chinese clinical populations (Wang et al., 2009). The respondents rated the degree to which they had experienced each symptom in the past 2 weeks on a 4-point Likert scale ranging from 0 to 3. The summary scores of all items on the scale were calculated, with a higher score indicating more severe symptoms. Cronbach’s alpha was 0.78 at T1 and 0.83 at T2.

Social support was measured by the Multidimensional Scale of Perceived Social Support (MSPSS) at T1. The 12-item MSPSS was developed by Zimet et al. (1988), which measures perceived social support from family, friends, and significant others. Each item was rated on a 7-point Likert scale ranging from 1 (disagree very strongly) to 7 (agree very strongly). A total score was calculated, with a higher score indicating greater social support. Cronbach’s alpha was 0.89 at T1.

Sociodemographic information was reported by the participants, including gender, age, education, marital status, employment status, and self-perceived financial burden. Marital status included single, married/partnered, divorced, widowed, separated, and others. Employment status was measured by the item “Do you currently have a job or do any unpaid work outside the home.” The participants rated self-perceived financial burden related to disease and treatment on a 5-point Likert scale ranging from 1 (no) to 5 (very much).

Clinical information included the length of dialysis, the presence of comorbid conditions (yes/no), and physical mobility. The physician’s diagnoses of comorbid conditions included diabetes, hypertension, cardiovascular disease, respiratory disease, endocrine and metabolic disease, and other diseases. A count of comorbid conditions was calculated, which ranged from 0 to 6. The presence of comorbid conditions was reported by the participants, and it was checked with the doctors if the participants were not clear about whether they had the disease or not. Physical mobility was assessed by an item at T1 in which the participants rated the level of difficulty in walking around on a 5-point scale including 1 (no), 2 (slight), 3 (moderate), 4 (severe), and 5 (unable to walk around).

Data analysis

Descriptive statistics were presented for the sample characteristics and main study variables. The association between the sample characteristics and PTG was examined using analysis of variance for categorical factors and correlation analysis for continuous factors. Post hoc comparisons among groups were estimated using a Bonferroni test. Multiple regression analyses were used to examine the predictive effect of physical activity on PTG at baseline and follow-up, respectively. Total physical activity and each aspect of physical activity (i.e. transport-related, household, and leisure-time physical activity) were examined in separate models. Since only 19 participants had current employment, the effect of occupational physical activity was not examined in the multiple regression analyses. The sample characteristics that were associated with outcome variables at a significance level of 0.05 in the univariate analyses were controlled for in the multiple regression analyses. Baseline PTG was controlled for in the analyses for PTG at follow-up. The ratio of context-specific physical activity to total physical activity was controlled for in the analyses for context-specific physical activity. A minimum sample size of 81 is required for detecting a small to medium effect size (f² = 0.1) of a predictor in a multiple linear regression model with eight predictors at a power of 0.8 and a significance level of 0.05 (Faul et al., 2009).

Missing data were minimal in this study (Table 1). The participants who completed the follow-up assessment (n = 117) did not differ from the patients who died (n = 10) or dropped out (n = 23) after the first assessment on all sample characteristics. Multiple imputations were conducted using a chained equations approach, which calculated the missing values of the time-invariant variables for all the participants (0.7% of total samples) and the missing values of the time-varying variables (i.e. marital status, emotional distress, comorbid conditions, PTG) for alive patients (15.3% of total samples). The data analyses were conducted by Stata version 15.1. Standardized betas in the multiple regression with imputed data sets were calculated by using the mibeta package (Harel, 2009).

Results

Descriptive statistics

The descriptive statistics for physical activity and PTG are presented in Table 2. The average level of total weekly physical activity expenditure reported in the IPAQ-LF was 2086.00 ± 2226.81 METs. Leisure-time physical activity contributed most to the total physical activity expenditure, followed by household and transport-related physical activity. Only 6% of the participants reported occupational physical activity. The average scores of PTGI-SF at T1 and T2 were 27.12 ± 9.33 and 21.71 ± 9.95, respectively, indicating small to moderate levels of PTG in general.

Table 2.

Correlation analyses of association between physical activity and PTG.

	M (SD)	1	2	3	4	5	6
1. Occupational activity	119.10 (712.43)	1
2. Transport-related activity	628.21 (866.96)	0.143	1
3. Household activity	658.02 (959.97)	−0.009	0.194*	1
4. Leisure-time activity	680.67 (1007.38)	0.118	0.246**	0.351***	1
5. Total physical activity	2086.00 (2226.81)	0.425***	0.630***	0.662***	0.737***	1
6. PTG T1	27.12 (9.33)	0.112	0.188*	0.231**	0.185*	0.292***
7. PTG T2	21.71 (9.95)	0.045	0.157	0.302**	0.328***	0.362***	0.356***

p < 0.05; **p < 0.01; ***p < 0.001.

The association of the sample characteristics with PTG at T1 is presented in Supplemental Table S1. Married patients tended to report higher PTG than patients those who were single, divorced, or widowed. Social support and length of dialysis were positively correlated with PTG, whereas depressive symptoms, anxiety symptoms, and limitations in physical mobility were negatively correlated with PTG.

Correlation between physical activity and PTG

The correlation analyses of the association between physical activity and PTG are presented in Table 2. Total levels of physical activity were associated with higher PTG, with a moderate correlation of 0.292 (p < 0.001) at T1 and 0.362 (p < 0.001) at T2. The association between physical activity and PTG appeared to differ according to the context of physical activity. Occupational physical activity was not significantly correlated with PTG over time. Transport-related physical activity was weakly and positively correlated with PTG at T1. The associations of household and leisure-time physical activity with PTG were relatively stronger and consistent, with higher levels of household and leisure-time physical activity being associated with higher PTG at T1 and T2.

The predictive effect of physical activity on PTG

The predictive effects of total physical activity on PTG are presented in Table 3. The sample characteristics that were identified to be significantly associated with PTG at T1 were controlled for in the multiple regression, including marital status, social support, depressive symptoms, anxiety symptoms, length of dialysis, and limitation in physical mobility. After controlling for the covariates, the predictive effects of total physical activity on PTG at T1 and T2 were significant. Patients who had higher levels of daily physical activity were more likely to report higher PTG at T1 and T2, compared with those who were less physically active.

Table 3.

The predictive effect of total physical activity on PTG.

	PTG at T1			PTG at T2
	Unstandardized beta	Standardized beta	p	Unstandardized beta	Standardized beta	p
Covariates ^#
Marital status	2.255 (1.722)	0.095	0.19	−1.893 (2.343)	−0.073	0.42
Social support	3.220 (0.585)	0.386	<0.001	1.259 (0.892)	0.137	0.16
Depressive symptoms	−0.398 (0.209)	−0.176	0.060	−1.015 (0.255)	−0.409	<0.001
Anxiety symptoms	−0.020 (0.227)	−0.008	0.93	0.825 (0.265)	0.274	0.003
Length of dialysis	0.314 (0.110)	0.202	0.005	0.068 (0.138)	0.040	0.62
Limitation in physical mobility	−0.521 (0.682)	−0.062	0.45	0.556 (0.883)	0.056	0.53
Total physical activity	0.001 (0.0003)	0.151	0.044	0.001 (0.0004)	0.224	0.014
PTG at T1	–			0.131 (0.105)	0.116	0.22

Sample characteristics that were associated with PTG at T1 at a significance level of 0.05 were controlled in the analyses.

The predictive effects of context-specific physical activity on PTG are presented in Table 4. Transport-related physical activity could predict subsequent PTG. Patients who engaged in higher levels of transport-related physical activity were more likely to report higher PTG at T2, even after controlling for the levels of PTG at T1 and other covariates. Household physical activity was not significantly associated with higher PTG at T1 and T2 in the multiple regression models. Leisure-time physical activity predicted higher levels of subsequent PTG at a significance level of 0.056.

Table 4.

The predictive effect of context-specific physical activity on PTG.

	PTG at T1			PTG at T2
	Unstandardized beta	Standardized beta	p	Coef.	Standardized beta	p
Context 1 ^#
Transport-related activity	0.001 (0.001)	0.082	0.32	0.002 (0.001)	0.199	0.036
PTG at T1	–			0.171 (0.105)	0.152	0.11
Context 2 ^#
Household activity	0.001 (0.001)	0.067	0.49	0.0003 (0.001)	0.031	0.79
PTG at T1	–			0.174 (0.106)	0.155	0.11
Context 3 ^#
Leisure-time activity	0.001 (0.001)	0.117	0.15	0.002 (0.001)	0.217	0.056
PTG at T1	–			0.149 (0.106)	0.133	0.16

Marital status, social support, depressive symptoms, anxiety symptoms, length of dialysis, limitation in physical mobility, and ratio of context-specific activity to total activity were controlled in the models.

Discussion

This study is original in using a prospective design to examine the association between physical activity and PTG in patients with ESRD receiving maintenance hemodialysis. Transport-related, household, leisure-time, and total physical activity were positively associated with PTG in patients receiving maintenance hemodialysis over time. The findings add to the body of knowledge of PTG among patients receiving maintenance hemodialysis by indicating the beneficial role of daily physical activity in facilitating their positive psychological growth.

The patients reported small to moderate levels of PTG on average, which were comparable to the levels observed in previous studies among dialysis-treated ESRD (Cui et al., 2017; de Alegria et al., 2017; Li et al., 2018). In particular, our findings indicate that daily physical activity could be a modifiable behavioral factor that contributes to PTG among patients receiving maintenance hemodialysis. Those patients who engaged more in daily physical activity were more likely to report higher PTG. This positive association remained after controlling for the limitation in physical mobility and other covariates. The positive predictive effect of daily physical activity on PTG may be explained by psychosocial mechanisms (Lubans et al., 2016; Paluska and Schwenk, 2000). Being physically active in daily life was associated with reduced emotional distress in patients receiving maintenance hemodialysis (Dziubek et al., 2016; Lopes et al., 2014), which may facilitate the constructive processing of the event and foster PTG. Moreover, patients may experience uncertainty and loss of control due to the adversities associated with the disease and treatment. However, participation in daily physical activity may provide opportunities for regaining control of their body and surroundings, fostering a sense of independence, and developing a sense of mastery (Chen et al., 2020), which could facilitate the development of PTG.

In addition to the total daily physical activity, this study also investigated the context-specific physical activity in relation to PTG among patients receiving maintenance hemodialysis. Significant positive associations were observed between transport-related, household, and leisure-time physical activity and PTG in the bivariate analyses, with small to medium effect sizes (r: 0.188–0.328). However, the association between context-specific physical activity and PTG was attenuated in the multivariate analyses. In general, our findings were in line with findings of previous studies that showed a positive association between leisure-time physical activity and PTG in people living with cancer (Andysz et al., 2015; Wang et al., 2014) or HIV (Littlewood et al., 2008; Milam, 2004). Engagement in leisure-time physical activity may relieve emotional distress, increase positive emotions, and enhance a sense of mastery (Chen et al., 2020), which may contribute to the development of PTG.

Moreover, this study expanded the understanding of the relationship between physical activity and PTG in the non-leisure-time contexts. Non-leisure-time physical activity, including transport-related and household physical activity, contributed to a considerable proportion of total physical activity expenditures among patients receiving maintenance hemodialysis. Our findings suggested that transport-related and household physical activity appeared to have beneficial effects on PTG among patients receiving maintenance hemodialysis. Traveling to and from the hospital to receive treatment on a regular basis provided opportunities for incidental physical activity (Nazly et al., 2013). Active ways of transportation, such as walking, cycling, and public transport, may help the patients to sustain a sense of independent mobility, which may in turn facilitate the development of PTG. Equally important was household physical activity. Many patients expressed that they could not live a fully independent life with hemodialysis (Bayhakki and Hatthakit, 2012). Nevertheless, active engagement in some household chores may maintain a sense of independence and improve the patients’ role in family functioning, which may thus strengthen family relationships and fostered PTG. Of note, the attenuation of the association in the multivariate analyses suggested that other factors may have an impact on the association between context-specific physical activity and PTG. Therefore, to better predict PTG, further study is necessary to investigate how context-specific physical activity interacts with other factors.

Apart from physical activity, more social support and lower levels of depressive symptoms were found to be associated with high levels of PTG. Because the time-consuming and long-term treatment may have reduced dialysis patients’ social participation, the social support of their family and friends played an important role for these patients, perhaps providing a sense of companionship and reducing social isolation (Cohen et al., 2007). Patients with lower or manageable levels of depressive symptoms may have a higher likelihood of engaging in more constructive cognitive processing, which opens up opportunities for the development of PTG (Tedeschi et al., 2018).

Limitations and directions for future research

The study has several limitations. First, this study used self-reported measures to assess physical activity and limitations in physical mobility. Although the long form of the International Physical Activity Questionnaire (IPAQ) has demonstrated good measurement properties and provided more stable estimates than the short form of the IPAQ (Kim et al., 2013), responses may still be subject to recall bias compared with objective measurements of physical activity. Second, this study used a prospective observational study design. The lack of a randomized controlled design limited the ability to make an inference on the causal relationship between physical activity and PTG. Further study is needed to adopt a randomized controlled trial and use objective measures for assessing physical activity (e.g. accelerometry) and physical mobility (e.g. a 6-minute walk test) to examine the effect of physical activity on PTG. Third, the data were collected from a convenience sample from two hospitals in Shanghai, which is a major metropolitan city in China. The generalizability of the findings to other populations is yet to be examined, such as people living in a rural area, who could have different characteristics of physical activity in various daily contexts (Zhu et al., 2016). Fourth, this study did not assess other significant stressors in the patients’ life, which might have an impact on PTG.

Implications for clinical practice

The findings of this study have implications for clinical practice. The findings suggest that clinical practice may take advantage of physical activity in facilitating PTG among patients receiving maintenance hemodialysis. Although the promotion of physical activity does not target PTG directly, it may provide health benefits, strengthen the perceptions of self, and reduce emotional distress (Chen et al., 2020), all of which may contribute to the development of PTG. Clinicians are encouraged to assess, counsel, and monitor a broad range of physical activity by including both leisure-time and non-leisure-time physical activity among dialysis patients. Focusing on lifestyle-embedded physical activity over a typical day could be a good place to start and may lead more readily to behavioral changes in patients who are physically inactive (Manns et al., 2012). Tailored interventions may be developed to encourage physical activity in daily life, such as using more active transportation for traveling to and from the hospital, performing household chores, and doing exercises that are compatible with patients’ physical capacities.

Conclusion

The findings from a prospective study showed that daily physical activity could be a modifiable behavioral factor associated with PTG among patients receiving maintenance hemodialysis. Engagement in transport-related, household, leisure-time, and total physical activity was associated with a higher level of PTG. Further study is needed using a randomized controlled design and objective measures of physical activity (e.g. accelerometry) to determine the effect of physical activity on PTG.

Supplemental Material

Supplementary_materials – Supplemental material for Physical activity and posttraumatic growth in patients receiving maintenance hemodialysis: A prospective study

Supplemental material, Supplementary_materials for Physical activity and posttraumatic growth in patients receiving maintenance hemodialysis: A prospective study by Jieling Chen, Lingling Liu, Jing Chen, Weijie Jiang, Bibo Wu, Jingfen Zhu, Vivian WQ Lou and Yaping He in Journal of Health Psychology

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by National Natural Science Foundation of China under Grant No. 71874111, Shanghai Municipal Health Bureau Foundation under Grant No. 201740116 and Shanghai Jiao Tong University Scientific and Technological Innovation Funds under Grant No. YG2020YQ01, YG2020YQ06.

Adherence to ethical standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Ethical approval

The study was approved by the IRB of the School of Medicine at Shanghai Jiao Tong University (SJUPN-201801) and the participating institutions (2016SL020).

ORCID iD

Yaping He

Data availability statement

The data could be requested via email to Yaping He.

Supplemental material

Supplemental material for this article is available online.

References

Andysz

Najder

Merecz-Kot

(2015) Posttraumatic growth in women after breast cancer surgery—Preliminary results from a study of Polish patients. Health Psychology Report 3(4): 336–344.

Barskova

Oesterreich

(2009) Post-traumatic growth in people living with a serious medical condition and its relations to physical and mental health: A systematic review. Disability and Rehabilitation 31(21): 1709–1733.

Bayhakki Hatthakit

(2012) Lived experiences of patients on hemodialysis: A meta-synthesis. Nephrology Nursing Journal 39(4): 295–304.

Cann

Calhoun

Tedeschi

, et al. (2010) A short form of the Posttraumatic Growth Inventory. Anxiety Stress and Coping 23(2): 127–137.

Chan

Brooks

Steel

, et al. (2012) The psychosocial correlates of quality of life in the dialysis population: A systematic review and meta-regression analysis. Quality of Life Research 21(4): 563–580.

Chen

Xiang

Lee

JLC

, et al. (2020) Physical activity and posttraumatic growth: A systematic review of quantitative and qualitative studies. Psychology of Sport and Exercise 49: 101679.

Cohen

Sharma

Acquaviva

, et al. (2007) Social support and chronic kidney disease: An update. Advances in Chronic Kidney Disease 14(4): 335–344.

Craig

Marshall

Sjostrom

, et al. (2003) International physical activity questionnaire: 12-country reliability and validity. Medicine and Science in Sports and Exercise 35(8): 1381–1395.

Crawford

Gayman

Tracey

(2014) An examination of post-traumatic growth in Canadian and American ParaSport athletes with acquired spinal cord injury. Psychology of Sport and Exercise 15(4): 399–406.

10.

Cui

Wang

, et al. (2017) Current status and influencing factors of post-traumatic growth in maintenance hemodialysis. International Journal of Nursing Sciences 4(4): 362–366.

11.

Day

(2013) The role of initial physical activity experiences in promoting posttraumatic growth in Paralympic athletes with an acquired disability. Disability and Rehabilitation 35(24): 2064–2072.

12.

de Alegria

Basabe

De Lorenzo

(2017) Evolution of post-traumatic growth during the first 12 months of dialysis: A longitudinal study. Journal of Renal Care 43(2): 108–113.

13.

Dziubek

Kowalska

Kusztal

, et al. (2016) The level of anxiety and depression in dialysis patients undertaking regular physical exercise training—A preliminary study. Kidney and Blood Pressure Research 41(1): 86–98.

14.

Ekelund

Andersson

(2007) Elucidating issues stressful for patients in predialysis and dialysis—From symptom to context. Journal of Health Psychology 12(1): 115–126.

15.

Faul

Erdfelder

Buchner

, et al. (2009) Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods 41(4): 1149–1160.

16.

Harel

(2009) The estimation of R2 and adjusted R2 in incomplete data sets using multiple imputation. Journal of Applied Statistics 36(10): 1109–1118.

17.

Joseph

Murphy

Regel

(2012) An affective-cognitive processing model of post-traumatic growth. Clinical Psychology & Psychotherapy 19(4): 316–325.

18.

Karamanidou

Weinman

Horne

(2014) A qualitative study of treatment burden among haemodialysis recipients. Journal of Health Psychology 19(4): 556–569.

19.

Kim

Park

Kang

(2013) Convergent validity of the International Physical Activity Questionnaire (IPAQ): Meta-analysis. Public Health Nutrition 16(3): 440–452.

20.

Liu

Han

, et al. (2018) The relationship among resilience, rumination and posttraumatic growth in hemodialysis patients in North China. Psychology Health & Medicine 23(4): 442–453.

21.

Littlewood

Vanable

Carey

, et al. (2008) The association of benefit finding to psychosocial and health behavior adaptation among HIV plus men and women. Journal of Behavioral Medicine 31(2): 145–155.

22.

Liu

Chen

, et al. (2018) Relationships between family resilience and posttraumatic growth in breast cancer survivors and caregiver burden. Psycho-Oncology 27(4): 1284–1290.

23.

Lopes

Lantz

Morgenstern

, et al. (2014) Associations of self-reported physical activity types and levels with quality of life, depression symptoms, and mortality in hemodialysis patients: The DOPPS. Clinical Journal of the American Society of Nephrology 9(10): 1702–1712.

24.

Lubans

Richards

Hillman

, et al. (2016) Physical activity for cognitive and mental health in youth: A systematic review of mechanisms. Pediatrics 138(3): e20161642.

25.

Macfarlane

Chan

Cerin

(2011) Examining the validity and reliability of the Chinese version of the International Physical Activity Questionnaire, long form (IPAQ-LC). Public Health Nutrition 14(3): 443–450.

26.

Manns

Dunstan

Owen

, et al. (2012) Addressing the nonexercise part of the activity continuum: A more realistic and achievable approach to activity programming for adults with mobility disability? Physical Therapy 92(4): 614–625.

27.

McDonough

Sabiston

Ullrich-French

(2011) The development of social relationships, social support, and posttraumatic growth in a dragon boating team for breast cancer survivors. Journal of Sport & Exercise Psychology 33(5): 627–648.

28.

Milam

(2004) Posttraumatic growth among HIV/AIDS patients. Journal of Applied Social Psychology 34(11): 2353–2376.

29.

Nazly

Ahmad

Musil

, et al. (2013) Hemodialysis stressors and coping strategies among Jordanian patients on hemodialysis: A qualitative study. Nephrology Nursing Journal 40(4): 321–327.

30.

Paluska

Schwenk

(2000) Physical activity and mental health—Current concepts. Sports Medicine 29(3): 167–180.

31.

Pascoe

Edvardsson

(2013) Benefit finding in cancer: A review of influencing factors and health outcomes. European Journal of Oncology Nursing 17(6): 760–766.

32.

Robinson

Akizawa

Jager

, et al. (2016) Factors affecting outcomes in patients reaching end-stage kidney disease worldwide: Differences in access to renal replacement therapy, modality use, and haemodialysis practices. The Lancet 388(10041): 294–306.

33.

Sawyer

Ayers

Field

(2010) Posttraumatic growth and adjustment among individuals with cancer or HIV/AIDS: A meta-analysis. Clinical Psychology Review 30(4): 436–447.

34.

Tedeschi

Calhoun

(2004) Posttraumatic growth: Conceptual foundations and empirical evidence. Psychological Inquiry 15(1): 1–18.

35.

Tedeschi

Shakespeare-Finch

Taku

, et al. (2018) Posttraumatic Growth. New York: Routledge.

36.

van der Borg

Verdonk

de Jong-Camerik

JGA

, et al. (2019) A continuous juggle of invisible forces: How fatigued dialysis patients manage daily life. Journal of Health Psychology. Epub ahead of print 6 June 2019. DOI: 10.1177/1359105319853340.

37.

Wang

M-L

Liu

J-E

Wang

H-Y

, et al. (2014) Posttraumatic growth and associated socio-demographic and clinical factors in Chinese breast cancer survivors. European Journal of Oncology Nursing 18(5): 478–483.

38.

Wang

Chair

Thompson

, et al. (2009) A psychometric evaluation of the Chinese version of the Hospital Anxiety and Depression Scale in patients with coronary heart disease. Journal of Clinical Nursing 18(13): 1908–1915.

39.

White

Babic

Parker

, et al. (2017) Domain-specific physical activity and mental health: A meta-analysis. American Journal of Preventive Medicine 52(5): 653–666.

40.

Kaminga

Dai

, et al. (2019) The prevalence of moderate-to-high posttraumatic growth: A systematic review and meta-analysis. Journal of Affective Disorders 243: 408–415.

41.

Yang

(2017) An alternative view of living well: survival trajectory of Korean patients with kidney failure on hemodialysis. Nephrology Nursing Journal 44(3): 219–249.

42.

Zang

(2016) Age, period and cohort effects on adult physical activity levels from 1991 to 2011 in China. International Journal of Behavioral Nutrition and Physical Activity 13 (Article no. 40). DOI: 10.1186/s12966-016-0364-z.

43.

Zhu

Chi

Sun

(2016) Physical activity among older Chinese adults living in urban and rural areas: A review. Journal of Sport and Health Science 5(3): 281–286.

44.

Zigmond

Snaith

(1983) The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica 67(6): 361–370.

45.

Zimet

Dahlem

Zimet

, et al. (1988) The multidimensional scale of perceived social support. Journal of Personality Assessment 52(1): 30–41.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB