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Journal of Child and Family Studies (2021) 30:1416–1427
https://doi.org/10.1007/s10826-021-01951-1
ORIGINAL PAPER
The Association between Home Environment and Quality of Life in
Children and Adolescents in Hangzhou City, China
Xianhong Huang1●Le Hua2●Xueyang Zhou3●Hao Zhang1●Meng Zhang1●Sheng Wang1●Shangren Qin1●
Jie Chen3●XiaoHe Wang1
Accepted: 26 March 2021 / Published online: 14 April 2021
© The Author(s) 2021
Abstract
To investigate the influence of the home environment, defined as family socioeconomic status (SES) (parent education level,
household income), student resource-mediated SES (access to nutritional resources and cognitively stimulating experiences),
reading ability, and difficulty with homework on quality of life in children and adolescents residing in urban and suburban
areas in Hangzhou City, Zhejiang Province, China. This study included 3200 Grade 3–6 students from 8 elementary schools
in Hangzhou City. Assessments included questionnaires that evaluated student quality of life, family SES, resource-mediated
SES (dietary behavior and the home literacy environment), reading ability, and difficulty with homework. The effects of the
home environment on student quality of life were analyzed by univariate analysis, multiple linear regression analysis, and
structural equation modeling. Overall, 80.6% of students had a medium or better quality of life. Young age (Grade 3 or 4),
female sex, household income of 10000–15000 RMB, high breakfast consumption, daily intake of fruit, a balanced diet, and
good reading habits were positively correlated with student quality of life (P< 0.05), while overuse of electronic devices was
negatively correlated with quality of life (P< 0.05). Dietary behaviors, home literacy environment, and student reading
ability and difficulty with homework directly affected quality of life. Family SES indirectly affected student quality of life.
Children and adolescents in China should have access to good nutrition and cognitively stimulating experiences to enhance
their well-being and provide them with social and academic advantages.
Keywords Quality of life ●Home literacy environment ●Dietary behavior ●Social economic status ●Structural equation
model
Highlights
●The influence of the home environment on quality of life in students in China was explored with structural equation
modeling.
●Student quality of life was directly affected by diet, the literacy environment, reading ability and difficulty with
homework.
●Student quality of life was indirectly affected by family socioeconomic status.
●These findings will inform the development of programs that promote improved quality of life in Chinese children.
Childhood and adolescence represent crucial phases in the
development of physical, psychological, behavioral, and
social maturity (Hosokawa and Katsura, 2017; Lee &
Jackson, 2017; Rashid et al., 2018; Zou et al., 2018). During
*XiaoHe Wang
xhewang@163.com
1Department of Health Service Management, School of Medicine
Hangzhou Normal University, Hangzhou, China
2Affiliated Xixi Hospital, College of Medicine, Zhejiang
University, Hangzhou, China
3The First Affiliated hospital, College of Medicine, Zhejiang
University, Hangzhou, China
Supplementary information The online version contains
supplementary material available at https://doi.org/10.1007/s10826-
021-01951-1.
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these phases, family socioeconomic status (SES) and the
home environment influence child and adolescent quality of
life (QOL), defined as their physical, emotional, and social
well-being (Maatta et al., 2017).
Widely used markers of SES include social indicators,
such as occupation and educational level, and economic
indicators that are material and resource related (Roubinov
& Boyce, 2017), such as income. The link between SES and
QOL in children and adolescents is mediated by accessi-
bility to resources, including nutrition and cognitively sti-
mulating experiences. Nutritional intake influences child
and adolescent growth and development, cognitive ability,
immunity, morbidity, and mortality, with poor nutrition a
key component of poor health (Boe et al., 2018; Bradley &
Corwyn, 2002; Hong, 2007). Access to cognitively stimu-
lating experiences provides children and adolescents direct
and peer or adult-mediated learning opportunities that
impact their cognitive ability and potential for benefiting
from school (Bradley & Corwyn, 2002; Rowland et al.,
2018; Russell et al., 2018). The connection between SES,
access to cognitively stimulating experiences, and QOL in
children and adolescents is related to parental behavior (Jin
& Lu, 2017). High-SES parents, defined as those with better
education and an economic advantage, typically spend more
time reading and communicating with their children and
adolescents than low-SES parents (Sun et al., 2013). This
might be because higher-SES parents have the time and
income to make better interpersonal and material invest-
ments in their children’s development than lower-SES
parents, who must focus on more basic needs (Sohr-Preston
et al., 2013). A good home literacy environment is posi-
tively related to child and adolescent language and literacy
development and might improve child and adolescent
reading ability (He et al., 2014; Noble et al., 2006; Sun
et al., 2013). Children and adolescents with reading diffi-
culties might experience concomitant psychosocial pro-
blems in three dimensions, including self-belief, social
cognitive ability, and interpersonal ability (Nathan, 2006).
In Hungary, children <18 years with a reading disability had
a lower QOL than controls without a reading disability
(Balazs et al., 2016).
Studies that investigated the influence of the home
environment on QOL in children and adolescents focused on
children and adolescents with chronic disease or those who
were obese. Research that investigated the QOL in children
and adolescents in the general population is limited and
mainly explored associations between family economic sta-
tus, parenteral education level, family structure, number of
siblings, household crowding, and parenting style (Hosokawa
& Katsura, 2017; Lee & Jackson, 2017; Ran et al., 2018;
Rashid et al., 2018; Zou et al., 2018). In the United States,
socioeconomic disadvantages were associated with a sig-
nificant negative impact on the cognitive achievement of
children aged 1–9 years (Lee & Jackson, 2017). In Japan,
family income was related to social skills in preschoolers
aged 5 years, and maternal and paternal education levels were
related to internalizing and externalizing problems in first
graders aged 6 years (Hosokawa & Katsura, 2017) In Brazil
(Paula et al., 2012), clinical, socioeconomic and home
environment (family structure; number of siblings; use of
cigarettes, alcohol and drugs in the family; household over-
crowding) factors exerted a negative impact on the oral
health-related QOL of schoolchildren aged 12 years. In
Wuhan, China, youth optimism was a mechanism by which
family SES was associated with life satisfaction in children
and adolescents from primary and high schools (Zou et al.,
2018). In Shapingba district, Chongqing, China, inadequate
health literacy might have contributed to poor QOL among
junior middle school students.
Reports on the influence of other factors on the QOL in
children and adolescents in China are limited. Compared
with adults, QOL research studies in children and adoles-
cents in China are scarce, possibly because of the relatively
stronger emphasis on parent-centeredness than child-
centeredness in Chinese culture (Daniel & Britta, 2007).
Previous studies that investigated related topics used tradi-
tional statistical methodologies, such as single factor and
multiple linear regression analysis (Matthews et al., 2014;
Paula et al., 2012; Wang et al., 2007). However, when
applying traditional multiple linear regression analysis, the
dependent variable and independent variables were defined
and only the direct effect between variables was determined.
The effect of latent variables, such as factors in the home
environment, can only be inferred from other variables that
are observed. Structural equation modeling uses a combi-
nation of factor and multiple regression analysis that indi-
cates measurement errors, and represents, estimates, and
tests a theoretical network between variables (Joreskog &
Sorbom, 1979; Meuleners et al., 2003). Therefore, structural
equation modeling evaluates the relationship between latent
constructs and observed variables. This study investigated
the QOL in children and adolescents residing in urban and
suburban areas in Hangzhou City, Zhejiang Province, China
using structural equation modeling. Hangzhou City is an
economically developed municipality that is located on the
southeast coast of China with a per capita GDP of USD
20,419 2017. This study explored how the home environ-
ment, defined as family SES (parent education level,
household income), resource-mediated SES (access to
nutritional resources and cognitively stimulating experi-
ences), reading ability, and difficulty with homework
directly and indirectly influenced the QOL in children and
adolescents in Hangzhou City. SES, reading ability and
difficulty with homework were chosen as the variables
measured in this study because evidence suggests (Bradley
& Corwyn, 2002) that the effect of SES on child health is
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mediated by the availability of environmental (reading and
nutritional) resources and psychological factors, which
influence reading ability and difficulty with homework.
Cognitive stimulation is a particularly important con-
sideration. Low-SES children lack resources and experi-
ence, which limits the development of their cognitive
abilities, reflected by their reading skills, and their potential
to benefit from school. In addition, exposure to resources
and culture is a mediating variable between SES and child
intelligence or academic performance and behavioral pro-
blems (Poulain et al., 2020). Understanding how inequal-
ities in the home environment effect children and
adolescents informs the development of programs that
promote improved QOL in Chinese children.
Methods
Participants
The sample include 3200 students with a mean age of 11.19
years (standard deviation [SD] =1.96, range 9–14 years).
Based on a dichotomous (male/female) measure of gender,
53.8% of the students were male and 46.2% of the students
were female. There were 848 (26.5%) Grade 6 students, 768
(24.0%) Grade 5 students, 836 (26.1%) Grade 4 students, and
748 (23.4%) Grade 3 students. Most of the students (96.9%)
lived with both biological parents, who were married. In
addition, this study included 3200 parents (89.3% mothers)
that reported on their children. Parents had a mean age of
38.67 years (SD =4.21). 52.6% of parents lived in urban
areas, and 47.4% of parents lived in the suburbs. For eco-
nomic status, 1075 (33.6%), 1217 (38.0%), and 908 (28.4%)
families had an income CNY < 10000, CNY 10,000–15,000,
or CNY > 15,000 per month, respectively. Regarding parents
occupation, 29.6% of fathers were professional technical
staff, and 22.9% of mothers were unemployed. For education
level, 23.9% of fathers had a college diploma or above,
45.9% of fathers reached junior college level, and 30.2% of
fathers finished senior high school or below. Between the
mothers, 22.0% were educated to junior high school or
below, and 17.0% had a college diploma or above.
Procedure
This study was conducted in Hangzhou, a city that is in the
north of Zhejiang Province on the southeast coast of China.
There are 10 districts in Hangzhou City and approximately
10–15 primary schools in each district; therefore, a multistage
cluster sampling design was used to select eight primary
schools. First, four urban and four suburban primary schools
were randomly selected. Then, three to five classes were
randomly selected from Grade 3–6 in each primary school.
Inclusion criteria for students were: (1) a normal intelligence
quotient, as reported by teachers; (2) no history of brain
trauma or brain disease, visual or auditory dysfunction, or
psychiatric disorders; (3) able to speak and read Chinese; and
(4) parental consent.
This cross-sectional study was conducted between Sep-
tember and December 2016 by two researchers and six
students with master’s degrees who had experience in
conducting epidemiological surveys. Before data collection,
the scientific research ethics committee of Hangzhou Nor-
mal University reviewed and approved the study protocol,
the informed consent forms, and the questionnaires. Per-
mission to conduct this study in the schools was obtained
from each head teacher, and informed consent was obtained
from the students and their parents (through a letter sent
home). When consent was given, the Children and Ado-
lescents’Quality of Life Scale and instructions on its
completion were provided to the included students, who
completed the questionnaire independently. During this
process, researchers were available to answer student
questions. Then, questionnaires were retrieved. Each stu-
dent took home the Chinese Reading Ability and its Influ-
encing Factors questionnaire for their mother or father to
complete. The students returned this questionnaire to their
teacher within 1 week. Parents and students were not
obliged to complete the questionnaires, even if they had
provided informed consent. Anonymity and confidentiality
were assured. Questionnaires with a response rate of 90%
were included in the analyses. Missing data were input
using medians. This study used double data entry and
validation, and the logical range of each variable was con-
sidered to minimize errors. Finally, 3391 students returned
the Children and Adolescents’Quality of Life Scale ques-
tionnaire with a 94.2% response rate, 3360 parents returned
the Chinese Reading Ability and its Influencing Factors
questionnaire with a 93.3% response rate, and 3200 families
completed both questionnaires, for an 88.9% response rate.
Measures
Student quality of life
Students completed the Children and Adolescents’Quality
of Life Scale that consisted of 49 items (Wu et al., 2006a,c)
and measured 4 domains, and 13 dimensions. One domain
evaluated social psychological function (21 items) and
assessed five dimensions, including teacher–student rela-
tionship (5 items, e.g., Are you satisfied with the relation-
ship between you and your teacher? 1 =not at all
satisfied–4=very satisfied), peer relationships (5 items,
e.g., Is your classmate friendly toward you? 1 =not
friendly–4=very friendly), and parent–child relationship (4
items, e.g., Do you like staying with your parents?),
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learning ability and attitude (3 items, e.g., Do you remem-
ber new things easily?), and self-concept (4 items, e.g., Do
you feel you are an important member of the team?), which
are scored as 1 =never–4=always. Another domain eval-
uates physical–mental health (12 items) and assesses three
dimensions, including: physical perception (5 items, e.g.,
Do you often feel tired after getting up?), negative emotions
(4 items, e.g., Do you often feel regretful for what you have
done?), and attitude toward homework (3 items, e.g., Do
you need a lot of time to finish homework?), which are
scored as 1 =never–4=always. A third domain evaluates
living environment (8 items) and assesses three dimensions,
including: convenience (2 items, e.g., Is there convenient
transportation near your home? 1 =not at all–4=very
convenient), opportunities for activity (3 items, e.g., Can
you participate in your favorite extracurricular activities? 1
=rarely–4=too many opportunities), and athletic ability (3
items, e.g., Are you satisfied with your ability to participate
in sports? 1 =not at all–4=very satisfied). Finally, there is
a life satisfaction domain that assesses a self-satisfaction
dimension (8 items, e.g., Are you satisfied with your sleep?
1=not at all satisfied–4=very satisfied). Total, domain,
and dimension scores were calculated, with higher scores
indicating better QOL. Scores lower than two SDs below
the mean were considered very poor QOL. Scores between
one and two SDs below the mean were considered poor
QOL. Scores that were one SD above or below the mean
were considered moderate QOL. Scores that were between
one and two SDs above the mean were considered better
QOL. Scores that were two SDs above the mean were
considered excellent (Wu et al., 2006a,c) QOL.
Total scores were transformed to a T score metric,
which was referenced to means and SD stratified by gen-
der, age, and region of residency for the Chinese general
population. A lower T score represented a poorer QOL
according to the following categorization: T < 30 =worst
QOL; 30 ≤T<40=bad QOL; 40 ≤T<60=medium
QOL; 60 ≤T<70=good QOL; and T ≥70 =best QOL.
During development and validation of the Children and
Adolescents’Quality of Life Scale, internal consistency and
reliability for the entire questionnaire, each domain, and
each dimension were evaluated as acceptable (Tavakol &
Dennick, 2011) using Cronbach’s alpha (between 0.73 and
0.95). Content validity of the scale was assessed as good
using a correlation coefficient between each dimension and
the overall score (0.56–0.89), and the four domains and the
overall score (0.52–0.83) (Wu et al., 2006b). The Children
and Adolescents’Quality of Life Scale is widely used in
China to assess children and adolescents aged 7–18 years.
In previous studies (Peng et al., 2005a; Wu et al., 2006b),
Cronbach’s alpha was reported at 0.855–0.872, and content
validity was reported at 0.661–0.866. The cumulative var-
iance contribution rate was 75.44% and the factor loading
was between 0.71 and 0.89, which indicated good construct
validity. In this study, confirmatory factor analysis was used
to assess the factorial structure of the Children and Ado-
lescents’Quality of Life Scale. Findings confirmed the 13-
factor structure (Lance et al., 2006), because the goodness
of fit index (GFI) was 0.972, the adjusted goodness of fit
index (AGFI) was 0.954, the comparative fit index (CFI)
was 0.948, the Tucker-Lewis index (TLI) was 0.945, the
Chi-squared (χ2/df) was 2.273, and the root mean square
error of approximation (RMSEA) was 0.045. The cumula-
tive percent variance was 75.44%. Factor loading was used
to identify whether items loaded strongly onto their hypo-
thesized latent variable. Factor loading was from 0.48 to
0.89, which indicated good construct validity.
Student home environment
Student home environment was defined by family SES,
resource-mediated SES, reading ability, and difficulty with
homework. Family SES was defined by parent education
level and household income. Resource-mediated SES was
defined by child and adolescent access to nutritional
resources and cognitively stimulating experiences, such as
reading materials and electronic devices. These variables
were measured using the Chinese Reading Ability and its
Influencing Factors questionnaire, which was completed by
one parent from each family. Internal consistency and
reliability for the entire questionnaire and each domain were
evaluated using Cronbach’s alpha, with values between
0.76 and 0.94 considered acceptable. Content validity of the
scale was assessed as good using the correlation coefficient
between each dimension and the overall score (0.67 to 0.91)
(p< 0.05). The cumulative percent variance was 72.37%
and factor loading was from 0.52 to 0.87, which indicated
good construct validity.
Family SES and resource-mediated SES
In Part 1 of the Chinese Reading Ability and its Influencing
Factors questionnaire, parents reported on items that assessed
the general home environment, including student gender and
date of birth, parent educational level, family SES (household
income, annual cost of extracurricular books: CNY < 300 =
1, CNY 300–500 =2, 500–800 =3, CNY > 8000 =4), stu-
dent dietary behavior (frequency of breakfast intake, fre-
quency of fruit intake, and balanced diet), and the home
literacy environment (student reading-related behavior,
weekly use of electronic devices, and rules for use of elec-
tronic devices). Variables were defined based on previously
published studies (He et al., 2014;Wangetal.,2013).
Reading-related behavior was scored based on the time
spent reading each day (none =0, 0–0.5 h =1, 0.5–1h=2,
>1 h =3) plus the frequency of participation in extracurricular
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activities such as reading (2–3 times per week =1, >3 times
per week =2, every day =3). Weekly use of electronic
devices was scored according to the time spent watching
television each day (<1 h =0.5, 1–2h=1.5, 2–3h=2.5,
>3 h =3) multiplied by 7 days plus the frequency of com-
puter use per week (1 day/week =1, approximately 3 days/
week =3, approximately 6 days per week =6, every day =
7) multiplied by the time spent using the computer (<1 h =
0.5, 1–2h=1.5, 2–4h=3, >4 h =4). Rules for use of
electronic devices were defined according to the parental
attitude concerning student television and computer use and
were scored from 1 to 6. Total scores for reading-related
behavior, weekly use of electronic devices, and rules for use
of electronic devices was from 0 to 27.
Student reading ability
In Part 2 of the Chinese Reading Ability and its Influencing
Factors questionnaire, parents reported on student reading
ability using the Dyslexia Checklist for Chinese Children
(Wu et al., 2006a,c), which consists of 57 items and 8
dimensions, including barriers to spoken language (six
items, e.g., lack of competence in oral communication and
not good at oral communication); problems with written
expression (seven items, e.g., writing very slowly and fin-
ishing homework very late); bad reading habits (six items,
e.g., reading the same sentence over again or skipping
sections); attention deficit disorder (7 items, e.g., cannot
concentrate during class or doing homework); visual dis-
turbance (seven items, e.g., confuses the letters d and b);
disturbance in auditory perception (seven items, e.g., writ-
ing very slowly and finishing homework very late); dys-
graphia (six items, e.g., does not understand normal speech,
only understands when speech is slow or repeated); and
difficulty understanding (nine items, e.g., often does not
understand the meaning of words in sentences). The
responses to each item were scored as 1 =never–5=
always. Higher scores represented a lower reading ability.
The eight dimensions were clustered into two categories
using principal component analysis: dyslexia (defined as a
specific and significant impairment in reading ability that
cannot be explained by deficits in intelligence, learning
opportunity, motivation, or sensory acuity (Fisher et al.,
2002), and included the first six dimensions) and bad
reading habits (defined as reading habits that impede read-
ing speed or are harmful to health).
Internal consistency and reliability for the total score and
the factors that composed the Dyslexia Checklist for Chi-
nese Children were evaluated using Cronbach’s alpha, with
values of 0.974 and 0.752–0.901, respectively. Factor
loadings for all items were satisfactory, from 0.383 to 0.856
(Hou et al., 2018). In this study, confirmatory factor ana-
lysis was used to assess the factorial structure of the
Dyslexia Checklist for Chinese Children. Findings con-
firmed the eight factor structure (Lance et al., 2006),
because the GFI was 0.943, the AGFI was 0.931, the CFI
was 0.927, the TLI was 0.925, the χ2/df was 2.863, and the
RMSEA was 0.051. The cumulative variance contribution
rate was 75.68%. Factor loading was used to identify
whether items loaded strongly onto their hypothesized latent
variable. Factor loading was between 0.41 and 0.91, which
indicated good construct validity.
Student difficulty with homework
Difficulty with homework was scored based on the need for
parental pressure to ensure the homework was finished
(seldom =1, sometime s =2, always =3) plus the time
each day required to finish the homework (<1 h =1, 1–2h
=2, 2–3h=3>3h=4) according to a previously pub-
lished report (He et al., 2014).
Statistical Analysis
Initially, normality, outliers, and multicollinearity were
evaluated. Normality was assessed using coefficients of
skewness (sk) and kurtosis (ku). Values fell within the
acceptable ranges of −0.58–1.54 for sk and −2.15–2.63 for
ku. The multivariate normality test gave a value of 8.45,
which indicated that the data followed a multivariate normal
distribution, because this value was <10. The existence of
outliers were identified by Cook’s distance. The maximum
Cook’s distance was <0.5 (0.028), which indicated there
were no outliers in these data. Multicollinearity was tested
by the tolerance rate and variance inflation factor (VIF). The
findings showed no tolerance rate <0.10 or VIF > 10. All the
tolerance values were >0.78 and the VIF was <3.9, which
indicated no multicollinearity.
Statistical analysis was performed using SPSS16.0 and
AMOS22.0. A descriptive analysis was conducted using
mean ± SD for quantitative variables and frequencies ±
percentages for qualitative variables. Reliability was
examined with Cronbach’s alpha and content validity was
measured using Pearson’s correlation coefficient. Differ-
ences in QOL scores based on gender, student grade,
parent education level, annual cost of extracurricular
books, and student dietary habits were analyzed using a
Student’s t-test or analysis of variance (ANOVA) with
Bonferroni post-hoc analysis. The factors that affected
QOL were identified using multiple regression analysis
with the four domains and total scores for QOL as
dependent variables, and student gender and grade, parent
education level, home literacy environment, and student
reading ability as independent variables. Factors with p<
0.1 in the univariate analysis were included in the multiple
regression analysis.
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Based on the data from the multiple regression analysis
and previously published literature, factors in the home
environment, including family SES, resource-mediated
SES, student reading ability, and difficulty with home-
work were included in structural equation modeling to
analyze their effect on student QOL. The costs of extra-
curricular books and monthly household income were used
to adjust family SES. Student reading ability, which is
affected by the home literacy environment, and difficulty
with homework were included in the model. Structural
equation modeling was performed using AMOS24.0. A
Chi-squared χ2test was used to assess model fit. In addition,
the CFI, TLI, RMSEA, and the standardized root mean
square residual (SRMR) were used to assess goodness of
fit of models. Acceptable criteria were set according to
guidelines reported in literature: CFI > 0.93, TLI > 0.90,
RMSEA < 0.08, and SRMR < 0.08. Full information max-
imum likelihood with robust standard errors (MLR) was
adopted to deal with missing data and non-normality. The
level of significance in the analyses was set to 0.05.
Results
Student Quality of Life
Overall, 15.1% of students had the worst QOL, 4.3% had a
bad QOL, 62.5% had a medium QOL, 14.4% had a good
QOL, and 3.7% of had the best QOL. 80.6% of students had
a medium or better QOL.
Between the four domains assessed by the QOL measure,
scores for psychosocial function, physical–mental health,
and life satisfaction were significantly higher among female
students compared with male students (p< 0.001). Scores
for psychosocial function, physical–mental health, living
environment, and life satisfaction were significantly higher
among younger students (Grade 3 or 4) compared with
older students (Grade 5 or 6) (p< 0.001). Scores for psy-
chosocial function and living environment were sig-
nificantly higher among students residing in urban regions
compared to students residing in suburban regions (p<
0.01), and scores for life satisfaction was significantly
higher among students residing in suburban regions (p<
0.01) (Table S1).
Influence of Parents Educational Level and Family
SES on Student Quality of Life
Scores for all four domains assessed by the QOL measure
(psychosocial function, physical–mental health, living
environment, life satisfaction) significantly increased with
increases in parent educational level and family SES (p<
0.05) (Table S2).
Influence of Student Eating Habits on their Quality
of Life
Scores for psychosocial function, physical–mental health,
and life satisfaction were significantly increased in students
who ate breakfast every day compared with those who ate
breakfast less frequently (p< 0.05). Scores for psychosocial
function, physical–mental health, living environment, and
life satisfaction were significantly increased in students who
ate fruit everyday compared with those who ate fruit often
or seldom and in those who ate fruit often compared with
those who ate fruit seldom (p< 0.05). Scores for psycho-
social function, physical–mental health, living environment,
and life satisfaction were significantly increased in students
who ate a balanced diet compared with those who ate pre-
dominantly meat or vegetables, and scores for psychosocial
function, physical–mental health, and living environment
were significantly increased in students who ate pre-
dominantly meat compared with predominantly vegetables
(p< 0.05) (Table S3).
Multiple Linear Regression Analysis
Univariate analysis and multiple linear regression analysis
were performed using the four domains assessed by the
QOL measure (psychosocial function, physical–mental
health, living environment, life satisfaction) as dependent
variables and student gender, age, region of residence,
family SES (parent education level, household income),
dietary behavior (breakfast intake, fruit intake, balanced
diet), and home literacy environment (reading-related
behavior, weekly use of electronic devices, rules for use of
electronic devices at home), as independent variables.
Factors with p< 0.1 on univariate analysis were included in
multiple regression analysis. The findings revealed eight
factors were associated with higher psychosocial function,
including young age (junior grades), having a father with a
college level or above of education, household income
CNY 10,000–15,000, frequent breakfast consumption, daily
intake of fruit, good reading-related behavior, and limited
time spent using electronic devices; the most influential
factor was reading-related behavior. Eight factors were
associated with higher physical–mental health, including
young age (Grade 3 or 4), household income CNY
10,000–15,000, frequent breakfast consumption, balanced
diet, daily intake of fruit, good reading-related behavior,
limited time spent using electronic devices, and lenient rules
for use of electronic devices. The most influential factor was
reading-related behavior. Eight factors were associated with
a better living environment, including living in an urban
area, having a father with college level or above of educa-
tion, having a mother with a junior college level of edu-
cation, frequent breakfast consumption, daily intake of fruit,
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good reading-related behavior, and strict rules for use of
electronic devices. The most influential factor was reading-
related behavior. Eight factors were associated with better
life satisfaction, including living in a rural area, young age
(Grade 3 or 4), frequent breakfast consumption, balanced
diet, daily intake of fruit, good reading-related behavior,
limited time spent using electronic devices, and lenient rules
for use of electronic devices. The most influential factor was
reading-related behavior (Table 1).
Structural Equation Modeling
Structural equation modeling was used to describe the
influence of the home environment on student QOL
(Fig. 1). Dietary behavior, difficulty with homework,
reading ability, home reading environment and other
influencing factors were identified using univariate analysis,
multiple linear regression analysis, and exploratory factor
analysis. Measurement and structural models were con-
structed. The measurement model was tested with con-
firmatory factor analysis. The strength of relationships in the
structural model were estimated using correlation coeffi-
cients generated by AMOS24.0. Model fit was assessed,
and the model was respecified. The final model had good fit,
because χ2/df, GFI, AGFI, NFI, IFI, and CFI were >0.9, and
the RMSEA was <0.05 (Table 2).
The estimated path coefficients from one independent
latent variable to the dependent latent variable are
Table 1 Multivariate linear regression for students quality of life scores (standardized coefficients)
Independent variables Categories Quality of life
Psychological
function
Physical
mental health
Living
environment
Life
satisfaction
Total
score of QOL
Residence (suburb =0) Urban 0.021 −0.027 0.118* −0.057** 0.017
Grade (three, four =0) Five and six −0.097** −0.103 −0.018 −0.136 −0.11*
Gender (boys =0) Girls 0.086** 0.022 −0.012 0.028 0.056**
Students reading
behavior
0.23* 0.16* 0.19* 0.18* 0.24*
Students time using
electronic devices
−0.062** −0.078** −0.011 −0.065** −0.069**
Rules for using
electronic devices
at home
0.013 −0.051** 0.054** −0.039** 0.003
Students
breakfast intake
0.092** 0.084** 0.052** 0.087** 0.095**
Monthly household income (<5000 =0)
5001–1000 yuan 0.014 0.014 0.009 −0.016 0.012
10001–150000 yuan 0.073** 0.058** 0.032 0.025 0.070
>15000 yuan 0.020 0.008 0.054** −0.006 0.021
Father’s education (junior high and below =0)
Senior high school 0.019 0.008 0.022 0.003 0.022
Associate degree 0.025 0.021 0.027 0.011 0.030
Bachelor’s degree
and above
0.045** −0.005 0.056** 0.003 0.027
Mother’s education (junior high and below =0)
Senior high school 0.015 0.014 0.021 0.018 0.023
Associate degree 0.011 0.006 0.061** −0.002 0.019
Bachelor’s degree
and above
0.012 0.003 0.054** 0.002 0.017
Students balanced diet intake (more vegetables =0)
More meat 0.020 −0.045** 0.053** −0.002 0.014
Balanced diet 0.018 0.046** 0.072** 0.076** 0.064**
Students vegetable and fruit intake (never =0)
Often 0.021 0.013 0.012 0.004 0.022
Everyday 0.068* 0.061** 0.087* 0.065** 0.089**
* < 0.01; ** < 0.05
1422 Journal of Child and Family Studies (2021) 30:1416–1427
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
summarized in Table 3. The direct effect of parent educa-
tional level on family SES was positive and significant. The
direct effect of family SES on dietary behavior and the
home literacy environment was positive and significant.
The direct effect of dietary behavior on reading ability was
negative and significant. The direct effect of the home lit-
eracy environment on reading ability was negative and
significant. The direct effect of reading ability on difficulty
with homework was positive and significant. The total
effect of parent education level on family SES, dietary
behavior, and the home literacy environment was positive
and significant. The total effect of parent education level on
reading ability and difficulty with homework was negative
and significant. The total effect of family SES on dietary
behavior and the home literacy environment was positive
and significant. The total effect of family SES on reading
ability and difficulty with homework was negative and
significant. The total effects of dietary behavior and the
home literacy environment on reading ability and difficulty
with homework were negative and significant. The total
effect of reading ability on difficulty with homework was
positive and significant.
The estimated path coefficients from latent variables to
QOL are summarized in Table 4. The direct effect of dietary
behavior on QOL was positive and significant. The direct
effect of difficulty with homework on QOL was negative
and significant. The total effect of family SES (parent
education level, household income), dietary behavior, home
literacy environment, and reading ability on QOL was
positive and significant and the total effect of difficulty with
homework was negative and significant.
Discussion
This study investigated the QOL in children and adolescents
residing in urban and suburban areas in Hangzhou City,
Zhejiang Province, China. Compared with national data
(Wu et al., 2006b), the overall QOL for primary school
students in Hangzhou City was good, because 80.6% of
students had a medium or better QOL. Compared with
children and adolescents in the general Chinese population
(Wu et al., 2006a,c), children and adolescents residing in
Hangzhou City had lower scores for psychosocial function
(3.04 ± 0.47 versus national 3.72 ± 0.71) and living envir-
onment (2.73 ± 0.54 versus national 3.67 ± 0.79) and higher
scores for physical–mental health (3.04 ± 0.49 versus
national 2.33 ± 0 .61) and life satisfaction (3.11 ± 0.44
versus national 1.52 ± 0.44). These data suggest that stra-
tegies should be implemented to improve the psychosocial
function and living environment of children and adolescents
residing in Hangzhou City. However, overall QOL and
several aspects of QOL (psychological function, physical
mental health, living environment, and life satisfaction)
were significantly higher in children and adolescents
residing in Hangzhou City compared to primary school
students in Hubei province (Huang & Shan, 2006),
Fig. 1 Structural equation model
Table 2 Fit indices of final model
Fit indices GFI AGFI CFI NFI IFI χ2/df RMSEA
Reference
value scale
>0.9 >0.9 >0.9 >0.9 >0.9 <5 <0.08
Fitted value 0.95 0.94 0.93 0.92 0.93 5.44 0.041
GFI goodness of fit index, AGFI adjusted goodness of fit index, CFI
comparative fit index, NFI normed fit index, IFI incremental fit index,
RMSEA root mean square error of approximation
Journal of Child and Family Studies (2021) 30:1416–1427 1423
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Shanghai, and Suzhou (Peng et al., 2005b; Shen et al.,
2004). This might be explained by the geographical location
of the children and adolescents. Previous studies showed
that QOL scores for primary school students varied between
regions and were higher in cities than in rural areas (Chen
et al., 2007). Accordingly, evidence suggests (Fang, 2001)
that student QOL is a comprehensive indicator reflecting
health and living standards, varying with socio-economic
and cultural levels.
Previous studies that investigated similar topics always
used traditional statistical methodologies (C et al., 2014;
Paula et al., 2012; Wang et al., 2007), which did not eval-
uate the effect of latent variables. In addition, variables
might be collinear by chance, which might lead to the
incorrect identification of relevant predictors in the statis-
tical model. In this study, by using structural equation
modeling, the direct (parent education level and monthly
household income) and indirect effects of family SES on
student QOL by mediating variables, such as dietary
behavior and the home literacy environment were analyzed.
Bradley et al, (Bradley & Corwyn, 2002) reported that
accessibility to resources and cultural activities was a
mediating variable between SES and child intelligence and
behavior. The home literacy environment is an important
mediating variable. A good home literacy environment
provides resources for the development of a child’s reading
ability, and it is a protective factor against dyslexia (He
et al., 2014; Noble et al., 2006; Sun et al., 2013). Since
structural equation modeling investigates indirect effects,
reading ability and difficulty with homework were included
as factors in the home environment and were analyzed as
mediating variables in this study.
Evidence suggests that fruit and vegetable intake and the
variety of fruit consumed are associated with some aspects
of QOL in children (Matthews et al., 2014), and an inap-
propriate diet and lack of breakfast might be important
environmental risk factors that lead to learning disorders in
children (Wang et al., 2013). In addition, it has been reported
that parent beliefs and behaviors (Davis-Kean 2005;He
et al., 2014) were indirectly related to child academic
achievement. Therefore, in this study, student eating habits
were chosen as a measure of nutritional resources, and the
home literacy environment was chosen as a measure of
student exposure to cognitively stimulating experiences. The
results showed that dietary behaviors and the home literacy
environment of students living in Hangzhou City directly
affected their QOL by influencing their reading ability and
their ability to do homework. Family SES indirectly affected
student QOL. These data imply that a higher family SES, as
shown by well-educated parents and a high household
income, healthy dietary behaviors among children and
adolescents, and a good home literacy environment, could
improve student reading ability, decrease risks of dyslexia,
Table 4 Standardized total effect, standardized direct effect, and
standardized indirect effect of latent variables on students quality
of life
Latent variables Total effect Direct effect Indirect effect
Family
socioeconomic status
0.362* 0.000 0.362*
Students dietary
behavior
0.513* 0.430* 0.083**
Home literacy
environment
0.0575** 0.000 0.0575**
Students reading ability −0.213** 0.000 −0.213**
Student’difficulty with
homework
−0.260** −0.260** 0.000
** < 0.05,* < 0.01
Table 3 Standardized total effect, standardized direct effect, and standardized indirect effect between latent variables
Independent variable Dependent variable Standardized
total effect
Standardized
direct effect
Standardized
indirect effect
Family socioeconomic status Students dietary behavior 0.62* 0.62* 0.00
Home literacy environment 0.94* 0.94* 0.00
Students reading ability −0.49* 0.00 −0.49*
Students difficulty in finishing
homework
−0.41** 0.00 −0.41**
Students dietary behavior Students reading ability −0.39** −0.39** 0.00
Students difficulty in finishing
homework
−0.32** 0.00 −0.32**
Home literacy environment Students reading ability −0.27** −0.27** 0.00
Students difficulty in finishing
homework
−0.22** 0.00 −0.22**
Students reading ability Students difficulty finishing
homework
0.82* 0.82* 0.00
* < 0.01; ** < 0.05
1424 Journal of Child and Family Studies (2021) 30:1416–1427
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
improve student ability to do homework, and enhance stu-
dent QOL. Similar to the results of this study, previous
research showed that SES was not related to child mental
health or psychological well-being in a sample of
19,487 school‐aged children collected from the 2013–2014
China Education Panel Survey; however, SES indirectly
affected child mental health and psychological well-being
through parent–child relations, peer relations, and
teacher–student relations (Jarman et al., 2015; Ge, 2017).
Previous studies have shown that there is an association
between parental education level and nutrition in children.
In Europe, the IDEFICS (identification and prevention of
dietary- and lifestyle-induced health effects in children and
infants) cohort study (Arvidsson et al., 2017) of 16,229
children aged 2–9 years revealed that children of parents
with a lower level of education had a higher sugar and fat
intake than children of parents with a higher level of edu-
cation. Studies of 34,366 children in Brazil and 7474 chil-
dren in the United Kingdom found that the unhealthy
dietary habits of children were associated with a low level
of maternal education (Cribb et al., 2011; Saldiva et al.,
2014). In the United States (Shonkoff et al., 2017) a study
of 599 parent–child dyads showed that clearly explained
parent rules about the types of foods children could eat
might decrease sugar intake.
A good home literacy environment provides children and
adolescents direct and indirect learning opportunities and
encourages a culture of continuous learning. Children from
low-SES families often lack access to cognitively stimu-
lating resources, such as educational materials and outdoor
activities that enhance cognitive ability. Previous reports
(Noble et al., 2006; Park et al., 2017) show that family SES,
time parents spend reading with children, and the number of
books in a home are associated with the home literacy
environment; student weekday and weekend screen time
decrease as parental education level increases (Sharif &
Sargent, 2006); and a good home literacy environment
might improve student reading abilities (He et al., 2014;
Noble et al., 2006; Sun et al., 2013).
The precise determination of the processes how the home
environment influences QOL in children and adolescents is
challenging; therefore, this study has several limitations.
First, it was a cross-sectional study and causality could not
be inferred. Second, factors that contribute to QOL are
complex and important indicators might not have been
considered in this model, including student to parental
exposure to stress inducing conditions and health-relevant
behaviors or lifestyle.
In conclusion, this study showed that the home envir-
onment influenced the QOL in children and adolescents in
Hangzhou City, China. These findings emphasize that
children and adolescents should have access to good
nutrition and cognitively stimulating experiences to
enhance their well-being and provide them with social and
academic advantages.
Acknowledgements The project was supported by Provincial Natural
Science Foundation of Zhejiang (No. LQ14H260001).
Compliance with Ethical Standards
Conflict of Interest The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to
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Open Access This article is licensed under a Creative Commons
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