Original Article

Yonago Acta medica 2016;59:279–287

Resilience Associated with Self-Disclosure and Relapse Risks in Patients with
Alcohol Use Disorders
Ayako Yamashita*† and Shin-ichi Yoshioka‡*Graduate School of Medical Sciences, Tottori University, Yonago 683-8503, Japan, †Department of Nursing, Niimi College, Niimi 7188585, Japan and ‡Department of Nursing Care Environment and Mental Health, School of Health Science, Tottori University Faculty of
Medicine, Yonago 683-8503, Japan

cation Test (AUDIT)2 is 1.07 million.3 However, according to a 2014 survey, an estimated 49,000 patients withalcohol dependency were in psychiatric hospitals4; thisseems to indicate that many patients with AUD did notreceive the required treatment. AUD requires an extended period of time before a patient can obtain intervention
treatment. These problems were part of the context inwhich the Basic Act on Measures Against Alcohol-related Health Harm to prevent damage to health due toalcohol was enacted in June 2014 in Japan.
The biopsychosocial model5 is often utilized when
discussing factors influencing AUD.6, 7 As for the cognitive-behavioral model8 that attempts to prevent relapsesof AUD, some have reported on influencing factors, suchas individual factors, coping skills for mental anguish,
and a variety of environmental factors, like the means ofacquiring alcohol and interpersonal relationships. As atreatment for AUD, people receive medical treatment forphysical complications caused by alcohol and are assisted in being re-socialized through regular life experiences in rehabilitation facilities.9
One treatment for AUD is participation in self-helpgroups, which is empirically proven as one of the effective recovery methods.10 An assessment of relapse risk
with a consideration of a variety of factors was conducted to indicate recovery from AUD, and rehabilitation
was offered to improve people’s quality of life.
Resilience has been noticed as individually differing
among those with AUD, chemical dependency, or inrecovery from these conditions.11 Resilience is the phenomenon whereby individuals attempt to adapt, regardless of serious risks.12–14 Indeed, psychiatry has begun touse resilience as a recovery concept. More specifically,
resilience refers to the ability to recover psychologicallyeven if one is temporarily faced with adversity and mal-

ABSTRACT
Background

The aim of this study was to clarify the
self-disclosure and risks of relapse associated with promoting resilience of patients with alcohol use disorders(AUD) and participating in self-help groups.
Methods An anonymous, self-administered questionnaire survey was administered to 48 patients with AUD
and participating in self-help groups; this questionnaire
consisted of basic attributes, a bidimensional resilience
scale to assess both innate and acquired resilience factors, a scale to assess depth of self-disclosure, and ascale assessing relapse risks. We conducted an evaluation by dividing the respondents into a high group andlow group based on their median values for both innate
and acquired resilience.
Results Innate/acquired resilience had a mutually reinforcing relationship, and, compared with the low resilience group, the high resilience group had significantly
reduced risks for relapses and resulted in deeper self-disclosure.
Conclusion Patients with high resilience had lower
risk of alcohol relapse and deeper self-disclosure. The
results suggest that one way of supporting patients with
AUD in recovery is assisting them in building personal
relationships with others and in deepening self-disclosure in a setting where they can relax, thus promoting
their natural ability to recover.
Key words

alcohol-related disorders; self-disclosure;self-help groups; recurrence; rehabilitation

Harmful use of alcohol causes biological, social, andpsychological damages. It is one of the greatest risk factors for health disorders across the globe. In May 2010,
the 63rd World Health Assembly adopted “Global Strategies to Reduce the Harmful Use of Alcohol,” leading toprevention measures against alcohol-related problems on
a global level.1
At present, the estimated number of people withalcohol use disorders (AUD) in Japan is approximately
570,000, and the estimated number with scores of 20
points or higher on the Alcohol Use Disorders Identifi-

Corresponding author: Ayako Yamashita
ayamashita@niimi-c.ac.jp
Received 2016 September 14
Accepted 2016 October 20
Abbreviations: AI, awareness of illness; ARRS, Alcohol Relapse
Risk Scale; AUD, alcohol use disorders; CA, compulsivity for alcohol; CI, confidence interval; EP, emotionality problems; NE, lack ofnegative expectancy for alcohol; OR, odds ratio; PE, positive expectancy for alcohol; SV, stimulus-induced vulnerability; Z, Z-score279

A. Yamashita and S. Yoshioka

adapted to the situation. Resilience includes a variety of
individual inherent factors and environmental factors acquired in various circumstances.15 Therefore, this study
investigated changes in resilience of patients with AUD
who participated in self-help groups and effects that thechanges have on relapse risk and self-disclosure. We
conducted this investigation with the objective of promoting support to reduce risk of relapse in patients with
AUD.

indicating greater resilience. In this study, the Cronbach’s α coefficient for the bidimensional resilience scale
was 0.910; thus, internal consistency was maintained.
Self-disclosure scale

An anonymous, self-administered questionnaire survey
was given to 135 AUD patients who participated in selfhelp group meetings across Japan and who agreed withthe study’s objective. The area of the survey covers Tottori, Shimane and Okayama Prefectures.

The scale to assess depth of self-disclosure22 was created based on the social penetration theory. This scaleconsists of 24 items on four levels: hobbies (level 1), difficult experiences (level 2), foibles (level 3), and inferior
personality characteristics and abilities (level 4). The
evaluation uses a seven-point scale, and higher scoresindicate a greater extent of self-disclosure. The evaluation uses a seven-point scale, and higher scores indicate
a greater extent of self-disclosure. For the scale to assessthe depth of self-disclosure in this study, the Cronbach’s
α coefficient was 0.957; thus, internal consistency wasmaintained.

Measures

Alcohol relapse risk scale

The study used an anonymous, self-administered questionnaire survey. It was conducted from February to
April 2015. The questionnaire consisted of demographics of participants, resilience scale, self-disclosure scale,and alcohol relapse risk scale.

To measure risk of AUD relapse, we used the Alcohol
Relapse Risk Scale (ARRS).23 This scale consists of 32
items on five sub-scales as follows: stimulus-induced
vulnerability (SV); emotionality problems (EP); compulsivity for alcohol (CA); lack of negative expectancy foralcohol (NE); and positive expectancy for alcohol (PE).
The ARRS includes 5 items to measure insight into
mental condition: awareness of illness (AI). The ARRS
total score is the average score from the five sub-scales(or their total score). When five supplementary items indicating the respondent’s intensity of awareness of theirillness were included, this scale consisted of a total of
32 items. The evaluation uses a three-point scale, andhigher scores indicate greater risk for alcohol relapse. In
this study, the Cronbach’s α coefficient for the 32 itemsin the ARRS was 0.915; thus, internal consistency wasmaintained.

SUBJECTS AND METHODS
Study samples

Demographics

Demographic features constituting the questionnaire
were as follows: age, gender, whether or not they lived
with another person or had a key person with whom toconsult in their lives, employment status, age when diagnosed with AUD, treatment time period, abstinence
period, number of sessions and time period participating
in self-help groups, other psychological disorders, andphysical disorders.
Resilience scale

Resilience is a term used to describe relative resistanceto psychosocial risk experiences.16 Dynamic processes
with positive changes are defined in inconvenient situations clarified by resilience definition.17 Inherent resilience factors which are inherited effects and biologicaleffects are difficult to change.18 However, resilience isformed into an interaction between an environment anda growth.19 This study used the bidimensional resilience
scale developed in Japan.20, 21 This scale is comprised ofinnate-resilience factors (12 questions related to individual factors) and acquired-resilience factors (9 questions
related to environmental factors). The innate resilience
factors indicate those that are strongly related to theindividual’s inherent nature; acquired factors indicate
learned methods of resilience. This scale uses a fivepoint rating for the questionnaire with higher total scores

Statistical analysis

The target group’s resilience scores were divided into
two groups based on median scores from their innateand acquired-resilience factors. The cut-off point ofresilience scales was not established. The number of thesample is small, and the scores of the resilience scales
exhibited a non-normal distribution. Hence, to clarify
the characteristics of the resilience, the target groups
were divided into two groups depending on the both
median innate and acquired resilience scores. Then, acomparison descriptive test was performed by dividing
these two groups into a high and low group. Spearman’srank correlation coefficient analysis was used to assess
the correlation relationships among resilience, ARRS,
and self-disclosure scale.
280

Resilience of patients with AUD

The Mann-Whitney U test was used on each quantitative factor from all scales. A multiple logistic regression analysis (maximum likelihood method) was performed by setting the high and low group of both innateand acquired- resilience factors as dependent variables
and by using as independent variables the items which
the comparison test’s results indicated as having a significant difference. We measured the variance inflation factor of the variables used in logistics analysis and studied
multicollinearity. We conducted analysis of missing values data. We conducted missing values pattern analysis
for data.
Statistical analyses were performed using IBM
SPSS 24.0 J for Windows (SPSS, Chicago, IL), and thesignificance level was set at 5% for all tests.

employed and 90% married, the target group for thisstudy not only had favorable treatment results, but they
had continued abstaining from alcohol for a long period
through continued treatment.
Table 1. Demographic characteristics of the sample
Items

ValuesNumber of participants
48
Gender (% male)
97.9
Age (years)
Employment status (% work)
68.8
Presence of key person (% yes)
85.4
Living with someone (% yes)
89.6
Treatment state (n)Outpatient
17
Inpatient
4
Untreated
7
Cessation of treatment
10
Unknown
10
Age when diagnosed with AUD (years)
Treatment time period (months)
Abstinence period (months)
Number of sessions and time period
participating in self-help groups (weekly)
Ther psychological disorders (n)12Physical disorders (n)18Bidimensional resilience scale
Innate-resilience factors
Acquired-resilience factors
Self-disclosure scale (A scale to assess
depth of self- disclosure)
Level 1 (hobbies)
Level 2 (difficult experiences)
Level 3 (foibles)
Level 4 (inferior personality characteristics and abilities)
Total
Alcohol Relapse Risk Scale
Stimulus-induced vulnerability
Emotionality problems
Compulsivity for alcohol
Lack of negative expectancy for alcohol
Positive expectancy for alcohol
Awareness of illness
Total

Ethical considerations

After receiving approval from the Tottori University
Faculty of Medicine Ethics Review Committee (approval
number 2646), this study was conducted in accordancewith the fundamental principles set forth in the Helsinki
Declaration. Informed consent was obtained from eachparticipant after the procedure(s) had been fully explained.
RESULTSA total of 86 questionnaires were collected (a collection
rate of 63.7%) from the participants, and 48 questionnaires had valid responses (a valid response rate of
35.6%). Table 1 displays the basic demographic features. The study sampled 86 subjects. After eliminating
missing values, 48 were eliminated so the respondents’
percentage was 55.8%. Therefore, by eliminating all
missing values, there is possibility for bias to occur. The
analysis results judged the “missing completely at random” and used the list-wise case deletion.
The sample group’s average age was 60.1, s = 10.5
years old (29–75), and the average age was 4 years
younger than the age group (65–69 years old),4 with thehighest AUD rate in Japan. Based on the average lengthof treatment, assumingly 14.7 years based on average
age and average age of diagnosis [45.4, s = 10.7 years
old (27–70)] , it was found that diagnosing people with
AUD typically took a long period.
The average number of self-help group meetings
that respondents attended per week was 2.6, s = 2.9, andthese respondents participated regularly and were able tocontinue participating.
The target group’s average period of abstinence fromdrinking alcohol was 133.1, s = 114.8 months. Due to thefact that the average number of weekly self-help groupsessions attended (2.6) and that approximately 70% were

M ± SD

60.1 ± 10.5

45.4 ± 10.7
39.6 ± 57.6
133.1 ± 114.8
2.6 ± 2.9

38.2 ± 8.1
30.1 ± 5.5

31.7 ± 9.5
18.6 ± 4.9
24.5 ± 7.7
28.5 ± 9.1
103.3 ± 26.9
11.7 ± 4.7
12.3 ± 3.7
3.8 ± 1.5
6.5 ± 2.2
4.3 ± 2.1
10. 6 ± 2.8
38.5 ± 10.5

AUD, alcohol use disorders; M, mean; n, number of participants.

The histograms of the total scores of each scale areshown in the Fig. 1 (innate-resilience scores: Fig. 1a, acquired-resilience scores: Fig. 1b, Alcohol Relapse Risk
281

A. Yamashita and S. Yoshioka

7
6
5
4
3
2
1
0

(b)
Number of patients

Number of patients

(a)

0

10

20

30

40

50

Innate-resilience score

60

7
6
5
4
3
2
1
0

0

Number of patients

Number of patients

5
4
3
2
1

10

20

30

30

40

50

(d)

6

0

20

Acquired-resilience score

(c)

0

10

40

50

60

70

Alcohol Relapse Risk Scale score

3
2
1
0

0

20 40 60 80 100 120 140 160

Self-disclosure scale score

Fig. 1. Histograms of the total scores of each scale. (a) Innate-resilience scores. (b) Acquired-resilience scores. (c) Alcohol Relapse
Risk Scale scores. (d) Self-disclosure scale scores.

self-disclosure for level 1 (Z = 2.560, P = 0.010), and level 2 (Z = 2.627, P = 0.009).
A comparison test was conducted on the target
group’s attributes after dividing them into high (30 ≤ )and low (29 ≥ ) groups based on their acquired-resilience
scores (Table 3).
The results indicated that the high acquired-resilience group had significantly low ARRS scores for PE(Z = –2.722, P = 0.006) and EP (Z = –2.630, P = 0.009);
this group also had significantly high innate-resilience
scores (Z = 3.950, P < 0.001). The results further indicated that this group had deep self-disclosure at all fourlevels: level 1 (Z = 2.890, P = 0.004); level 2 (Z = 2.906,
P = 0.004); level 3 (Z = 2.199, P = 0.028); and level 4 (Z= 2.378, P = 0.017).
Table 4 shows the correlations among resilience,
ARRS, and self-disclosure scale. Negative correlations
were found between both innate and acquired resilience,

Scale scores: Fig. 1c, self-disclosure scale scores: Fig. 1d).
The target group’s median values of innate-, andacquired-resilience scores were 37.0, 30.0, respectively.
A comparison test was conducted on the target group’s
attributes-after dividing them into high (37 ≤ ) and low (36≥) groups based on their innate-resilience scores (Table2). There were 21 subjects (43.8%) in the high category
for both innate-resilience and acquired-resilience, and 14
subjects (29.2%) in both the high and the low categories.
The subjects of this study were irregularly distributed.
Therefore, the 2 categories did not show same numbers
because the samples were concentrated in the middle.
The results indicated that the high innate-resiliencegroup had significantly low ARRS scores for PE [Z-score(Z) = –2.884 P = 0.004], SV (Z = –2.566, P = 0.010),
and EP (Z = –3.294, P = 0.001); additionally, the results
show that this group had high acquired-resilience scores(Z = 3.106, P < 0.001) and also that this group had deep282

Resilience of patients with AUD
Table 2. Comparison of variables between high and low groups based on innate-resilience scores

Variables

Innate-resilienceLow group
High group( n = 21)( n = 27)
M ± SDM ± SDZ

P-valueAge (years)
56.6 ± 13.2
62.9 ± 7.0
1.540
0.124
Age when diagnosed with AUD (years) ( n = 47)
44.3 ± 11.1
46.2 ± 10.4
0.482
0.630
Treatment time period (months) ( n = 42)45.6 ± 67.9
34.1 ± 47.3
–0.6200.536
Abstinence period (months)
103.4 ± 98.1
156.2 ± 123.1
1.434
0.151
Number of sessions and time period participating in self3.0 ± 4.2
2.3 ± 1.1
0.367
0.713
help groups (1week time) ( n = 45)
Scale
Alcohol Relapse Risk ScaleSV
13.4 ± 5.9
10.4 ± 3.0–2.5660.010**
EP14.1 ± 3.6
10.8 ± 3.0–3.2940.001***
CA
4.2 ± 2.0
3.5 ± 0.8
–1.1040.270
NE6.8 ± 2.1
6.2 ± 2.3
–1.0730.283
PE5.4 ± 2.5
3.5 ± 1.3
–2.884
0.004**
AI
11.6 ± 2.7
9.9 ± 2.7
–1.9290.054
Total
43.9 ± 12.2
34.4 ± 6.6–3.1860.001***
Acquired-resilience27.2 ± 5.2
32.3 ± 4.9
3.106< 0.001***
Self-disclosure scale
Level 127.9 ± 8.9
34.6 ± 9.0
2.560
0.010
**
Level 2
16.6 ± 5.0
20.1 ± 4.2
2.627
0.009
**
Level 323.9 ± 8.6
25.0 ± 7.0
0.853
0.393
Level 427.8 ± 9.2
29.1 ± 9.1
0.864
0.388
Total
96.1 ± 27.1
108.8 ± 25.8
1.819
0.069
Statistical evaluation is performed by the Mann-Whitney U test. **P < 0.01, ***P < 0.001.
The Self-disclosure scale assesses the depth of how much a person reveals about him- or herself. AI , awareness of illness; CA, compulsivity for alcohol; EP, emotionality problems; Level 1, hobbies; Level 2, difficult experiences; Level 3, foibles; Level 4, inferior personality
characteristics and abilities; M, mean; n, number of participants; NE, lack of negative expectancy for alcohol; PE, positive expectancy foralcohol; SV, stimulus-induced vulnerability; Z, Z-score.

(CI) (0.846–0.997); P = 0.042] and the acquired-resilience score [OR = 1.200, 95% CI (1.017–1.415); P =0.031].
Another multiple logistic regression analysis (maximum likelihood method) was performed by setting thehigh and low groups for acquired-resilience factors as
dependent variables and by using the three items (ARRS
scores; innate-resilience; and self-disclosure) as independent variables which the comparison test’s results
indicated as having a significant difference. The results
indicated that the factor significantly correlated with thehigh acquired-resilience group was the innate-resilience
score [OR = 1.289, 95% CI (1.095–1.517); P = 0.002].

and ARRS, while positive correlations between both innate and acquired resilience, and self-disclosure scale.
The variable we used to analyze the logistics is thevariance inflation factor. The VIF value was less than 2.2.
Because we took multicollinearity into consideration
as the variable for logistics analysis, we did not use a subscale but rather used the total score and the variables
that showed the significant difference.
A multiple logistic regression analysis (maximum
likelihood method; results shown in Table 5) was performed by setting the high and low groups of innate-resilience factors as dependent variables and by using thethree items (ARRS scores; acquired resilience; self-disclosure) as independent variables which the comparison
test’s results indicated as having a significant difference.
The results indicated that the factors significantly correlated with the high innate-resilience group were ARRS
score [OR (odds ratio) = 0.918, 95% confidence interval

DISCUSSION
Upon clarifying attributes and characteristics of innate
and acquired resilience of AUD patients who participate
in self-help groups, we now examine factors associated283

A. Yamashita and S. Yoshioka
Table 3. Comparison of variables between high and low groups based on acquired-resilience scores
Acquired-resilienceLow group
High group(n = 20)(n = 28)
M ± SDM ± SD

VariablesZ

P-valueAge (years)
56.1 ± 13.0
63.0 ± 7.4
1.644
0.100
Age when diagnosed with AUD (years) (n = 47)
43.8 ± 9.7
46.6 ± 11.4
0.657
0.511
Treatment time period (months) (n = 42)36.5 ± 63.8
42.1 ± 53.3
0.698
0.485
Abstinence period (months)
110.0 ± 114.2
149.6 ± 114.4
1.590
0.112
Number of sessions and time period participating in self3.3 ± 4.2
2.1 ± 1.1
–0.9660.334
help groups(1week time) (n = 45)
Scale
Alcohol Relapse Risk ScaleSV
13.2 ± 6.0
10.7 ± 3.2–1.7020.089
EP13.8 ± 3.8
11.1 ± 3.2–2.6300.009**
CA4.1 ± 1.8
3.6 ± 1.3
–0.6640.507
NE
6.7 ± 2.0
6.3 ± 2.4
–0.9400.347
PE5.2 ± 2.4
3.7 ± 1.6
–2.7220.006**
AI11.5 ± 2.5
10.0 ± 2.9–1.835
0.066
Total
42.9 ± 12.1
35.4 ± 8.1–2.6190.009**
Innate-resilience25.1 ± 3.7
33.7 ± 3.4
3.950< 0.001***
Self-disclosure scale
Level 126.9 ± 10.2
35.1 ± 7.3
2.890
0.004
**
Level 2
16.2 ± 5.1
20.3 ± 4.0
2.906
0.004
**
Level 322.1 ± 9.2
26.3 ± 6.0
2.199
0.028
**
Level 425.4 ± 10.3
30.7 ± 7.5
2.378
0.017
**
Total
90.5 ± 28.8
112.4 ± 21.5
2.814
0.005
**
Statistical evaluation is performed by the Mann-Whitney U test. *P < 0.05, **P < 0.01, ***P < 0.001.The Self-disclosure scale assesses
the depth of how much a person reveals about him- or herself. AI , awareness of illness; CA, compulsivity for alcohol; EP, emotionality
problems; Level 1, hobbies; Level 2, difficult experiences; Level 3, foibles; Level 4, inferior personality characteristics and abilities; M,mean; n, number of participants; NE, lack of negative expectancy for alcohol; PE, positive expectancy for alcohol; SV, stimulus-induced
vulnerability; Z, Z-score.
Table 4. Bivariate correlations among variable 2 vs. variable 2’s resilience
Innateresilience SV

EP

Alcohol Relapse Risk Scale
CA
NE
PE
AI

Innate-resilience
1.000–0.528 ** –0.572 ** –0.219
Acquired-resilience 0.653 ** –0.400 ** –0.477 ** –0.003

Total

Self-disclosure scale
Level 1 Level 2 Level 3 Level 4 Total–0.294 * –0.507 ** –0.413 ** –0.629 ** 0.532 ** 0.584 ** 0.282
0.311 * 0.458 **–0.275 –0.244 –0.193 –0.461 ** 0.468 ** 0.493 ** 0.390 ** 0.432 ** 0.497 **Values are expressed as Spearman’s ρ. *P < 0.05, **P < 0.01.
The Self-disclosure scale assesses the depth of how much a person reveals about him- or herself. AI , awareness of illness; CA, compulsivity for alcohol; EP, emotionality problems; Level 1, hobbies; Level 2, difficult experiences; Level 3, foibles; Level 4, inferior personality
characteristics and abilities; NE, lack of negative expectancy for alcohol; PE, positive expectancy for alcohol; SV, stimulus-induced vulnerability.

with self-disclosure and relapse risks.
The target group’s resilience scores were nearly
the same values as those obtained from prior researchwith university students using the same scale 24; theirinnate-resilience scores were 38.6, s = 6.9, and acquired-resilience scores were 29.9, s = 4.7. Considering
the fact that the target group voluntarily works toward

their recovery while maintaining interpersonal relationships within their families and self-help groups,
presumably, the target group originally had both highinnate and acquired resilience. This could potentiallyhave been influenced by the fact that the target group being members of self-help groups. This could have been
influenced by the fact that the target group already had
284

Resilience of patients with AUD
Table 5. Summary of logistic regression analysis for variables contributing to high and low groups of innate- andacquired-resilience factors

Innate-resilience
ARRS
Acquired-resilience
Acquired-resilience
Innate-resilience

β

SE

Wald

df

P-value

OR (95% CI)–0.085
0.182

0.042
0.084

4.154
4.654

1
1

0.042*
0.031*

0.918 (0.846–0.997)
1.200 (1.017–1.415)

0.254

0.083

9.266

1

0.002**

1.289 (1.095–1.517)

OR and 95% CI were calculated with the use of logistic regression analysis. *P < 0.05, **P < 0.01.
ARRS, Alcohol Relapse Risk Scale; CI, confidence interval; df, degrees of freedom; OR, odds ratio; SE, standard error.

a support system; the respondents already belonged toself-help groups and approximately half of them continued with treatment.
The comparison of the high resilience group withthe low resilience group made it clear that patients withhigh resilience had deeper levels of self-disclosure.
Self-disclosure is defined as the method of conveying
one’s own thoughts, emotions, or feelings to other peopleusing linguistic communication.
In this study, self-disclosure levels of patients withhigh innate-resilience were significantly deeper for suchitems as level 1 and level 2. Approximately similar results were obtained in the correlation analysis. Self-disclosure levels for patients with high acquired-resilience
were significantly deeper for all items. It can be said thatthe level of self-disclosure deepens alongside the recovery process from the fact that high resilience patients
had deeper self-disclosure for acquired-resilience thanfor innate-resilience.
Self-disclosure of patients with AUD relates theirown experiences in self-help groups, but disclosing
in these settings is different from self-disclosure that
happens in daily life with friends and families. These
groups aim at a curative effect and are also a means of
empowerment.25 Moreover, the participants of self-helpgroups disclose themselves26 so that they can gain spiritual experiences, which leads to their recovery.27 Thisapparently indicate that patients deepen their levels of
self-disclosure by participating in self-help groups andthat being more empowered plays a part in increasing
resilience.
The comparison of the high resilience group withthe low resilience group made it clear that patients had
reduced risks of relapse. The correlation analysis showedsame similar results. Patients with high innate-resilience
had significantly lower scores on the three items—
SV, EP, and PE. They also tended to score significantly
lower on one item—intensity of AI. Patients with highacquired resilience had significantly lower scores on two
items such as EP and PE. Also, it was shown that they

had a tendency to have significantly lower scores on suchan item as intensity of AI.
These results indicate that relapse risks for patientswith high resilience were reduced by the following four
items: SV, EP, PE, and intensity of AI. Furthermore, thetwo items among those concerned with relapse risks that
did not have significant differences were NE and CA;
however, average scores for these items were low forpatients with high resilience. Having considered theseresults, patients with high resilience had reduced risks ofalcohol relapses.
The results of the logistic regression analysis indicated that innate-and acquired-resilience factors were
mutually reinforcing, which is a factor for the one withhigh innate-resilience to lower the ARRS score of positive expectancy for alcohol.
In supporting people’s recovery from AUD, the validity28 of an approach that utilizes intervention under
the transtheoretical model,29 which shows the processof behavior modification, has been demonstrated. Basedon positive expectancy decreasing in patients with highresilience, intervention that corresponds with stages of
behavior modification, seems to increase resilience effectively.
This study’s results suggested the following characteristics of resilience in patients with AUD whoparticipate in self-help groups: high resilience patients
deepened their acquired-resilience more than their innate one through self-disclosure, and this reduced riskof relapse. In fact, some have reported that the conceptof resilience, as the natural ability to recover intrinsic topatients, must be considered in connection with recovery
from AUD.30 Patients’ participation in self-help groups
allows them to acquire sociality31 and deepen self-disclosure32; a degree of accomplishing these can be anindicator of recovery from dependency. Since this study
suggested that patients’ participation in self-help groups
deepened acquired-resilience and reduce relapse risks,
it is required to lead those with AUD to attend self-helpgroups as a measure to enhance their self-disclosure.
285

A. Yamashita and S. Yoshioka

Because innate- and acquired-resilience factors aremutually reinforcing, it is essential to mutually improve
both individual and environmental factors as a way of
supporting patients with AUD for their resilience enhancement.
It was reported that traumatic experiences, including
abuse during childhood, can be one of the factors that
reduce resilience.33 One proven method for improving
the resilience of teenagers at high risk for alcohol ordrug dependency is improving the family’s resiliencethrough intervention in a form of community-based
programs.6, 34 This suggests the necessity of intervention
during childhood or with families to improve resilience.
This study has clarified that patients with high resilience have reduced risk of relapsing, and the resilienceof patients faces traumatic growth35 due to recovery.
The results suggest that one way of supporting patientsfor their recovery is to assist them in building personal
relationships and provide support that allows them todisclose deeply in a setting where they can relax, thus
promoting their natural ability to recover.
Self-disclosure is to know oneself and is an element
necessary to build close relations with others.36 The
help seeking skill is a means of self-disclosure and is an
important early intervention for AUD.37 The need to acquire help seeking skills for early intervention of AUD
was suggested.
This study has several limitations. First, the study
obtained valuable data from the small target group.
Thus, there were limitations to generalizing the findings
and sampling bias in the target group cannot be denied.
Second, this was a cross-sectional study. Conducting
further studies with larger target groups is necessary.
In conclusion, AUD patients with high resilience
had reduced risks for relapsing and self-disclosure wasdeepened during the recovery process as acquired-resilience increased. The preparing for an environment
conductive to promoting natural ability of patients with
AUD to recover is necessary.

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Acknowledgments: We would like to express our sincerest, heartfelt gratitude to everyone who has cooperated with this study. Additionally, this study was completed as part of research conducted
via the Grant-in-Aid for Scientific Research (C) (Grant Number
15K11835).
The authors declare no conflict of interest.

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