THE group, with decrease of the means of

THE EFFECTIVENESS OF SELF-HYPNOSIS TRAINING
IN REDUCING DEPRESSION AND INSOMNIA

 

 

 

  
Abstract. We investigated the effect of self-hypnosis (SH) training on depression and insomnia reduction in a sample of Mutah University students.
The sample consisted of 84 students suffering from depression and insomnia.
Participants were randomly assigned to two groups: the experimental group (n = 42) and the control group (n = 42). The
experimental group was trained on SH, whereas the control
group received no training. The training period lasted on average eight sessions
for each student. Depression and insomnia scales were administered as pre- and posttest
to all participants. MANCOVA revealed a significant training effect for the
experimental compared to the control group, with decrease of the means of
depression and insomnia in the posttest.

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Key words: Depression, insomnia, self-hypnosis

 

Introduction

 

Insomnia is a
subjective experience of inadequate sleep and is one of the most frequently
reported health complaints. ?t is defined as habitual sleeplessness or the inability to sleep. It
has high prevalence in the population (2-4%) (Roth & Drake 2004; Lalive, Rudolph, Luscher, & Tan, 2011). People who
suffer from insomnia have more somatic and mental disorders (Novak, Mucsi,
Shapiro, Rethelyi, & Kopp, 2004) compared to people who sleep sufficiently.
Epidemiological, cross-sectional,
and prospective studies suggest that insomnia, chronic pain,
and depression frequently co-occur and are mutually interacting
conditions. Specifically, several studies have found a relationship
between insomnia and depression and/or anxiety (American Psychiatric
Association, 2000; Breslau, Roth, Rosenthal, & Andreski 1996; Ford &
Kamerow, 1989; Johna, Meyera, Rumpfb, & Hapke, 2005; Popa & Cuza, 2013; Riemann & Voderholzer, 2003; Soldatos,
1994). Specifically, sleep is physiologically abnormal in
persons at risk for depression. For example, shortened REM sleep latency is
present not only during clinical episodes of depression, but also before the
clinical episode in individuals at risk for depression. Although insomnia
usually disappears as depression is treated, it may persist, indicating
heightened vulnerability to depressive relapse or recurrence (Lustberg & Reynolds, 2000). In addition, research suggests that common mechanisms underlie
insomnia and depression (Benca & Peterson, 2008; Finan & Smith, 2013; Staner, 2010). However,
the exact neurobiological and psychological mechanisms that link them remain largely unknown (Benca & Peterson, 2008).

 

Since research has demonstrated that insomnia increases the risk of
new-onset depression or recurrence of depression, optimal treatment of insomnia
associated with depression becomes an important clinical goal. Various
treatment options have been suggested for patients presenting insomnia and
depression symptoms, including single agents, combination strategies and
behavioral interventions (Jindal & Thase, 2004; Lam, 2017). Insomnia is
most often treated with pharmacotherapy, which can be effective in the
short-term (Fiorentino & Ancoli-Israel, 2007; Holbrook, Crowther, Lotter, Cheng,
& King, 2000; Krystal, 2009; National Institutes of Health, 2005), but
fails to treat the underlying condition, Psychological treatments for insomnia
include cognitive behavioral therapies along with pharmacological therapies
gives the best results in overcoming insomnia symptoms (Glaze, 2004; Savard
& Morin, 2001; Silber, 2005).

In general, there are five main psychological
strategies for treating insomnia. Three of them emerged from mainstream
behavioral therapy: relaxation, cognitive therapy, and stimulus control. Two
others originated from empirical investigations of sleep disorders: bedtime
restriction and sleep hygiene (Nowell, Mazumdar, & Buysse, 1997). Another way
to group behavioral interventions for the treatment of insomnia is the degree
to which they emphasize internal vs. external (environmental) factors. In
relaxation and cognitive therapy, the emphasis is on internal change. In
stimulus control, bedtime restriction and sleep hygiene, the emphasis is on
changing external aspects of sleep (Carmack, 1988).

Self-hypnosis is one of the most common cognitive behavioral
therapies used to overcome insomnia and consists of two main stages: 1) the
induction stage, designed to produce a change in the state of consciousness,
and 2) the suggestions application stage (Mazzoni et al., 2009). Suggestions
may act at the level of cognition, perception, affect, or behavior, and
depending on the problem may include suggestions for anesthesia, relaxation, increased
self-efficacy, stress reduction or sleep induction (Batty, Bonnington, Tang,
Hawken, & Gruzelier, 2006). The suggestions formulated for treatment of insomnia
are usually associated with notions related to sleep (e.g., bedroom,
sleepiness, eye closing, falling asleep and deep sleep) (Assen, 2007). In this
way, hypnosis can be used as both a method of sleep inductions (e.g., self-induction),
as well as a way of accessing problems that may induce insomnia, such as
depression and anxiety, beyond mere symptomatic treatments used in other types
of hypnotherapy (Popa & Cuza, 2013).

Most of the early published research on the use of SH as a cognitive
therapy for insomnia has involved depressive and anxious adults. For example, instruction
of SH in two sessions aiming to induce relaxation to 18 participants aged 29 to
60 years was shown to be more effective in improving insomnia than use of the
pharmaceutical Nitrazepam or a placebo (Anderson, Dalton, & Basker, 1979).
Stanton (1989) and Becker (1993) found that SH is more effective than placebo
or reinforcement treatment in dealing with insomnia. In addition, the treatment
of depression through hypnosis significantly contributes to overcome the
negative thoughts related to the difficulty of going to sleep, and helps break
the cycles of thinking about the concerns of the future and the suffering of
the past (Alladin, 2006; Alladin & Alibhai, 2007).

Kohen and Murray (2006) explored the treating of depression in
children and adolescents primarily from the standpoint of clinical
intervention. Result found that SH treatment can be applied to help reduce
depressive symptoms, and encourage young people to apply these skills to help
themselves. Furthermore, Alladin (2009, 2010) described cognitive hypnotherapy
(CH) as an evidence-based multimodal treatment for depression, which can be
applied to a wide range of patients with depression. Specifically, cognitive
hypnotherapy is combined with cognitive behavior therapy, and the two have
substantial positive effects. Finally, Farrell-Carnahan et al. (2010) investigated
the feasibility and preliminary efficacy of SH recordings in cancer survivors.
Overall, adjusted effect sizes showed small SH treatment effects in the case of
sleep, fatigue, mood, and quality of life.

Overall, there is evidence supporting the effectiveness of SH training
to reduce depressive symptoms and
help getting smooth sleep and overcoming insomnia. The present study aimed to further explore whether training on SH can reduce depression and insomnia symptoms. The
hypothesis was that the SH training group, compared to a control group that
received treatment after the completion of the intervention to the experimental
group, will have lower depression and insomnia scores at the posttest compared
to the pretest.

 

METHOD

Participants

 

Eighty-four students
of the Mutah University participated in the present study. They were selected randomly
from seven courses of Science College, that were taught at the same day and
time. Forty-two were assigned to the experimental group (EG) and 42 to the control
group (CG). There were no psychology students in the two groups. The students who
met the conditions of the study were limited to 84 students (out of 432), 45.14
% males (38), 54.76 % females (46). The mean age of the entire sample was 20
years (ranging from 18 to 21 years, SD=3.25). The sample of students suffering
from insomnia and depression were chosen based on the cut-off points of the
Athens Insomnia Scale and the Beck Depression Inventory-II (see Instruments).

 

Instruments

 

Athens
Insomnia Scale

The Athens Insomnia Scale (AIS) (Soldatos,
Dikeos, & Paparrigopoulos, 2000) aims
to measure sleep induction, awakenings during the night, final awakening, total
sleep duration, general sleep quality, the consequences of insomnia during the
following day ?specifically, well-being, functional capacity (both physical and
mental) and sleepiness.  It comprises of
eight items. Participants were asked to respond to each item in a Likert-type
scale ranging from 0 = no problem at all
to 3 = very serious problem. They also
reported whether they had experienced any difficulty sleeping at least three
times a week during the last month. The total score of the AIS ranges from zero
to 24. A cut-off score of ?6 on the AIS was used to
establish the diagnosis of insomnia and select the study sample. This kind of insomnia is called adjustment insomnia or short-term
insomnia, disturbs one’s sleep and usually is due to stress. The sleep problem
ends when the source of stress is gone or when you adapt to the stressful
situation. The stress does not always come from a negative experience.
Something positive can make one too excited to sleep well (Benca & Peterson, 2008; Soldatos,
Dikeos, & Paparrigopoulos, 2002).

Seven bilingual
(English and Arabic) translators from different universities in Jordan and Oman
translated the English version of the AIS into Arabic. The translators were instructed
to retain both the formal aspects of language and the meaning of the items of
the original as closely as possible, but to give priority to meaning
equivalence. When the Arabic translation was finalized, the AIS was then
back-translated (from Arabic to English) by other seven bilingual professors The
back-translated items were then evaluated by a group of eight
faculty members to ensure that the item meanings were
equivalent between the original English version and the back-translated
version. The differences found between items, were re-entered into the forward
and back-translation process, until the evaluators were satisfied with substantial
meaning equivalence.

Content
validity for the AIS was established by asking twelve expert raters to evaluate
candidate items on quality (clarity, lack of bias, and lack of offensiveness),
and goodness-of-fit with the intended AIS. On a scale ranging from 1 = poor to 4
= excellent the average quality rating was 3.69, and the average goodness-of-fit
rating was 3.78. Unanimously, 100 % of the expert reviewers agreed on all eight
items. The internal consistency of the instrument was determined using a group
of the same participants as a pilot study (N=217). The calculated coefficient
alpha reliability for the (AIS) was (0.90). Finally, test-retest reliability
was applied and accounted on a sample of study participants, (n=52 students). The
AIS demonstrated good test-retest reliability (r = .91 for the total score). Depending
on these psychometric properties of AIS we can considered the AIS to be an effective
tool in insomnia diagnosis, as well as it was validated in various countries by
testing it on local samples. 

 

Beck Depression
Inventory-second edition (BDI-II)

 

The 21 items of
the BDI-II (Beck, Epstein, Brown, & Steer, 1988; Beck, Steer, & Garbin,
1993) are designed to assess the severity of the affective, cognitive,
motivational, psychomotor and vegetative components of depression. BDI-II items
are rated on a 4-point scale ranging from 0 (I do not feel sad) to 3 (I am so
sad or unhappy that I can’t stand it) based on severity of the symptom depicted
in the item. The maximum total score is 63. Higher total scores indicate more
severe depressive symptoms. The standardized cutoffs used differ from the
original: 0–13: minimal depression, 14–19: mild depression, 20–28: moderate
depression, 29–63: severe depression. Eleven was the cut point between normal
and depressed. (Aldahadha, 2008; Beck, Steer, Ball & Ranieri, 1996). For
the purposes of this study, the degree 20 and above was adopted to be among the
members of the study.

 Alpha reliability coefficients of the BDI-II
have been found to exceed .90 in a range of populations. The 21 items of the BDI-II
can be separated into two subscales: 1) a cognitive-affective subscale (the sum
of the first 13 items), and 2) a somatic-performance subscale. These subscales,
as well as the total score of the BDI-II were used in the analyses of the
current study. Finally, in accordance with guidelines stipulated by Beck, Steer,
and Garbin (1993), the cut-offs for assessing different levels of depression
were as follows:

To achieve the
accredited standards of psychometric properties, content validity for the
BDI-II was established by asking twelve expert raters to evaluate candidate
items on quality unanimously, 100 % of the expert reviewers agreed on all scale
items. For further confirmation of the validity, the scale was tested by
construct validity. The correlation coefficients between the items and the
total score of the questioner ranged from 78 and 91. For the purpose of this study,
the cut point of 50 correlation coefficient score and above was adopted to
accept the construct validity. Reliability measures for the depressed cases
revealed a coefficient alpha of .94 (p

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