Differences in anticipation capacity in depressive and mixed anxiety–depressive disorders

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BACKGROUND

The ability to predict future events is seen as a significant factor for successful adaptation. One of the psychological indicators for the assessment of these abilities is the prognostic competence, i.e. anticipatory competence (AC) [1]. Thanks to it, a person can anticipate the course of events with a high degree of probability, predict situations and his/her own reactions to them [2–4]. Patients with mental disorders have been shown to have lower levels of AC than healthy individuals [5], which is reflected in the anticipation concept of neurosis development [6]. The 10th revision of the International Classification of Diseases (ICD-10) makes a distinction between neurotic and affective disorders [7]. In practice, however, there is considerable overlap between anxiety and depressive disorders [8, 9]. The development of predictive competence is considered to be a target for psycho-corrective techniques [10]. It is therefore relevant to investigate whether the differences in the manifestations of AC in depressive and mixed anxiety and depressive disorders are significant, which was the aim of this study.

The hypotheses of the study:

1. There are statistically significant differences in the AC between the samples of patients and the control group;
2. Patients with mixed anxiety and depressive disorders and depressive disorders differ in prognostic competence;
3. AC is correlated with the severity of depression symptoms.

MATERIALS AND METHODS

Characteristics of the study group

A total of 336 people participated in the study:

• The first group (C) included 132 people (55 men and 77 women) aged between 18 and 55 years (median age: 22 years) who, according to self-report data, did not seek psychological help at the time of the study and were not observed by a psychiatrist.
• The second group (D) included 109 people (31 men and 78 women) aged between 19 and 59 years (median age: 32.5 years) with a confirmed diagnosis of the depressive disorder spectrum (ICD-10 code F32-33).
• The third group (A/D) included 95 people (21 men and 74 women) aged 19 to 67 years (median age: 42.5 years) with mixed anxiety and depressive disorder (ICD-10 code F41.2).

All patients were treated at the 2nd Women’s Department of V.M. Bekhterev Republican Clinical Psychiatric Hospital and the Psychotherapy Department of State Clinical Hospital No. 18 named after K.Sh. Zyatdinov (Kazan); the diagnoses were established by the attending physician on the basis of the ICD-10 criteria.

Study methods

Experimental psychological testing was chosen as the main study method. The techniques used are as follows:

• A. Beck Depression Inventory, including 3 scales: general (BDI), cognitive-affective symptoms (CS), and somatic symptoms(SS) to assess the severity of depression symptoms [11];
• L.A. Regush’s “Predictive Ability” methodology [12];
• V.D. Mendelevich’s AC (prognostic competence) test [13], including 4 scales: personal-situational (PSAC), spatial (SAC), temporal (TAC), and overall anticipatory competence (OAC).

RESULTS

The descriptive statistics are summarized in Table 1 below. The AC was higher in the control group than in the patient groups. The AC scores of patients in the D and A/D groups were similar. The severity of depression symptoms was greater in the D group than in the A/D group, but the differences were not statistically significant (Mann–Whitney test).

 

Table 1. Descriptive statistics
Data	Group	Methods
		GAC	TAC	SAC	PSAC	PA	BGI	CS	СС
Mean value	MAD	250	39.3	45.3	165	11.1	22.4	14.2	8.25
	D	250	37.8	46.2	166	10.8	25.4	16.7	8.69
	CG	260	40.2	49.1	171	—	—	—	—
Standard error	MAD	2.24	0.703	0.886	1.62	0.289	2.67	2.06	0.937
	D	2.26	0.728	0.900	1.55	0.268	1.45	0.992	0.747
	CG	1.79	0.664	0.706	0.952	—	—	—	—
Standard deviation	MAD	21.7	6.82	8.59	15.7	1.29	12.0	9.20	4.19
	D	23.6	7.60	9.40	16.2	1.80	9.72	6.66	5.01
	CG	20.5	7.63	8.11	10.9	—	—	—	—
Note. MAD — mixed anxiety–depressive disorder group; D — depressive disorder group; CG — control group; GAC — General Anticipation Capacity Scale; TAC — Temporal Anticipation Capacity Scale; SAC — Spatial Anticipation Capacity Scale; PSAC — Personal and Situational Anticipation Capacity Scale; PA — prediction ability; BGI — Beck’s General Inventory; CS — Cognitive and Affective Symptom Scale; SS — the Somatic Symptom Scale.

 

One-way analysis of variance (Kruskal–Wallis test) was used to compare the parameters of the three groups. Statistically significant results were obtained for all of the AC scales (Table 2).

 

Table 2. One-way analysis of variance
Anticipation capacity scales	χ²	p	ε²
GAC	23.40	<0.001	0.07
TAC	7.91	0.019	0.023
SAC	9.34	0.009	0.028
PSAC	14.8	<0.001	0.044
Note. GAC — General Anticipation Capacity Scale; TAC — Temporal Anticipation Capacity Scale; SAC — Spatial Anticipation Capacity Scale; PSAC — Personal and Situational Anticipation Capacity Scale.

 

Post-hoc analysis by paired comparisons with the Dwass–Steel–Critchlow–Fligner test revealed that all AC parameters, except SAC, in the D group were lower than in the C group: OAC (W=6.41, p <0.001), SAC (W=3.124, p=0.070),TAC (W=3.73, p=0.023), PSAC (W=5.158, p <0.001). The A/D group had statistically significant differences with the C group on all scales except TAC: ОАС (W=4.80, p=0.002), SAC (W=4.049, p=0.012), TAC (W=1.26, p=0.646), PSAC (W=3.834, p=0.018). The A/D and D groups had no statistically significant differences so they were combined for comparison with the C group. The patient group had lower scores on all AC scales (Table 3).

 

Table 3. Differences between the patient group and the control group
Anticipation capacity scales	Welch’s t	p	Mean difference	Cohen’s d
GAC	−4.29	<0.001	−10.27	−0.475
TAC	−2.03	0.043	−1.7	−0.228
SAC	−3.46	<0.001	−3.28	−0.383
PSAC	−3.66	<0.001	−5.37	−0.393
Note. GAC — General Anticipation Capacity Scale; TAC — Temporal Anticipation Capacity Scale; SAC — Spatial Anticipation Capacity Scale; PSAC — Personal and Situational Anticipation Capacity Scale.

 

Correlation analysis showed no relationship between severity of depressive symptoms and prognostic competence. The Predictive Ability scale correlated with the OAC scales (r=0.446, p <0.001), TAC (r=0.404, p <0.001), PSAC (r=0.278, p=0.025).

DISCUSSION

It was hypothesized that the AC would be lower in the patient groups than in the control group. The results confirm this. However, no differences were found between the D and A/D groups. It seems that less successful prediction is equally common in depression with and without anxiety. Similar anticipation inconsistency levels have been reported in neurotic disorders [5, 14–16]. The findings may suggest that neurotic and depressive disorders share underlying mechanisms.

No correlations of AC with the severity of depression were found. Predictive ability also did not correlate with symptoms of depression, but it did correlate with anticipation. This may be because both prognostic competence and predictive ability are defined as personality traits, meaning that they are more enduring than dynamic symptoms of depression.

Limitations of the study

This study has a number of limitations. The correlational design does not explain causality, i.e. whether the AC decreases due to the disease or serves as a predisposing factor. The questions in the test are mainly aimed at retrospective evaluation. In our opinion, this fact, together with the absence of correlations with symptoms, favors the second interpretation, but its verification requires experimental and prospective designs.

CONCLUSION

The study showed statistically significant differences in AC scores between patients with depressive and anxiety disorders and depressive disorders and the control group: AC was lower in patients with these disorders. Meanwhile, no differences were found between patients with depressive disorders and those with mixed anxiety and depressive disorder. There was also no association found between AC and the severity of the symptoms of depression. The results allow the correlation of depressive disorders with other disorders of the neurosis level, for which a decrease in prognostic ability has been previously observed [1, 2, 5, 14–16]. Based on our data, we can justify the promising nature of psycho-corrective measures aimed at developing prognostic competence in depression with and without anxiety.

ADDITIONAL INFORMATION

Funding source. The work and publication of the article were carried out at the personal expense of the author's team

Competing interests. The authors declare that there is no potential conflict of interest requiring disclosure in this article.

Authors’ contribution. All authors confirm that their authorship complies with the international ICMJE criteria (all authors have made a significant contribution to the development of the concept, research, and preparation of the article, as well as read and approved the final version before its publication). A.S. Granitsa — literature review, concept and design of the study, collection and processing of materials, writing the text, making final edits; D.A. Makarova — collection and processing of materials, analysis of the received data, writing of the text; A.I. Mashtakova — collection and processing of materials, analysis of the received data, writing of the text; M.S. Chertishchev — collection and processing of materials, analysis of the received data, writing of the text.

Acknowledgments. The authors express their gratitude to the chief physician of the RCPB named after Academician V.M. Bekhtereva to I.I. Akhmetzyanovand the head. 2 by the women's department to F.G. Kalimullin, to the chief physician of the State Clinical Hospital No. 18 of Kazan R.S. Bakirovand the head. by the Psychotherapy department to B.I. Akberovfor administrative support of the study.

About the authors

Alexander S. Granitsa

Kazan Federal University

Author for correspondence.
Email: hebechblu@yandex.ru
ORCID iD: 0000-0002-0498-7397
SPIN-code: 4775-7844

MD, Cand. Sci. (Med.), Associate Professor, Depart. of Neurology with courses in Psychiatry, Clinical Psychology and Medical Genetics

Russian Federation, Kazan

Dinara A. Makarova

Kazan Federal University

Email: Dinara.makarova@mail.ru
ORCID iD: 0009-0003-1854-8664

Student of the Faculty of Medicine

Russian Federation, Kazan

Alexandra I. Mashtakova

Kazan Federal University

Email: mashtakovaA@mail.ru
ORCID iD: 0009-0003-6750-430X

Student of the Faculty of Medicine

Russian Federation, Kazan

Mikhail S. Chertishev

Kazan State Medical University

Email: chertishev.mihail@mail.ru
ORCID iD: 0000-0002-8692-1868

MD, Cand. Sci. (Med.), Associate Professor, Depart. of Psychiatry and Medical Psychology

Russian Federation, Kazan

References

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