| Literature DB >> 34309776 |
Vittoria Cuteri1,2, Giulia Minori3, Gloria Gagliardi3, Fabio Tamburini3, Elisabetta Malaspina1, Paola Gualandi1, Francesca Rossi1, Milena Moscano1, Valentina Francia1, Antonia Parmeggiani4,5.
Abstract
PURPOSE: Attention has recently been paid to Clinical Linguistics for the detection and support of clinical conditions. Many works have been published on the "linguistic profile" of various clinical populations, but very few papers have been devoted to linguistic changes in patients with eating disorders. Patients with Anorexia Nervosa (AN) share similar psychological features such as disturbances in self-perceived body image, inflexible and obsessive thinking and anxious or depressive traits. We hypothesize that these characteristics can result in altered linguistic patterns and be detected using the Natural Language Processing tools.Entities:
Keywords: Adolescence; Anorexia nervosa; Clinical linguistics; Eating Disorders; Linguistic marker
Mesh:
Year: 2021 PMID: 34309776 PMCID: PMC8311399 DOI: 10.1007/s40519-021-01273-7
Source DB: PubMed Journal: Eat Weight Disord ISSN: 1124-4909 Impact factor: 3.008
Inclusion criteria for participant enrollment
| AN | CG |
|---|---|
Age: 14–18 Diagnosis of Anorexia Nervosa (DSM-5, EDI-3) Fair level of communication skills in standard Italian (Language History Questionnaire) Written informed consent | Age: 14–18 BMI ≥ 18.5 Fair level of communication skills in standard Italian (Language History Questionnaire) Written informed consent |
Fig. 1The Cookie theft
Demographic characteristics of the sample
| Group | N | Age (mean ± sd) | Years of education (mean ± sd) |
|---|---|---|---|
| ANG | 17 | 16 ± 1.37 | 11.06 ± 1.34 |
| CG | 34 | 16 ± 1.35 | 11.15 ± 1.28 |
Text length produced in the three tasks by ANG and CG
| Task | ANG (mean ± sd) | CG (mean ± sd) |
|---|---|---|
| Task1 (Personal) | 98.63 ± 42.94 | 105.5 ± 35.05 |
| Task2 (Neutral) | 61.53 ± 40.98 | 68.56 ± 31.55 |
| Task 3 (Description of picture) | 81.50 ± 40.02 | 77.15 ± 24.13 |
| Overall | 80.22 ± 43.16 | 83.74 ± 34.18 |
Results of the linguistic analysis
| Linguistic variable | Task 1 (Personal) | Task 2 (Neutral) | Task 3 (Description of picture) | Overall |
|---|---|---|---|---|
| LEX_ContDens | D = 0.441 | |||
| LEX_PoS_ADV | D = 0.412 | |||
| LEX_PoS_CONJ | D = 0.235 | |||
| LEX_PDEIXIS | D = 0.412 | |||
| LEX_HonoreR | D = 0.412 | D = 0.255 | ||
| SYN_NPLENSD | D = 0.411 | |||
| SYN_GRAPHDISTM | D = 0.284 | |||
| SYN_SLENM | D = 0.412 | D = 0.284 | ||
| SYN_SLENSD | D = 0.412 | D = 0.245 | ||
| LIWC_WPS | D = 0.412 | D = 0.245 | ||
| LIWC_SIXLTR | D = 0.441 | D = 0.333 | ||
| LIWC_DIC | D = 0.441 | D = 0.588 | ||
| LIWC_PERCP | D = 0.412 | |||
| LIWC_PRES | D = 0.412 |
The significant p-value is indicated for the corresponding feature and task, with *p < 0.05; **p < 0.01; ***p < 0.001