https://dx.doi.org/10.24016/2025.v11.466

ORIGINAL ARTICLE

 

 

Factors associated with dropout in a self-guided transdiagnostic intervention for a Mexican population

 

Omar Hernández-Orduña1*, Anabel de la Rosa-Gómez2, Pablo D. Valencia2, Lorena Flores-Plata1, Alejandra Mares1

1 Iztacala Iztacala Higher Education Faculty, National Autonomous University of Mexico, Mexico City, Mexico.

2 National Autonomous University of Mexico, Mexico City, Mexico.

 

* Correspondence: fomarhernandez.o@gmail.com

 

Received: June 16, 2025 | Revised: July 26, 2025 | Accepted: August 16, 2025 | Published Online: August 30, 2025

 

CITE IT AS:

Hernández-Orduña, O., de la Rosa-Gómez, A., Valencia, P., Flores-Plata, L., Mares, A. (2025). Factors associated with dropout in a self-guided transdiagnostic intervention for a Mexican population. Interacciones, 11, e466. https://dx.doi.org/10.24016/2025.v11.466

 

 

ABSTRACT

Background: Technology has revolutionized mental health, enabling access to diverse and more accessible therapies. Telepsychology interventions, such as self-guided ones, offer personalized treatments through digital platforms. Although long-term studies are limited, these approaches have shown effectiveness in various disorders. Modality and the specific disorder influence the effectiveness of these therapies. Understanding and identifying factors that influence the abandonment of self-guided treatments is crucial to improving their design and increasing the likelihood of their completion.

Objective: This study seeks to identify factors that influence the abandonment of self-guided treatments for emotional problems related to stress and trauma. With this, it is possible to include tools that improve the effectiveness of these treatments.

Methods: The sample included 89 adults evaluated through diagnostic interviews, who met criteria for emotional or stress-related disorders and were assigned to a self-guided transdiagnostic treatment with eight modules that could be accessed at any time.

Results: A dropout rate of 81% of participants was identified, mainly during the first sessions. Through logistic regression analysis, some significant factors for abandonment were identified, such as place of residence and anxiety levels.

Conclusion: Strategies such as personalized reminders, continuous clarification of treatment objectives, and tools that drive patient motivation towards treatment could prevent abandonment. The results offer valuable information for optimizing telepsychology and ensuring success in self-guided psychological treatments in diverse technological and cultural contexts.

Keywords: Telepsychology, self-guided treatments, drop-out, transdiagnostic treatment, emotional issues, trauma issues.

 

 

INTRODUCTION

Current technology has enabled a wider variety of modalities for providing psychological care, making it more accessible to individuals with different mental health issues. In recent years, technology-mediated interventions have improved access to mental health services, reducing the time to receive care and eliminating some barriers, such as stigma (Dworschak et al., 2022, 2022). These interventions, which do not rely on traditional physical settings, have proven effective in addressing a variety of issues related to emotions, eating disorders, and substance use (Lin et al., 2022; Taylor et al., 2021).

Different intervention modalities in telepsychology utilize different media, such as videoconferences, telephone calls, messaging, apps, and websites. Some may be conducted with the support of an individual therapist or in group settings, either synchronously or asynchronously, while others may be self-guided (McCord et al., 2020; Saenz et al., 2020). Some such modalities have already proven more effective than others; for example, videoconference interventions are more effective than telephone conversations for treating depression and trauma (Turgoose et al., 2018; Tobak, 2021).

The self-guided modality in telepsychology involves a psychological treatment where individuals seeking treatment independently follow a guided protocol organized into steps they follow at their own pace and convenience (Cuijpers & Schuurmans, 2007). Patients utilize various resources, including reading material, videos, activities, and evaluations. This type of treatment has been developed for various issues, such as alcohol use, anxiety, depression, eating disorders, and insomnia (Jiménez-Molina et al., 2019; McClellan et al., 2022; Sablone et al., 2024). The strategies are well-accepted and suitably adapted to an online format. However, most studies have reported short-term results, either due to challenges in adherence or a lack of long-term follow-up (Jiménez-Molina et al., 2019; Tiburcio et al., 2018). 

Self-guided treatments have also proven effective for various issues, including anxiety. However, they may be more effective for specific typologies, such as panic attacks and obsessive-compulsive disorder, than for others, such as specific phobias and social anxiety, whose nature may require contextual elements to maximize the treatment's effects (Hirai & Clum, 2006; Gadelha et al., 2021).

Technology-mediated interventions have increasingly been used in Latin America since the COVID-19 pandemic (Dominguez-Rodriguez et al., 2023; Celleri et al., 2023; De la Rosa et al., 2022; Rodante et al., 2022; Benjet et al., 2023), providing greater access to mental health care services. These interventions have proven effective for different issues, including substance use, depression, and anxiety, in countries such as Chile, Mexico, Brazil, and Argentina (Jiménez-Molina et al., 2019).

Some studies have shown that dropout rates are comparable between in-person and telepsychology modalities (Walton et al., 2023). Karyotaki and associates (2015) conducted a systematic review to identify the characteristics that predict dropout in self-guided interventions for depression. Their multivariate analysis revealed that male gender, a lower level of education, and comorbidity with anxiety symptoms significantly increase the risk of dropping out (Karyotaki et al., 2015).

Difficulties accessing the internet or the app were identified as factors leading to treatment dropout (Cárdenas López et al., 2014). These challenges may exacerbate some individuals' symptoms, such as the urgency to achieve quick results (Dennison et al., 2013). Forgetfulness may be another reason people do not adhere to the interventions and drop out (Donkin & Glozier, 2012; Horsch et al., 2015, 2017). This issue may hinder our ability to assess such treatments' effectiveness and follow up on their outcomes.

Psychological treatments require follow-up and continuity to achieve their objectives and are particularly relevant in self-guided, technology-mediated interventions. Knowing which factors influence treatment dropout may help take on strategies to meet the needs of individuals using this approach, thus increasing the likelihood that they will complete the treatment. Therefore, this study aimed to determine the rate of dropout from a transdiagnostic self-guided intervention designed for emotional issues related to stress and trauma, identify the characteristics of those who dropped out, and discern whether any of these characteristics affected the likelihood of treatment dropout.

 

METHODS

Design

This study presents a secondary analysis from the principal study, which evaluated the efficacy, clinical change moderators, and acceptability of a transdiagnostic treatment by comparing three intervention modalities (self-guided, self-guided with an advisor, and waitlist). The technological development of these modalities is placed in official public record no. 03-2023-121412443800-01, and the randomized clinical trial was registered under the number NCT05225701 on the website ClinicalTrial.gov in February 2022. (De La Rosa-Gómez et al., 2022). We utilized a longitudinal design to collect data throughout the self-guided sessions, with zero to eight measurements to record the number of completed sessions and the characteristics of participants who dropped out of treatment.

 

Participants

The sample included 89 adult participants from Mexico who voluntarily signed up on the website https://e-motion.iztacala.unam.mx/register. These participants were evaluated by psychologists trained in administering the MINI interview (Mini-International Neuropsychiatric Interview) (Heinze et al., 2000; Sheehan et al., 1997) to confirm whether they met the specified criteria and symptoms. Participants were assigned to a self-guided intervention, and this appointment was confirmed by email. The sample comprised 17% (n=15) men and 83% (n=74) women, with an average age of 35. Regarding relationship status, 55% (n=49) reported being single at the time of the intervention, while 45% (n=49) had partners. Geographically, 70% (n=62) resided in Mexico City, whereas 30% (n=27) lived in another state. Concerning education, 33% (n=29) reported an intermediate level, having graduated from middle or high school, while 67% (n=60) had a higher education level, having attended college or beyond. Regarding employment, 53% (n=47) were unemployed, while 47% (n=42) reported being employed. 

 

Procedure

The participants were recruited through a social media announcement. Those interested in the intervention were asked to complete a series of psychometric assessments and undergo a MINI interview to confirm that they met the symptomatology of an emotional, stress, or trauma-related issue. Individuals who did not meet the criteria were referred to a public mental health service directory.  Once accepted, each participant's user profile was activated on the self-guidance platform, and they were sent an email confirming their username and password to begin. The intervention was designed to be self-guided and consisted of eight modules that participants could access at any time (24 hours, any day of the week).  At the end of each intervention module, participants were evaluated to gain access to the next module. This process allowed the system to track its progress. We extracted the participants' data assigned to the intervention between March 2023 and April 2024 from the database and considered a module completed on the date the module evaluation was recorded. Individuals who did not finish one or more of the eight intervention modules over a year were classified as having dropped out.

 

Assessment instruments

Beck Anxiety Inventory (Beck et al., 1988). An instrument consisting of 21 questions to identify the severity of manifested anxiety symptoms. It uses a four-point scale, ranging from 0 to 3, where 0 indicates the absence of a symptom and three represents the maximum severity of that symptom. With established cut-off points, anxiety levels in the Mexican population are: 0-7 points indicate minimal anxiety, 8-15 points suggest mild anxiety, 16-25 points correspond to moderate anxiety, and 26-63 points reflect severe anxiety. The questions cover symptoms typically included in diagnosing an anxiety disorder. Several studies suggest that this measurement exhibits high internal consistency and validity as both a divergent and convergent construct (α = 0.83), and the original version has shown 85% sensitivity and 81% specificity in identifying these symptoms. (Robles et al., 2001).

Beck Depression Inventory II (Beck et al., 1988). Questionnaire with 21 items evaluating the clinical symptoms of melancholy and thoughts that surface during depression. It is a self-report tool that uses a four-point scale (0 to 3), where 0 indicates the absence of a symptom and three the maximum severity of that symptom. It has a total score range of 0 to 63, with established cut-off points of 0-13 points indicating minimal depression, 14-19 points suggesting mild depression, 20-28 points corresponding to moderate depression, and 29-63 points reflecting severe depression. The original version has shown 83% sensitivity and 95% specificity in identifying these symptoms. This instrument has been validated for the Mexican population. We utilized version II, demonstrating strong psychometric properties (α = 87-0.92) (Jurado et al., 1998).

Difficulties in Emotional Regulation Scale (DERS) (Gratz & Roemer, 2004). The self-report instrument consists of 15 items assessing two dimensions: emotional regulation strategies and awareness. We used the version validated for the Mexican population, with a Cronbach's alpha value ranging from 0.74 to 0.84 (De La Rosa-Gómez et al., 2021).

 

Statistical analysis

Sociodemographic data were analyzed and compared using central tendency measures in descriptive statistics to identify variables of interest. A logistic regression was conducted to determine which variables were more likely to contribute to treatment dropout. Only statistically significant variables and findings from other articles were included. Dropout was treated as a dependent variable, while sociodemographic and psychological factors were considered independent variables.

 

Ethical considerations

This project originates from a larger initiative approved by the Ethics Committee at the Facultad de Estudios Superiores Iztacala (College of Higher Studies Iztacala Campus) of the Universidad Nacional Autónoma de México (National Autonomous University of Mexico), under registration number (CE/FESI/082020/1363). The project complies with the guidelines outlined in the Ethics Code for Mexican Psychologists and adheres to recommendations for online psychotherapy. We ensured the participants' information confidentiality by using an encryption algorithm accessible only to the principal researcher. Participation was voluntary. All study participants signed an online informed consent form before receiving the intervention (De La Rosa-Gómez et al., 2022; Sociedad Mexicana de Psicología, 2010).

 

RESULTS

Of the 89 participants assigned to the intervention, 18% (n=16) did not complete any of the modules. Among those who started treatment, 81% (n=72) did not finish and were classified as dropouts. In all, 23% (n=20) of participants dropped out after finishing module 1, 18% (n=16) after module 2, and 10% (n=9) after module 3; that is, half the participants dropped out during the first three modules. Module 7 had the lowest dropout percentage at 1%; only one person started this module but did not complete the treatment. Also, 19% of participants completed treatment (n=17). The above is summarized in Table 1. On average, participants who did not finish using the platform for 52 days to complete one or more modules, whereas those who completed the treatment used the platform for an average of 75 days. 

 

 

Table 1. Dropout percentages and frequencies per module (n=89)

Module

Frequency dropout

Cumulative dropout

Percentage dropout

0

16

16

18

1

20

36

22.5

2

16

52

18

3

9

61

10.1

4

3

64

3.4

5

2

66

2.2

6

5

71

5.6

7

1

72

1.1

Total

72

100

Note: Participants who did not complete their assigned module could not advance to the next one and were considered to have dropped out

 

 

Regarding the differences between individuals who completed the intervention and those who did not, 93% of men who started treatment did not finish (n=14), and 78% of women dropped out of some modules (n=58). Regarding relationship status, 86% of single participants (n=42) and 75% of those in a relationship (n=30) did not complete the treatment. Regarding residence, 87% (n=54) of participants living outside of Mexico City and 67% (n=18) of those residing there withdrew from the treatment. Concerning the level of education, 93% (n=27) of individuals with an intermediate level (middle and high school) and 75% (n=45) of those with a higher level of education (college and beyond) did not complete the treatment. Lastly, regarding occupation, 83% (n=39) of unemployed individuals and 79% (n=33) of employed persons did not finish the treatment. The above is summarized in Table 2.

 

 

Table 2. Differences in sociodemographic and in psychological variables between participants who did and did not drop out of the intervention (n=89).

 

 

Dropout
n=72 (%)

Completed
n=17 (%)

Age (years)

 

33.8 (mean)

39.7 (mean)

Sex

Men

14 (93.3)

1 (6.7)

 

Women

58 (78.4)

16 (21.6)

Relationship status

Single

42 (85.7)

7 (14.3)

 

With a partner

30 (75)

10 (25)

Place of residence

Outside of Mexico City

54 (87.1)

8 (12.9)

 

Mexico City

18 (66.7)

9 (33.3)

Level of education

Intermediate

27 (93.1)

2 (6.9)

 

High

45 (7)

15 (25)

Occupation

Unemployed

39 (83)

8 (17)

 

Employed

33 (78.6)

9 (21.4)

Level of anxiety

Minimum

17 (94.4)

1 (5.6)

Mild

27 (81.8)

6 (18.2)

Moderate

25 (78.1)

7 (21.9)

 

Severe

3 (50)

3 (50)

Level of depression

Mild

12 (80)

3 (20)

Moderate

47 (78.3)

13 (21.7)

Severe

13 (92.9)

1 (7.1)

Note: Intermediate level of education: middle and high school; higher level of education: undergraduate and graduate.

 

 

As for psychological variables, the dropout percentage was lower when the anxiety level was higher (in the minimum and mild ranges). Conversely, a higher dropout percentage was associated with a higher level of depression (in the moderate and severe ranges) (Table 2).

The bivariate logistic regression analysis indicated that individuals living in Mexico City (OR=3.38, 95%CI=1.13-10.05, p=0.29) and those with the highest anxiety scores (OR=1.07, 95%CI=1.01-1.14, p=0.47) were the most likely to drop out of treatment. In the multiple logistic regression, which accounted for additional variables that could influence dropout, the results showed that living in Mexico City (aOR=4.583, 95%CI=1.35-15.25, p=0.15) and presenting anxiety symptoms (aOR=1.08, 95%CI=1.01-1.16, p=0.44) remained associated with the likelihood of dropping out of treatment. These findings are shown in Table 3.

 

 

Table 3. Logistic regression analysis results (n=89)

Variable

Simple

Multiple

OR [CI 95%]

p

OR [CI 95%]

p

Sex

 

 

 

 

Woman

Ref. Group

Ref. Group

Man

0.26 [0.03, 2.12]

0.208

0.25 [0.03, 2.25]

0.217

Age

1.05 [1.00, 1.10]

0.055

 

 

Marital status

 

 

 

 

Single

Ref. Group

In a relationship

2.00 [0.68, 5.85]

0.206

 

 

Residence

 

 

 

 

Outside of Mexico City

Ref. Group

Ref. Group

Mexico City

3.38 [1.13, 10.05]

0.029

4.53 [1.35, 15.25]

0.015*

Level of education

 

 

 

 

High

Ref. Group

Intermediate

0.22 [0.05, 1.05]

0.222

 

 

Employed

 

 

 

 

No

Ref. Group

Yes

1.33 [0.46, 3.84]

0.598

 

 

BAI score

1.07 [1.00, 1.14]

0.047

1.08 [1.00, 1.16]

0.044*

BDI-II score

0.94 [0.87, 1.03]

0.178

0.93 [0.85, 1.02]

0.119

Note: Dependent variable: Dropout. Statistically significant variables were included, as well as findings from other articles

 

 

DISCUSSION

Our study found that single, unemployed individuals who identified as men, lived outside of Mexico City, and had lower levels of education and higher depression scores exhibited the highest dropout percentages, particularly during the initial weeks of self-guided treatment. Therefore, it may be important to pay attention to this population, as these characteristics could be a factor to be considered when providing long-distance services through technology.

Different strategies can be implemented to avoid dropout from teletherapy, specifically during self-guided interventions and at both individual and treatment levels (Heron & Smyth, 2010; Horsch et al., 2017; Karyotaki et al., 2015; Nordby et al., 2022; Titov et al., 2013). One practical approach is psychoeducation, which provides evidence-based information about a patient’s condition and has been shown to influence treatment adherence (Oliveira & Dias, 2023; Prashant Srivastava & Rishi Panday, 2016). By investing resources that may be easily and continuously accessed through platforms, dropout rates could be decreased.

The results indicate that it could be effective to implement these strategies during the initial sessions, even when access to the platform is offered, since the highest dropout percentage occurred during the first two sessions. Some individuals did not even begin treatment. This finding aligns with the literature on in-person treatments related to premature dropout and the need to perform interventions during early sessions (Strauss et al., 2010).

Another interesting finding was that individuals with high anxiety symptoms completed the treatment, possibly due to a greater perception of need and greater concern about their symptoms, as has been noted in other studies (Lopes et al., 2015; Krebs et al., 2012). In contrast, those with depression symptoms were more likely to drop out of treatment. Other studies have also observed this pattern (Swift & Greenberg, 2012; Strauss et al., 2010). Using behavioral strategies, such as providing an agenda linked to personal accounts, sending automated personalized reminders, and establishing initial contact with users, may improve treatment follow-up and adherence by increasing motivation among patients, as well as exploring willingness toward this type of intervention (Perski et al., 2017; Dennison et al., 2013; Heron & Smyth, 2010).

 

Limitations and conclusions

Motivation and expectations regarding treatment before its commencement are highly relevant factors that should be considered in future studies. Our study did not measure these aspects, but they may provide information on the patients’ predisposition. Implementing strategies such as treatment goal reminders (Dennison et al., 2013) within the programs and frequently presenting the decisional balance throughout treatment could be beneficial. These strategies may help improve the patient’s intrinsic motivation, create a sense of control, help visualize their progress (for example, through weekly graphs of anxiety and depression questionnaire results), and enhance their ability to identify with the program. Such strategies could increase the program’s perceived value, leading to greater adherence (Donkin & Glozier, 2012) and reduced dropout rates.

Additionally, we recognize that our study did not include a formal evaluation of the mechanisms of data loss, nor did we determine whether missing data were consistent with Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR). This is a significant limitation, and future research should prioritize this type of analysis to enhance the robustness of the findings.

We also recommend increasing the sample size and the number of persons with other attributes, such as varying levels of education, psychological symptoms, knowledge, and experience with devices and platforms. This study helps identify variables to be addressed by telepsychology. Online interventions may benefit from the insights gained through this type of research to adapt the identified variables, prevent dropout for individuals with risk factors of a Mexican population, and recognize these characteristics to guide them towards the most appropriate treatment options. Furthermore, the findings provide valuable information for developing and implementing digital interventions, strengthening these tools, and laying the groundwork for future research and controlled studies.

 

ORCID

Omar Hernández-Orduña: https://orcid.org/0000-0002-4868-876X

Anabel de la Rosa-Gómez: https://orcid.org/0000-0002-3527-1500

Lorena Flores-Plata: https://orcid.org/0000-0003-1306-0718

Pablo D. Valencia: https://orcid.org/0000-0002-6809-1805

Alejandra Mares: https://orcid.org/0009-0004-6969-532X

 

AUTHORS’ CONTRIBUTION

Omar Hernández-Orduña: Conceptualization, Writing – original draft, Visualization, Project administration, Writing – review & editing.

Anabel de la Rosa-Gómez: Methodology, Conceptualization, Supervision, Writing – review & editing.

Lorena Flores-Plata: Methodology, Project administration, Writing – review & editing.

Pablo D. Valencia: Formal analysis, Data curation, Writing – review & editing.

Alejandra Mares: Investigation, Writing – review & editing

 

FUNDING SOURCE

The study is part of a main project supported by external funding of the National Council of Humanities, Science and Technology. Call 2020–04: Research Projects and Social Incidence in Mental Health and Addictions. Grant number 1401. It is part of a postdoctoral research stay with number 697266 at the same institution. The fundings institution had no role in the design of the study; data collection, analysis, and interpretation; and will not have any role in the writing of the manuscript.

 

CONFLICT OF INTEREST

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

 

ACKNOWLEDGMENTS

Not applicable.

 

REVIEW PROCESS

This study has been reviewed by external peers in double-blind mode. The editor in charge was Anthony Copez-Lonzoy. The review process is included as supplementary material 1.

 

DATA AVAILABILITY STATEMENT

Data will be made available on request.

 

DECLARATION OF THE USE OF GENERATIVE ARTIFICIAL INTELLIGENCE

We have not used generative artificial intelligence in any form. The final version of the manuscript was reviewed and approved by all authors.

 

DISCLAIMER

The authors are responsible for all statements made in this article.

 

REFERENCES

Beck, A. T., Epstein, N., Brown, G., & Steer, R. A. (1988). An inventory for measuring clinical anxiety: Psychometric properties. Journal of Consulting and Clinical Psychology, 56(6), 893-897. https://doi.org/10.1037/0022-006X.56.6.893

Beck, A. T., Steer, R. A., & Carbin, M. G. (1988). Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clinical Psychology Review, 8(1), 77-100. https://doi.org/10.1016/0272-7358(88)90050-5

Benjet, C., Zainal, N. H., Albor, Y., Alvis-Barranco, L., Carrasco-Tapias, N., Contreras-Ibáñez, C. C., ... & Kessler, R. C. (2023). A precision treatment model for internet-delivered cognitive behavioral therapy for anxiety and depression among university students: a secondary analysis of a randomized clinical trial. JAMA psychiatry, 80(8), 768-777. https://doi.org/10.1001/jamapsychiatry.2023.1675

Cárdenas López, G., Botella Arbona, C., Quero Castellano, S., De La Rosa Gómez, A., & Baños Rivera, R. M. (2014). Programa de telepsicología para el tratamiento de la fobia a hablar en público en población mexicana. Psicología Iberoamericana, 22(1), 45-54. https://doi.org/10.48102/pi.v22i1.145

Celleri, M., Cremades, C. F., Etchevers, M. J., & Garay, C. J. (2023). Effectiveness of the Unified Protocol for the transdiagnostic treatment of emotional disorders in online and group format in Argentina: Study protocol for a randomized controlled trial. Trials, 24(1), 678. https://doi.org/10.1186/s13063-023-07428-4

Cuijpers, P., & Schuurmans, J. (2007). Self-help interventions for anxiety disorders: An overview. Current Psychiatry Reports, 9(4), 284-290. https://doi.org/10.1007/s11920-007-0034-6

De La Rosa-Gómez, A., Flores-Plata, L. A., Esquivel-Santoveña, E. E., Santillán Torres Torija, C., García-Flores, R., Dominguez-Rodriguez, A., Arenas-Landgrave, P., Castellanos-Vargas, R. O., Berra-Ruiz, E., Silvestre-Ramírez, R., Miranda-Díaz, G. A., Díaz-Sosa, D. M., Hernández-Posadas, A., Flores-Elvira, A. I., Valencia, P. D., & Vázquez-Sánchez, M. F. (2022). Efficacy of a transdiagnostic guided internet-delivered intervention for emotional, trauma and stress-related disorders in Mexican population: Study protocol for a randomized controlled trial. BMC Psychiatry, 22(1), 537. https://doi.org/10.1186/s12888-022-04132-6

De La Rosa-Gómez, A., Hernández-Posadas, A., Valencia, P. D., & Guajardo-Garcini, D. A. (2021). Análisis dimensional de la Escala de Dificultades en la Regulación Emocional (DERS-15) en universitarios mexicanos. Revista Evaluar, 21(2), 80-97. https://doi.org/10.35670/1667-4545.v21.n2.34401

Dennison, L., Morrison, L., Conway, G., & Yardley, L. (2013). Opportunities and Challenges for Smartphone Applications in Supporting Health Behavior change: Qualitative Study. Journal of Medical Internet Research, 15(4), e86. https://doi.org/10.2196/jmir.2583

Dominguez-Rodriguez, A., Sanz-Gomez, S., González Ramírez, L. P., Herdoiza-Arroyo, P. E., Trevino Garcia, L. E., de la Rosa-Gómez, A., González-Cantero J. O., Macias-Aguinaga, V. & Miaja, M. (2023). The efficacy and usability of an unguided web-based grief intervention for adults who lost a loved one during the COVID-19 pandemic: randomized controlled trial. Journal of medical internet research, 25, e43839. https://doi.org/10.2196/43839

Donkin, L., & Glozier, N. (2012). Motivators and Motivations to Persist With Online Psychological Interventions: A Qualitative Study of Treatment Completers. Journal of Medical Internet Research, 14(3), 34-34. https://doi.org/10.2196/jmir.2100

Dworschak, C., Heim, E., & Maercker, A. (2022). Efficacy of internet-based interventions for common mental disorder symptoms and psychosocial problems in older adults: A systematic review and meta-analysis. Internet Interventions, 27, 100498. https://doi.org/10.1016/j.invent.2022.100498

Gadelha, M. J. N., Silva, L. R. M. D., Oliveira, A. P. D., Cavalcante, N. A., Araújo, V. S. D., Lopes, R. T., & Silva, N. G. D. (2021). Internet Cognitive Behavioral Therapy for social anxiety disorder: a systematic review. Revista Brasileira de Terapias Cognitivas, 17(2), 96-104. https://doi.org/10.5935/1808-5687.20210022.

Gratz, K. L., & Roemer, L. (2004). Multidimensional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of the difficulties in emotion regulation scale. Journal of psychopathology and behavioral assessment, 26, 41-54. https://doi.org/10.1023/B:JOBA.0000007455.08539.94

Heron, K. E., & Smyth, J. M. (2010). Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behaviour treatments. British Journal of Health Psychology, 15(1), 1-39. https://doi.org/10.1348/135910709X466063

Hirai, M., & Clum, G. A. (2006). A Meta-Analytic Study of Self-Help Interventions for Anxiety Problems. Behavior Therapy, 37(2), 99-111. https://doi.org/10.1016/j.beth.2005.05.002

Horsch, C., Lancee, J., Beun, R. J., Neerincx, M. A., & Brinkman, W.-P. (2015). Adherence to Technology-Mediated Insomnia Treatment: A Meta-Analysis, Interviews, and Focus Groups. Journal of Medical Internet Research, 17(9), e214. https://doi.org/10.2196/jmir.4115

Horsch, C., Spruit, S., Lancee, J., Van Eijk, R., Beun, R. J., Neerincx, M., & Brinkman, W.-P. (2017). Reminders make people adhere better to a self-help sleep intervention. Health and Technology, 7(2-3), 173-188. https://doi.org/10.1007/s12553-016-0167-x

Jiménez-Molina, Á., Franco, P., Martínez, V., Martínez, P., Rojas, G., & Araya, R. (2019). Internet-Based Interventions for the Prevention and Treatment of Mental Disorders in Latin America: A Scoping Review. Frontiers in Psychiatry, 10, 664. https://doi.org/10.3389/fpsyt.2019.00664

Jurado, S., Villegas, M. E., Méndez, L., Rodríguez, F., Loperena, V., & Varela, R. (1998). La estandarización del Inventario de Depresión de Beck para los residentes de la Ciudad de México. Salud mental, 26-31.

Karyotaki, E., Kleiboer, A., Smit, F., Turner, D. T., Pastor, A. M., Andersson, G., Berger, T., Botella, C., Breton, J. M., Carlbring, P., Christensen, H., De Graaf, E., Griffiths, K., Donker, T., Farrer, L., Huibers, M. J. H., Lenndin, J., Mackinnon, A., Meyer, B., … Cuijpers, P. (2015). Predictors of treatment dropout in self-guided web-based interventions for depression: An ‘individual patient data’ meta-analysis. Psychological Medicine, 45(13), 2717-2726. https://doi.org/10.1017/S0033291715000665

Krebs, M., González, L. M., Rivera, A., Herrera, P., & Melis, F. (2012). Adherencia a psicoterapia en pacientes con trastornos de ansiedad. Psykhe (Santiago), 21(2), 133-147.

Lin, T., Heckman, T. G., & Anderson, T. (2022). The efficacy of synchronous teletherapy versus in-person therapy: A meta-analysis of randomized clinical trials. Clinical Psychology: Science and Practice, 29(2), 167-178. https://doi.org/10.1037/cps0000056

Lopes, R. T., Gonçalves, M. M., Sinai, D., & Machado, P. P. (2015). Predictors of dropout in a controlled clinical trial of psychotherapy for moderate depression. International journal of clinical and health psychology, 15(1), 76-80. https://doi.org/10.1016/j.ijchp.2014.11.001

McCord, C., Bernhard, P., Walsh, M., Rosner, C., & Console, K. (2020). A consolidated model for telepsychology practice. Journal of Clinical Psychology, 76(6), 1060-1082. https://doi.org/10.1002/jclp.22954

McClellan, M. J., Osbaldiston, R., Wu, R., Yeager, R., Monroe, A. D., McQueen, T., & Dunlap, M. H. (2022). The effectiveness of telepsychology with veterans: A meta-analysis of services delivered by videoconference and phone. Psychological Services, 19(2), 294.  http://dx.doi.org/10.1037/ser0000522

Nordby, E. S., Gjestad, R., Kenter, R. M. F., Guribye, F., Mukhiya, S. K., Lundervold, A. J., & Nordgreen, T. (2022). The Effect of SMS Reminders on Adherence in a Self-Guided Internet-Delivered Intervention for Adults With ADHD. Frontiers in Digital Health, 4, 821031. https://doi.org/10.3389/fdgth.2022.821031

Oliveira, C. T. D., & Dias, A. C. G. (2023). How can psychoeducation help in the treatment of mental disorders? Estudos de Psicologia (Campinas), 40, e190183. https://doi.org/10.1590/1982-0275202340e190183

Perski, O., Blandford, A., West, R., & Michie, S. (2017). Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Translational behavioral medicine, 7(2), 254-267. https://doi.org/10.1007/s13142-016-0453-1

Prashant Srivastava & Rishi Panday. (2016). Psychoeducation an Effective Tool as Treatment Modality in Mental Health. International Journal of Indian Psychology, 4(1). https://doi.org/10.25215/0401.153

Robles, R., Varela, R., Jurado, S., & Páez, F. (2001). Versión mexicana del Inventario de Ansiedad de Beck: Propiedades psicométricas. Revista mexicana de psicología, 18(2), 211-218.

Rodante, D. E., Kaplan, M. I., Olivera Fedi, R., Gagliesi, P., Pascali, A., José Quintero, P. S., Compte, E. J., Perez, A. I., Weinstein, M., Chiapella, L. C., & Daray, F. M. (2022). CALMA, a Mobile Health Application, as an Accessory to Therapy for Reduction of Suicidal and Non-Suicidal Self-Injured Behaviors: A Pilot Cluster Randomized Controlled Trial. Archives of Suicide Research, 26(2), 801-818. https://doi.org/10.1080/13811118.2020.1834476

Sablone, S., Bellino, M., Lagona, V., Franco, T. P., Groicher, M., Risola, R., Violante, M. & Grattagliano, I. (2024). Telepsychology revolution in the mental health care delivery: a global overview of emerging clinical and legal issues. Forensic Sciences Research, 9(3), owae008. https://doi.org/10.1093/fsr/owae008

Saenz, J. J., Sahu, A., Tarlow, K., & Chang, J. (2020). Telepsychology: Training perspectives. Journal of Clinical Psychology, 76(6), 1101-1107. https://doi.org/10.1002/jclp.22875

Strauss, J. L., Guerra, V. S., Marx, C. E., Eggleston, A. M., & Calhoun, P. S. (2010). Adherence and psychotherapy. Improving Patient Treatment Adherence: A Clinician’s Guide, 215-240.

Swift, J. K., & Greenberg, R. P. (2012). Premature discontinuation in adult psychotherapy: a meta-analysis. Journal of consulting and clinical psychology, 80(4), 547. https://doi.org/10.1037/a0028226

Taylor, C. B., Graham, A. K., Flatt, R. E., Waldherr, K., & Fitzsimmons-Craft, E. E. (2021). Current state of scientific evidence on Internet-based interventions for the treatment of depression, anxiety, eating disorders and substance abuse: An overview of systematic reviews and meta-analyses. European Journal of Public Health, 31(Supplement_1), i3-i10. https://doi.org/10.1093/eurpub/ckz208

Tiburcio, M., Lara, Ma. A., Martínez, N., Fernández, M., & Aguilar, A. (2018). Web-Based Intervention to Reduce Substance Abuse and Depression: A Three Arm Randomized Trial in Mexico. Substance Use & Misuse, 53(13), 2220-2231. https://doi.org/10.1080/10826084.2018.1467452

Titov, N., Dear, B. F., Johnston, L., Lorian, C., Zou, J., Wootton, B., Spence, J., McEvoy, P. M., & Rapee, R. M. (2013). Improving adherence and clinical outcomes in self-guided internet treatment for anxiety and depression: Randomised controlled trial. PloS One, 8(7), e62873. https://doi.org/10.1371/journal.pone.0062873

Tobak, B. A. (2021). Investigating the Effectiveness of Telepsychology Administered Cognitive Behavioral Therapy to Treat Anxiety and Depression Among College Age Individuals. William James College. Disponible en: ProQuest One Academic. (2716536833).

Turgoose, D., Ashwick, R., & Murphy, D. (2018). Systematic review of lessons learned from delivering tele-therapy to veterans with post-traumatic stress disorder. Journal of Telemedicine and Telecare, 24(9), 575-585. https://doi.org/10.1177/1357633X17730443

Walton, C., Gonzalez, S., Cooney, E. B., Leigh, L., & Szwec, S. (2023). Engagement over telehealth: Comparing attendance and drop-out between Dialectical Behaviour Therapy delivered face-to-face and via telehealth for programs in Australia and New Zealand during the Covid-19 pandemic. https://doi.org/10.21203/rs.3.rs-2516011/v1

Factores asociados al abandono en una intervención transdiagnóstica autoguiada para una población mexicana

 

 

RESUMEN

Introducción: La tecnología ha revolucionado la salud mental al facilitar el acceso a terapias más diversas y accesibles. Las intervenciones en telepsicología, como las autoguiadas, ofrecen tratamientos personalizados mediante plataformas digitales. Aunque los estudios a largo plazo son limitados, estas intervenciones han mostrado efectividad en distintos trastornos. La modalidad y el tipo de trastorno influyen en su eficacia. Comprender e identificar los factores que influyen en el abandono de tratamientos autoguiados es clave para mejorar su diseño y aumentar su tasa de finalización.

Objetivo: Este estudio busca identificar los factores que influyen en el abandono de tratamientos autoguiados dirigidos a problemas emocionales relacionados con el estrés y el trauma. Esto permitirá incluir herramientas que mejoren su efectividad.

Método: La muestra incluyó a 89 adultos evaluados mediante entrevistas diagnósticas, quienes cumplían criterios para trastornos emocionales o relacionados con el estrés, y fueron asignados a un tratamiento transdiagnóstico autoguiado compuesto por ocho módulos accesibles en cualquier momento.

Resultados: Se identificó una tasa de abandono del 81 %, principalmente durante las primeras sesiones. Mediante un análisis de regresión logística se identificaron algunos factores significativos asociados al abandono, como el lugar de residencia y los niveles de ansiedad.

Conclusión: Estrategias como recordatorios personalizados, clarificación continua de los objetivos del tratamiento y herramientas que fomenten la motivación del paciente podrían prevenir el abandono. Los resultados ofrecen información útil para optimizar la telepsicología y favorecer el éxito de tratamientos psicológicos autoguiados en contextos tecnológicos y culturales diversos.

Palabras claves: Telepsicología; Tratamientos autoguiados; Abandono; Tratamiento transdiagnóstico; Problemas emocionales; Problemas relacionados con trauma.