SWK+6401 social work practice and evaluation research
Sal — Single Subject Design for Sleep
Sal is a 25 year old female who is having trouble sleeping, leaving her drowsy, irritable, and inattentive during daytime hours; this is causing her problems at work and with her friends and family. Sal reports that she uses her cellphone extensively and has been taking her cellphone to bed with her at night so she won’t miss any messages or alerts (e.g., texts, Instagram, Facebook, YouTube, phone calls); each alert is accompanied with an audible “dine — when she hears the alert, even during hours that are reserved for sleep, she attends to each one. These frequent alerts make it difficult for her to get to sleep, and once she is asleep, they often wake her up. Sal also runs an app which detects and counts her total number of sleep hours.
You will provide Cognitive Behavioral Therapy to address her sleeping problem. Part of the treatment that you and Sal derive is that before going to bed, she will put the cell phone in a charging station that’s in the living room — far enough away from the bedroom that the audio alerts cannot be heard.
Sal has been using the sleep monitoring app for several weeks; you ask her to continue using it. Specifically, you ask Sal to provide the daily sleep hours for each day for the one week prior to therapy, and then to continue gathering the daily sleep data for the duration of the therapy. You will see Sal twice a week for seven weeks. Her scores are coded in Excel.
Data file: SaISSD.xlsx
Variables: • sleep
Total number of hours of sleep each night
• psleep Phase of each sleep measurement (A = Baseline, B = Treatment)

Cognitive Behavioral Therapy for Insomnia: A Single-Subject Design Approach

Insomnia, characterized by persistent difficulty falling or staying asleep, is a prevalent sleep disorder that significantly impacts an individual’s quality of life (Patel et al., 2018). Cognitive Behavioral Therapy for Insomnia (CBT-I) is an evidence-based treatment that combines cognitive restructuring techniques with behavioral interventions, such as sleep restriction and stimulus control (Edinger & Carney, 2015). This case study employs a single-subject design (SSD) to evaluate the effectiveness of CBT-I in treating Sal, a 25-year-old female experiencing sleep difficulties due to excessive cellphone usage before and during sleep.

Single-Subject Design and Its Applications
Single-subject designs, also known as single-case experimental designs, are widely used in various fields, including psychology, education, and healthcare (Kratochwill et al., 2010). SSDs involve the repeated measurement of a single individual or a small group of individuals over time, allowing researchers to evaluate the effects of an intervention or treatment (Byiers et al., 2012). This approach is particularly useful when working with individuals who require tailored interventions or when studying rare or complex conditions (Vannest & Ninci, 2015).

The A-B Design: Baseline and Treatment Phases
In this case study, an A-B design is employed, where Phase A represents the baseline phase, and Phase B represents the treatment phase. During the baseline phase (A), Sal’s sleep duration is monitored for one week without any intervention. This phase establishes a baseline measure of her sleep patterns and provides a reference point for evaluating the effectiveness of the subsequent treatment.

The treatment phase (B) involves the implementation of CBT-I, specifically the stimulus control technique. Sal agrees to place her cellphone in a charging station in the living room before bedtime, thereby minimizing auditory distractions and disruptions from notifications during sleep hours. This intervention aims to create an association between the bedroom and sleep, as well as reduce the impact of external stimuli on sleep quality (Edinger & Carney, 2015).

Data Collection and Analysis
Throughout the study, Sal utilizes a sleep monitoring app to record her daily sleep duration in hours. These data are coded in an Excel file, with the “sleep” variable representing the total number of hours of sleep each night and the “psleep” variable indicating the phase of each sleep measurement (A for baseline or B for treatment).

Visual analysis and statistical techniques are commonly employed in SSDs to evaluate the effectiveness of the intervention (Kratochwill et al., 2013). Researchers may examine the level, trend, and variability of the data within and across phases, as well as calculate effect sizes to quantify the magnitude of the intervention’s impact (Vannest & Ninci, 2015). Additionally, advanced statistical methods, such as multilevel modeling or interrupted time series analysis, can be applied to account for potential confounding variables or to evaluate the sustainability of the treatment effects (Shadish et al., 2015).

Ethical Considerations and Limitations
When conducting SSDs, it is crucial to adhere to ethical principles, such as obtaining informed consent, ensuring confidentiality, and minimizing potential risks or harm to participants (Byiers et al., 2012). Additionally, researchers should be aware of the limitations of SSDs, including the potential for carry-over effects, reactivity to measurement, and limited generalizability (Vannest & Ninci, 2015).

Single-subject designs offer a valuable approach for evaluating the effectiveness of interventions tailored to individual needs, particularly in the context of sleep disorders and CBT-I. By combining empirical data collection with visual and statistical analysis, researchers can gain insights into the impact of treatment on individuals’ sleep patterns and overall well-being.


Byiers, B. J., Reichle, J., & Symons, F. J. (2012). Single-subject experimental design for evidence-based practice. American Journal of Speech-Language Pathology, 21(4), 397-414. https://doi.org/10.1044/1058-0360(2012/11-0036)

Edinger, J. D., & Carney, C. E. (2015). Overcoming insomnia: A cognitive-behavioral therapy approach, therapist guide. Oxford University Press.

Kratochwill, T. R., Hitchcock, J., Horner, R. H., Levin, J. R., Odom, S. L., Rindskopf, D. M., & Shadish, W. R. (2010). Single-case designs technical documentation. Retrieved from What Works Clearinghouse website: https://ies.ed.gov/ncee/wwc/Docs/ReferenceResources/wwc_scd.pdf

Kratochwill, T. R., Levin, J. R., Horner, R. H., & Swoboda, C. M. (2013). Visual analysis of single-case intervention research: Conceptual and methodological issues. In T. R. Kratochwill & J. R. Levin (Eds.), Single-case intervention research: Methodological and statistical advances (pp. 91-125). American Psychological Association.

Patel, D., Steinberg, J., & Patel, P. (2018). Insomnia in the elderly: A review. Journal of Clinical Sleep Medicine, 14(6), 1017-1024. https://doi.org/10.5664/jcsm.7172

Shadish, W. R., Kyse, E. N., & Rindskopf, D. M. (2015). Analysis of single-case designs: An overview of recent developments. Current Psychology, 34(3), 389-411. https://doi.org/10.1007/s12144-015-9320-y

Vannest, K. J., & Ninci, J. (2015). Evaluating intervention effects in single-case research designs. Journal of Counseling & Development, 93(4), 403-411. https://doi.org/10.1002/jcad.12038

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