*2.4. Intervention*

During the intervention phase of the study, participants used weighted blankets for 14 consecutive nights. These weighted blankets were the SensaCalm ® brand, custom made, and were provided by the PI. The weighted blankets were designed to be 10% of each child's body weight adhering to the prototypical weighted blanket protocol [13–16]. The SensaCalm ® blankets used for the study ranged from 3–7 pounds to accommodate the varying weights of potential participants and ranged between USD 40.00 and 80.00. The blanket brand was chosen based upon the a ffordable cost, durability, equal distribution of weight across the blanket. When the weighted blankets were provided, caregivers were given instructions on safely and e ffectively using them. Caregivers were instructed to only use the blankets at night (i.e., not during nap time or quiet time); only use the blanket if the child was able to remove it on their own; cover the child's body, arms, and feet but not their head or face; check on the child occasionally while using the blanket; adjust other bedding while using the weighted blanket to ensure the child was not too hot, and to contact the PI if the weighted blanket was showing signs of wear. Additionally, caregivers of the participants reported that they slept in their own bed (as opposed to the caregivers, or another location) through this study's duration.

### *2.5. Method of Analysis*

Data were analyzed through visual analysis of repeated measure graphs generated using Microsoft Excel, version 16, as described by Kennedy [31]. Visual analysis is widely accepted as a mechanism to analyze data for single-subject designs [32]. The literature supports visual inspection as the preferred method of analysis among single-subject designs because it is sensitive and able to capture intervention e ffects significant to clinicians working outside research labs within clients' natural or typical context [32]. Moreover, the visual analysis approach is preferred because it has lower error rates and is conservative enough to identify reliable treatment e ffects [32].

In addition to visual analysis, this study used the percentage of non-overlapping data (PND) [33] as an additional analysis tool. PND is a statistical method widely used in behavioral science research, particularly for analyzing the small data sets, which are commonplace with single-subject design studies. PND is calculated by identifying the most extreme data point in the baseline phase (either the highest or lowest value depending on whether the intervention is intended to reduce or increase a behavior). The PND is the percentage of data in the intervention phase, which falls above or below this point based on its intended outcome.
