Hi Brianne,

First thing to say is that correlation not the same as causation. A well explaination of this can be found in this link:

https://www.abs.gov.au/statistics/understanding-statistics/statistical-terms-and-concepts/correlation-and-causation

Related to your question, assuming that your model looks like this:

Y(sleep_quality) = intercept + IV(ASD) + IV(ADHD) + IV(Anxiety) +IV(Depression)

1.- Explore the relationship between the parameters with a scatter plot. Sometimes the independent and dependent variables don't have a linear relationship and transformation of the variables might improve the fit. An example can be found here: https://pages.cms.hu-berlin.de/EOL/gcg_quantitative-methods/Lab06_Multiple-regression.html

2.- Explore the interaction between the independent parameters (x's) and the effect on sleep quality. Check the model assumptions. By doing this, the model should show a better model fit. Examples are also in the previous link.

4.- Exhaustive model selection: Use AIC, BIC and/or adjusted R^2 to determine if all of the four variables are explaining the response in sleep quality. Compare the models. An example can be also found in the previous link.

5.- Sometimes is hard to go through this type of questions online since there are many variables that I'm probably not mentioning, then I would recommend you to contact a statistician near you for a better understanding of the model analysis.

I hope this can give you a guideline in your statistical analysis. Let me know if you have any more questions.

Best wishes!

1. Yes, you can use the results from the correlation analysis to provide context and interpretation for the non-significant results in your regression analysis. Since the correlation analysis indicates significant relationships between all four independent variables (IVs) and the dependent variable (DV), it suggests that there are associations between these variables, even if they did not all contribute significantly in the regression model. You could discuss how these correlations highlight the interrelationships among the variables and provide insights into potential underlying mechanisms affecting sleep problems.

2. It is valid to hypothesize that high levels of autistic and ADHD traits may indirectly lead to more anxiety and depressive symptoms, which in turn contribute to sleep problems. The significant correlations between autistic and ADHD traits with anxiety and depressive symptoms support this hypothesis. You could use these correlation results to propose a potential pathway or mechanism by which these traits influence sleep problems through the mediation of anxiety and depressive symptoms. However, it's important to note that correlation does not imply causation, so further research would be needed to establish causality and validate this interpretation.

Your inquiry delves into the intricacies of interpreting statistical analyses, particularly when the findings from multiple regression and correlation analyses do not fully align. Let's address your questions one by one:

Interpreting Non-Significant Results in Regression Using Correlation Analysis:

When your multiple regression analysis shows that autistic and ADHD traits are not significant predictors of sleep problems, yet Spearman’s correlation indicates significant correlations among all variables, it suggests a nuanced relationship that might not be direct.

You can indeed use the correlation results to inform your interpretation of the regression analysis, but with caution. While correlation shows a relationship between two variables, it does not imply causation or the nature of the relationship in the presence of other variables. In your regression model, when you control for other factors (anxiety and depressive symptoms), the unique contributions of autistic and ADHD traits to sleep problems might be overshadowed or accounted for by the other variables.

This discrepancy can be interpreted in several ways. It could be that the effects of autistic and ADHD traits on sleep problems are mediated through anxiety and depressive symptoms. It could also suggest multicollinearity, where the independent variables are correlated with each other, potentially inflating the variance and making it hard to isolate the effect of each predictor.

Explaining the Relationship Between Traits and Sleep Problems:

Saying that high levels of autistic and ADHD traits lead to more anxiety and depressive symptoms, which then lead to sleep problems, is a plausible hypothesis. However, it is essential to note that correlation results alone do not establish causation or the directionality of these relationships.

To assert such a causal pathway, you would ideally need to conduct a mediation analysis. This statistical approach helps to understand if the relationship between your independent variables (autistic and ADHD traits) and the dependent variable (sleep problems) is mediated by another variable or set of variables (anxiety and depressive symptoms).

If you choose to suggest a possible underlying mechanism based on your correlation results, you should frame it as a speculative hypothesis that needs further investigation. It's crucial to acknowledge the limitations of correlation analysis and the need for more sophisticated analyses to test the proposed mediation model.

In sum, while your correlation findings can offer insights and guide the interpretation of your regression results, any conclusions about causal relationships or underlying mechanisms should be made cautiously and framed as hypotheses for future research rather than definitive conclusions.

The concept you're touching upon is quite intricate, involving the overlap and distinctions between autistic regression or burnout and depression, particularly given that both can present with seemingly similar symptoms but have different underlying etiologies and implications.

1. **Autistic Regression/Burnout**: This term refers to a period where an individual on the autism spectrum experiences a significant reduction in functional skills, which they previously possessed. This can manifest in various domains such as social skills, communication, and self-care. Autistic burnout, closely related, is characterized by intense chronic fatigue, loss of skills, and reduced tolerance to stimuli. These phenomena are often triggered by sustained stress and overexertion, particularly in environments that are not accommodating to the individual's needs.

2. **Depression**: This is a common mental health disorder characterized by persistent sadness, lack of interest or pleasure in activities, and an array of physical and cognitive symptoms that significantly impair one's daily life. Depression can affect anyone, regardless of their neurotype, and has been well-studied in various populations, including those on the autism spectrum.

The challenge in distinguishing between autistic regression/burnout and depression lies in their symptomatic overlaps, such as withdrawal from social situations, decreased activity levels, and apparent loss of skills or interests. However, the underlying causes and contexts may differ significantly:

- **Context and Triggers**: Autistic regression or burnout may stem from long-term exhaustion of coping mechanisms and the constant strain of adapting to a neurotypical world, whereas depression can have a variety of triggers, including biological, psychological, and social factors.

- **Symptom Manifestation**: While both conditions can lead to withdrawal and loss of interest, the reasons might be different. For instance, an autistic individual might withdraw due to sensory overload or social exhaustion, whereas a person with depression might withdraw due to feelings of worthlessness or pervasive sadness.

- **Treatment and Support**: Approaches to support and intervention might differ. Autistic burnout may require adjustments to the environment, increased support for autistic traits, and strategies that respect the individual's neurotype. In contrast, depression often involves psychological therapies, medication, and lifestyle changes that target the mood disorder's specific symptoms and causes.

The challenge in current research and clinical practice is the need for a nuanced understanding of these differences. There's a growing recognition of the importance of differentiating between the two to provide appropriate support and interventions. However, more research is necessary to develop clear diagnostic criteria and treatment plans that acknowledge the unique experiences of autistic individuals, especially as they intersect with symptoms of depression.

In conclusion, while we have some understanding of both conditions, there's a significant gap in our knowledge about how autistic regression/burnout and depression interact, how they can be distinguished from one another in clinical practice, and how best to support individuals experiencing these challenges. Addressing these gaps requires targeted research and a clinical approach that is sensitive to the nuances of neurodiversity.

Sleep problems can be quantified using various methods, each offering insights into different aspects of sleep quality and quantity. Heart rate variability (HRV) is one of the advanced metrics used to assess sleep, especially when considering the unique sleep patterns in autistic individuals. Let's explore the different methods used to quantify sleep problems, focusing on HRV and its relevance in the context of autism.

1. **Subjective Measures**:

- **Sleep Diaries**: Individuals record their sleep and wake times, sleep quality, and any sleep disturbances.

- **Questionnaires**: Tools like the Pittsburgh Sleep Quality Index (PSQI) or the Insomnia Severity Index (ISI) assess various sleep-related problems through self-reported measures.

2. **Objective Measures**:

- **Polysomnography (PSG)**: Considered the gold standard for sleep measurement, PSG records brain waves, oxygen levels in the blood, heart rate, breathing, as well as eye and leg movements during sleep. It provides comprehensive data on sleep stages, interruptions, and overall sleep architecture.

- **Actigraphy**: This method involves wearing a device (similar to a wristwatch) that tracks movement and can infer sleep and wake periods. It's less invasive than PSG and can be used for longer monitoring periods, providing a more representative picture of an individual's sleep patterns.

3. **Heart Rate Variability (HRV) and Sleep**:

- HRV, the variation in time intervals between heartbeats, is an indicator of the autonomic nervous system's (ANS) activity. During sleep, HRV patterns can reflect the depth and quality of sleep, with higher HRV generally associated with better sleep quality and greater restorative processes.

- In the context of autism, HRV can be particularly insightful. Research suggests that individuals with autism might exhibit different ANS responses during sleep. For instance, the HRV patterns in autistic individuals might show variations in the autonomic balance, potentially correlating with the unique sleep disturbances observed in this population.

4. **Applying HRV to Study Autistic Sleep**:

- Studying HRV in autistic individuals during sleep can provide insights into how their sleep differs from that of neurotypical individuals. It can help identify specific patterns or anomalies in autonomic regulation during sleep.

- Such data can be crucial for developing tailored interventions to improve sleep quality in autistic individuals. For example, if HRV data indicate less restorative sleep, interventions might focus on enhancing sleep hygiene, environmental modifications, or exploring other therapeutic options.

In summary, quantifying sleep problems involves a blend of subjective and objective measures. HRV stands out as a valuable tool, especially in understanding the nuanced sleep patterns of autistic individuals. It offers a window into the autonomic processes during sleep, aiding in the development of more personalized and effective interventions.

The concept of AudHD, which unofficially combines traits of autism (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), acknowledges the co-occurrence of these conditions but is not formally recognized as a separate diagnostic entity. While individuals can have both ASD and ADHD, the interplay of symptoms is complex, particularly when considering sensory needs.

1. **Divergent Sensory Processing**: Autism is often associated with unique sensory sensitivities or sensory processing disorder. Individuals with autism might experience hyper- or hypo-sensitivity to sensory inputs, which can significantly affect their daily functioning. In contrast, ADHD is characterized by difficulties with attention, impulsivity, and hyperactivity, but it does not inherently include the same kind of sensory processing issues.

2. **Competing Sensory Needs**: When someone has both ASD and ADHD, they might face what you've termed "competing sensory needs." For instance, the need for a calm, minimally stimulating environment to reduce sensory overload (common in ASD) may clash with the need for high stimulation sought by someone with ADHD to maintain focus and engagement. This intersection can create unique challenges for individuals with both conditions.

3. **Behavioral and Cognitive Interactions**: Beyond sensory needs, ASD and ADHD intersect in ways that can complicate the presentation and management of each condition. For example, the impulsivity and attention challenges of ADHD can exacerbate the social communication difficulties in ASD. Conversely, the intense focus on specific interests, a common trait in ASD, might counteract ADHD symptoms by providing a channel for sustained attention, albeit in a limited context.

4. **Clinical Implications**: Recognizing the concurrent presentation of ASD and ADHD is crucial for developing effective support and intervention strategies. Interventions must be nuanced to address the dual needs arising from both conditions, such as creating environments that balance sensory needs and employing strategies that cater to attentional challenges without exacerbating sensory sensitivities.

5. **Research and Understanding**: The overlapping yet distinct nature of ASD and ADHD calls for more research to understand how these conditions interact at neurobiological, cognitive, and behavioral levels. Enhanced understanding can lead to better diagnostic criteria and more tailored interventions, helping individuals with co-occurring ASD and ADHD navigate their unique challenges more effectively.

In summary, while AudHD is not a formal diagnosis, it represents a real and complex interplay of ASD and ADHD traits in individuals. The term underscores the necessity of acknowledging and addressing the intricate and sometimes conflicting needs of those who live with both conditions, particularly in the realm of sensory processing and attentional demands.