According to the World Drug Report 2023, in 2021, over 296 million people used substances worldwide [1]. Substance Use Disorders (SUDs) are characterized by patterns of substance use ranging from mild to severe, often taking significant toll on individuals’ physical and mental health. One risk is premature death, stemming from overdoses and long-lasting negative effects. These lasting changes in the brain can include alterations in the reward system, executive functioning, mood, self-awareness, and sleep [2]. Furthermore, analysis of the global prevalence of mental and substance use disorders in 2019 found that the worldwide prevalence of SUDs was 2.2%, with alcohol use disorder (AUD) being the most common at 1.5%, followed by other substances such as cannabis, opioids, amphetamine, and cocaine [3]. Among those affected, 13% experience psychiatric issues, with depression affecting about 5.7% [4]. In addition, sleep disturbances are also common among this population.

While there is no exact number of individuals with SUDs who experience sleep disturbances due to variability among substance types, research consistently shows that sleep problems are highly prevalent in this population, including difficulty falling asleep, difficulty staying asleep, impaired daytime functioning, and more [5]. For instance, a study done by Mahfoud et al. [6] indicated that self-reported sleep impairment – using the Pittsburgh Sleep Quality Index (PSQI) – was the primary issue among participants with AUD (80%), polysubstance (66%), and narcotics (40%). These findings indicate a high prevalence among this population compared to the general population, where only a small number of individuals are affected (1.5%). Furthermore, the most common sleep-related disorders are insomnia, circadian rhythm disorders, and sleep-related breathing disorders [5]. The bidirectional relationship between SUDs and sleep makes it difficult for treatment-seeking patients to adhere to treatment. Substance use leads to worse sleep outcomes, which increases the risk of relapse [7]. Moreover, during withdrawal, individuals can experience many symptoms such as cravings and poor sleep quality, and in an attempt to alleviate sleep disturbances, they come back to the use of the substance previously used [8,9].

In sleep studies, it is common to use both objective and subjective measures, each with its own importance. Subjective measures are often assessed using tools such as the PSQI; these measures rely on self-reported experiences, such as how restful the individual feels or how long they believe they sleep [10]. It is important to capture and understand the psychological or perceptual factors that influence behavior and can be beneficial when access to objective measures is unavailable. However, these measures are prone to biases (e.g., recall biases), and patients tend to under- or over-report the severity of their sleep disturbance [11]. On the contrary, objective measures such as polysomnography (PSG) can control for these variables and measure sleep more precisely. PSG is widely considered to be the gold standard in sleep studies due to being a multiparameter assessment that can assess brain activity by using electroencephalography, assess eye movement by using electrooculography, assess muscle activity by using electromyography, and cardiac activity by using electrocardiography [11,12]. Although it can be widely used, the use of PSG is typically assessed in laboratory settings. This can bring many disadvantages, such as burden, cost, and discomfort due to having to adapt to a new setting, affecting sleep [12].

Therefore, several studies have opted for the use of actigraphy in clinical and research settings [10]. Actigraphy is a noninvasive measure that assesses rest-activity patterns and estimates sleep-wake characteristics [13]. It is typically used via a wrist-worn device, on the non-dominant wrist or the ankle [13]. It is capable of measuring sleep parameters such as total sleep time (TST), sleep efficiency (SE), sleep latency (SL), and wake after sleep onset (WASO). In addition, wrist-worn devices bring many benefits to sleep assessments. For instance, it is cost-effective, and it can be used in real-world settings, such as home. Because it allows ecological monitoring, it is easier to accomplish longitudinal studies [12]. Moreover, actigraphy is clinically validated and recommended for assessing and supporting the diagnosis of several sleep-related conditions, such as insomnia and circadian rhythm sleep-wake disorders (CRSWDs). This is because it can estimate sleep patterns and variability over time, and can be beneficial when PSG is not feasible or too burdensome [10,11]. However, actigraphy cannot assess sleep architecture, as it cannot distinguish between sleep stages, lacking electroencephalogram, electrooculogram, and electromyogram data [10]. Even though Actigraphy itself can assess sleep parameters, it is usually recommended to use sleep logs, as they can help define rest intervals such as bedtime [14,15]. Actigraphy also requires an event marker, a button that the subject can press to mark several events (e.g., naps, drowsiness) [10]. While actigraphy can identify insomnia and CRSWDs [16,17], there are some variabilities that can affect its effectiveness [18–20].

For instance, actigraphy has a high sensitivity and accuracy similar to PSG, but low specificity, which makes it difficult to detect wakefulness within sleep periods [10,21]. Additionally, a variability of definitions and technical details (e.g., algorithms and sensitivity threshold settings) in wrist-worn devices was noted among studies [12]. For example, determining bedtime was not systematically defined in studies, as ‘bedtime’ was based on the sleep logs, while other studies based it on event markers [12]. Failure to report data was also common among studies; while some studies completely relied on actigraphy algorithms to estimate SL per instance, other studies failed to report these details [22–24]. Moreover, the selection of instruments, data processing, and analysis are not always described in studies [25]. Studies also failed to include circadian rhythm variables, which are crucial, as these variables track patterns of rest and activity over time, which reflect the individuals’ biological clock [25]. Lastly, a meta-analysis by Smith et al. [12] studied the use of actigraphy in sleep disorders and CRSWDs, and findings could not be generalized due to its small and heterogeneous sampling. Similarly, a systematic review found similar limitations in regard to sampling limitations [10].

Despite the increasing use of actigraphy in sleep research, a systematic review is needed to further analyze and summarize actigraphy-based sleep outcomes in individuals with SUDs. Due to the variabilities in the methodology, such as the type of device used or how sleep parameters are measured, as well as its heterogeneous sampling, it is difficult to compare among studies. This review aims to identify and describe sleep parameters derived from actigraphy across various substance types, including alcohol, benzodiazepines, cannabis, cocaine, opioids, methamphetamine, and nicotine. Methodological characteristics and study quality will be evaluated to assess what is currently in literature and what the limitations are. Lastly, this review seeks to address these limitations by comprehensively synthesizing previous research and providing a better understanding, and informing future research to come for later meta-analytical analyses, as well as the application in clinical settings. This will further help understand sleep alterations in populations with SUDs and how to better develop treatments suitable for each substance.