Addiction research & theory · 2025
Social and behavioral correlates of sleep health among adults receiving medication treatment for opioid use disorder.
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Sadler Lois S, Jeon Sangchoon, Ibrahim Ahmad, Barry Declan, Nwananji-Enwerem Uzoji, Scheinost Dustin, Yaggi Henry, Redeker Nancy S
Аннотация
OBJECTIVES: Opioid use disorder (OUD) and its treatment (MOUD) are associated with altered sleep health. The purposes are to (1) describe profiles of sleep health among adults using medication for opioid use disorder (MOUD) and (2) examine the associations between multi-level individual, family, neighborhood, and social characteristics and sleep profiles. We hypothesized that poor quality of life, adverse life experiences, addiction behavior, dysfunctional family and social interactions, and negative neighborhood characteristics are associated with negative profiles of sleep health.
METHODS: This study comprised baseline analyses of the NIH/HEAL-funded CLOUDS study (Collaboration Linking Opioid Use Disorder and Sleep). We obtained self-report measures of sleep health and indicators of multi-level individual, family, and neighborhood factors. We identified sleep health profiles with K-means cluster analysis and examined the associations between these multi-level factors and sleep health profiles.
RESULTS: The sample included 165 participants (M age = 42, SD =11.4 years; N = 73/42.2% female; N = 37/22.4% Black or more than one race). We identified four sleep health profiles: Healthy sleep (Profile A; 30.3%); mild insomnia/late sleep timing (Profile B: 20%); clinical insomnia/long sleep (Profile C: 25.5%); and insomnia with excessive daytime sleepiness (Profile D: 23.6%). There were statistically significant differences across sleep profiles in physical and psychological health, addiction use and risk, family function, neighborhood esthetic quality, and perceptions of community support, with more adverse factors associated with poorer sleep health.
CONCLUSIONS: Research is needed to understand the causal directions of these relationships and promote multi-level interventions to promote sleep health.
METHODS: This study comprised baseline analyses of the NIH/HEAL-funded CLOUDS study (Collaboration Linking Opioid Use Disorder and Sleep). We obtained self-report measures of sleep health and indicators of multi-level individual, family, and neighborhood factors. We identified sleep health profiles with K-means cluster analysis and examined the associations between these multi-level factors and sleep health profiles.
RESULTS: The sample included 165 participants (M age = 42, SD =11.4 years; N = 73/42.2% female; N = 37/22.4% Black or more than one race). We identified four sleep health profiles: Healthy sleep (Profile A; 30.3%); mild insomnia/late sleep timing (Profile B: 20%); clinical insomnia/long sleep (Profile C: 25.5%); and insomnia with excessive daytime sleepiness (Profile D: 23.6%). There were statistically significant differences across sleep profiles in physical and psychological health, addiction use and risk, family function, neighborhood esthetic quality, and perceptions of community support, with more adverse factors associated with poorer sleep health.
CONCLUSIONS: Research is needed to understand the causal directions of these relationships and promote multi-level interventions to promote sleep health.