Would I Have Your Support? Family Network Features and Past Support Exchanges Associated with Anticipated Support for a Substance Problem.

Objective: Assessment of social processes underlying anticipation for recovery-related support from family in the event of a substance problem. We drew from literature on social support, substance use, and social networks to develop a path model connecting emotionally close family relationships, closeness among members in the wider family network (density), previous emotional support exchanges, and anticipated support. Subjects and Methods: We used a sample from the 2019 Nebraska Annual Social Indicators Survey (284 adults; 57% female; 94% white; 46.26% living in rural areas) and employed generalized structural equation modeling with logistic regression equations for our binary dependent variable (anticipated support). Results: Denser family networks were associated with individuals' close relations with family (b = .18, p < .001), close family relations were associated with support received by (b = .25, p < .05) and given to (b = .47, p < .001) family, and only support given to family increased the odds of anticipated support (IRR = 4.32, CI = 1.13, 16.48). Conclusions: Family-wide dynamics are important for understanding how support exchange relates to anticipated support. Prioritizing efforts to strengthen family relationships and improve the likelihood that at-risk individuals, especially in rural areas, can overcome substance problems is important.

What Kinds of Support are Alaska Native Youth and Young Adults Reporting? An Examination of Types, Quantities, Sources, and Frequencies of Support.

American Indian and Alaska Native (AI/AN) youth, particularly males, experience disproportionately high rates of suicide compared to other young people in the United States. Therefore, enacting suicide prevention efforts for AI/AN youth is especially important. Since research shows that strengthening social, cultural, and emotional support can reduce suicide risk, many recent prevention efforts focus on these strategies. Yet, to reinforce and to extend the positive impact of these strategies for suicide risk reduction, we argue it is useful to identify baseline levels and other features of already-existing support. Toward this end, we describe the types (i.e., category), quantities (i.e., distribution and average number), sources (i.e., from whom), and frequencies (i.e., how often) of social support that AN young people report receiving, and we examine if these "support profiles" differ by age and sex. We use survey data from 165 ANs under age 30, collected as part of a participatory intervention study focused on Promoting Community Conversations About Research to End Suicide (PC CARES). We find that: 1) most ANs reported receiving nearly all supports, 2) compared with females, males reported receiving fewer supports on average, 3) family was the most selected support source, followed by close friends and service providers, and 4) family (e.g., parents, siblings, and grandparents) provided support regularly (i.e., monthly or more). Though our findings may suggest fruitful avenues for interventions targeted toward AN males, we discuss these findings in relation to the gendered nature of suicide prevention and assessment.

Short Take: Collecting Data from a Vulnerable Population during the COVID-19 Pandemic.

Conducting field research with a vulnerable population is difficult under the most auspicious conditions, and these difficulties only increase during a pandemic. Here, we describe the practical challenges and ethical considerations surrounding a recent data collection effort with a high-risk population during the COVID-19 pandemic. We detail our strategies related to research design, site selection, and ethical review.

Patterns of Missing Data With Ecological Momentary Assessment Among People Who Use Drugs: Feasibility Study Using Pilot Study Data.

BACKGROUND: Ecological momentary assessment (EMA) is a set of research methods that capture events, feelings, and behaviors as they unfold in their real-world setting. Capturing data in the moment reduces important sources of measurement error but also generates challenges for noncompliance (ie, missing data). To date, EMA research has only examined the overall rates of noncompliance. OBJECTIVE: In this study, we identify four types of noncompliance among people who use drugs and aim to examine the factors associated with the most common types. METHODS: Data were obtained from a recent pilot study of 28 Nebraskan people who use drugs who answered EMA questions for 2 weeks. We examined questions that were not answered because they were skipped, they expired, the phone was switched off, or the phone died after receiving them. RESULTS: We found that the phone being switched off and questions expiring comprised 93.34% (1739/1863 missing question-instances) of our missing data. Generalized structural equation model results show that participant-level factors, including age (relative risk ratio [RRR]=0.93; P=.005), gender (RRR=0.08; P=.006), homelessness (RRR=3.80; P=.04), personal device ownership (RRR=0.14; P=.008), and network size (RRR=0.57; P=.001), are important for predicting off missingness, whereas only question-level factors, including time of day (ie, morning compared with afternoon, RRR=0.55; P<.001) and day of week (ie, Tuesday-Saturday compared with Sunday, RRR=0.70, P=.02; RRR=0.64, P=.005; RRR=0.58, P=.001; RRR=0.55, P<.001; and RRR=0.66, P=.008, respectively) are important for predicting expired missingness. The week of study is important for both (ie, week 2 compared with week 1, RRR=1.21, P=.03, for off missingness and RRR=1.98, P<.001, for expired missingness). CONCLUSIONS: We suggest a three-pronged strategy to preempt missing EMA data with high-risk populations: first, provide additional resources for participants likely to experience phone charging problems (eg, people experiencing homelessness); second, ask questions when participants are not likely to experience competing demands (eg, morning); and third, incentivize continued compliance as the study progresses. Attending to these issues can help researchers ensure maximal data quality.

Practical problems and positive experiences with ecological momentary assessment: reflections from people who use drugs.

Background: Ecological momentary assessment (EMA) is an increasingly popular and feasible form of data collection, but it can be intensive and intrusive. Especially for at-risk, vulnerable populations like people who use drugs (PWUD), poor experiences with EMA may exacerbate existing chronic struggles while decreasing response rates. However, little research queries participants' experiences with EMA studies.Objectives: We explore participants' positive and negative experiences with EMA, identifying what they liked about the study, the problems they experienced, and suggested solutions to these problems.Methods: Results come from semi-structured interviews from 26 PWUD (6 women; 20 men) in Nebraska who participated in a two-week EMA pilot study on drug use with a study-provided smartphone. Participant responses were recorded by interviewers into open-text fields in Qualtrics. Data were analyzed with an iterative open coding procedure.Results: We found that many participants enjoyed the study and seamlessly incorporated the phone into their daily lives. There were a number of negative study aspects identified, however, as many participants experienced functional issues (e.g., running out of high-speed data, trouble keeping the phone charged, not able to answer questions within the two-hour timeframe) that detracted from their experience, especially if they were homeless.Conclusion: Our findings provide methodological considerations for studies with EMA components among at-risk, vulnerable populations, like PWUD. These suggestions are targeted toward the continued ethical collection of high-quality data in clinical and non-clinical settings.