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Daily factors driving daily substance use and chronic pain among older adults with HIV: An exploratory study using ecological momentary assessment.

ABSTRACT: BACKGROUND:Adults 50 and older make up approximately 50% of persons living with HIV. Multiple co-morbidities are common among this group, including chronic pain and substance abuse, yet little is known about the daily factors that either enhance or inhibit these experiences or behaviors. This study explored daily drivers of substance use, pain, and relief from pain among older adults living with HIV utilizing ecological momentary assessment (EMA). METHOD:Participants (N = 55), ages 49-71, completed seven consecutive days of daily EMA online surveys prior to treatment initiation within a randomized controlled trial. Multilevel modeling tested predictors of pain, substance use, and relief from pain by examining within- and between-person relationships. RESULTS:Results revealed an associational, reciprocal relationship between daily worst pain and daily drinking, where greater worst pain ratings predicted heavier drinking and heavier drinking predicted greater daily and overall pain. Greater happiness and poorer quality of sleep predicted greater daily worst pain. Exercising and overall confidence to cope with pain without medication were associated with lower levels of daily worst pain. Finally, spending less time with a loved one over time and reporting any coping behavior were associated with relief from pain. CONCLUSION:Investigation of daily factors that drive pain and substance use behaviors among this unique population help inform which daily factors are most risky to their health and well-being. Alcohol use emerged as the only substance associated with both driving pain and responding to pain. Findings suggest key points for prevention and intervention.

SUBMITTER: Kuerbis A

PROVIDER: S-EPMC6456439 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Daily factors driving daily substance use and chronic pain among older adults with HIV: An exploratory study using ecological momentary assessment.

Kuerbis Alexis A Reid M Carrington MC Lake Jordan E JE Glasner-Edwards Suzette S Jenkins Jessica J Liao Diana D Candelario Jury J Moore Alison A AA

Alcohol (Fayetteville, N.Y.) 20181009

<h4>Background</h4>Adults 50 and older make up approximately 50% of persons living with HIV. Multiple co-morbidities are common among this group, including chronic pain and substance abuse, yet little is known about the daily factors that either enhance or inhibit these experiences or behaviors. This study explored daily drivers of substance use, pain, and relief from pain among older adults living with HIV utilizing ecological momentary assessment (EMA).<h4>Method</h4>Participants (N = 55), age ...[more]

PMID: 30308287

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Project description:BackgroundDaily activities have been associated with neurocognitive performance. However, much of this research has used in-person neuropsychological testing that requires participants to travel to a laboratory or clinic, which may not always be feasible and does not allow for the examination of real-time relationships between cognition and behavior. Thus, there is a need to understand the real-time relationship between activities in the real world and neurocognitive functioning to improve tracking of symptoms or disease states and aid in the early identification of neurocognitive deficits among at-risk individuals.ObjectiveWe used a smartphone-based ecological momentary cognitive assessment (EMCA) platform to examine real-time relationships between daily activities and neurocognitive performance (executive functioning and verbal learning) in the everyday environment of middle-aged and older adults with and without HIV.MethodsA total of 103 adults aged 50-74 years (67 persons with HIV; mean age 59 years, SD 6.4) were recruited from the University of California, San Diego HIV Neurobehavioral Research Program and the San Diego community. Participants completed our EMCA protocol for 14 days. Participants reported their current daily activities 4 times per day; following 2 of the 4 daily ecological momentary assessment (EMA) surveys, participants were administered the mobile Color-Word Interference Test (mCWIT) and mobile Verbal Learning Test (mVLT), each once per day. Activities were categorized into cognitively stimulating activities, passive leisure activities, and instrumental activities of daily living (IADLs). We used multilevel modeling to examine the same-survey and lagged within-person and between-person effects of each activity type on mobile cognitive performance.ResultsOn average, participants completed 91% of the EMA surveys, 85% of the mCWIT trials, and 80% of the mVLT trials, and they reported engaging in cognitively stimulating activities on 17% of surveys, passive leisure activities on 33% of surveys, and IADLs on 20% of surveys. Adherence and activity percentages did not differ by HIV status. Within-persons, engagement in cognitively stimulating activities was associated with better mCWIT performance (β=-1.12; P=.007), whereas engagement in passive leisure activities was associated with worse mCWIT performance (β=.94; P=.005). There were no lagged associations. At the aggregate between-person level, a greater percentage of time spent in cognitively stimulating activities was associated with better mean mVLT performance (β=.07; P=.02), whereas a greater percentage of time spent in passive leisure activities was associated with worse mean mVLT performance (β=-.07; P=.01). IADLs were not associated with mCWIT or mVLT performance.ConclusionsSmartphones present unique opportunities for assessing neurocognitive performance and behavior in middle-aged and older adults' own environment. Measurement of cognition and daily functioning outside of clinical settings may generate novel insights on the dynamic association of daily behaviors and neurocognitive performance and may add new dimensions to understanding the complexity of human behavior.

Project description:Repetitive negative thinking (RNT) is a transdiagnostic process and a promising target for prevention and treatment of mental disorders. RNT is typically assessed via self-report questionnaires with most studies focusing on one type of RNT (i.e., worry or rumination) and one specific disorder (i.e., anxiety or depression). However, responses to such questionnaires may be biased by memory and metacognitive beliefs. Recently, Ecological Momentary Assessment (EMA) has been employed to minimize these biases. This study aims to develop an EMA paradigm to measure RNT as a transdiagnostic process in natural settings. Based on empirical and theoretical considerations, an item pool was created encompassing RNT content and processes. We then (1) tested model fit of a content-related and a process-related model for assessing RNT as an individual difference variable, (2) investigated the reliability and construct validity of the proposed scale(s), and (3) determined the optimal sampling design. One hundred fifty healthy participants aged 18 to 40 years filled out baseline questionnaires on rumination, worry, RNT, symptoms of depression, anxiety, and stress. Participants received 8 semi-random daily prompts assessing RNT over 14 days. After the EMA phase, participants answered questionnaires on depression, anxiety, and stress again. Multilevel confirmatory factor analysis revealed excellent model fit for the process-related model but unsatisfactory fit for the content-related model. Different hybrid models were additionally explored, yielding one model with satisfactory fit. Both the process-related and the hybrid scale showed good reliability and good convergent validity and were significantly associated with symptoms of depression, anxiety, and stress after the EMA phase when controlling for baseline scores. Further analyses found that a sampling design of 5 daily assessments across 10 days yielded the best tradeoff between participant burden and information retained by EMA. In sum, this paper presents a promising paradigm for assessing RNT in daily life.

Project description:BACKGROUND:Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devices could be used to collect data to classify older adults into depression groups. OBJECTIVE:The objective of this study was to develop a machine learning algorithm to predict the classification of depression groups among older adults living alone. We focused on utilizing diverse data collected through a survey, an Actiwatch, and an EMA report related to depression. METHODS:The prediction model using machine learning was developed in 4 steps: (1) data collection, (2) data processing and representation, (3) data modeling (feature engineering and selection), and (4) training and validation to test the prediction model. Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models). RESULTS:On the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960). CONCLUSIONS:This study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor.

Dataset Information

Daily factors driving daily substance use and chronic pain among older adults with HIV: An exploratory study using ecological momentary assessment.

Publications

Daily factors driving daily substance use and chronic pain among older adults with HIV: An exploratory study using ecological momentary assessment.

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