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Tag: ecological momentary assessment
01/30/2023

Feasibility and acceptability of using smartphone-based EMA to assess patterns of prescription opioid and medical cannabis use among individuals with chronic pain

Anderson Goodell EM, Nordeck C, Finan PH, Vandrey R, Dunn KE, & Thrul J. (2021). Feasibility and acceptability of using smartphone-based EMA to assess patterns of prescription opioid and medical cannabis use among individuals with chronic pain. Internet Interventions: the Application of Information Technology in Mental and Behavioural Health, 26, 100460–100460. https://doi.org/10.1016/j.invent.2021.100460

This paper described the feasibility and acceptability of a smartphone-based Ecological Momentary Assessment (EMA) data collection tool among people who use multiple substances and suffer from chronic pain. Forty-six participants were recruited through targeted Facebook and Instagram advertisements and completed screening via the link in the ads. Eligible participants had an opioid medication prescription, current opioid use, a pain disorder, and a referral for medical cannabis. Participants completed prompted EMA surveys on a mobile app for 30 days. Surveys included questions about opioid medication use, medical cannabis use, and pain symptoms. Participants were prompted to respond to four randomly timed surveys (assessing the past hour) and one daily diary per day. A subsample of 10 participants completed qualitative interviews. On average, participants responded to 70% of past-hour surveys and 92% of daily diaries. During qualitative interviews, participants reported an overall positive experience, but identified some issues related to smartphone notifications, redundant questions, or being prompted to complete assessments when they do not feel well. Findings demonstrate the feasibility and general acceptability of using this methodology for examining patterns of medical cannabis and prescription opioid medication use among individuals with chronic pain. Engagement with the digital tool over the 30-day duration was comparable to previous work. This study has implications for informing larger-scale epidemiology studies, interventions, and assessments on a wider geographic scale.

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08/15/2022

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Ng A, Wei B, Jain J, Ward E, Tandon S, Moskowitz J, Krogh-Jespersen S, Wakschlag L, Alshurafa N. Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation. JMIR Mhealth Uhealth 2022;10(8):e33850. DOI: 10.2196/33850

This study aimed to develop and evaluate a machine learning model to predict next-day physiological and prenatal stress by collecting sensor heart rate data and ecological momentary assessment (EMA) questionnaires. This study applied an explainability model for the prediction results. A total of 16 adult pregnant women from an obstetrics and gynecology clinic were enrolled in the study. Participants received a 12-week cognitive behavioral therapy intervention and wore a mobile electrocardiography (heart rate) sensor for 12 weeks. Participants completed EMAs for perceived stress on their mobile phones 5 times a day for 12 weeks. In total, about 4000 hours of data were collected and participants completed 2800 EMAs. Researchers used these data to train and evaluate 6 different machine learning models to select the best performing model for predicting next-day physiological and perceived stress. The random forest classifier performed the best for both physiological and perceived stress, with an average F1 score (a commonly used evaluation metric) of 81.9% and 72.5%, respectively. Two features significantly predicted both physiological and perceived stress: feeling unable to overcome difficulties and participants’ number of children. Results demonstrated that a machine learning model can predict next-day physiological and perceived stress among pregnant women. Future studies should validate the model with a larger sample size.

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05/09/2022

FOCUS mHealth Intervention for Veterans with Serious Mental Illness in an Outpatient Department of Veterans Affairs Setting: Feasibility, Acceptability, and Usability Study

Buck B, Nguyen J, Porter S, Ben-Zeev D, Reger GM. FOCUS mHealth Intervention for Veterans With Serious Mental Illness in an Outpatient Department of Veterans Affairs Setting: Feasibility, Acceptability, and Usability Study. JMIR Ment Health 2022;9(1):e26049. doi: 10.2196/26049

This study evaluates the feasibility, acceptability, and preliminary effectiveness of a mobile health intervention for veterans with serious mental illnesses (SMIs) in a VA outpatient care. Seventeen veterans with SMIs participated in a one-month pilot trial of FOCUS. FOCUS consists of a mobile app, a clinician dashboard, and a mHealth support specialist. The app provides brief self-management interventions based on the user’s responses to an ecological momentary assessment (EMA). Interventions include strategies to cope with auditory hallucinations, mood, sleep, social functioning, and medication use. A mHealth support specialist tracks and gives technical support for participants’ use of FOCUS. The specialist also gives weekly updates to the mental health treatment team on each veteran’s use of FOCUS and reported symptoms and functioning. Researchers collected data on mental health symptoms and functional recovery, as well as qualitative feedback on the acceptability of FOCUS. On average, participants completed 85 EMAs and used FOCUS on 19.29 out of 30 access days. Overall, participants reported the intervention as highly acceptable; 94% would recommend FOCUS to a friend, and 88% felt satisfied. Based on qualitative interviews, participants found FOCUS complements their VA services and suggested possible subgroups to target (i.e., combat veterans). During the pilot, participants reported statistically non-significant improvements in recovery, auditory hallucinations, and quality of life. The study administered surveys to clinicians who had patients participating in FOCUS to assess feasibility and acceptability. Based on this data, clinicians found the mHealth updates useful for informing their care. FOCUS appears to be feasible, acceptable, and useful for veterans with SMIs; future research could examine specific implementation strategies in the VA, as well as replicate the effectiveness of FOCUS with a larger sample.

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03/07/2022

Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia

Raugh IM, James SH, Gonzalez CM, Chapman HC, Cohen AS, Kirkpatrick B, Strauss GP (2021). Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia. Journal of Psychiatric Research, 138, 436–443. https://doi.org/10.1016/j.jpsychires.2021.04.022

Digital phenotyping is a method of using mobile technology to collect data from everyday life and can be used as an objective and ecologically valid symptom assessment for schizophrenia. Researchers evaluated levels of adherence, feasibility, and tolerability for active and passive digital phenotyping methods using smartphones and smart band devices. The study included 54 outpatients diagnosed with schizophrenia and 55 demographically matched healthy controls. The participants completed six days of digital phenotyping. Active digital phenotyping included intentional task completion such as signal and event ecological momentary assessments and passive digital phenotyping was collected without participants’ direct input such as geolocation, accelerometry, and ambulatory psychophysiology. The study found that adherence in active digital phenotyping was significantly lower among patients with schizophrenia compared to controls. However, there was no significant different for passive phenotyping. The study’s results also found higher psychosocial functioning predicts greater active recordings adherence. Higher education level, lower positive symptoms, lower negative symptoms, and higher psychosocial functioning were predictors of higher passive recordings adherence. Both groups found digital phenotyping tolerable and feasible.

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02/28/2022

Characterizing and modeling smoking behavior using automatic smoking event detection and mobile surveys in naturalistic environments: Observational study

Zhai D, van Stiphout R, Schiavone G, De Raedt W, Van Hoof C. (2022). Characterizing and modeling smoking behavior using automatic smoking event detection and mobile surveys in naturalistic environments: Observational study. JMIR Mhealth Uhealth 2022;10(2):e28159) doi: 10.2196/28159

A study was conducted to enable quantified monitoring of smoking behavior 24/7 using continuous automatic measurement techniques to identify and analyze smoking patterns. Researchers conducted a 4-week observational study among 46 current adult smokers. Participants tracked their smoking behavior by using an electronic lighter and smartphone app called ASSIST. The lighter was connected to the app and participants were informed to solely used the provided lighter when smoking. The app was used to prompt smoking-related ecological momentary assessment questionnaires and smoking rate was assessed by the timestamps of smoking. The study acquired data from a total of 8639 cigarettes smoked and 1839 ecological momentary assessments over 902 participant days. Among most participants, self-reported estimates of daily smoking were inaccurate and biased compared to the objectively measured smoking rate. Specifically, 74% of smokers made more than one wrong estimate and 70% overestimated smoking instances. Compared to light smokers, moderate and heavy smokers were significantly older in age and higher in nicotine dependence, craving, arousal, and difficulty resisting smoking. Results indicate that electronic lighters have potential for smoking behavior data in the real world. Technology-based methods for smoking behavior monitoring can be beneficial for smoking cessation applications. The study lends insights for future design and implementation of technology-based smoking cessation applications.

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04/05/2019

An ecological momentary assessment study examining posttraumatic stress disorder symptoms, prenatal bonding, and substance use among pregnant women

Sanjuan PM, Pearson MR, Poremba C, Amaro HdLA, Leeman L. (2019). An ecological momentary assessment study examining posttraumatic stress disorder symptoms, prenatal bonding, and substance use among pregnant women. Drug and Alcohol Dependence. 195:33-39. doi: 10.1016/j.drugalcdep.2018.11.019

Researchers recruited 33 pregnant women who had experienced trauma to complete ecological momentary assessments (EMAs) three times per day for 28 days. Researchers sought to examine relationships between trauma, substance use, and prenatal bonding. Read More

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08/17/2018

Before and after: Craving, mood, and background stress in the hours surrounding drug use and stressful events in patients with opioid-use disorder

Preston KL, Kowalczyk WJ, Philips KA, et al. (2018). Before and after: Craving, mood, and background stress in the hours surrounding drug use and stressful events in patients with opioid-use disorder. Psychopharmacology. doi: 10.1007/s00213-018-4966-9

As a part of a larger study, 182 people seeking treatment for opioid use disorder were recruited from a research clinic to complete ecological momentary assessments (EMAs) concerning substance use, mood, and stress for 16 weeks. Read More

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07/27/2018

mHealth for the detection and intervention in adolescent and young adult substance use disorder

Carriero S, Chai PR, Carey J, Lai J, Smelson D, Boyer EW. (2018). mHealth for the detection and intervention in adolescent and young adult substance use disorder. Current Addiction Reports. 5: 110-119. doi: 10.1007/s40429-018-0192-0

The authors describe 20 studies published since 2013 that evaluate screening and intervention mHealth approaches for substance use disorders (SUDs) in adolescents (13-17) and young adults (18-24). Read More

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08/25/2017

Efficacy of a web-based guided recommendation service for a curated list of readily available mental health and well-being mobile apps for young people: Randomized controlled trial.

Bidargaddi N, Musiat P, Winsall M, et al. (2017). Efficacy of a web-based guided recommendation service for a curated list of readily available mental health and well-being mobile apps for young people: Randomized controlled trial. Journal of Medical Internet Research. 19(5): e141. doi: 10.2196/jmir.6775

Reachout.com offers the Toolbox, a compendium of wellbeing mobile applications (apps) for adolescents and young adults (13-25 years old). Read More

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