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  Web-based and Mobile Health...

  Jan 10, 2020

  Effectiveness and Cost-Effectiveness of a Self-Guided Internet Intervention for Social Anxiety Symptoms in a General Population Sample: Randomized Controlled...

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  Authors List:

  • John Powell  
  • Veronika Williams  
  • Helen Atherton  
  • Kylie Bennett  
  • Yaling Yang  
  • Mina Davoudianfar  
  • Annika Hellsing  
  • Angela Martin  
  • Jill Mollison  
  • Milensu Shanyinde  
  • Ly-Mee Yu  
  • Kathleen M Griffiths  

  Abstract:

  Background: Many people are accessing digital self-help for mental health problems, often with little evidence of effectiveness. Social anxiety is one of the most common sources of mental distress in the population, and many people with symptoms do not seek help for what represents a significant public health problem. Objective: This study aimed to evaluate the effectiveness of a self-guided cognitive behavioral internet intervention for people with social anxiety symptoms in the general population. Methods: We conducted a two-group randomized controlled trial in England between May 11, 2016, and June 27, 2018. Adults with social anxiety symptoms who were not receiving treatment for social anxiety were recruited using online advertisements. All participants had unrestricted access to usual care and were randomized in a 1:1 ratio to either a Web-based unguided self-help intervention based on cognitive behavioral principles or a waiting list control group. All outcomes were collected through self-report online questionnaires. The primary outcome was the change in 17-item Social Phobia Inventory (SPIN-17) score from baseline to 6 weeks using a linear mixed-effect model that used data from all time points (6 weeks, 3 months, 6 months, and 12 months). Results: A total of 2122 participants were randomized, and 6 were excluded from analyses because they were ineligible. Of the 2116 eligible randomized participants (mean age 37 years; 80.24%, 1698/2116 women), 70.13% (1484/2116) had follow-up data available for analysis, and 56.95% (1205/2116) had data on the primary outcome, although attrition was higher in the intervention arm. At 6 weeks, the mean (95% CI) adjusted difference in change in SPIN-17 score in the intervention group compared with control was −1.94 (−3.13 to −0.75; P=.001), a standardized mean difference effect size of 0.2. The improvement was maintained at 12 months. Given the high dropout rate, sensitivity analyses explored missing data assumptions, with results that were consistent with those of the primary analysis. The economic evaluation demonstrated cost-effectiveness with a small health status benefit and a reduction in health service utilization. Conclusions: For people with social anxiety symptoms who are not receiving other forms of help, this study suggests that the use of an online self-help tool based on cognitive behavioral principles can provide a small improvement in social anxiety symptoms compared with no intervention, although dropout rates were high. Clinical Trial: ClinicalTrials.gov NCT02451878; https://clinicaltrials.gov/ct2/show/NCT02451878

  • J Med Internet Res 2020;22(1):e16804

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  Participatory Medicine & E-Patients

  Jan 10, 2020

  Prevalence and Outcomes of Web-Based Health Information Seeking for Acute Symptoms: Cross-Sectional Study

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  Authors List:

  • Lydia Aoun  
  • Najla Lakkis  
  • Jumana Antoun  

  Abstract:

  Background: The literature indicates that Web-based health information seeking is mostly used for seeking information on well-established diseases. However, only a few studies report health information seeking in the absence of a doctor’s visit and in the context of acute symptoms. Objective: This survey aimed to estimate the prevalence of Web-based health information seeking for acute symptoms and the impact of such information on symptom management and health service utilization. Methods: This was a cross-sectional study of a convenience sample of 287 Lebanese adults (with a response rate of 18.5% [54/291]) conducted between December 2016 and June 2017. The survey was answered by participants online or through phone-based interviews. Results: A total of 64.3% of the participants (178/277) reported checking the internet for health information when they had an acute symptom. The rate of those who sought to use Web-based health information first when experiencing acute symptom(s) in the past 12 months was 19.2% (25/130). In addition, 50% (9/18) visited the doctor because of the obtained information, and the rest self-medicated or sought a pharmacist’s advice; the majority (18/24, 75%) improved within 3-4 days. Conclusions: Higher education level and trust in Web-based medical information were two major predictors of Web-based health information seeking for acute symptoms. Seeking Web-based health information first for acute symptoms is common and may lead to self-management by avoiding a visit to the physician. Physicians should encourage their patients to discuss Web-based health information and guide them toward trusted online websites.

  • J Med Internet Res 2020;22(1):e15148

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  Web-based and Mobile Health...

  Jan 10, 2020

  The Effects of a Digital Well-Being Intervention on Patients With Chronic Conditions: Observational Study

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  Authors List:

  • Acacia C Parks  
  • Allison L Williams  
  • Gina M Kackloudis  
  • Julia L Stafford  
  • Eliane M Boucher  
  • Ryan D Honomichl  

  Abstract:

  Background: Chronic conditions account for 75% of health care costs, and the impact of chronic illness is expected to grow over time. Although subjective well-being predicts better health outcomes, people with chronic conditions tend to report lower well-being. Improving well-being might mitigate costs associated with chronic illness; however, existing interventions can be difficult to access and draw from a single theoretical approach. Happify, a digital well-being intervention program drawing from multiple theoretical traditions to target well-being, has already been established as an efficacious means of improving well-being in both distressed and nondistressed users. Objective: This study aimed to compare change in well-being over time after using Happify for users with and without a chronic condition. Methods: Data were obtained from Happify users, a publicly available digital well-being program accessible via website or mobile phone app. Users work on tracks addressing a specific issue (eg, conquering negative thoughts) composed of games and activities based on positive psychology, cognitive behavioral therapy, and mindfulness principles. The sample included 821 users receiving at least 6 weeks’ exposure to Happify (ranging from 42 to 179 days) who met other inclusion criteria. As part of a baseline questionnaire, respondents reported demographic information (age and gender) and whether they had any of the prespecified chronic conditions: arthritis, diabetes, insomnia, multiple sclerosis, chronic pain, psoriasis, eczema, or some other condition (450 reported a chronic condition, whereas 371 did not). Subjective well-being was assessed with the Happify Scale, a 9-item measure of positive emotionality and life satisfaction. To evaluate changes in well-being over time, a mixed effects linear regression model was fit for subjective well-being, controlling for demographics and platform usage. Results: At baseline, users with a chronic condition had significantly lower subjective well-being (mean 38.34, SD 17.40) than users without a chronic condition (mean 43.65, SD 19.13). However, change trajectories for users with or without a chronic condition were not significantly different; both groups experienced equivalent improvements in well-being. We also found an effect for time from baseline (b=0.071; SE=0.010; P<.01) and number of activities completed (b=0.03; SE=0.009; P<.01), and a 2-way interaction between number of activities completed and time from baseline (b=0.0002; SE=0.00006; P<.01), such that completing more activities and doing so over increasingly longer periods produced improved well-being scores. Conclusions: Data from this study support the conclusion that users with a chronic condition experienced significant improvement over time. Despite reporting lower subjective well-being on the whole, their change trajectory while using Happify was equivalent to those without a chronic condition. Consistent with past research, users who completed more activities over a longer period showed the most improvement. In short, the presence of a chronic condition did not prevent users from showing improved well-being when using Happify.

  • J Med Internet Res 2020;22(1):e16211

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  Theme Issue 2019: Blockchain in...

  Jan 9, 2020

  Examining the Potential of Blockchain Technology to Meet the Needs of 21st-Century Japanese Health Care: Viewpoint on Use Cases and Policy

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  Authors List:

  • Tim Mackey  
  • Hirofumi Bekki  
  • Tokio Matsuzaki  
  • Hiroshi Mizushima  

  Abstract:

  Japan is undergoing a major population health transition as its society ages, and it continues to experience low birth rates. An aging Japan will bring new challenges to its public health system, highlighted as a model for universal health coverage (UHC) around the world. Specific challenges Japan’s health care system will face include an increase in national public health expenditures, higher demand for health care services, acute need for elder and long-term care, shortage of health care workers, and disparities between health care access in rural versus urban areas. Blockchain technology has the potential to address some of these challenges, but only if a health blockchain is conceptualized, designed, localized, and deployed in a way that is compatible with Japan’s centralized UHC-centric public health system. Blockchain solutions must also be adaptive to opportunities and barriers unique to Japan’s national health and innovation policy, including its regulatory sandbox system, while also seeking to learn from blockchain adoption in the private sector and in other countries. This viewpoint outlines the major opportunities and potential challenges to blockchain adoption for the future of Japan’s health care.

  • J Med Internet Res 2020;22(1):e13649

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  E-Health / Health Services Research...

  Jan 8, 2020

  Effectiveness of Upper Limb Wearable Technology for Improving Activity and Participation in Adult Stroke Survivors: Systematic Review

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  Authors List:

  • Jack Parker  
  • Lauren Powell  
  • Susan Mawson  

  Abstract:

  Background: With advances in technology, the adoption of wearable devices has become a viable adjunct in poststroke rehabilitation. Upper limb (UL) impairment affects up to 77% of stroke survivors impacting on their ability to carry out everyday activities. However, despite an increase in research exploring these devices for UL rehabilitation, little is known of their effectiveness. Objective: This review aimed to assess the effectiveness of UL wearable technology for improving activity and participation in adult stroke survivors. Methods: Randomized controlled trials (RCTs) and randomized comparable trials of UL wearable technology for poststroke rehabilitation were included. Primary outcome measures were validated measures of activity and participation as defined by the International Classification of Functioning, Disability, and Health. Databases searched were MEDLINE, Web of Science (Core collection), CINAHL, and the Cochrane Library. The Cochrane Risk of Bias Tool was used to assess the methodological quality of the RCTs and the Downs and Black Instrument for the quality of non RCTs. Results: In the review, we included 11 studies with collectively 354 participants at baseline and 323 participants at final follow-up including control groups and participants poststroke. Participants’ stroke type and severity varied. Only 1 study found significant between-group differences for systems functioning and activity (P≤.02). The 11 included studies in this review had small sample sizes ranging from 5 to 99 participants at an average (mean) age of 57 years. Conclusions: This review has highlighted a number of reasons for insignificant findings in this area including low sample sizes and the appropriateness of the methodology for complex interventions. However, technology has the potential to measure outcomes, provide feedback, and engage users outside of clinical sessions. This could provide a platform for motivating stroke survivors to carry out more rehabilitation in the absence of a therapist, which could maximize recovery.

  • J Med Internet Res 2020;22(1):e15981

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  Participatory Medicine & E-Patients

  Jan 8, 2020

  Health Consumers’ Daily Habit of Internet Banking Use as a Proxy for Understanding Health Information Sharing Behavior: Quasi-Experimental Approach

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  Authors List:

  • Hyeyoung Hah  

  Abstract:

  Background: As the US health care system is embracing data-driven care, personal health information (PHI) has become a valuable resource for various health care stakeholders. In particularly, health consumers are expected to autonomously manage and share PHI with their health care partners. To date, there have been mixed views on the factors influencing individuals’ health data–sharing behaviors. Objective: This study aimed to identify a key factor to better understand health information sharing behavior from a health consumer’s perspective. We focused on daily settings, wherein health data–sharing behavior becomes a part of individuals’ daily information management activities. Considering the similarity between health and finance information management, we explicitly examined whether health consumers’ daily habit of similar data sharing from the financial domain affects their PHI-sharing behaviors in various scenarios. Methods: A Web-based survey was administered to US health consumers who have access to and experience in using the internet. We collected individual health consumers’ intention to share PHI under varying contexts, habit of financial information management (operationalized as internet banking [IB] use in this paper), and the demographic information from the cross-sectional Web-based survey. To isolate the effect of daily IB on PHI-sharing behaviors in everyday contexts, propensity score matching was used to estimate the average treatment effect (ATE) and average treatment effect on the treated (ATET) regarding IB use. We balanced the treatment and control groups using caliper matching based on the observed confounding variables (ie, gender, income, health status, and access to primary care provider), all of which resulted in a minimal level of bias between unmatched and matched samples (bias <5%). Results: A total of 339 responses were obtained from a cross-sectional Web-based survey. The ATET results showed that in terms of sharing contents, those who used IB daily were more likely to share general information (P=.01), current information (P=.003), and entire data (P=.04). Regarding occasions for sharing occasions, IB users were prone to share their information in all cases (P=.02). With regard to sharing recipients, daily IB users were more willing to share their personal health data with stakeholders who were not directly involved in their care, such as health administrators (P=.05). These results were qualitatively similar to the ATE results. Conclusions: This study examined whether daily management of similar information (ie, personal financial information) changes health consumers’ PHI-sharing behavior under varying sharing conditions. We demonstrated that daily financial information management can encourage health information sharing to a much broader extent, in several instances, and with many stakeholders. We call for more attention to this unobserved daily habit driven by the use of various nonhealth technologies, all of which can implicitly affect patterns and the extent of individuals’ PHI-sharing behaviors.

  • J Med Internet Res 2020;22(1):e15585

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  Viewpoint

  Jan 7, 2020

  Big Data in Context: Addressing the Twin Perils of Data Absenteeism and Chauvinism in the Context of Health Disparities Research

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  • Edmund W J Lee  
  • Kasisomayajula Viswanath  

  Abstract:

  Recent advances in the collection and processing of health data from multiple sources at scale—known as big data—have become appealing across public health domains. However, present discussions often do not thoroughly consider the implications of big data or health informatics in the context of continuing health disparities. The 2 key objectives of this paper were as follows: first, it introduced 2 main problems of health big data in the context of health disparities—data absenteeism (lack of representation from underprivileged groups) and data chauvinism (faith in the size of data without considerations for quality and contexts). Second, this paper suggested that health organizations should strive to go beyond the current fad and seek to understand and coordinate efforts across the surrounding societal-, organizational-, individual-, and data-level contexts in a realistic manner to leverage big data to address health disparities.

  • J Med Internet Res 2020;22(1):e16377

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  Web-based and Mobile Health...

  Jan 7, 2020

  Self-Care Monitoring of Heart Failure Symptoms and Lung Impedance at Home Following Hospital Discharge: Longitudinal Study

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  Authors List:

  • Ina Thon Aamodt  
  • Edita Lycholip  
  • Jelena Celutkiene  
  • Thomas von Lueder  
  • Dan Atar  
  • Ragnhild Sørum Falk  
  • Ragnhild Hellesø  
  • Tiny Jaarsma  
  • Anna Strömberg  
  • Irene Lie  

  Abstract:

  Background: Self-care is key to the daily management of chronic heart failure (HF). After discharge from hospital, patients may struggle to recognize and respond to worsening HF symptoms. Failure to monitor and respond to HF symptoms may lead to unnecessary hospitalizations. Objective: This study aimed to (1) determine the feasibility of lung impedance measurements and a symptom diary to monitor HF symptoms daily at home for 30 days following hospital discharge and (2) determine daily changes in HF symptoms of pulmonary edema, lung impedance measurements, and if self-care behavior improves over time when patients use these self-care monitoring tools. Methods: This study used a prospective longitudinal design including patients from cardiology wards in 2 university hospitals—one in Norway and one in Lithuania. Data on HF symptoms and pulmonary edema were collected from 10 participants (mean age 64.5 years; 90% (9/10) male) with severe HF (New York Heart Association classes III and IV) who were discharged home after being hospitalized for an HF condition. HF symptoms were self-reported using the Memorial Symptom Assessment Scale for Heart Failure. Pulmonary edema was measured by participants using a noninvasive lung impedance monitor, the CardioSet Edema Guard Monitor. Informal caregivers aided the participants with the noninvasive measurements. Results: The prevalence and burden of shortness of breath varied from participants experiencing them daily to never, whereas lung impedance measurements varied for individual participants and the group participants, as a whole. Self-care behavior score improved significantly (P=.007) from a median of 56 (IQR range 22-75) at discharge to a median of 81 (IQR range 72-98) 30 days later. Conclusions: Noninvasive measurement of lung impedance daily and the use of a symptom diary were feasible at home for 30 days in HF patients. Self-care behavior significantly improved after 30 days of using a symptom diary and measuring lung impedance at home. Further research is needed to determine if daily self-care monitoring of HF signs and symptoms, combined with daily lung impedance measurements, may reduce hospital readmissions.

  • J Med Internet Res 2020;22(1):e15445

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  Web-based and Mobile Health...

  Jan 7, 2020

  Efficacy of a Self-Help Web-Based Recovery Training in Improving Sleep in Workers: Randomized Controlled Trial in the General Working Population

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  Authors List:

  • Doerte Behrendt  
  • David Daniel Ebert  
  • Kai Spiegelhalder  
  • Dirk Lehr  

  Abstract:

  Background: Sleep complaints are among the most prevalent health concerns, especially among workers, which may lead to adverse effects on health and work. Internet-delivered cognitive behavioral therapy for insomnia (iCBT-I) offers the opportunity to deliver effective solutions on a large scale. The efficacy of iCBT-I for clinical samples has been demonstrated in recent meta-analyses, and there is evidence that iCBT-I is effective in the working population with severe sleep complaints. However, to date, there is limited evidence from randomized controlled trials that iCBT-I could also be an effective tool for universal prevention among the general working population regardless of symptom severity. Although increasing evidence suggests that negatively toned cognitive activity may be a key factor for the development and maintenance of insomnia, little is known about how iCBT-I improves sleep by reducing presleep cognitive activity. Objective: This study aimed to examine the efficacy of a self-help internet-delivered recovery training, based on principles of iCBT-I tailored to the work-life domain, among the general working population. General and work-related cognitive activities were investigated as potential mediators of the intervention’s effect. Methods: A sample of 177 workers were randomized to receive either the iCBT-I (n=88) or controls (n=89). The intervention is a Web-based training consisting of six 1-week modules. As the training was self-help, participants received nothing but technical support via email. Web-based self-report assessments were scheduled at baseline, at 8 weeks, and at 6 months following randomization. The primary outcome was insomnia severity. Secondary outcomes included measures of mental health and work-related health and cognitive activity. In an exploratory analysis, general and work-related cognitive activities, measured as worry and work-related rumination, were investigated as mediators. Results: Analysis of the linear mixed effects model showed that, relative to controls, participants who received iCBT-I reported significantly lower insomnia severity scores at postintervention (between-group mean difference −4.36; 95% CI −5.59 to − 3.03; Cohen d=0.97) and at 6-month follow-up (between-group difference: −3.64; 95% CI −4.89 to −2.39; Cohen d=0.86). The overall test of group-by-time interaction was significant (P<.001). Significant differences, with small-to-large effect sizes, were also detected for cognitive activity and for mental and work-related health, but not for absenteeism. Mediation analysis demonstrated that work-related rumination (indirect effect: a₁b₁=−0.80; SE=0.34; 95% boot CI −1.59 to −0.25) and worry (indirect effect: a₂b₂=−0.37; SE=0.19; 95% boot CI −0.85 to −0.09) mediate the intervention’s effect on sleep. Conclusions: A self-help Web-based recovery training, grounded in the principles of iCBT-I, can be effective in the general working population, both short and long term. Work-related rumination may be a particularly crucial mediator of the intervention’s effect, suggesting that tailoring interventions to the workplace, including components to reduce the work-related cognitive activity, might be important when designing recovery interventions for workers. Clinical Trial: German Clinical Trials Register DRKS00007142; https://www.drks.de/DRKS00007142

  • J Med Internet Res 2020;22(1):e13346

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  Web-based and Mobile Health...

  Jan 6, 2020

  A Web- and Mobile App–Based Mental Health Promotion Intervention Comparing Email, Short Message Service, and Videoconferencing Support for a Healthy...

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  Authors List:

  • Melanie Elise Renfrew  
  • Darren Peter Morton  
  • Jason Kyle Morton  
  • Jason Scott Hinze  
  • Peter James Beamish  
  • Geraldine Przybylko  
  • Bevan Adrian Craig  

  Abstract:

  Background: The rapid increase in mental health disorders has prompted a call for greater focus on mental health promotion and primary prevention. Web- and mobile app–based interventions present a scalable opportunity. Little is known about the influence of human support on the outcomes of these interventions. Objective: This study aimed to compare the influence of 3 modes of human support on the outcomes (ie, mental health, vitality, depression, anxiety, stress, life satisfaction, and flourishing) of a 10-week, Web- and mobile app–based, lifestyle-focused mental health promotion intervention among a healthy adult cohort. Methods: Participants were recruited voluntarily using a combination of online and offline advertising. They were randomized, unblinded into 3 groups differentiated by human support mode: Group 1 (n=201): standard—fully automated emails (S); Group 2 (n=202): standard plus personalized SMS (S+pSMS); and Group 3 (n=202): standard plus weekly videoconferencing support (S+VCS), hosted by 1 trained facilitator. Participants accessed the intervention, including the questionnaire, on a Web-based learning management system or through a mobile app. The questionnaire, administered at pre- and postintervention, contained self-reported measures of mental well-being, including the “mental health” and “vitality” subscales from the Short Form Health Survey-36, Depression Anxiety and Stress Scale-21, Diener Satisfaction With Life Scale (SWLS), and Diener Flourishing Scale. Results: Of 605 potential participants, 458 (S: n=157, S+pSMS: n=163, and S+VCS: n=138) entered the study by completing registration and the preintervention questionnaire. At post intervention, 320 out of 458 participants (69.9%; S: n=103, S+pSMS: n=114, and S+VCS: n=103) completed the questionnaire. Significant within-group improvements were recorded from pre- to postintervention in all groups and in every outcome measure (P≤.001). No significant between-group differences were observed for outcomes in any measure: mental health (P=.77), vitality (P=.65), depression (P=.93), anxiety (P=.25), stress (P.57), SWLS (P=.65), and Flourishing Scale (P=.99). Adherence was not significantly different between groups for mean videos watched (P=.42) and practical activity engagement (P=.71). Participation in videoconference support sessions (VCSSs) was low; 37 out of 103 (35.9%) participants did not attend any VCSSs, and only 19 out of 103 (18.4%) attended 7 or more out of 10 sessions. Stratification within the S+VCS group revealed that those who attended 7 or more VCSSs experienced significantly greater improvements in the domains of mental health (P=.006; d=0.71), vitality (P=.005; d=0.73), depression (P=.04; d=0.54), and life satisfaction (P=.046; d=0.50) compared with participants who attended less than 7. Conclusions: A Web- and mobile app–based mental health promotion intervention enhanced domains of mental well-being among a healthy cohort, irrespective of human support. Low attendance at VCSSs hindered the ability to make meaningful between-group comparisons. Supplementing the intervention with VCSSs might improve outcomes when attendance is optimized. Clinical Trial: Australian New Zealand Clinical Trials Registry (ANZCTR): 12619001009101; http://www.anzctr.org.au/ACTRN12619001009101.aspx

  • J Med Internet Res 2020;22(1):e15592

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  Web-based and Mobile Health...

  Jan 6, 2020

  Usability Evaluations of Mobile Mental Health Technologies: Systematic Review

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  Authors List:

  • Yavuz Inal  
  • Jo Dugstad Wake  
  • Frode Guribye  
  • Tine Nordgreen  

  Abstract:

  Background: Many mobile health (mHealth) apps for mental health have been made available in recent years. Although there is reason to be optimistic about their effect on improving health and increasing access to care, there is a call for more knowledge concerning how mHealth apps are used in practice. Objective: This study aimed to review the literature on how usability is being addressed and measured in mHealth interventions for mental health problems. Methods: We conducted a systematic literature review through a search for peer-reviewed studies published between 2001 and 2018 in the following electronic databases: EMBASE, CINAHL, PsycINFO, PubMed, and Web of Science. Two reviewers independently assessed all abstracts against the inclusion and exclusion criteria, following the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Results: A total of 299 studies were initially identified based on the inclusion keywords. Following a review of the title, abstract, and full text, 42 studies were found that fulfilled the criteria, most of which evaluated usability with patients (n=29) and health care providers (n=11) as opposed to healthy users (n=8) and were directed at a wide variety of mental health problems (n=24). Half of the studies set out to evaluate usability (n=21), and the remainder focused on feasibility (n=10) or acceptability (n=10). Regarding the maturity of the evaluated systems, most were either prototypes or previously tested versions of the technology, and the studies included few accounts of sketching and participatory design processes. The most common reason referred to for developing mobile mental health apps was the availability of mobile devices to users, their popularity, and how people in general became accustomed to using them for various purposes. Conclusions: This study provides a detailed account of how evidence of usability of mHealth apps is gathered in the form of usability evaluations from the perspective of computer science and human-computer interaction, including how users feature in the evaluation, how the study objectives and outcomes are stated, which research methods and techniques are used, and what the notion of mobility features is for mHealth apps. Most studies described their methods as trials, gathered data from a small sample size, and carried out a summative evaluation using a single questionnaire, which indicates that usability evaluation was not the main focus. As many studies described using an adapted version of a standard usability questionnaire, there may be a need for developing a standardized mHealth usability questionnaire.

  • J Med Internet Res 2020;22(1):e15337

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  Artificial Intelligence (other than...

  Jan 3, 2020

  The Detection of Opioid Misuse and Heroin Use From Paramedic Response Documentation: Machine Learning for Improved Surveillance

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  Authors List:

  • José Tomás Prieto  
  • Kenneth Scott  
  • Dean McEwen  
  • Laura J Podewils  
  • Alia Al-Tayyib  
  • James Robinson  
  • David Edwards  
  • Seth Foldy  
  • Judith C Shlay  
  • Arthur J Davidson  

  Abstract:

  Background: Timely, precise, and localized surveillance of nonfatal events is needed to improve response and prevention of opioid-related problems in an evolving opioid crisis in the United States. Records of naloxone administration found in prehospital emergency medical services (EMS) data have helped estimate opioid overdose incidence, including nonhospital, field-treated cases. However, as naloxone is often used by EMS personnel in unconsciousness of unknown cause, attributing naloxone administration to opioid misuse and heroin use (OM) may misclassify events. Better methods are needed to identify OM. Objective: This study aimed to develop and test a natural language processing method that would improve identification of potential OM from paramedic documentation. Methods: First, we searched Denver Health paramedic trip reports from August 2017 to April 2018 for keywords naloxone, heroin, and both combined, and we reviewed narratives of identified reports to determine whether they constituted true cases of OM. Then, we used this human classification as reference standard and trained 4 machine learning models (random forest, k-nearest neighbors, support vector machines, and L1-regularized logistic regression). We selected the algorithm that produced the highest area under the receiver operating curve (AUC) for model assessment. Finally, we compared positive predictive value (PPV) of the highest performing machine learning algorithm with PPV of searches of keywords naloxone, heroin, and combination of both in the binary classification of OM in unseen September 2018 data. Results: In total, 54,359 trip reports were filed from August 2017 to April 2018. Approximately 1.09% (594/54,359) indicated naloxone administration. Among trip reports with reviewer agreement regarding OM in the narrative, 57.6% (292/516) were considered to include information revealing OM. Approximately 1.63% (884/54,359) of all trip reports mentioned heroin in the narrative. Among trip reports with reviewer agreement, 95.5% (784/821) were considered to include information revealing OM. Combined results accounted for 2.39% (1298/54,359) of trip reports. Among trip reports with reviewer agreement, 77.79% (907/1166) were considered to include information consistent with OM. The reference standard used to train and test machine learning models included details of 1166 trip reports. L1-regularized logistic regression was the highest performing algorithm (AUC=0.94; 95% CI 0.91-0.97) in identifying OM. Tested on 5983 unseen reports from September 2018, the keyword naloxone inaccurately identified and underestimated probable OM trip report cases (63 cases; PPV=0.68). The keyword heroin yielded more cases with improved performance (129 cases; PPV=0.99). Combined keyword and L1-regularized logistic regression classifier further improved performance (146 cases; PPV=0.99). Conclusions: A machine learning application enhanced the effectiveness of finding OM among documented paramedic field responses. This approach to refining OM surveillance may lead to improved first-responder and public health responses toward prevention of overdoses and other opioid-related problems in US communities.

  • J Med Internet Res 2020;22(1):e15645

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