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Background: Voice-controlled smart speakers and displays have a unique but unproven potential for delivering eHealth interventions. Many laptop- and smartphone-based interventions have been shown to improve multiple outcomes, but voice-controlled platforms have not been tested in large-scale rigorous trials. Older adults with multiple chronic health conditions, who need tools to help with their daily management, may be especially good candidates for interventions on voice-controlled devices because these patients often have physical limitations, such as tremors or vision problems, that make the use of laptops and smartphones challenging., Objective: The aim of this study is to assess whether participants using an evidence-based intervention (ElderTree) on a smart display will experience decreased pain interference and improved quality of life and related measures in comparison with participants using ElderTree on a laptop and control participants who are given no device or access to ElderTree., Methods: A total of 291 adults aged ≥60 years with chronic pain and ≥3 additional chronic conditions will be recruited from primary care clinics and community organizations and randomized 1:1:1 to ElderTree access on a smart display along with their usual care, ElderTree access on a touch screen laptop along with usual care, or usual care alone. All patients will be followed for 8 months. The primary outcomes are differences between groups in measures of pain interference and psychosocial quality of life. The secondary outcomes are between-group differences in system use at 8 months, physical quality of life, pain intensity, hospital readmissions, communication with medical providers, health distress, well-being, loneliness, and irritability. We will also examine mediators and moderators of the effects of ElderTree on both platforms. At baseline, 4 months, and 8 months, patients will complete written surveys comprising validated scales selected for good psychometric properties with similar populations. ElderTree use data will be collected continuously in system logs. We will use linear mixed-effects models to evaluate outcomes over time, with treatment condition and time acting as between-participant factors. Separate analyses will be conducted for each outcome., Results: Recruitment began in August 2021 and will run through April 2023. The intervention period will end in December 2023. The findings will be disseminated via peer-reviewed publications., Conclusions: To our knowledge, this is the first study with a large sample and long time frame to examine whether a voice-controlled smart device can perform as well as or better than a laptop in implementing a health intervention for older patients with multiple chronic health conditions. As patients with multiple conditions are such a large cohort, the implications for cost as well as patient well-being are significant. Making the best use of current and developing technologies is a critical part of this effort., Trial Registration: ClinicalTrials.gov NCT04798196; https://clinicaltrials.gov/ct2/show/NCT04798196., International Registered Report Identifier (irrid): PRR1-10.2196/37522., (©David H Gustafson, Marie-Louise Mares, Darcie C Johnston, Gina Landucci, Klaren Pe-Romashko, Olivia J Vjorn, Yaxin Hu, David H Gustafson, Adam Maus, Jane E Mahoney, Bilge Mutlu. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 05.05.2022.)

Communicating within digital health interventions involves a range of behaviors that may contribute to the management of chronic illnesses in different ways. This study examines whether communication within a smartphone-based application for addiction recovery produces distinct effects depending on 1) the "level" of communication, defined as intraindividual communication (e.g., journal entries to oneself); dyadic communication (e.g., private messaging to other individuals); or network communication (e.g., discussion forum posts to all group members), and 2) whether individuals produce or are exposed to messages. We operationalize these communication levels and behaviors based on system use logs as the number of clicks dedicated to each activity and assess how each category of system use relates to changes in group bonding and substance use after 6 months with the mobile intervention. Our findings show that (1) intraindividual exposure to one's own past posts marginally predicts decreased drug use; (2) dyadic production predicts greater perceived bonding; while dyadic exposure marginally predicts reduced drug use; (3) network production predicts decreased risky drinking. Implications for digital health interventions are discussed.

Background: Successful long-term recovery from opioid use disorder (OUD) requires continuous lapse risk monitoring and appropriate use and adaptation of recovery-supportive behaviors as lapse risk changes. Available treatments often fail to support long-term recovery by failing to account for the dynamic nature of long-term recovery., Objective: The aim of this protocol paper is to describe research that aims to develop a highly contextualized lapse risk prediction model that forecasts the ongoing probability of lapse., Methods: The participants will include 480 US adults in their first year of recovery from OUD. Participants will report lapses and provide data relevant to lapse risk for a year with a digital therapeutic smartphone app through both self-report and passive personal sensing methods (eg, cellular communications and geolocation). The lapse risk prediction model will be developed using contemporary rigorous machine learning methods that optimize prediction in new data., Results: The National Institute of Drug Abuse funded this project (R01DA047315) on July 18, 2019 with a funding period from August 1, 2019 to June 30, 2024. The University of Wisconsin-Madison Health Sciences Institutional Review Board approved this project on July 9, 2019. Pilot enrollment began on April 16, 2021. Full enrollment began in September 2021., Conclusions: The model that will be developed in this project could support long-term recovery from OUD-for example, by enabling just-in-time interventions within digital therapeutics., International Registered Report Identifier (irrid): DERR1-10.2196/29563., (©Hannah Moshontz, Alejandra J Colmenares, Gaylen E Fronk, Sarah J Sant'Ana, Kendra Wyant, Susan E Wanta, Adam Maus, David H Gustafson Jr, Dhavan Shah, John J Curtin. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 07.12.2021.)

Background: The growing epidemic of opioid use disorder (OUD) and associated injection drug use has resulted in a surge of new hepatitis C virus (HCV) infections. Approximately half of the people with HCV infection are unaware of their HCV status. Improving HCV awareness and increasing screening among people with OUD are critical. Addiction-Comprehensive Health Enhancement Support System (A-CHESS) is an evidence-based, smartphone-delivered relapse prevention system that has been implemented among people with OUD who are receiving medications for addiction treatment (MAT) to improve long-term recovery., Objective: We incorporated HCV-related content and functionality into A-CHESS to characterize the HCV care continuum among people in early remission and receiving MAT for OUD and to determine whether incorporating such content and functionality into A-CHESS increases HCV testing., Methods: HCV intervention content, including dissemination of educational information, private messages tailored to individuals' stage of HCV care, and a public discussion forum, was implemented into the A-CHESS platform. Between April 2016 and April 2020, 416 participants with OUD were enrolled in this study. Participants were randomly assigned to receive MAT alone (control arm) or MAT+A-CHESS (experimental arm). Quarterly telephone interviews were conducted from baseline to month 24 to assess risk behaviors and HCV testing history. Cox proportional hazards regression was used to assess whether participants who used A-CHESS were tested for HCV (either antibody [Ab] or RNA testing) at a higher rate than those in the control arm. To assess the effect of A-CHESS on subsets of participants at the highest risk for HCV, additional analyses were performed to examine the effect of the intervention among participants who injected drugs and shared injection equipment., Results: Overall, 44.2% (184/416) of the study participants were HCV Ab positive, 30.3% (126/416) were HCV Ab negative, and 25.5% (106/416) were considered untested at baseline. At month 24, there was no overall difference in HCV testing uptake between the intervention and control participants. However, among the subset of 109 participants who engaged in injection drug use, there was a slight trend toward increased HCV testing uptake among those who used A-CHESS (89% vs 85%; hazard ratio: 1.34; 95% CI 0.87-2.05; P=.18), and a stronger trend was observed when focusing on the subset of 32 participants who reported sharing injection equipment (87% vs 56%; hazard ratio: 2.92; 95% CI 0.959-8.86; P=.06)., Conclusions: Incorporating HCV prevention and care information into A-CHESS may increase the uptake of HCV testing while preventing opioid relapse when implemented among populations who engage in high-risk behaviors such as sharing contaminated injection equipment. However, more studies that are powered to detect differences in HCV testing among high-risk groups are needed., Trial Registration: ClinicalTrials.gov NCT02712034; https://clinicaltrials.gov/ct2/show/NCT02712034., International Registered Report Identifier (irrid): RR2-10.2196/12620., (©Karli R Hochstatter, David H Gustafson Sr, Gina Landucci, Klaren Pe-Romashko, Olivia Cody, Adam Maus, Dhavan V Shah, Ryan P Westergaard. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 22.02.2021.)

The dynamics of zoonotic vector-borne diseases are determined by a complex set of parameters including human behavior that may vary with socio-ecological contexts. Lyme disease is the most common vector-borne disease in the United States. The Northeast and upper Midwest are the regions most affected - two areas with differing levels of urbanization and differing sociocultural settings. The probability of being infected with Lyme disease is related to the risk of encounters with Ixodes scapularis ticks infected with Borrelia burgdorferi sensu lato, which reflects both the environmental tick hazard and human behaviors. Herein, we compare behavioral and peridomestic risk factors perceived to influence the risk for human-tick encounters between two high-incidence states in the Northeast (New York and New Jersey) and one high-incidence state in the Midwest (Wisconsin). We used a smartphone application, The Tick App, as a novel survey tool, during spring and summer of 2018. Adaptive human behavior was identified in the relationship between outdoor activities and the use of methods to prevent tick bites. More frequent recreational outdoor activities and gardening (a peridomestic activity) were associated with a 1.4-2.3 times increased likelihood of using personal protective measures to prevent tick bites, when accounting for demographics and previous Lyme diagnosis. Most outdoor activities were more frequently reported by participants from the Midwest (n = 697), representing an older demographic, than the Northeast (n = 396). Participants from the Northeast were less likely to report use of personal protective measures to prevent tick bites, but a larger proportion of participants from the Northeast reported application of environmental pesticides targeting ticks or mosquitoes or other insects on their property (34 % of 279 versus 22 % of 616 participants) and interventions to reduce the presence of peridomestic deer compared to participants from the Midwest (e.g. 20 % of 278 versus 7% of 615 participants reported having a deer proof fence). Participants from the Midwest were more likely to kill rodents on their property (28 % versus 13 %). These differences illustrate the need for further assessment of personal behavior and tick exposure in these two Lyme disease-endemic regions to aid in targeted public health messaging to reduce tick-borne diseases., (Copyright © 2020 The Authors. Published by Elsevier GmbH.. All rights reserved.)

Background: Mobile health (mHealth) technology takes advantage of smartphone features to turn them into research tools, with the potential to reach a larger section of the population in a cost-effective manner, compared with traditional epidemiological methods. Although mHealth apps have been widely implemented in chronic diseases and psychology, their potential use in the research of vector-borne diseases has not yet been fully exploited., Objective: This study aimed to assess the usability and feasibility of The Tick App, the first tick research-focused app in the United States., Methods: The Tick App was designed as a survey tool to collect data on human behaviors and movements associated with tick exposure while engaging users in tick identification and reporting. It consists of an enrollment survey to identify general risk factors, daily surveys to collect data on human activities and tick encounters (Tick Diaries), a survey to enter the details of tick encounters coupled with tick identification services provided by the research team (Report a Tick), and educational material. Using quantitative and qualitative methods, we evaluated the enrollment strategy (passive vs active), the user profile, location, longitudinal use of its features, and users' feedback., Results: Between May and September 2018, 1468 adult users enrolled in the app. The Tick App users were equally represented across genders and evenly distributed across age groups. Most users owned a pet (65.94%, 962/1459; P<.001), did frequent outdoor activities (recreational or peridomestic; 75.24%, 1094/1454; P<.001 and 64.58%, 941/1457; P<.001, respectively), and lived in the Midwest (56.55%, 824/1457) and Northeast (33.0%, 481/1457) regions in the United States, more specifically in Wisconsin, southern New York, and New Jersey. Users lived more frequently in high-incidence counties for Lyme disease (incidence rate ratio [IRR] 3.5, 95% CI 1.8-7.2; P<.001) and in counties with cases recently increasing (IRR 1.8, 95% CI 1.1-3.2; P=.03). Recurring users (49.25%, 723/1468) had a similar demographic profile to all users but participated in outdoor activities more frequently (80.5%, 575/714; P<.01). The number of Tick Diaries submitted per user (median 2, interquartile range [IQR] 1-11) was higher for older age groups (aged >55 years; IRR 3.4, 95% CI 1.5-7.6; P<.001) and lower in the Northeast (IRR[NE] 0.4, 95% CI 0.3-0.7; P<.001), whereas the number of tick reports (median 1, IQR 1-2) increased with the frequency of outdoor activities (IRR 1.5, 95% CI 1.3-1.8; P<.001)., Conclusions: This assessment allowed us to identify what fraction of the population used The Tick App and how it was used during a pilot phase. This information will be used to improve future iterations of The Tick App and tailor potential tick prevention interventions to the users' characteristics., (©Maria P Fernandez, Gebbiena M. Bron, Pallavi A Kache, Scott R Larson, Adam Maus, David Gustafson Jr, Jean I Tsao, Lyric C Bartholomay, Susan M Paskewitz, Maria A Diuk-Wasser. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 24.10.2019.)

Background: People who inject drugs are at a disproportionate risk for contracting hepatitis C virus (HCV). However, use of HCV prevention and treatment services remains suboptimal among people with substance use disorders due to various health system, societal, and individual barriers. Mobile health applications offer promising strategies to support people in recovery from substance use disorders. We sought to determine whether the Addiction-Comprehensive Health Enhancement Support System (A-CHESS), an existing mobile health application for opioid use disorder, could be adapted to improve HCV screening and treatment., Objective: The goals of this paper are to describe: (1) the components and functionality of an HCV intervention incorporated into the existing A-CHESS system; and (2) how data are collected and will be used to evaluate HCV testing, linkage to care, and treatment., Methods: People with recent opioid use were enrolled in a randomized controlled trial to test whether A-CHESS reduced relapse. We developed and implemented HCV intervention content within the A-CHESS platform to simultaneously evaluate whether A-CHESS improved secondary outcomes related to HCV care. All A-CHESS users received the HCV intervention content, which includes educational information, private messages tailored to an individual's stage of HCV care, and a public discussion forum. Data on patients' HCV risk behaviors and stage of care were collected through quarterly telephone interviews and weekly surveys delivered through A-CHESS. The proportion of people with opioid use disorder who are HCV untested, HCV-negative, HCV antibody-positive, or HCV RNA-positive, as well as linked to care, treated and cured at baseline is described here. The 24-month follow-up is ongoing and will be completed in April 2020. Survey data will then be used to assess whether individuals who received the HCV-enhanced A-CHESS intervention were more likely to reduce risky injection behaviors, receive HCV testing, link to medical care, initiate treatment, and be cured of HCV compared to the control group., Results: Between April 2016 and April 2018, 416 individuals were enrolled and completed the baseline interview. Of these individuals, 207 were then randomly assigned to the control arm and 209 were assigned to the intervention arm. At baseline, 202 individuals (49%) self-reported ever testing HCV antibody-positive. Of those, 179 (89%) reported receiving HCV RNA confirmatory testing, 134 (66%) tested HCV RNA-positive, 125 (62%) were linked to medical care and 27 (13%) were treated and cured of HCV. Of the remaining 214 individuals who had never tested HCV antibody-positive, 129 (31%) had tested HCV antibody-negative within the past year and 85 (20%) had not been tested within the past year., Conclusions: The A-CHESS mobile health system allows for the implementation of a bundle of services as well as the collection of longitudinal data related to drug use and HCV care among people with opioid use disorders. This study will provide preliminary evidence to determine whether HCV-specific services embedded into the A-CHESS program can improve HCV outcomes for people engaged in addiction treatment., Trial Registration: ClinicalTrials.gov NCT02712034; https://clinicaltrials.gov/ct2/show/NCT02712034., International Registered Report Identifier (irrid): DERR1-10.2196/12620., (©Karli R Hochstatter, David H Gustafson Sr, Gina Landucci, Klaren Pe-Romashko, Adam Maus, Dhavan V Shah, Quinton A Taylor, Emma K Gill, Rebecca Miller, Sarah Krechel, Ryan P Westergaard. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 01.08.2019.)

Objectives: Longer retention in treatment is associated with positive outcomes. For women, who suffer worse drug-related problems than men, social technologies, which are more readily adopted by women, may offer promise. This naturalistic study examined whether a smartphone-based relapse-prevention system, A-CHESS (Addiction-Comprehensive Health Enhancement Support System), could improve retention for women with substance use disorders in an impoverished rural setting., Methods: A total of 98 women, age 18 to 40, in southeastern Kentucky and mandated to treatment, received A-CHESS with intensive outpatient treatment for 6 months. For comparison, data were obtained for a similar but non-equivalent group of 100 same-age women also mandated to treatment in the same clinics during the period. Electronic medical record data on length-of-stay and treatment service use for both groups were analyzed, with A-CHESS use data, to determine whether those using A-CHESS showed better retention than those without., Results: Women with A-CHESS averaged 780 service units compared with 343 for the comparison group. For those with discharge dates prior to the study's end, A-CHESS patients stayed in treatment a mean of 410 vs 262 days for the comparison group., Conclusions: Given associations between retention and positive outcomes, mobile health technology such as A-CHESS may help improve outcomes among women, especially in settings where access to in-person services is difficult. The findings, based on a non-equivalent comparison, suggest the need for further exploration with rigorous experimental designs to determine whether and to what degree access to a smartphone with A-CHESS may extend and support recovery for women., Competing Interests: Declaration of conflicting interests:The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: WDM is an independent consultant who once provided services to the NIATx Foundation of the University of Wisconsin–Madison Department of Industrial Engineering, the group that developed the A-CHESS smartphone app. DHG has a small shareholder interest in CHESS Health, a corporation that develops health care technology for patients and family members struggling with addiction; this relationship is managed by DHG and the University of Wisconsin–Madison’s Conflict of Interest Committee. The authors declare no other conflicts of interest.

To reduce exposure to risky and challenging driving situations and prolong mobility and independence, older drivers self-regulate their driving behavior. But self-regulation can be challenging because it depends on drivers' ability to assess their limitations. Studies using self-reports, survey data, and hazard and risk perception tests have shown that driving behavior feedback can help older drivers assess their limitations and adjust their driving behavior. But only limited work has been conducted in developing feedback technology interventions tailored to meet the information needs of older drivers, and the impact these interventions have in helping older drivers self-monitor their driving behavior and risk outcomes. The vehicles of 33 drivers 65 years and older were instrumented with OBD2 devices. Older drivers were provided access to customized web-based Trip Diaries that delivered post-trip feedback of the routes driven, low-risk route alternatives, and frequency of their risky driving behaviors. Data were recorded over four months, with baseline driving behavior collected for one month. Generalized linear mixed effects regression models assessed the effects of post-trip feedback on the route risk and driving behaviors of older drivers. Results showed that post-trip feedback reduced the estimated route risk of older drivers by 2.9% per week, and reduced their speeding frequency on average by 0.9% per week. Overall, the Trip Diary feedback reduced the expected crash rate from 1 in 6172 trips to 1 in 7173 trips, and the expected speeding frequency from 46% to 39%. Thus providing older drivers with tailored feedback of their driving behavior and crash risk could help them appropriately self-regulate their driving behavior, and improve their crash risk outcomes., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

What models can effectively guide the creation of eHealth and mHealth technologies? This paper describes the use of the NIATx model as a framework for the user-centered design of a new technology for older adults. The NIATx model is a simple framework of process improvement based on the following principles derived from an analysis of decades of research from various industries about why some projects fail and others succeed: (1) Understand and involve the customer; (2) fix key problems; (3) pick an influential change leader; (4) get ideas from outside the field; (5) use rapid-cycle testing. This paper describes the use of these principles in technology development, the strengths and challenges of using this approach in this context, and lessons learned from the process. Overall, the NIATx model enabled us to produce a user-focused technology that the anecdotal evidence available so far suggests is engaging and useful to older adults. The first and fourth principles were especially important in developing the technology; the fourth proved the most challenging to use.

Understanding influence networks among substance abuse treatment clinics may speed the diffusion of innovations. The purpose of this study was to describe influence networks in Massachusetts, Michigan, New York, Oregon, and Washington and test two expectations, using social network analysis: (1) Social network measures can identify influential clinics; and (2) Within a network, some weakly connected clinics access out-of-network sources of innovative evidence-based practices and can spread these innovations through the network. A survey of 201 clinics in a parent study on quality improvement provided the data. Network measures and sociograms were obtained from adjacency matrixes created by UCINet. We used regression analysis to determine whether network status relates to clinics' adopting innovations. Findings suggest that influential clinics can be identified and that loosely linked clinics were likely to join the study sooner than more influential clinics but were not more likely to have improved outcomes than other organizations. Findings identify the structure of influence networks for SUD treatment organizations and have mixed results on how those structures impacted diffusion of the intervention under study. Further study is necessary to test whether use of knowledge of the network structure will have an effect on the pace and breadth of dissemination of innovations.