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Digital health interventions could be a scalable approach to delivering at-home COVID-19 testing. SCALE-UP II aimed to investigate the effectiveness of three digital health interventions on the delivery of mailed at-home COVID-19 testing: text messaging (TM), automated chatbot (CA), and patient navigation upon request (PN). Pragmatic randomized controlled trial. Participants who self-reported that they had a smartphone were randomized in a 2x2x2 factorial design (Smartphone study) to receive (i) chatbot or TM; (ii) option to request PN; and (iii) intervention frequency every 10 or 30 days. All other participants were randomized in a 2x2 factorial design (Non-Smartphone study) to receive (i) option to request PN; and (ii) intervention frequency every 10 or 30 days. Study settings were safety net community health centers (CHCs) located across the state of Utah, USA. Eligible patients were >18 years old, with a primary care visit in the last three years, and a valid cellphone in the CHC electronic health record. The primary outcome was proportion of participants requesting at-home COVID-19 tests. The trial enrolled 2,117 in the Smartphone study and 31,439 in the Non-Smartphone study. In the Smartphone study, the proportion of participants who requested test kits in the Chatbot arm was lower than in TM (16.6% vs. 52.1%, aRR=0.317 [98.33% CI 0.27-0.38], P<.0001). In the Non-Smartphone study, the proportion of participants who requested test kits was higher if they were messaged every 10 days rather than every 30 days (5.5% vs 4.8%, aRR=1.144 [97.5% CI 1.03-1.28], P=.005). Yet, participants in the 10-day vs. 30-day condition were more likely to opt out of receiving study interventions (12.6% vs 7.3%, aRR=1.72 [97.5% CI 1.59-1.86], P<.0001). In the Non-Smartphone study, the proportion of participants who requested test kits was lower for those in the PN condition compared to No PN (4.3% vs 5.9%, aRR=0.729 [97.5% CI 0.65-0.81], P<.0001). Simple bidirectional TM was more effective than an interactive Web-based chatbot on the delivery of COVID-19 testing. Although messaging every 10 days was more effective than every 30 days, it also led to a larger opt-out rate. Digital health interventions based on automated bidirectional text messaging is a simple, scalable, and low-cost strategy to offer access to at-home COVID-19 testing. Similar approaches may be used to support public health response and other forms of at-home testing. Clinicaltrials.gov (NCT05533918 and NCT05533359). RR2-doi: 10.1136/bmjopen-2023-081455.

Background Tobacco use remains the leading cause of death and disability in the USA and is disproportionately concentrated among low socioeconomic status (SES) populations. Community Health Centers (CHCs) are a key venue for reaching low SES populations with evidence-based tobacco cessation treatment such as Quitlines. Electronic health record (EHR)-based interventions at the point-of-care, text messaging (TM), and phone counseling have the potential to increase Quitline reach and are feasible to implement within CHCs. However, there is a lack of data to inform how, when, and in what combination these strategies should be implemented. The aims of this cluster-randomized trial are to evaluate multi-level implementation strategies to increase the Reach (i.e., proportion of tobacco-using patients who enroll in the Quitline) and Impact (i.e., Reach × Efficacy [efficacy is defined as the proportion of tobacco-using patients who enroll in Quitline treatment that successfully quit]) and to evaluate characteristics of healthcare system, providers, and patients that may influence tobacco-use outcomes. Methods This study is a multilevel, three-phase, Sequential Multiple Assignment Randomized Trial (SMART), conducted in CHCs ( N  = 33 clinics; N  = 6000 patients). In the first phase, clinics will be randomized to two different EHR conditions. The second and third phases are patient-level randomizations based on prior treatment response. Patients who enroll in the Quitline receive no further interventions. In phase two, patients who are non-responders (i.e., patients who do not enroll in Quitline) will be randomized to receive either TM or continued-EHR. In phase three, patients in the TM condition who are non-responders will be randomized to receive either continued-TM or TM + phone coaching. Discussion This project will evaluate scalable, multi-level interventions to directly address strategic national priorities for reducing tobacco use and related disparities by increasing the Reach and Impact of evidence-based tobacco cessation interventions in low SES populations. Trial registration This trial was registered at ClinicalTrials.gov ( NCT03900767 ) on April 4th, 2019.

IntroductionAlthough evidence-based guidelines recommend non-pharmacologic treatments as first-line care for chronic low back pain (LBP), uptake has been limited, particularly in rural, low-income and ethnically diverse communities. The BeatPain study will evaluate the implementation and compare the effectiveness of two strategies to provide non-pharmacologic treatment for chronic LBP. The study will use telehealth to overcome access barriers for persons receiving care in federally qualified health centres (FQHCs) in the state of Utah.Methods and analysisBeatPain Utah is a pragmatic randomised clinical trial with a hybrid type I design investigating different strategies to provide non-pharmacologic care for adults with chronic LBP seen in Utah FQHCs. The intervention strategies include a brief pain consult (BPC) and telehealth physical therapy (PT) component provided using either an adaptive or sequenced delivery strategy across two 12-week treatment phases. Interventions are provided via telehealth by centrally located physical therapists. The sequenced delivery strategy provides the BPC, followed by telehealth PT in the first 12 weeks for all patients. The adaptive strategy uses a stepped care approach and provides the BPC in the first 12 weeks and telehealth PT to patients who are non-responders to the BPC component. We will recruit 500 English-speaking or Spanish-speaking participants who will be individually randomised with 1:1 allocation. The primary outcome is the Pain, Enjoyment and General Activity measure of pain impact with secondary outcomes including the additional pain assessment domains specified by the National Institutes (NIH) of Health Helping to End Addiction Long Initiative and implementation measures. Analyses of primary and secondary measures of effectiveness will be performed under longitudinal mixed effect models across assessments at baseline, and at 12, 26 and 52 weeks follow-ups.Ethics and disseminationEthics approval for the study was obtained from the University of Utah Institutional Review Board. On completion, study data will be made available in compliance with NIH data sharing policies.Trial registration numberNCT04923334.

IntroductionSCALE-UP II aims to investigate the effectiveness of population health management interventions using text messaging (TM), chatbots and patient navigation (PN) in increasing the uptake of at-home COVID-19 testing among patients in historically marginalised communities, specifically, those receiving care at community health centres (CHCs).Methods and analysisThe trial is a multisite, randomised pragmatic clinical trial. Eligible patients are >18 years old with a primary care visit in the last 3 years at one of the participating CHCs. Demographic data will be obtained from CHC electronic health records. Patients will be randomised to one of two factorial designs based on smartphone ownership. Patients who self-report replying to a text message that they have a smartphone will be randomised in a 2×2×2 factorial fashion to receive (1) chatbot or TM; (2) PN (yes or no); and (3) repeated offers to interact with the interventions every 10 or 30 days. Participants who do not self-report as having a smartphone will be randomised in a 2×2 factorial fashion to receive (1) TM with or without PN; and (2) repeated offers every 10 or 30 days. The interventions will be sent in English or Spanish, with an option to request at-home COVID-19 test kits. The primary outcome is the proportion of participants using at-home COVID-19 tests during a 90-day follow-up. The study will evaluate the main effects and interactions among interventions, implementation outcomes and predictors and moderators of study outcomes. Statistical analyses will include logistic regression, stratified subgroup analyses and adjustment for stratification factors.Ethics and disseminationThe protocol was approved by the University of Utah Institutional Review Board. On completion, study data will be made available in compliance with National Institutes of Health data sharing policies. Results will be disseminated through study partners and peer-reviewed publications.Trial registration numberClinicalTrials.gov: NCT05533918 and NCT05533359.

Interventions designed to address COVID-19 needed to be rapidly scaled up to the population level, and to address health equity by reaching historically marginalized populations most affected by the pandemic (e.g., racial/ethnic minorities and rural and low socioeconomic status populations). From February 2021 to June 2022, SCALE-UP Utah used text messaging interventions to reach 107 846 patients from 28 clinics within seven safety-net health care systems. Interventions provided informational and motivational messaging regarding COVID-19 testing and vaccination, and were developed using extensive community partner input. (Am J Public Health. 2024; 114(11):1207-1211. https://doi.org/10.2105/AJPH.2024307770)

Interventions designed to address COVID-19 needed to be rapidly scaled up to the population level, and to address health equity by reaching historically marginalized populations most affected by the pandemic (e.g., racial/ethnic minorities and rural and low socioeconomic status populations). From February 2021 to June 2022, SCALE-UP Utah used text messaging interventions to reach 107 846 patients from 28 clinics within seven safety-net health care systems. Interventions provided informational and motivational messaging regarding COVID-19 testing and vaccination, and were developed using extensive community partner input. ( Am J Public Health. 2024;114(11):1207–1211. https://doi.org/10.2105/AJPH.2024.307770 )

Digital health interventions could be a scalable approach to delivering at-home COVID-19 testing. SCALE-UP II aimed to investigate the effectiveness of 3 digital health interventions on the delivery of mailed at-home COVID-19 testing: SMS text messaging, automated chatbot, and patient navigation upon request. The study was a pragmatic randomized controlled trial. Participants who self-reported that they had a smartphone were randomized in a 2×2×2 factorial design (smartphone study) to receive (1) chatbot or text messaging, (2) the option to request patient navigation, and (3) intervention frequency every 10 or 30 days. All other participants were randomized in a 2×2 factorial design (nonsmartphone study) to receive the option to request patient navigation and intervention frequency every 10 or 30 days. Study settings were safety net community health centers located across the state of Utah, United States. Eligible patients were >18 years old, with a primary care visit in the last 3 years, and a valid cellphone in the community health centers electronic health record. The primary outcome was the proportion of participants requesting at-home COVID-19 tests. The trial enrolled 2117 in the smartphone study and 31,439 in the nonsmartphone study. In the smartphone study, the proportion of participants who requested test kits in the Chatbot arm was lower than in SMS text messaging (174/1051, 16.6% vs 555/1066, 52.1%; adjusted risk ratio (aRR) 0.317, 98.33% CI 0.27-0.38; P<.001). In the nonsmartphone study, the proportion of participants who requested test kits was higher if they were messaged every 10 days rather than every 30 days (860/15,717, 5.5% vs 752/15,722, 4.8%; aRR 1.144, 97.5% CI 1.03-1.28; P=.005). However, participants in the 10-day versus 30-day condition were more likely to opt out of receiving study interventions (1977/15,717, 12.6% vs 1147/15,722, 7.3%; aRR 1.72, 97.5% CI 1.59-1.86; P<.001). In the nonsmartphone study, the proportion of participants who requested test kits was lower for those in the patient navigation condition compared with no patient navigation (680/15,718, 4.3% vs 932/15,721, 5.9%; aRR 0.729, 97.5% CI 0.65-0.81; P<.001). Simple bidirectional text messaging was more effective than an interactive web-based chatbot on the delivery of COVID-19 testing. Although messaging every 10 days was more effective than every 30 days, it also led to a larger opt-out rate. Digital health interventions based on automated bidirectional SMS text messaging are a simple, scalable, and low-cost strategy to offer access to at-home COVID-19 testing. Similar approaches may be used to support public health response and other forms of at-home testing.

Abstract Racial/ethnic minority, low socioeconomic status, and rural populations are disproportionately affected by COVID-19. Developing and evaluating interventions to address COVID-19 testing and vaccination among these populations are crucial to improving health inequities. The purpose of this paper is to describe the application of a rapid-cycle design and adaptation process from an ongoing trial to address COVID-19 among safety-net healthcare system patients. The rapid-cycle design and adaptation process included: (a) assessing context and determining relevant models/frameworks; (b) determining core and modifiable components of interventions; and (c) conducting iterative adaptations using Plan-Do-Study-Act (PDSA) cycles. PDSA cycles included: Plan. Gather information from potential adopters/implementers (e.g., Community Health Center [CHC] staff/patients) and design initial interventions; Do. Implement interventions in single CHC or patient cohort; Study. Examine process, outcome, and context data (e.g., infection rates); and, Act. If necessary, refine interventions based on process and outcome data, then disseminate interventions to other CHCs and patient cohorts. Seven CHC systems with 26 clinics participated in the trial. Rapid-cycle, PDSA-based adaptations were made to adapt to evolving COVID-19-related needs. Near real-time data used for adaptation included data on infection hot spots, CHC capacity, stakeholder priorities, local/national policies, and testing/vaccine availability. Adaptations included those to study design, intervention content, and intervention cohorts. Decision-making included multiple stakeholders (e.g., State Department of Health, Primary Care Association, CHCs, patients, researchers). Rapid-cycle designs may improve the relevance and timeliness of interventions for CHCs and other settings that provide care to populations experiencing health inequities, and for rapidly evolving healthcare challenges such as COVID-19. SCALE-UP Utah used real-time information on changes in COVID-19 policy (e.g., vaccination authorization), local case rates, and the capacity of safety-net healthcare systems to iteratively change interventions to be relevant and timely for patients. Lay Summary Racial/ethnic minority, low socioeconomic status, and rural populations experience a disproportionate burden of COVID-19. Finding ways to address COVID-19 among these populations is crucial to improving health inequities. The purpose of this paper is to describe the rapid-cycle design process for a research project to address COVID-19 testing and vaccination among safety-net healthcare system patients. The project used real-time information on changes in COVID-19 policy (e.g., vaccination authorization), local case rates, and the capacity of safety-net healthcare systems to iteratively change interventions to ensure interventions were relevant and timely for patients. Key changes that were made to interventions included a change to the study design to include vaccination as a focus of the interventions after the vaccine was authorized; change in intervention content according to the capacity of local Community Health Centers to provide testing to patients; and changes to intervention cohorts such that priority groups of patients were selected for intervention based on characteristics including age, residency in an infection “hot spot,” or race/ethnicity. Iteratively improving interventions based on real-time data collection may increase intervention relevance and timeliness, and rapid-cycle adaptions can be successfully implemented in resource constrained settings like safety-net healthcare systems.