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Background: The impact of using direct-to-consumer wearable devices as a means to timely detect atrial fibrillation (AF) and to improve clinical outcomes is unknown., Methods: Heartline is a pragmatic, randomized, and decentralized application-based trial of US participants aged ≥65 years. Two randomized cohorts include adults with possession of an iPhone and without a history of AF and those with a diagnosis of AF taking a direct oral anticoagulant (DOAC) for ≥30 days. Participants within each cohort are randomized (3:1) to either a core digital engagement program (CDEP) via iPhone application (Heartline application) and an Apple Watch (Apple Watch Group) or CDEP alone (iPhone-only Group). The Apple Watch Group has the watch irregular rhythm notification (IRN) feature enabled and access to the ECG application on the Apple Watch. If an IRN notification is issued for suspected AF then the study application instructs participants in the Apple Watch Group to seek medical care. All participants were "watch-naïve" at time of enrollment and have an option to either buy or loan an Apple Watch as part of this study. The primary end point is time from randomization to clinical diagnosis of AF, with confirmation by health care claims. Key secondary endpoint are claims-based incidence of a 6-component composite cardiovascular/systemic embolism/mortality event, DOAC medication use and adherence, costs/health resource utilization, and frequency of hospitalizations for bleeding. All study assessments, including patient-reported outcomes, are conducted through the study application. The target study enrollment is approximately 28,000 participants in total; at time of manuscript submission, a total of 26,485 participants have been enrolled into the study., Conclusion: The Heartline Study will assess if an Apple Watch with the IRN and ECG application, along with application-facilitated digital health engagement modules, improves time to AF diagnosis and cardiovascular outcomes in a real-world environment., Trial Registration: ClinicalTrials.gov Identifier: NCT04276441., Competing Interests: Conflict of Interest CMG: Research grant support from Johnson & Johnson and Apple. SS: Research grant from Janssen Pharmaceuticals; Employment with PhysIQ, Inc. DL: Research grant from AtriCure and Biosense Webster. Speakers engagement and advisory boards with Medtronic, Abbott, Boston Scientific, Biosense Webster. MPT: Related to the submitted work: Consulting fees from Johnson & Johnson. Outside of the submitted work: consulting fees from Medtronic Inc, Abbott, Pfizer, Sanofi, InCarda, 100Plus, AliveCor, Acutus, Sanofi, Bristol Myers Squibb, Medtronic; grants from Bristol Myers Squibb, American Heart Association, Sanofi, Apple, Bayer, Gilead, US Food and Drug Administration, employment from iRhythm Technologies. RP: Advisory boards for Johnson & Johnson, Abbott, Medtronic; research support Abbott and American Heart Association. WSJ: Research grants from Bayer, Boehringer Ingelheim, Janssen, Merck, Novartis, National Institute on Aging, Patient-Centered Outcomes Research Institute. TJB: Research grant support from Boehringer Ingelheim, Altathera, Boston Scientific. ABC: Advisory board for Janssen Pharmaceuticals, Medtronic Inc., Abbott, Sanofi Aventis, Milestone Pharmaceuticals, Eagle Pharmaceuticals; honoraria for speaking from Medtronic Inc., Abbott, Sanofi Aventis, Milestone Pharmaceuticals. EP: Research support from Janssen, Bristol-Myers Squibb, Amgen, Esperion; Advisory boards for Novartis, Novo Nordisk, Cerner. JR: Advisory boards for Acesion Pharma and InCarda; consultant for Advanced Medical Education, Element Science, GV, Inc.; steering committee for Janssen; stock options for AblaCor, Element Science, InfoBionic, LuxCath, NewPace, Celero Systems. LS: consultant for Abbott and executive committee member for Johnson & Johnson Heartline trial. MT: Employee of Tiltas Solutions; no other competing interests to disclose. KGT: Full-time employee of Medtronic. NM: Consultant for AtriCure Biosense Webster, Sanofi, Pfizer; honoraria for speaking: Biotronik, Bristol Myers Squibb, Sanofi; research grants from Biosense Webster, St. Jude Medical, Johnson & Johnson, Medtronic, Boston Scientific. MP: Employee of and may hold stock in Apple. AH, SK, JN, SJ, KW, and CVD: employees of Janssen and may hold stock in Johnson & Johnson. JAS: Principal investigator of grants from NIH, Abbott Vascular, and the American College of Cardiology Foundation; consultant to Janssen, Novartis, Amgen, Bristol Myers Squibb, AstraZeneca, Bayer, Terumo, Merck; Scientific Advisory Board of UnitedHealthcare and Board of Directors for Blue Cross and Blue Shield of Kansas City; Copyright owner of the KCCQ, SAQ, and PAQ., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Perinatal depression is estimated to affect ~ 12% of pregnancies and is linked to numerous negative outcomes. There is currently no model to predict perinatal depression at multiple time-points during and after pregnancy using variables ascertained early into pregnancy., Methods: A prospective cohort design where 858 participants filled in a baseline self-reported survey at week 4-10 of pregnancy (that included social economics, health history, various psychiatric measures), with follow-up until 3 months after delivery. Our primary outcome was an Edinburgh Postnatal Depression Score (EPDS) score of 12 or more (a proxy for perinatal depression) assessed during each trimester and again at two time periods after delivery. Five gradient boosting machines were trained to predict the risk of having EPDS score >  = 12 at each of the five follow-up periods. The predictors consisted of 21 variables from 3 validated psychometric scales. As a sensitivity analysis, we also investigated different predictor sets that contained: i) 17 of the 21 variables predictors by only including two of the psychometric scales and ii) including 143 additional social economics and health history predictors, resulting in 164 predictors., Results: We developed five prognostic models: PND-T1 (trimester 1), PND-T2 (trimester 2), PND-T3 (trimester 3), PND-A1 (after delivery 1) and PND-A2 (delayed onset after delivery) that calculate personalised risks while only requiring that women be asked 21 questions from 3 validated psychometric scales at weeks 4-10 of pregnancy. C-statistics (also known as AUC) ranged between 0.69 (95% CI 0.65-0.73) and 0.77 (95% CI 0.74-0.80). At 50% sensitivity the positive predictive value ranged between 30%-50% across the models, generally identifying groups of patients with double the average risk. Models trained using the 17 predictors and 164 predictors did not improve model performance compared to the models trained using 21 predictors., Conclusions: The five models can predict risk of perinatal depression within each trimester and in two post-natal periods using survey responses as early as week 4 of pregnancy with modest performance. The models need to be externally validated and prospectively tested to ensure generalizability to any pregnant patient., (© 2022. The Author(s).)

Background: Collecting longitudinal data during and shortly after pregnancy is difficult, as pregnant women often avoid studies with repeated surveys. In contrast, pregnant women interact with certain websites at multiple stages throughout pregnancy and the postpartum period. This digital connection presents the opportunity to use a website as a way to recruit and enroll pregnant women into a panel study and collect valuable longitudinal data for research. These data can then be used to learn new scientific insights and improve health care., Objective: The objective of this paper is to describe the approaches applied and lessons learned from designing and conducting an online panel for health care research, specifically perinatal mood disorders. Our panel design and approach aimed to recruit a large sample (N=1200) of pregnant women representative of the US population and to minimize attrition over time., Methods: We designed an online panel to enroll participants from the pregnancy and parenting website BabyCenter. We enrolled women into the panel from weeks 4 to 10 of pregnancy (Panel 1) or from weeks 28 to 33 of pregnancy (Panel 2) and administered repeated psychometric assessments from enrollment through 3 months postpartum. We employed a combination of adaptive digital strategies to recruit, communicate with, and build trust with participants to minimize attrition over time. We were transparent at baseline about expectations, used monetary and information-based incentives, and sent personalized reminders to reduce attrition. The approach was participant-centric and leveraged many aspects of flexibility that digital methods afford., Results: We recruited 1179 pregnant women-our target was 1200-during a 26-day period between August 25 and September 19, 2016. Our strategy to recruit participants using adaptive sampling tactics resulted in a large panel that was similar to the US population of pregnant women. Attrition was on par with existing longitudinal observational studies in pregnant populations, and 79.2% (934/1179) of our panel completed another survey after enrollment. There were 736 out of 1179 (62.4%) women who completed at least one assessment in both the prenatal and postnatal periods, and 709 out of 1179 (60.1%) women who completed the final assessment. To validate the data, we compared participation rates and factors of perinatal mood disorders ascertained from this study with prior research, suggesting reliability of our approach., Conclusions: A suitably designed online panel created in partnership with a digital media source that reaches the target audience is a means to leverage a conveniently sized and viable sample for scientific research. Our key lessons learned are as follows: sampling tactics may need to be adjusted to enroll a representative sample, attrition can be reduced by adapting to participants' needs, and study engagement can be boosted by personalizing interactions with the flexibility afforded by digital technologies., (©Beth McGee, Marie Leonte, Kevin Wildenhaus, Marsha Wilcox, Jenna Reps, Lauren LaCross. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org), 27.04.2021.)

Depressive symptoms during and after pregnancy confer risks for adverse outcomes in both the mother and child. Postpartum depression is traditionally diagnosed after birth of the child. Perinatal depression is a serious, prevalent heterogeneous syndrome that can occur during the period from conception through several months after childbirth. Onset and course are not well understood. There is a paucity of longitudinal studies of the disorder that include the antenatal period in population-based samples. We used an Internet panel of pregnant women recruited in 2 cohorts; 858 ascertained in the first and 322 ascertained in the third trimesters of pregnancy. We recruited the second cohort in order to assure sufficient sample to examine depressive symptoms later in pregnancy and in the postpartum period. Assessments included standard psychometric measures, health history, and pregnancy experience. The Edinburgh Postnatal Depression Scale was used for the assessment of depressive symptoms. Nearly 10% of women entered the pregnancy with depressive symptoms. Prevalence was about the same at 4 weeks and 3 months postpartum. During pregnancy, prevalence increased to 16% in the third trimester. Among incident cases, 80% occurred during pregnancy, with 1/3 occurring in the first trimester. We describe predictors of incident depressive symptoms and covariates associated with time-to-onset which include health history (psychiatric and medical) and social support covariates. The majority of incident depressive symptoms occur during pregnancy rather than afterward. This finding underscores the mandate for mental health screening early in pregnancy and throughout gestation. It will be important to find safe and effective interventions that prevent, mitigate, or delay the onset of depressive symptoms that can be implemented during pregnancy.

Background: The perinatal period is a time of high risk for onset of depressive disorders and is associated with substantial morbidity and mortality, including maternal suicide. Perinatal depression comprises a heterogeneous group of clinical subtypes, and further refinement is needed to improve treatment outcomes. We sought to empirically identify and describe clinically relevant phenotypic subtypes of perinatal depression, and further characterise subtypes by time of symptom onset within pregnancy and three post-partum periods., Methods: Data were assembled from a subset of seven of 19 international sites in the Postpartum Depression: Action Towards Causes and Treatment (PACT) Consortium. In this analysis, the cohort was restricted to women aged 19-40 years with information about onset of depressive symptoms in the perinatal period and complete prospective data for the ten-item Edinburgh postnatal depression scale (EPDS). Principal components and common factor analysis were used to identify symptom dimensions in the EPDS. The National Institute of Mental Health research domain criteria functional constructs of negative valence and arousal were applied to the EPDS dimensions that reflect states of depressed mood, anhedonia, and anxiety. We used k-means clustering to identify subtypes of women sharing symptom patterns. Univariate and bivariate statistics were used to describe the subtypes., Findings: Data for 663 women were included in these analyses. We found evidence for three underlying dimensions measured by the EPDS: depressed mood, anxiety, and anhedonia. On the basis of these dimensions, we identified five distinct subtypes of perinatal depression: severe anxious depression, moderate anxious depression, anxious anhedonia, pure anhedonia, and resolved depression. These subtypes have clear differences in symptom quality and time of onset. Anxiety and anhedonia emerged as prominent symptom dimensions with post-partum onset and were notably severe., Interpretation: Our findings show that there might be different types and severity of perinatal depression with varying time of onset throughout pregnancy and post partum. These findings support the need for tailored treatments that improve outcomes for women with perinatal depression., Funding: Janssen Research & Development., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Public health in the United States can be improved by building workplace "cultures of health" that support healthy lifestyles. The Affordable Care Act (ACA), which includes the Prevention and Public Health Fund, will support a new focus on prevention and wellness, offering opportunities to strengthen the public's health through workplace wellness initiatives. This article describes the opportunity the ACA provides to improve worker wellness.

Purpose: This study evaluated the economic impact of an online disease management program within a broader population health management strategy., Design: A retrospective, quasi-experimental, cohort design evaluated program participants and a matched cohort of nonparticipants on 2003-2007 claims data in a mixed model., Sample: The study was conducted through Highmark Inc, Blue Cross Blue Shield, covering 4.8 million members in five regions of Pennsylvania. Overall, 413 online self-management program participants were compared with a matched cohort of 360 nonparticipants., Measures: The costs and claims data were measured per person per calendar year. Total payments were aggregated from inpatient, outpatient, professional services, and pharmacy payments. The costs of the online program were estimated on a per-participant basis. All dollars were adjusted to 2008 values., Intervention: The online intervention, implemented in 2006, was a commercially available, tailored program for chronic condition self management, nested within the Blues on Call(SM) condition management strategy., Analysis: General linear modeling (with covariate adjustment) was used. Data trends were also explored using second-order polynomial regressions., Results: Health care costs per person per year were $757 less than predicted for participants relative to matched nonparticipants, yielding a return on investment of $9.89 for every dollar spent on the program., Conclusions: This online intervention showed a favorable and cost-effective impact on health care cost.

Background: Research increasingly supports the conclusion that well-designed programs delivered over the Internet can produce significant weight loss compared to randomized controlled conditions. Much less is known about four important issues addressed in this study: (1) which recruitment methods produce higher eHealth participation rates, (2) which patient characteristics are related to enrollment, (3) which characteristics are related to level of user engagement in the program, and (4) which characteristics are related to continued participation in project assessments., Methods: We recruited overweight members of three health maintenance organizations (HMOs) to participate in an entirely Internet-mediated weight loss program developed by HealthMedia, Inc. Two different recruitment methods were used: personal letters from prevention directors in each HMO, and general notices in member newsletters. The personal letters were sent to members diagnosed with diabetes or heart disease and, in one HMO, to a general membership sample in a particular geographic location. Data were collected in the context of a 2x2 randomized controlled trial, with participants assigned to receive or not receive a goal setting intervention and a nutrition education intervention in addition to the basic program., Results: A total of 2311 members enrolled. Bivariate analyses on aggregate data revealed that personalized mailings produced higher enrollment rates than member newsletters and that members with diabetes or heart disease were more likely to enroll than those without these diagnoses. In addition, males, those over age 60, smokers, and those estimated to have higher medical expenses were less likely to enroll (all P < .001). Males and those in the combined intervention were less likely to engage initially, or to continue to be engaged with their Web program, than other participants. In terms of retention, multiple logistic regressions revealed that enrollees under age 60 (P < .001) and those with higher baseline self-efficacy were less likely to participate in the 12-month follow-up (P = .03), but with these exceptions, those participating were very similar to those not participating in the follow-up., Conclusions: A single personalized mailing increases enrollment in Internet-based weight loss. eHealth programs offer great potential for recruiting large numbers of participants, but they may not reach those at highest risk. Patient characteristics related to each of these important factors may be different, and more comprehensive analyses of determinants of enrollment, engagement, and retention in eHealth programs are needed.

The prevention and management of chronic disease necessitates the effective treatment of health risk behaviors. Evaluating methods for maximizing change among individuals with a combination of these behaviors is an area of continued research. The University of Michigan's Health Media Research Laboratory, in collaboration with the Henry Ford Health System (HFHS), is testing a computer-based tailored print material intervention and complementary telecounseling intervention among individuals served by the HFHS. Both interventions are informed by theoretical constructs, particularly those derived from the Transtheoretical Framework and the Health Belief Model. Through a randomized, 2x2 factorial trial, we intend to determine effectiveness of the two interventions in achieving behavior change of three health risk behaviors: cigarette smoking, low vegetable consumption and sedentary behavior. Participants with at least two of these behavioral risk factors will receive four treatments over an 18-week intervention period. Follow-up at 3 and 12 months will assess both short- and long-term behavioral effects of the individual and combined treatments against a group receiving generic print materials. Through this research, we intend to develop a better understanding of how the presence of multiple risk behaviors affects the probability of behavior change and to evaluate the joint action of these behaviors.