Smoking remains a major public health problem, and it is important to provide a variety of efficacious and appealing options to encourage smokers to quit smoking. Scheduled smoking is a method of gradual reduction, preparing smokers to quit by systematically reducing cigarette consumption according to a predetermined schedule that increases the time between cigarette consumption. Gradual reduction may be preferred to abrupt quitting, but the efficacy of this cessation approach is unclear.This study aims, first, to evaluate the overall effectiveness of scheduled smoking alone, or in combination with precessation nicotine replacement therapy (NRT), versus standard NRT starting on the quit date with no prior smoking reduction and, second, to evaluate the impact of schedule compliance on the effectiveness of the intervention.A total of 916 participants recruited from the Houston metropolitan area were randomly assigned to 1 of the following 3 groups: scheduled smoking plus a precessation nicotine patch , scheduled smoking only with no precessation patch , and enhanced usual care  control. The primary abstinence outcomes were carbon monoxide–verified, self-reported, 7-day point prevalence abstinence at 2 and 4 weeks after the quit date. Unadjusted and adjusted logistic regression analyses were performed to evaluate the intervention effect. Scheduled smoking was implemented using a handheld device for 3 weeks before quitting. This trial was not registered because data collection began before July 1, 2005.Results for the first aim showed no overall differences in abstinence among the 3 groups in both the unadjusted and adjusted models. However, the results for the second aim showed a clear effect on abstinence by schedule compliance at 2 and 4 weeks and 6 months after quitting , 4 weeks , and 6 months , with the differences at 2 and 4 weeks after quitting being the most robust. We also found that scheduled smoking was related to a reduction in nicotine withdrawal, negative affect, and craving when compared with the controls.Scheduled smoking, when combined with precessation use of NRT, can result in significantly higher abstinence rates than usual care (abrupt quitting with NRT), particularly in the early postquit phase (2 and 4 weeks after cessation) when smokers are compliant with the procedure. Scheduled smoking also produced a better overall quitting experience by reducing symptoms of nicotine withdrawal and craving, in comparison with usual care, which could encourage future quit attempts. Studies in this area should focus on the use of counseling or other methods to improve adherence. Smoking remains a major public health problem, and it is currently responsible for approximately 6 million premature deaths every year worldwide and is predicted to cause approximately 8 million deaths annually by the year 2030 . TobaccoApproximately 70% of smokers report that they want to quit smoking every year , and altsmoke-free periods) [An assortment of pharmacological and behavioral treatment strategies has been shown to increase cessation rates in adult smokers -12. Tradperiods) , using nperiods) ,16, settperiods) , or reduperiods) .Scheduled smoking is a gradual reduction method of preparing to quit smoking in which smokers reduce their smoking level by systematically increasing the time between cigarette consumption using a predetermined schedule -22. ThisNRT was formulated to partially replace the nicotine that would have been consumed in a cigarette without the additional harmful components of combustible tobacco , therebyThe public health impact of smoking cessation treatments  depends on the uptake of treatments in the smoking population as well as their effectiveness in real-world use . PreviouThis study aimed to (1) evaluate the overall effectiveness of scheduled smoking alone, or in combination with precessation NRT, versus standard NRT starting on the quit date with no prior smoking reduction and (2) assess and evaluate the impact of schedule compliance on the effectiveness of the intervention.Participants were recruited from the Houston metropolitan area using newspaper and billboard advertisements. A total of 1773 smokers were screened via telephone for study eligibility. Only healthy smokers who were interested in quitting smoking, had no uncontrolled medical illness, were fluent in English, and smoked at least 10 cigarettes per day were included in the study. Individuals were excluded if they were taking psychotropic medications, met the criteria for a current psychiatric disorder, or were involved in current smoking cessation activities. Of the 1773 smokers, 1035 (58.38%) met all the criteria and were invited to enroll in the study. However, of the 1035 smokers, 119 (11.5%) did not attend the baseline visit, resulting in 916 (88.5%) participants being randomized and issued a study device. Of the 916 randomized participants, 96 (10.5%) returned the device unused and thus were not exposed to treatment and were therefore excluded from the outcome analyses.Participants could earn up to US $115 in gift certificates by completing study procedures. All participants received a US $50 gift certificate for the completion of visits through the end-of-treatment visit and a US $15 gift certificate for completion of the 2 follow-up visits. Participants also received a total of US $5 to US $50 in gift certificates based on completion of random mood and smoking assessments generated by their computer while in the study.The protocol was approved by the institutional review board of The University of Texas MD Anderson Cancer Center. This trial was not registered because the data collection began before July 1, 2005. Data collection for this study was conducted from 2000 to 2006, before the advent of smartphones. The data analysis for this study began in 2018. The data from this study and the customized program provided the basis for a new scheduled smoking smartphone app designed to run on the current Android platform. This program is currently being tested and will be the subject of a future study. A clinical version (nonresearch) will be made available for download following testing.Participants were randomly assigned to 1 of the following 3 groups: scheduled smoking plus a precessation nicotine patch (SSNP), scheduled smoking only with no precessation patch (SS), and enhanced usual care (EUC) control. The groups were stratified by sex, race, cigarettes smoked per day, and depression history. The study was divided into 4 phases: baseline (2 days), precessation date intervention , postquit date intervention phase 1 , and postquit date intervention phase 2 (8 weeks). During the baseline period, all groups were instructed to smoke ad libitum and use a handheld device (HD) to record their smoking, and to receive other programming, as described in the section below. During the precessation period, both scheduled smoking groups (SSNP or SS) were exposed to a 21-day computerized intervention delivered on the HD, designed to progressively reduce daily cigarette consumption before their quit date. The SSNP group also began using a nicotine patch simultaneously with the reduction schedule. The EUC group did not receive a precessation patch or cigarette reduction intervention. During the postquit date intervention (phase 1), which included the quit date, all groups used the nicotine patch and HD to continue recording smoking behavior and receive other programming, as noted in the Scheduled Smoking section below. During the postquit date intervention (phase 2), all groups stopped using the HD but continued using the nicotine patch for an additional 8 weeks.During the 3-week precessation period, smokers in both the SSNP and SS groups were instructed to smoke at scheduled times only. The computer-assisted scheduled smoking intervention application, active on the HD for the SSNP and SS groups only, prompted the smoker to smoke only at specific times during the day. Over a period of 3 weeks, the program gradually increased the interval between prompted cigarettes  such that only 3 to 4 cigarettes were scheduled to be consumed the day before the quit date. The reduction schedule was implemented using the following algorithm: 1) using the daily smoking mean and wake time derived from the participants’ self-report at baseline, the program calculated an intercigarette interval by dividing the average number of minutes awake per day by the reduced percentage of the smoker’s average daily smoking frequency. (2) For days 1 to 12, the baseline smoking rate was reduced by 15% every 3 days, and for days 13 to 21, a 10% reduction every 3 days was used . (3) The process outlined in step 2 was used to determine the maximum allowable cigarettes for that period; however, a 15-minute delay to the first cigarette of the day was added to the schedule each week, and because of this delay, any cigarette consumption that would have occurred past the participants’ bedtime were not added to the schedule. Moreover, once the smoking frequency was reduced to 3 to 4 cigarettes per day, no further reductions took place until the quit date, when zero cigarettes were expected. (4) All smoking times were adjusted to fall on the nearest quarter within the hour. A sample schedule over the 3-week scheduled smoking period is provided in Figure S1 in  using thParticipants in the SSNP and SS groups attended 9 in-person visits as follows: baseline, 3 weeks precessation, 2 weeks precessation, 1 week precessation, 2 days after quitting, 2 weeks after quitting, 4 weeks after quitting, 6 months after quitting, and 12 months after quitting.Participants in the EUC group had no precessation scheduled smoking or reduction intervention and hence attended only 6 in-person visits .At the baseline visit (approximately 45 minutes), participants were instructed on the use of the HD, which included watching a short video explaining the group-specific and general features of the program, and were assessed on their understanding of device use. All other visits (approximately 20 minutes) were used to obtain questionnaire information, assess abstinence, and download data from the HD. No smoking cessation counseling was provided during these visits.During the 21-day precessation date intervention period , an HD that was a precursor to today’s smartphones , 3 weeks precessation, and 2 weeks after cessation . The EUC group used the device for 2 days precessation and 2 weeks after cessation.The HD shared common functionality across groups, including an embedded electronic cessation tip guide; programmed assessments of smoking behavior, mood, and other conditions delivered at both fixed (daily diary) and random times ; and user-initiated recording of actual smoking, temptations (urges) to smoke, and quit attempts. The wake-up time and bedtime were recorded/confirmed daily  . The PRIParticipants completed a questionnaire to assess the demographics; medical and health history of respondents and their families; current medications; alcohol intake; and smoking history, including years smoked, previous quit attempts, relapse, current smoking rate, and other nicotine or tobacco use. This questionnaire has been used in a number of our studies on smoking cessation and relapse ,61.The Fagerström Test for Cigarette Dependence (formerly the Fagerström Test for Nicotine Dependence) ,63 is a The subsequent questionnaires were administered at baseline and at each subsequent in-person visit.The Wisconsin Smoking Withdrawal Scale is a 28-item nicotine withdrawal scale that includes the following 7 subscales: anger, anxiety, concentration, craving, hunger, sadness, and sleep .The Positive and Negative Affect Scale comprises two 10-item mood scales: positive affect and negative affect. Various time instructions  have been used with acceptably high α reliability ranging from .86 to .90 for positive affect and .84 to .87 for negative affect .The Center for Epidemiologic Studies Depression Scale is a 20-item self-report measure developed to assess depressive symptoms in community  populations  and in rThe Self-Efficacy Scale is a 14-item scale rating confidence (0-10) at resisting smoking urges within several types of situations ,69. The Group differences in the 7-day point prevalence abstinence were assessed at 2 weeks, 4 weeks, 6 months, and 12 months after the quit date. Point prevalence abstinence was defined as a self-report of no smoking during the previous 7 days and an expired carbon monoxide (CO) level of <10 ppm (the standard at the time the study was conducted). Abstinence values were computed in two ways: (1) using an intention-to-treat (ITT) approach in which missing smoking status information at any assessment was imputed as smoking and (2) using a respondent-only (RO) approach in which no imputation for missing information was conducted and abstinence was based on observed data only.Compliance with the scheduled smoking intervention was assessed in 2 ways. The first method involved a simple calculation of the proportion of scheduled cigarettes smoked on schedule (within 5 minutes of the scheduled time) and the number of cigarettes smoked off schedule. Information for these calculations was obtained directly from the HD. The second method involved assessing the actual level of smoking reduction achieved by examining changes in expired CO between baseline and their last precessation visit (before the to-quit-day). Specifically, a percent reduction score was calculated using the following equation: (expired CO level measured at baseline − expired CO level measured at last precessation visit)/(expired CO level measured at baseline × 100). We prefer the second approach over the one that assesses compliance only via computerized schedule self-report adherence, which may be vulnerable to experimental demand characteristics. That is, participants who desire to present themselves in a better light may make false reports about adhering to the computerized schedule—recording a scheduled cigarette that has not been smoked or failing to record an off-schedule cigarette. In addition, this method accounts for the influence of unscheduled cigarettes on the expired CO.Using expired CO as a measure of compliance has other advantages over characterizing compliance in terms of the number of cigarettes smoked on schedule. For instance, under the scheduled smoking “rules,” smokers may “miss” a scheduled cigarette as long as they refrain from smoking until the next scheduled cigarette. Therefore, from the point of view of schedule compliance, “missing” a scheduled cigarette is permissible, while smoking an “off-schedule” cigarette to compensate for the loss is not. On the basis of our scheduling algorithm, participants who adhered to their smoking schedule were projected to have reduced their cigarette intake by 75% during the final week of smoking reduction. However, smokers vary in their smoking topography , and it is difficult, if not impossible, to determine a reduction in expired CO level that will accurately reflect a 75% reduction in cigarette intake. Therefore, after considering the variation of the time to the last cigarette smoked by participants before they attended their last precessation visit , we deemed it reasonable to expect smokers who complied with the reduction schedule to have reduced their expired CO level by ≥50%. Other studies evaluating the use of precessation patches and denicotinized cigarettes for smoking cessation have adopted a similar approach of using the level of baseline CO reduction as a measure of treatment exposure and have noted a considerable treatment effect among those achieving >50% (55.6%) level of reduction .Using this cutoff (≥50% CO reduction from baseline), we created subsets of participants within the SSNP and SS groups who were categorized as compliant (SSSP compliant and SS compliant) or noncompliant (SSNP noncompliant and SS noncompliant). These subsets were compared with the controls (EUC) in the secondary analyses described below in the Compliance Analysis section.P values calculated based on the number of comparisons within each analysis. For both the unadjusted and adjusted logistic regressions, our primary comparisons were between the 2 designated treatment groups (SSNP and SS) and the control group (EUC). Secondary analyses were also conducted using the 4 group subsets based on CO reduction–derived compliance  versus control (EUC). In both the primary and secondary analyses, abstinence values were reported using both ITT and RO approaches to missing data. The statistical program Stata  was used for all analyses.The primary abstinence outcomes were CO-verified, self-reported, 7-day point prevalence abstinence at 2 and 4 weeks after the quit date. In addition, we examined self-reported 7-day point prevalence abstinence recorded at 6-month and 12-month follow-up visits. Logistic regression analysis was used to regress abstinence outcome in the treatment groups, which were initially unadjusted for any covariates. Additional multiple logistic regression analyses were conducted to adjust for the covariates of age, gender, race and ethnicity, and education. We report Bonferroni-adjusted Although our secondary analyses took scheduled smoking compliance into account when assembling the subgroups, we acknowledge that this type of analysis can be biased because noncompliance with the scheduled smoking cannot be assumed to be random with respect to abstinence, and therefore, confounding of abstinence by compliance may occur . To accoThe IV analytical approach involved a 2-stage estimation of the treatment effects. In the first stage, we estimated the decomposition between the 2 error terms, accounting for unobserved confounding in the relationship between compliance and abstinence . We estiWritten informed consent was obtained from each participant at screening. All participants received a US $50 gift certificate for completion of visits through the end-of-treatment visit and a US $15 gift certificate for the completion of the follow-up visits. Participants also received a total of US $5 to US $50 gift certificates based on completion of ecological momentary assessments. This research was approved by MD Anderson’s institutional review board (PA15-0675).As shown in The baseline demographic characteristics for the 3 main treatment groups  and the 4 compliance groups  are presented in F1,487=0.61; P=.43) or the number of off-scheduled cigarettes smoked  during the 3 precessation week (reduction) period.Collectively, participants in the SSNP and SS groups did not differ from each other in the percentage of scheduled cigarettes smoked  during the precessation phase. Specifically, 50.9% (139/273) of all participants assigned to the SSNP group achieved ≥50% reduction in expired CO level between their baseline and last precessation visit, whereas only 26.5% (71/268) of those assigned to the SS group reduced their expired CO level by ≥50%. Smokers in the SSNP group who achieved ≥50% CO reduction were more likely than those who failed to reduce their CO by 50% to have smoked their scheduled cigarettes on time . However, the 2 groups did not differ in the number of cigarettes smoked off schedule (unscheduled cigarettes) during the 3-week precessation period . Similarly, smokers in the SS group who reduced their CO by ≥50% were more likely than those in the SS group who failed to reduce their CO by 50% to smoke on schedule . The 2 groups did not differ in the number of unscheduled cigarettes smoked during the 3-week precessation period .Significantly more participants from the SSNP group than those in the SS group achieved ≥50% reduction in expired CO  and 4 weeks  after the target quit date. Across the 4 compliance groups, use of nicotine patch was also similar with no significant differences for the 2 weeks postquit  as well as for the 4 weeks postquit time points .Participants assigned to the SSNP group reported a high rate of compliance to precessation use of the nicotine patch, applying an average of 97% (SD 10.7%) of all patches as instructed before their target quit date. Prequit nicotine patch compliance for SSNP compliant was 97% and for SSNP noncompliant was 95%. Postquit nicotine patch use was also assessed, and we found no significant difference in compliance across all groups at 2 weeks  were essentially the same, that is, scheduled smoking was completed, and all were now equally exposed to a standard regimen of NRT.Our primary abstinence outcome  was examined at 2 weeks, 4 weeks (primary), 6 months, and 12 months after the target quit date. Overall abstinence rates for the 3 main treatment groups  and 4 compliance groups are presented in Although no main effects of SSNP versus EUC group were observed in relation to the smoking cessation outcome, this could partially be owing to varying degrees of compliance with the scheduled smoking intervention and its downstream effects on the actual reduction in smoke exposure. To account for the effect of smoking reduction compliance on treatment efficacy, as noted above in the section Compliance Analysis, we used a ≥50% reduction in expired CO as a cutoff criterion to subdivide each of the 2 treatment groups into either compliant (SSNP compliant and SS compliant) or noncompliant groups (SSNP noncompliant and SS noncompliant).P values for the 4-week and 6-month comparisons were attenuated following the Bonferroni adjustment for 10 comparisons. No significant difference in abstinence was found between the SSNP-compliant and EUC groups 12 months after cessation. The adjusted and RO results reflected the same findings, with the P value of 0.04 for the 2-week SSNP compliant versus EUC comparison remaining significant after correction. Both the SSNP-noncompliant and SS-noncompliant groups were significantly less likely to abstain than EUC at 2 weeks, 4 weeks, and 12 months postcessation in the adjusted and unadjusted ITT models but not in the RO models.As shown in SSNP-compliant participants were also more likely than SS-compliant participants to report 7-day abstinence at 2 weeks after the quit date  in the ITT unadjusted comparison, with similar findings for the adjusted ITT and RO analyses, although these later comparisons did not survive the Bonferroni-adjusted correction. Although SSNP-compliant smokers continued to outperform SS-compliant smokers in the 7-day abstinence at 4 weeks, 6 months, and 12 months after cessation, the differences were not statistically significant. No other significant differences in abstinence outcomes were found between SSNP-compliant and SS-compliant participants at either 6 or 12 months after cessation.We complemented the ITT and as-treated analyses with the LATE method using the IV analysis. In the LATE analyses , we examWe modeled withdrawal and affect outcomes using the flexible mixed effects model framework to account for repeated measures, including the 2- and 4-week and 6- and 12-month time points, using the “xtmixed” command in Stata . The timThis innovative clinical trial evaluated the effects of a mobile smoking cessation intervention designed to progressively reduce smoking before quitting using SSNP, in comparison with SS and an EUC control group. EUC smokers used the nicotine patch on the quit date and carried the same mobile device to record cigarettes and manage their quit attempt, with no scheduled reduction. No counseling was provided in this study beyond the explanation of the use and rationale of the procedures and an electronic tip guide available on the mobile device. The results showed no overall differences in abstinence in the 3 groups over multiple time points  in both unadjusted and adjusted (for covariates) analyses using both ITT (missing smoking status imputed as smoking) and RO (no imputation) approaches to the treatment of missing data. However, when taking schedule compliance into account, a different pattern of results emerged.We evaluated schedule compliance in 2 types of analyses, and the results in both were consistent, showing that smokers who were compliant with the scheduled smoking procedure and used the nicotine patch before the quit date were more likely to be abstinent at several time points, with the differences at 2 weeks and 4 week after quitting (primary outcome of the study) being the most robust.Specifically, when we subdivided the 2 scheduled smoking groups into those who complied with the scheduled reduction, as defined by a 50% drop in expired CO between baseline and final week of scheduled smoking, and those who did not, we found that the SSNP compliers abstained significantly more often at the 2-week, 4-week, and 6-month postquit dates when compared with the EUC group. Although the differences at 4 weeks and 6 months were not significant following the correction for multiple comparisons, they continued to trend in the correct direction, with ORs >1.5, which have generally been considered as a clinically meaningful difference between treatments.causal effect of the smoking cessation intervention among compliers. In comparison with the EUC group, those who complied with the intervention in the SSNP group averaged a 26%, 29%, and 22% increase in abstinence at the 2-week, 4-week, and 6-month postquit time points, respectively, although the 6-month outcome was not statistically different. Thus, this LATE analysis offers support for the value of the SSNP intervention when compliance is considered. The LATE analysis complements the standard effectiveness analyses because the LATE analysis evaluates the effect of actually receiving the intervention. We found no differences across the 3 groups in postquit compliance with patch use. However, precessation patch use was related to schedule compliance, as noted by the fact that >50% of the smokers in the SSNP group achieved a >50% reduction in CO (a criterion of compliance) compared with 26% in the SS group. However, both the direction and the magnitude of the effects of SSNP schedule compliance in both compliance analyses gives us confidence that compliance with the scheduled smoking procedure is a key factor for the intervention to work. This conclusion is further supported by the absence of precessation differences in compliance with the nicotine patch between the 2 SSNP compliance groups (SSNP compliant: 97% and SSNP noncompliant: 95%). Thus, although use of the nicotine patch before cessation may make it easier for smokers to comply with the scheduled reduction procedure, compliance with precessation patch use alone is insufficient to improve smoking cessation outcomes over usual care. Moreover, we noted that the SSNP intervention had other beneficial effects on the quitting experience, such as reducing withdrawal, negative affect, and craving, which could reduce the probability of relapse, and may encourage future quit attempts should that occur, particularly because we know that it may take multiple quit attempts before a smoker eventually succeeds [We also took an IV approach to evaluate compliance, using LATE estimates of the direct succeeds . SubsequAs noted earlier, previous studies on gradual reduction methods have been largely inconclusive when compared with abrupt quitting ; however, the efficacy of precessation nicotine patch use has received modest support. Our results suggest that the combined use of nicotine patch and scheduled smoking  can be effective when compliance with the scheduled smoking procedure is observed. This is consistent with a study by Rose et al  that shoAlthough the overall comparisons showed no differences across the groups, there are limitations in the study design that should be noted. First, the main component of this intervention was implemented before the quit date and involved simultaneous use of both the nicotine patch and the scheduled smoking procedure. However, the postquit intervention was essentially the same for all groups and consisted of continued use of the HD for an additional 2 weeks plus use of the nicotine patch. Thus, the expectation that a 3-week prequit intervention might have long-lasting effects on postquitting behavior would require a very large precessation treatment effect, when averaging across all participants within a group. Our compliance analyses suggest that the effects of the combined scheduled smoking and nicotine patch can persist into the postquitting environment, with clinically meaningful effects up to 6 months after quitting among smokers who adhere to the procedure. This is analogous to the notion that a drug cannot work if it is not taken, and a key factor is to determine why it is not used and to intervene if possible. This intervention by design did not involve smoking cessation counseling, which we know is essential to produce improved outcomes when combined with pharmacotherapy . AlthougAnother limitation was the provision of HD in the control group. This was done to achieve internal validity, that is, all smokers carried a device that delivered the scheduled smoking intervention to both scheduled groups in the precessation period and functioned essentially the same for all smokers in the postquit environment. Thus, the control group had access to the relapse management routines, tip guides, and self-assessments, all of which might have had an impact on postquit date smoking behavior and raising cessation rates beyond what might be expected through usual care . None of these features would normally be available under true usual care control groups; hence, this group was designated as “Enhanced Usual Care.”This study evaluated the combination of a low-touch behavioral treatment (scheduled smoking) delivered on a HD with and without NRT against a usual care NRT control condition. It could be argued that because our findings and program used to produce the data were >20 years old, they may have limited applicability to the current treatment standards, technology, and the current population of smokers. However, we believe that our findings are highly relevant to the current smoking cessation treatment for several reasons. First, the basic standard of care for smoking cessation till date is NRT, typically using the nicotine patch . It is bScheduled smoking, when combined with precessation use of NRT, can result in significantly higher abstinence rates than usual care (abrupt cessation with NRT), particularly in the early postquit phase (2 and 4 weeks after cessation) when smokers are compliant with the procedure. There was also evidence for a clinically meaningful effect (OR>1.5) at 6 months after quitting, although statistical differences were reduced with adjustments for multiple comparisons. Scheduled smoking also produces a better overall quitting experience by reducing symptoms of nicotine withdrawal and craving in comparison with usual care, which could encourage more future quit attempts. Future studies in this area should focus on the use of counseling or other methods to improve adherence.


Investigate diagnostic stability, daily life functioning and social situation in womendiagnosed with ADHD and/or ASD in childhood.M = 27, 4 years old) with semi-structuredinterviews and questionnaires, close relatives were interviewed.Prospective 17 to 20-year follow-up study of 100 girls of whom 92 diagnosed inchildhood with main DSM-IV ADHD or ASD, and 60 comparison girls. Ninety and 54 of thesewomen were examined (At follow-up, 89% of women with ADHD or ASD in childhood still met the criteria foreither of these diagnoses. Very few women were “in remission.” In 34% the main diagnosisshifted from ADHD to ASD. Women with ADHD and ASD had significantly more disability andunfavorable social situation than comparison women.Women with ADHD and/or ASD in childhood had impairing problems 17 to 20 years later.Early ADHD changed to ASD in adulthood in some cases. Nearly all with ASD met criteriafor ADHD as adults. Diagnostic and Statistical Manual ofMental Disorders  and autism spectrum disorder (ASD) areheterogeneous neurodevelopmental disorders (NDDs) with onset early in life. ADHD is defined byimpairing symptoms of inattention (ADHD-I), impulsivity/hyperactivity (ADHD-H), or acombination of both (ADHD-C) according to the The main symptoms of both ADHD and ASD lead to substantial impairments in many importantdomains of functioning.Until recently, most ADHD and ASD research has focused on boys and men . As a coDuring the past decade, several studies focusing on females have been carried outparticularly with regard to ADHD . A serieStudies on the stability and developmental trajectories of ASD diagnoses in females with ASDare few .HoweverWhile in the past considered to be easily separable disorders, it is now clear that ASD andADHD are often comorbid with each other, share many genetic factors and show similarfunctional and structural brain characteristics and cognitive profiles .Together with other NDDs  and Tic Disorders (TD)) ASD and ADHD are sometimesgrouped under the acronym of ESSENCE  .The prevalence of ASD with ADHD for children in clinical studies have been estimated at 53%to 78%  and in oADHD affects about 5% to 7% of the general population of children  with 1.6Studies focusing on the ADHD symptoms and its effect on women’s psychosocial lives,compared to women without ADHD are few. However adverse outcomes have been reported foryoung women with ADHD in academic, occupational and social areas . TeenageASD has a population prevalence of about 1% to 1.5%  with a mStudies focusing on the ASD symptoms and its effect on women’s psychosocial lives, comparedto women without ASD is rare. However two reports have shown greater risk for negativesexual experiences compared to women without ASD .There arSimilarities between causes of missed or delayed diagnoses in ADHD and in ASD have beenfound . SeveralThe aim was to study diagnostic stability in ADHD and ASD according to DSM-IV establishedin childhood/adolescence into early adulthood in a clinical group of women. Another aim wasto compare the main diagnoses examined according to the DSM-IV and DSM-5. Further, to studyoverall function level and psychosocial situation compared to a group of women without thesediagnoses.The present follow-up study occurred at Time two (T2) after the original diagnostic studyat Time one (T1). It was carried out, as part of a larger prospective longitudinal follow-upstudy of 100 Swedish girls with ESSENCE, the vast majority was diagnosed with ADHD and/orASD in childhood/adolescence, 17 to 20 years after diagnosis . IncludeM = 13.0,SD3.4) than those with ASD  (p < .001).Between 1999 and 2001, 100 girls aged between 3 and 18 years were assessed for socialimpairment and/or attention problems at the Child Neuropsychiatry Clinic, QueenSilvia’s Children and Youth Hospital, Gothenburg, Sweden. The investigation wasmulti-professional and was performed by experienced clinicians. Of the 100 girlsparticipating at T1, 46 received a main diagnosis of ADHD, and 46 a main diagnosis ofASD  of whom the vast majority also had “comorbid” diagnosis ofADHD. Of the remaining eight, three were diagnosed with TD and five with other maindiagnoses (OMD). Although the inclusion criterion of the study was the absence of ID,12 girls (10 with ASD) were found to meet the criteria for ID despite havingoriginally been screened ID negative (see reference ). All din =60) were selected and matched for age (±2 months) with 60 randomly selectedschoolgirls from the register of a pediatric outpatient clinic in a community in theGöteborg region. Exclusion criteria for the community girls included serious medicalillness, ID or having a sibling or parent with a known NDD. Three of 60 communitygirls were excluded after full assessment at T1. The final community group composedtherefore of 57 girls. Among the 60 matched clinic girls ADHD was received in 34 andof them 32% also had autistic trait. Twenty girls received an ASD diagnosis of the 60matched clinic girls.All clinic girls 7 to 16 years of age with a tested FSIQ ≥ 80 when they were ≤18 years of age were invited to participate in a follow-up assessment(T2). Ten of them declined participation. Four further women declined a personalinterview but granted permission for a proxy interview with a close relative. Fivewere not able to participate on their own due to communicative disabilities  but were seen and gave permission themselves or through their legalguardian, for an interview with a close relative . Three womenwanted to participate themselves but did not agree to the participation of a relative.We thus received information from both the woman and her relatives in 78 cases, fromthe woman only in three and from a relative only, in nine cases.MClinG and Non-MClinG, 54 and 36, respectively . All clinic women with ID wereincluded in the Non- MClinG.All the 90 women in FClinG were included at T2 and were distributed betweenvely see . In the In the MComG 54 of the 57 women participated at follow-up, three were not reachableor declined participation. Two of the women agreed to participate but declinedparticipation of a relative. Altogether, information was obtained from both woman andher relative in 52 cases and from the woman only, in two cases. These 54 women wereincluded in the MComG . After fThe Central Ethical Review board of the University of Gothenburg has approved the study. All the 144 participating women or their legal guardians,provided written or orally informed consent to participate. Special arrangements wererequired to ensure that potential participants were fully informed. After the formalinvitation was sent, a second simplified version of the letter, was sent as a supplement.Oral information was also provided to ensure that all participants understood what theinterview/assessment would entail.Appendix1). Most of the clinic women were interviewed during one single session,lasting 4 to 8 hr, but some required two or more sessions. The women in the FClinG wereall assessed by one of three physicians  clinically experienced in the assessment and treatment of NDD. All interviews wereconducted in Swedish and all instruments used were translated and previously used inseveral Swedish studies.All the 144 women were examined with the same instruments according to their ability toparticipate. The interview included 16 instruments for the women and eight instruments forthe first degree relative , andfour in restricted/repetitive behavior and interest domain (RRBI). In DSM-5 this ismodified into three items in the SOC and four in the RRBI domain. To ensure that allcriteria were examined, in addition to what emerged during the interview with thewoman and her relative, data from the instruments below were used.Data were collected using a semi-structured interview schedule including 114questions, in all women who were able to participate by their own. The topics coveredin this interview 11 domains. In the actual study we used data from following topics:housing, relations, studies, economy and work/daily activity. We also asked aboutviolence, physically, mentally and/or sexually  and ifso, by whom. This type of interview has earlier been used in follow-up studies at ourcenter. However our research team added some “female-oriented” questions specificallydeveloped for this study.In the examination process of ADHD, results obtained on the Wender-Reimherr AdultAttention Deficit Disorder Scale (WRAADS) a self-report questionnaire completed by thewoman, and the Wender Riktad ADHD Symtom Skala (WRASS)-scale, a Swedish version of theTargeted Attention Deficit Disorder Rating Scale (TADDS) completed by her relativewere used. The WRAADS is based on the Utah Criteria  for ADHDThe Autism Symptoms Self-Report (ASSERT) were used in the diagnostic process ofautism, completed by both the woman and her relative . The oriThe Asperger Syndrome Diagnostic Interview (ASDI)—parent version completed byrelatives was included in the assessment . ASDI isFunctional level was assessed by the first degree relative with the Adaptive BehaviorAssessment System, second edition (ABAS II) . ABAS IIThe woman’s Global Assessment of Functioning (GAF) score  was estiProblems related to perception/sensory reactions were investigated from the RRBIDSM-5 criteria of ASD and question eight in the ASSERT form; “Do you feel thatyou/your relative are too sensitive or not at all sensitive to for example, sound,touch, different materials, pain, temperature or smell?.” We reviewed the reportsabout sensory reactions at T1 in the medical records and in the Autism BehaviorChecklist (ABC), perception domain in this follow-up study .The DSM-IV criteria were applied to establish diagnoses at both T1 and T2, forexamining the diagnostic stability. DSM-5 was used for comparison at T2 between the maindiagnoses according to variability in criteria across the two manuals. Diagnosesaccording to DSM-IV and DSM-5 were assigned when both symptoms and impairment criteriawere met. Exclusionary criteria were disregarded, as in T1, using the DSM-IV. “Maindiagnosis” was used to define the problems considered most handicapping at the time ofassessment. If the woman was on medication with stimulants at T2, the diagnosis of ADHDwas considered met if clinical interview did not contradict this. ADHD Not OtherwiseSpecified (ADHD NOS) was defined if five symptom criteria according to DSM-IV or four toDSM-5 were met in one of the three ADHD subtypes together with impairment.Only full ADHD syndrome is included in the ADHD analyses. PDD NOS was diagnosed if fourto five criteria for AD were met, including at least one criterion from the socialinteraction domain. Autistic traits was “diagnosed” if two to three criteria werefullfilled for AD. Remission for the main diagnoses assigned at T1 was defined if fullsymptom criteria were not met at T2 and with a calculated functional GAF score>70.All collected information from the FClinG was evaluated conjointly by the two authors, and from the MComG by KA and SL. Diagnoses of four ambiguous cases werediscussed in detail with one of the senior authors (CG). The interviewers were not blindto baseline ascertainment group.However the majority of young women in the MClinG were assessed by the two interviewer,who did not have met any of the young women earlier and were mostly blind for their maindiagnoses.T-testswere used for comparisons between groups. Chi-square test were used for groupcomparisons of proportions. All analyses were two- tailed and a p-valueof less than .05 was considered statistically significant.Statistical analyses were calculated with IBM SPSS version 23 software. Continuousvariables are presented with means and standard deviations, and Results across the two main diagnostic categories, ADHD and ASD , diagnosSD4.4). There was a significant difference (p = .001)in mean age between women diagnosed with ADHD and ASD as main diagnoses at T1, 29.5(SD3.4) and 25.5 (SD4.5). No important difference wasfound in the distribution of the main diagnoses ADHD and ASD at T1 between the 90 clinicwomen at T2 and those 10 who were lost to follow up (p = .726).At T2 the women in the FClinG were between 19 and 37 years of age, mean age of 27.5 women diagnosed at T1 with ADHD or ASD still met thediagnostic criteria for either of these main diagnoses and 19/41 (46%) of women withADHD remained in the ADHD group, and four (10%) fulfilled fewer criteria (ADHD-NOS).Four of the 41 women (10%) were in remission . The comp =.061) and 23% met criteria for ADHD with autistic traits. Twenty two percent from theoriginal ADHD group met an ADHD diagnosis only at T2. All together 54% of the womenreceiving an ADHD at T2 (10 from ADHD group and two from ASD group) also had autistictraits. The combined subtype was diagnosed in 73% with a main diagnosis of ADHD atfollow-up. Only three women met ADHD-I.Fourteen women (34%) with a previously main ADHD diagnosis at T1 changed to ASD at T2.Seven of them had also autistic traits at T1 . In the ADHD groupwithout autistic traits at T1 23% met the criteria for ASD at T2 (At T2 eight more women met a main diagnosis of ADHD according to DSM-5.At follow-up 88% of women with a main diagnosis of ASD in childhood in the FClinGretained in the ASD group. The vast majority 35/37 (95%) of them fullfilled AD . AD persAt T2, seven fewer women were diagnosed with ASD in accordance to DSM-5 than to DSM-VIand met instead the full criteria for ADHD with autistic traits. The diagnosticdifference was mainly due to more women (67 and 56 women respectively) with the requiredcriteria in RRBI met in DSM-IV than in DSM-5 (one criteria respectively 2).Three of seven women retained their diagnoses at T2; one women with TD, one with MildIntellectual Disability, one with LD NOS. One woman with ADHD NOS changed to ADHD-H atT2. One with Sleep Disorder at T1 was later diagnosed with Restless Legs Syndrome butwas now in remission. One woman with Intermittent Explosive Disorder at T1, wasdiagnosed with ASD and co- occurred ADHD- C at T2.ADHD persisted in 14/31 (45%) of women in the MClinG and ASD in 15/18 (83%). The samepattern was seen in the FClinG for these main diagnoses. The diagnostic distributionbetween the main ADHD and ASD differed strongly from the original assessment at T1 . The sixAt follow-up, two women in the MComG met DSM-IV criteria for ASD, three met criteria forADHD. These five women were excluded from the MComG in the follow-up analyses, leaving 49individuals.p < .001). The same pattern was seen in the MClinG betweenthe two main diagnostic groups (ADHD 25% vs. ASD 96%). All women with ADHD and perceptualproblems at T2 also had autistic traits. In the entire ASD group at T1 93% reportedsensory reactions leading to a high consistency for sensory problems from T1 to T2 (81%).Fifteen women with main ADHD, reported no sensory problems. In the 14 cases with a changefrom ADHD at T1 to ASD at T2, 78% already had reported sensory problems at the firstassessment time. Very few women in the matched community group reported perceptualproblems compared to two of three in the collapsed ADHD + ASD group (p =.001).Based on diagnosis in the FClinG of 90 women at T2 sensory reactions were reportedsignificantly less often in the women with a main diagnosis of ADHD compared to ASD and with ASD (n = 42) diagnosed at T1 was measured to study the outcomeat T2. The ADHD and the ASD group did not differ significantly in FSIQ (p= .270) measured at T1 . However the functional ability waslower in the ASD group (nine women with ID) compared to the ADHD group on ABAS-II, with asignificant difference for practical index (p = .002). Nearly all womenwith ASD also had “comorbid” ADHD.The level of functioning in daily life in all women with ADHD p = .08) with lowerscores for the ASD group. Strongly significant differences were found on the results fromABAS-II between the collapsed ASD and ADHD group and the MComG group with neitherdiagnosis.As described in M = 49,SD11) compared to the ADHD group  (p = .001). The collapsed group of ADHD + ASDdiffered significantly in comparison to the MComG in the GAF scores. The six women inremission scored highest on the GAF (M = 78).The global functioning (GAF) score differed between the two diagnostic groups withsignificantly lower GAF scores in the MClinG with ASD (p = .006). Allbut one of these clinic women were diagnosed with ASD. Living with a partner were reported in 77% of women in the MComG and, in 41% of the MClinG with NDD(p = .002). The majority of women who never had a relationshiplasting more than 3 months is found in the ASD group.As described in p = .083) found in comparison betweenthe ADHD group and the ASD group in children that they had, most children lived withmothers in the ADHD group. One of two women with ADHD had performed 1 to 2 abortions andonly women with ADHD had went through more than one abortion (p =.038).There was a statistical trend (p = .002) compared to 14% in the MComG.In the MClinG 50% respectively 40% stated that they felt socially isolated, asignificant difference  with ADHD and ASD reported that theyhad received some type of support during their schooling.p = .002) (There were major significant differences in how to provide for oneself between theMClinG and the MComG ( = .002) . Fiftyfop = .004) as well as physical andmental abuse (p < .001). The mental and physical abuse in the ADHDand ASD groups were mainly carried out by family members or peers and women with ADHDreported the highest degree of exposure.A high rate of being exposed to sexual abuse were self-reported in all study groups.However rape was significantly more frequently reported in the collapsed MClinG withmain ADHD and ASD compared to MComG . Our findingscorrespond to a meta-analysis, which indicated that AD was stable in 76% over time and PDDNOS was less stable (35%), however all these studies included young children . OtheroOur finding of 46% of diagnostic stability from T1 to T2 for a full ADHD diagnosis and10% for ADHD NOS  is in line with the two major female ADHDlongitudinal studies . In the To our knowledge, we have not found any longitudinal study in females with ASD. The malefollow- up study in AS  reportedThe combined subtype had the highest persistence (48%) of the ADHD conditions and wasalso the most common subtype both in the main ADHD group and in ASD with co-occurrenceADHD. The persistence for ADHD-I was only 15%. The Berkley Girls Study mentioned aboveconcluded that a comparatively larger proportion of girls with ADHD-C retain theirdiagnosis compared to ADHD-I . These fThe high change from ADHD to ASD in one of three women from the original study toassessment two was not expected, even though half of them had autistic traits already atT1. For some of these women there had been suspicion about ASD at the first assessmenttime. We found coexisting ASD and ADHD, in 83% and several women with ADHD also had autistictraits (54%). These symptoms partly overshadow each other and cause diagnosticdifficulties . These rThere was a trend toward more severe ASD diagnosis over time with an increase in thenumber of girls fulfilling all 4 DSM-IV SOC criteria at T2 compared to T1. Our findingsfor adult women expand on those from the ALSPAC birth cohort study that found anescalation of ASD across adolescence that was greater in females compared to males .Nearly all women with ASD in the FClinG had perception/sensory problems at T2 and allwith reported perceptual problems at the first assessment retained their sensory problemsat T2. Sensory reactions were not reported in the main ADHD group without autistic traits,but most of the women with ADHD and autistic traits reported such problems. The findingsare consistent with previous work showing that the majority of all children with ASD havehad sensory problems remaining into adulthood . ConstanIn the MClinG of women the significantly larger general impairment of functional levelcompared to the women in the MComG, remained from the first assessment at T1 . This paIn this study the FClinG women with a main ADHD diagnosis or co- existent ADHD in T1 wereoffered treatment with stimulants and psychoeducation, after the study, during theirupbringing. In our previous report we found that one-third of these women today hadongoing treatment with stimulants . In prevThe MClinG women with ADHD and/or ASD differed significantly from the women in the MComGin multiple domains. The important differences found in our study in social situation,educational level, source of income and personal security in life have earlier beendescribed for mostly clinically referred women with ADHD . A populStudies regarding exposure to sexual abuse in adulthood appear to be sparse. There aresome recent work who has pointed out the increased risk of sexual victimization that womenwith ADHD and ASD are exposed to . The bacNearly half of the women with ADHD or ASD reported an experience of involuntary socialisolation, whereas this was infrequently reported in the MComG. Social difficulties are acore symptom of autism, and social problems in a person with ADHD may reflect socialmaladjustment due to impulsivity and inattention toward social codes . HoweverWhile the vast majority of women in the MComG supported themselves with a salary/studygrant, a fourth of those with ADHD and more than half of the women with ASD had some typeof social welfare grant. A partial explanation in addition to their disabling symptoms,might be the large difference in completed education. It is currently challenging to behired in Sweden without schooling beyond elementary school. Secondary school appears to benecessary. Becoming dependent on income through social insurance, early in life, means alife in relative poverty, this too being a factor contributing to poorer physical andmental health .Large and significant differences were seen when regarding exposure to physical or mentalabuse from peers, partners or strangers. Other work by Although the number of participants in our study is relatively small, one of thestrengths is that it had a clinical approach from the beginning. At T1 the group was wellcharacterized and examined with rigorous diagnostic criteria. At T2 the information wasobtained from the women, their first degree relative, and questionnaires as well asthrough observations by the researchers during the interviews. This procedure haspreviously been deemed necessary to obtain a complete picture of the current situation. TheparAvailable data was the decisive factor for the number of participants and for example,the wide age span, which is a limitation to the study with regard to the changes indiagnostic status observed and presented.Clinical samples generally include more severe cases than population samples, and due toknown referral bias especially among girls, they may not fully provide the whole pictureregarding the transition into adulthood among girls with ASD and/or ADHD . ThecomThe fact that one of the researchers, who was responsible for the assessments in T1, alsoparticipated in the evaluations in T2 can be seen as a weakness. The interviews were notblinded. To avoid bias as much as possible, the majority of the women and their relativesin the matched groups, were examined by colleagues who did not participate in T1. Whenconducting the study it was a strength, that good knowledge of the families facilitatedthe contact. It also made it easier to adapt the assessments where the women could notparticipate in the interviews (according to severe autism with ID and/or languagedisabilities) and the parent became the source of information. This fact most likely alsocontributed to the high retention in participation.Diagnoses are to be considered as the best estimates of the condition. As in clinicalwork, it is difficult to get distinct responses from persons with neuropsychiatricsymptomatology, and disabilities are often underreported . In the Most of the women with ADHD and/or ASD diagnosed in childhood/adolescence, still met thecriteria in early adult life for these. A “transition” from ADHD in childhood to ASD inadulthood occurred in some cases, both from the ADHD-C and ADHD-I childhood groups.Co-occurrence of ADHD and ASD was extremely common. Perceptual/sensory problems occurred inalmost everyone with ASD and frequently in women with ADHD and autistic traits. Dailyadaptive functioning were impaired in both matched clinic diagnostic groups, however evenmore so practically in the full ASD group. The low GAF scores in both groups indicate theburden of impairments and the need for support. Further, findings indicate an increased riskof violence and sexual abuse/exploitation compared to peers in the community.It seems important that when ADHD or ASD is suspected in a girl or an adult woman, thepresence of coexisting ADHD and ASD should always be investigated. Clinicians should alsoplan for continued support that takes into account potential changes in the symptom profileand social situation that may occur in early adulthood.Further research should be done to better understand the consequences of living with ADHDand/or ASD in adulthood. With better knowledge, medical care, community, educationalinstitutions and employers would be better at detecting the unmet needs of these women andprovide more functional support.Click here for additional data file.Supplemental material, sj-docx-1-jad-10.1177_10870547231158751 for Girls With Socialand/or Attention Deficit Re-Examined in Young Adulthood: Prospective Study of DiagnosticStability, Daily Life Functioning and Social Situation by Svenny Kopp, Karin SusannaAsztély, Sara Landberg, Margda Waern, Stefan Bergman and Christopher Gillberg in Journalof Attention Disorders


Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers.This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors.Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period.A total of 120 participants were enrolled . The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days.Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear.ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394RR2-10.1016/j.cvdhj.2021.07.002 Atrial fibrillation (AF) is a common heart rhythm disorder that affects over 6 million Americans and more than 30 million individuals worldwide ,2. IndivOver the last decade, consumer wearable technologies capable of detecting AF have transformed how health care providers and their patients diagnose and manage heart rhythm disorders ,8. SeverDespite the large number of participants in these studies, relatively few participants were at high risk for AF based on age or comorbidities. Furthermore, all participants were existing smartwatch owners, limiting the generalizability to most older populations in which digital technologies are less commonly used ,14. StroWe present the primary findings of the Pulsewatch study (NCT03761394), a randomized trial conducted to evaluate the accuracy and usability of a novel smartwatch-smartphone dyad designed for AF detection among stroke survivors. The Pulsewatch smartphone and smartwatch app were designed on the Android operating system by researchers with significant patient and provider input. The primary aims of this study were to (1) examine the accuracy and usability of a smartwatch for the detection of AF over 14 days compared with a comparator standard electrocardiogram (ECG) patch monitor and (2) describe the extent of adherence to wearing a smartwatch throughout the monitoring period.All participants were enrolled from ambulatory neurology and cardiovascular clinics affiliated with the University of Massachusetts Memorial Health Care (UMMHC), an academic tertiary care center in central Massachusetts, from September 2019 to August 2021. Ambulatory patients were eligible for participation if they were aged 50 years or older, had a history of ischemic stroke or transient ischemic attack (TIA) within the past decade, were willing to use the Pulsewatch system for at least 44 days, and were proficient in written and spoken English. Exclusion criteria included an absolute contraindication to the receipt of anticoagulation therapy , the inability to provide informed consent, a known allergy or hypersensitivity to medical-grade hydrocolloid adhesives or hydrogel, the presence of a life-threatening arrhythmia that required in-patient monitoring for immediate analysis, and having an implantable pacemaker.The Pulsewatch study is a clinical trial with 2 phases, both involving randomized assignment of participants to intervention versus control. The first phase is designed to evaluate Pulsewatch system accuracy versus a comparator standard (a 14-day ECG patch) and usability, followed by a second 30-day phase intended to evaluate adherence to wearing the smartwatch daily as the primary study outcome. The Pulsewatch study protocol, including more detailed calculations with regard to sample size and randomization, has been described previously .Trained research staff screened electronic medical records for eligible patients with upcoming neurology and cardiology clinic appointments from September 2019 to May 2021. An invitation letter providing a description of the Pulsewatch study as well as contact information was mailed to all participants in case they wished to ask questions or opt out of the study. Patients were then approached in person by research coordinators at the time of their ambulatory clinic visit to gauge interest in the study and obtain informed consent, if appropriate. Upon enrollment, participants were asked to complete a baseline questionnaire and were then randomized to either the control or intervention groups in a 1:3 ratio. The random allocation sequence was generated a priori by statisticians, to which all other study staff were blinded. Both groups received a comparator standard US Food and Drug Administration (FDA)–cleared ECG patch  and were asked to wear the patch daily over 14 days, whereas participants in the intervention group received the same ECG patch and instructions for use plus a Pulsewatch system . Participants were asked to wear the smartwatch daily and to keep the smartwatch and smartphone charged regularly. Research staff provided training to all intervention group participants, as well as to any caregiver or family member who accompanied the participant to the study visit, and all participants were provided a training packet with detailed instructions for the successful use of the patch and the Pulsewatch system .During the 14-day follow-up period for phase 1 of the study, research staff contacted participants on days 3 and 7 to encourage the use of the Pulsewatch system, address any questions or concerns expressed by participants, and troubleshoot any technical challenges the participant may be experiencing. This training was designed to improve wear time and ensure adequate sampling to evaluate the accuracy of the Pulsewatch system.Upon completion of the 14-day follow-up period, participants returned for a study visit, at which time they completed a follow-up questionnaire to assess key domains, including device use experience and a multitude of psychosocial factors, and those in the intervention arm were asked about their experience with the smartphone, app, and smartwatch. At this time, all participants, irrespective of their initial assignment to intervention or control, were randomized in a 1:1 fashion to be provided the Pulsewatch system for an additional 30 days. This 30-day follow-up period was designed to evaluate longer-term adherence to smartwatch use. During this phase of the study, the research staff did not initiate any calls with participants. The staff did, however, provide advice and instructions to participants who called the study hotline during the follow-up period. An overview of the study design is presented in The Pulsewatch system consists of 2 apps developed for the Android operating system for use on the Samsung smartphone and smartwatch. The Pulsewatch user interface was designed through an iterative process involving participants who consented to participate in focus groups and a Hack-a-thon. Participants included stroke survivors, their caregivers, and health care providers. Development and programming took place over the course of a year, and 4 focus groups were held with patients and their caregivers. During these sessions, iterations of the app interface were presented to the participants to elicit their feedback and refine the app for better usability.Upon completion of the focus groups, additional patients and their caregivers, together with local cardiologists, cardiac electrophysiologists, stroke neurologists, and the primary engineering and app development teams, were invited to a day-long programming event where participants reviewed the prototype app, offered their perspectives, and made suggestions for improvement.The final version of the Pulsewatch smartwatch app shows the user their heart rate, the time, and their rhythm status based on the recordings of the participants’ pulse (5-minute recordings are used to assess rhythm). If at least 90 consecutive seconds of potential AF are detected during a 5-minute detection window, the watch app displays an alert asking the user to “please stay still” along with an accompanying audio or vibration notification. After this alert, if another 60 consecutive seconds of AF are detected, the user is presented with an alert on the watch that reads “abnormality detected.” This approach was designed to reduce the likelihood of motion noise artifact simulating AF and the number of false positives among participants. “Abnormality detected” was our primary outcome, indicating Pulsewatch’s detection of AF.The Pulsewatch smartphone app pulls pulse data from the smartwatch, allows users to label arrhythmia episodes with any symptoms they may be experiencing, provides informational links regarding AF, and allows participants to review their heart rate ranges. Screenshots from the final version of the smartphone and smartwatch apps are presented in Participant demographic characteristics and medical history information were abstracted from the electronic medical record by trained research staff. Accuracy of the smartwatch’s “abnormality detected” (or AF detection) was determined by comparing whether a participant received any AF alerts over the 2-week monitoring period compared to whether AF was adjudicated as present based on a cardiologist’s over-read of all possible AF episodes detected on the FDA-cleared ECG patch monitor. Raw ECG data from these patches were extracted, and each episode categorized as AF by the patch monitor’s built-in FDA-approved AF detection algorithm was divided into 30-second segments. Each 30-second segment was reviewed by a noncardiologist physician (SN), and those determined to be potentially AF were reviewed by a board-certified cardiologist (MFG) for confirmation. Any segments on which the reviewers disagreed were reviewed by another board-certified cardiologist (KVT) as a tiebreaker. If any participant failed to wear the Pulsewatch smartwatch during the 14-day study period, they were excluded from the device accuracy analysis.Usability was measured at the 14-day study examination by the System Usability Scale (SUS), a validated scale ranging from 0 to 100, with scores of 68 or greater indicating high usability, and investigator-generated Likert scale usability questions specific to smartwatch use . PulsewaDue to the unprecedented challenges presented by the COVID-19 pandemic, this study protocol was adapted in June 2020 and approved by the Institutional Review Board to be performed entirely through remote means to ensure the safety of study participants, research staff, and clinic staff. All in-person study visits were replaced by phone encounters, including informed consent, enrollment, baseline visit, and follow-up visits for both phases of the study. All participants approached after June 2020 were given the option of interfacing with study staff in person per the original study protocol or entirely remotely as described above. The only questionnaire that could not be administered over the phone as it was in person was the Montreal Cognitive Assessment (MoCA) . A validDeidentified pulse data collected from the smartwatches were transferred to the paired smartphone through Bluetooth in real time, and these data were transmitted to secure study servers for storage. All patient identifiers, questionnaire data, and information extracted from medical records were stored on Health Insurance Portability and Accountability Act (HIPAA)–compliant secure servers at UMMHC.Descriptive statistics were calculated for all participant demographic, medical history, and psychosocial characteristics. The performance of the Pulsewatch system for AF detection was determined at the individual participant level and presented as sensitivity, specificity, and accuracy compared to the comparator standard. For usability questions, Likert scale responses of “strongly agree” and “agree” were collapsed, as were responses of “strongly disagree” and “disagree.” Descriptive statistics of usability questions were calculated. Overall device wear time in hours was quantified for each participant and descriptive statistics were calculated. A Cuzick rank sum test for the trend of ordered groups was used to assess the linear trend in daily adherence (binary outcome). All analyses were completed using Stata 14.0 and SAS 9.3.Pulsewatch has been approved by the UMass Chan IRB . Written informed consent was collected from all participants, and all study data has been deidentified. Study participants were compensated US $100 for their participation in phase 1, then another US $100 for phase 2. A small convenience sample of participants was selected for qualitative interviews about their experience in the study and compensated US $60 for their time.A total of 90 participants were randomized into the intervention arm of phase 1 of the trial and 57 into the phase 2 intervention group. The average age of study participants in the first phase was 65.1 (SD 9.3) years, 41% (37/90) were female, most were non-Hispanic White , 56% (46/82) were college graduates, and 35% (27/77) had an income higher than US $100,000. More than 3 out of 4 participants had been previously diagnosed with hypertension and hyperlipidemia, and 2 out of every 5 were cognitively impaired. Most owned smartphones  and engaged with apps on their devices daily , though a much smaller proportion of participants owned smartwatches . The characteristics of participants randomized into the phase 2 intervention group were similar. Participant characteristics are further detailed in AF was detected in 6 out of the 90 participants (incidence 6.67%). Participants with AF detected varied widely with regard to the number of episodes and the total AF burden (time spent in AF), ranging from a minimum of 2 episodes lasting a total of 16 minutes to 5012 episodes lasting a total of 1712 minutes . HoweverThe Pulsewatch system detected AF correctly in 3 out of the 5 participants who were determined to have AF by cardiologist overread of the ECG patch monitors  and correctly indicated no AF in 76 of the 80 participants determined to be free from AF based on cardiologist overread of the ECG patch monitors . Overall, the smartwatch exhibited an accuracy of 92.9% for AF identification in our 14-day study  and 5.Although a participant may be wearing the watch, the Pulsewatch app may not have been constantly running in the background, most often due to accidental termination of the app by the participant or activation of power-saving mode to conserve battery. Thus, active rhythm recording did not occur for the entirety of watch wear time for all participants. Accuracy analysis is therefore limited to the duration that the Pulsewatch app was on and recording pulse .The average Pulsewatch SUS was 62.8 out of a possible 100, and 37.5% (33/88) of participants reported the system as highly usable (SUS ≥68). When asked about the watch and phone apps separately, 63.6% (56/88) of participants agreed or strongly agreed that the watch app was easy to use, while 52.3% (46/88) indicated the same for the phone app. Around 42% (37/88) of participants agreed or strongly agreed that using the Pulsewatch system made them feel more connected to their doctor. Most participants did not experience anxiety or worry because of using the Pulsewatch system , and 58% (51/88) of participants indicated a willingness to use the system daily for 6 months for heart rhythm monitoring. Overall, more than one-half of participants, 51.1% (45/88), agreed or strongly agreed with the statement that they enjoyed using the system over the course of the study. All 90 participants who used the Pulsewatch system indicated that they would be comfortable allowing their doctors access to health information collected by this device.P<.05). Similarly, about 55% (28/51) of participants wore the watch for at least 5 hours on day 30 of the study  compared to similar cohorts. A meta-analysis of 50 studies examining AF diagnosis poststroke demonstrated a yield of 15.3% with mobile cardiac outpatient telemetry (MCOT) devices . HoweverThe Pulsewatch algorithm for AF detection was designed to minimize false-positive alerts to avoid introducing patient anxiety. This is particularly important in the context of our recent finding that smartwatch alerts for AF negatively impact patients’ perceptions of physical health . In thisOur analysis of concurrent watch and patch wear demonstrated that while a portion of the hours in which AF was detected on the patch were not detected by the smartwatch, it was still able to identify AF in 3 of the 5 participants who had this condition over the 2-week monitoring period. This further highlights the importance of adequate duration of monitoring, which may be significantly higher for a smartwatch than a traditional ECG monitor affixed to a patient’s chest, as a smartwatch may need to analyze more episodes of AF to detect the condition, as observed in this study. We observed that 55% (28/51) of participants in this study wore the watch for greater than 5 hours daily at 30 days, suggesting that in real-world use, smartwatches would be worn for an adequate duration to allow for AF detection, albeit with a slight decline in adherence over time.Few studies have examined the accuracy of wearable devices among older adults, and fewer still among those naïve to technologies at baseline. The Apple Heart Study recruited individuals who already owned an Apple Watch , asked them to use the device’s built-in pulse analysis feature, and sent ECG patch monitors to all participants who received a potential AF alert on their smartwatch . Among tOlder adults are a particularly understudied population with respect to digital health and telemedicine, and information on the perceptions of smartwatch usability among this population is scant. A systematic review of wearable sensors deployed in older adults found that studies largely focus on the systems and technical performance aspects of technology and that few existing studies address usability and acceptability challenges encountered by older adults .A preliminary feasibility study we conducted in a small cohort of older adults  using a smartwatch for cardiac rhythm monitoring found an average SUS score of 73 . In the Initial adherence to the Pulsewatch app and smartwatch was high but declined marginally over the course of the 30-day monitoring period. Our findings are consistent with other studies that prescribe the use of commercial wearables among older adults with risk factors for stroke. For example, in a substudy of the mHealth Screening to Prevent Strokes (mSToPS) trial, which consisted of 230 participants with numerous risk factors for AF , it was observed that 43% of participants who received a smartwatch to monitor their heart rhythm did not record any data on the device despite initial enthusiasm for receiving such a device . The disOur results illustrate the opportunities for using wearables for AF detection but also highlight gaps that may impede the successful integration of smartwatches for long-term arrhythmia monitoring in at-risk populations that do not regularly use such devices. As discussed in the recent European Heart Rhythm Association (EHRA) position paper on AF screening and detection, photoplethysmograph-based wearables potentially provide a useful means of initial screening but should not guide anticoagulation therapy without further investigation . Since tOur results should be examined in light of several limitations. First, our small sample size and subsequent low rate of AF result in large confidence intervals for the accuracy of AF detection, and thus the accuracy values should be interpreted with this in mind. Additionally, watch wear time in the study was relatively low and may have resulted in inadequate coverage to capture AF in subjects with arrhythmia, further complicating accuracy calculations. Finally, this study population is rather homogenous, including a high proportion of individuals of high socioeconomic status, which may not be representative of other populations.A smartphone-app smartwatch dyad designed with and for patients with a previous stroke demonstrates high accuracy for detecting undiagnosed AF and was found to be highly usable by stroke survivors. Daily adherence to the system declined over a 30-day unsupported monitoring period, suggesting that the use of commercial wearables for AF detection outside of populations who previously owned such devices will require new strategies to improve adherence for effective integration of wearables into clinical settings.